From 0ea1cb2a6d789518470ac5e26600ae2297b80022 Mon Sep 17 00:00:00 2001
From: Vincent van Hees <v.vanhees@accelting.com>
Date: Thu, 23 Jan 2025 12:56:23 +0100
Subject: [PATCH] rebuild pages alongside 3.1-11 release

---
 docs/404.html                                 |   2 +-
 docs/CODE_OF_CONDUCT.html                     |   2 +-
 docs/CONTRIBUTING.html                        |   2 +-
 docs/LICENSE-text.html                        |   2 +-
 docs/RELEASE_CYCLE.html                       |   2 +-
 docs/articles/Cookbook.html                   |   9 +-
 docs/articles/CutPoints.html                  |   2 +-
 docs/articles/ExternalFunction.html           |   2 +-
 docs/articles/GGIR.html                       |   2 +-
 docs/articles/GGIRParameters.html             |  52 ++--
 docs/articles/GGIRoutput.html                 | 270 +++++++++++-------
 docs/articles/HouseHoldCoanalysis.html        |   2 +-
 docs/articles/TutorialDaySegmentAnalyses.html |   2 +-
 docs/articles/chapter0_Contributing.html      |   2 +-
 docs/articles/chapter0_GetStarted.html        |   2 +-
 docs/articles/chapter0_Installation.html      |   2 +-
 docs/articles/chapter0_Support.html           |   2 +-
 docs/articles/chapter10_SleepAnalysis.html    |   2 +-
 .../chapter11_DescribingDataCutPoints.html    |   2 +-
 docs/articles/chapter12_TimeUseAnalysis.html  |   2 +-
 docs/articles/chapter13_CircadianRhythm.html  |  24 +-
 .../chapter14_BehaviouralFragmentation.html   |   2 +-
 docs/articles/chapter1_WhatIsGGIR.html        |   2 +-
 docs/articles/chapter2_Pipeline.html          |   2 +-
 docs/articles/chapter3_QualityAssessment.html |   2 +-
 docs/articles/chapter4_AccMetrics.html        |   2 +-
 docs/articles/chapter5_StudyProtocol.html     |   2 +-
 docs/articles/chapter6_DataImputation.html    |   2 +-
 ...er7_DescribingDataWithoutKnowingSleep.html |   2 +-
 .../chapter8_SleepFundamentalsSibs.html       |   2 +-
 .../chapter9_SleepFundamentalsGuiders.html    |   9 +-
 docs/articles/chapterInProgress.html          |   2 +-
 docs/articles/index.html                      |   2 +-
 docs/articles/readmyacccsv.html               |   2 +-
 docs/authors.html                             |  10 +-
 docs/index.html                               |   2 +-
 docs/news/index.html                          |  36 ++-
 docs/pkgdown.yml                              |   2 +-
 docs/search.json                              |   2 +-
 39 files changed, 299 insertions(+), 175 deletions(-)

diff --git a/docs/404.html b/docs/404.html
index d243f345a..e9cf4cd2c 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="https://wadpac.github.io/GGIR/index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/CODE_OF_CONDUCT.html b/docs/CODE_OF_CONDUCT.html
index 75b56fd07..c7dd67171 100644
--- a/docs/CODE_OF_CONDUCT.html
+++ b/docs/CODE_OF_CONDUCT.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/CONTRIBUTING.html b/docs/CONTRIBUTING.html
index 53b585cbc..4a761dd4b 100644
--- a/docs/CONTRIBUTING.html
+++ b/docs/CONTRIBUTING.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html
index 4daf355b5..7d68480f3 100644
--- a/docs/LICENSE-text.html
+++ b/docs/LICENSE-text.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/RELEASE_CYCLE.html b/docs/RELEASE_CYCLE.html
index 1a541b85d..ff6c9fc74 100644
--- a/docs/RELEASE_CYCLE.html
+++ b/docs/RELEASE_CYCLE.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/Cookbook.html b/docs/articles/Cookbook.html
index 51e7eba66..3f9aae1f8 100644
--- a/docs/articles/Cookbook.html
+++ b/docs/articles/Cookbook.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -111,10 +111,12 @@ <h1>Cookbook</h1>
     
     
 <p>In this cook book you will find recipes for using GGIR in specific
-scenarios.</p>
+non-default scenarios.</p>
 <div class="section level2">
 <h2 id="handling-externally-derived-data">Handling externally derived data<a class="anchor" aria-label="anchor" href="#handling-externally-derived-data"></a>
 </h2>
+<p>This refers to the situation where external software or hardware was
+used to derive epoch level aggregates from accelerometer data.</p>
 <div class="section level3">
 <h3 id="actiwatch-data">Actiwatch data<a class="anchor" aria-label="anchor" href="#actiwatch-data"></a>
 </h3>
@@ -144,6 +146,9 @@ <h4 id="in--csv-format">in .CSV format<a class="anchor" aria-label="anchor" href
 <div class="section level3">
 <h3 id="uk-biobank-data-in--csv-format">UK Biobank data in .CSV format<a class="anchor" aria-label="anchor" href="#uk-biobank-data-in--csv-format"></a>
 </h3>
+<p>This is only applicable the UK Biobank 5 second epoch csv exports and
+NOT to the raw accelerometer data. Please see <a href="https://wadpac.github.io/GGIR/" class="uri">https://wadpac.github.io/GGIR/</a> for all documentation on
+processing raw accelerometer data (multiple values per second).</p>
 <pre><code>GGIR(datadir = "/media/ukbiobank",
    outputdir = "/media/myoutput",
    dataFormat = "ukbiobank_csv",
diff --git a/docs/articles/CutPoints.html b/docs/articles/CutPoints.html
index 10688980d..c882e2297 100644
--- a/docs/articles/CutPoints.html
+++ b/docs/articles/CutPoints.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/ExternalFunction.html b/docs/articles/ExternalFunction.html
index bb52570e8..2e51b5321 100644
--- a/docs/articles/ExternalFunction.html
+++ b/docs/articles/ExternalFunction.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/GGIR.html b/docs/articles/GGIR.html
index d38dec1e0..8a16c2699 100644
--- a/docs/articles/GGIR.html
+++ b/docs/articles/GGIR.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/GGIRParameters.html b/docs/articles/GGIRParameters.html
index 7f4f79d58..c8688fef3 100644
--- a/docs/articles/GGIRParameters.html
+++ b/docs/articles/GGIRParameters.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -2498,24 +2498,26 @@ <h3 id="physical-activity-parameters">Physical activity Parameters<a class="anch
 <div class="section level4">
 <h4 class="unnumbered" id="mvpathreshold">mvpathreshold<a class="anchor" aria-label="anchor" href="#mvpathreshold"></a>
 </h4>
-<p>Numeric (default = 100). Acceleration threshold for MVPA estimation
-in GGIR g.part2. This can be a single number or an vector of numbers,
-e.g., mvpathreshold = c(100, 120). In the latter case the code will
-estimate MVPA separately for each threshold. If this variable is left
-blank, e.g., mvpathreshold = c(), then MVPA is not estimated.</p>
+<p>Numeric (default = NULL). Legacy parameter, if not provided GGIR uses
+the value of threshold.mod for this. Acceleration threshold for MVPA
+estimation in GGIR g.part2. This can be a single number or an vector of
+numbers, e.g., mvpathreshold = c(100, 120). In the latter case the code
+will estimate MVPA separately for each threshold. If this variable is
+left blank, e.g., mvpathreshold = c(), then MVPA is not estimated.</p>
 </div>
 <div class="section level4">
 <h4 class="unnumbered" id="boutcriter">boutcriter<a class="anchor" aria-label="anchor" href="#boutcriter"></a>
 </h4>
-<p>Numeric (default = 0.8). A number between 0 and 1, it defines what
-fraction of a bout needs to be above the mvpathreshold, only used in
-GGIR g.part2.</p>
+<p>Numeric (default = NULL). Legacy parameter, if not provided GGIR uses
+the value of boutcriter.mvpa for this. A number between 0 and 1, it
+defines what fraction of a bout needs to be above the mvpathreshold,
+only used in GGIR g.part2.</p>
 </div>
 <div class="section level4">
 <h4 class="unnumbered" id="mvpadur">mvpadur<a class="anchor" aria-label="anchor" href="#mvpadur"></a>
 </h4>
-<p>Numeric (default = 10). The bout duration(s) for which MVPA will be
-calculated. Only used in GGIR g.part2.</p>
+<p>Numeric (default = c(1, 5, 10)). The bout duration(s) for which MVPA
+will be calculated. Only used in GGIR g.part2.</p>
 </div>
 <div class="section level4">
 <h4 class="unnumbered" id="boutcriter-in">boutcriter.in<a class="anchor" aria-label="anchor" href="#boutcriter-in"></a>
@@ -2845,19 +2847,22 @@ <h4 class="unnumbered" id="epochvalues2csv">epochvalues2csv<a class="anchor" ari
 <div class="section level4">
 <h4 class="unnumbered" id="save_ms5rawlevels">save_ms5rawlevels<a class="anchor" aria-label="anchor" href="#save_ms5rawlevels"></a>
 </h4>
-<p>Boolean (default = FALSE). In g.part5: Whether to save the time
-series classification (levels) as csv or RData files (as defined by
+<p>Boolean (default = TRUE). In g.part5: Whether to save the time series
+classification (levels) as csv or RData files (as defined by
 save_ms5raw_format). Note that time stamps will be stored in the column
 timenum in UTC format (i.e., seconds from 1970-01-01). To convert
 timenum to time stamp format, you need to specify your desired time
-zone, e.g., as.POSIXct(mdat$timenum, tz = “Europe/London”).</p>
+zone, e.g., as.POSIXct(mdat$timenum, tz = “Europe/London”). If you are
+not using GGIR part 6, not interested in the visualreport generation,
+and not interested in time series then you may want to consider setting
+this parameter to FALSE.</p>
 </div>
 <div class="section level4">
 <h4 class="unnumbered" id="save_ms5raw_format">save_ms5raw_format<a class="anchor" aria-label="anchor" href="#save_ms5raw_format"></a>
 </h4>
-<p>Character (default = “csv”). In g.part5: To specify how data should
-be stored: either “csv” or “RData”. Only used if save_ms5rawlevels =
-TRUE.</p>
+<p>Character (default = “RData”). In g.part5: To specify how data should
+be stored: “csv”, “RData”, or both via c(“csv”, “RData”). Only used if
+save_ms5rawlevels = TRUE.</p>
 </div>
 <div class="section level4">
 <h4 class="unnumbered" id="save_ms5raw_without_invalid">save_ms5raw_without_invalid<a class="anchor" aria-label="anchor" href="#save_ms5raw_without_invalid"></a>
@@ -2954,13 +2959,12 @@ <h4 class="unnumbered" id="do-visual">do.visual<a class="anchor" aria-label="anc
 <div class="section level4">
 <h4 class="unnumbered" id="do-sibreport">do.sibreport<a class="anchor" aria-label="anchor" href="#do-sibreport"></a>
 </h4>
-<p>Boolean (default = FALSE). In g.part4: To indicate whether to
-generate report for the sustained inactivity bouts (SIB). If set to TRUE
-and when an advanced sleep diary is available in part 4 then part 5 will
-use this to generate summary statistics on the overlap between
-self-reported nonwear and napping with SIB. Here, SIB can be filter
-based on parameter possible_nap_edge_acc and the first value of
-possible_nap_dur</p>
+<p>Boolean (default = TRUE). In g.part4: To indicate whether to generate
+report for the sustained inactivity bouts (SIB). If set to TRUE and when
+an advanced sleep diary is available in part 4 then part 5 will use this
+to generate summary statistics on the overlap between self-reported
+nonwear and napping with SIB. Here, SIB can be filter based on parameter
+possible_nap_edge_acc and the first value of possible_nap_dur</p>
 </div>
 <div class="section level4">
 <h4 class="unnumbered" id="do-part2-pdf">do.part2.pdf<a class="anchor" aria-label="anchor" href="#do-part2-pdf"></a>
diff --git a/docs/articles/GGIRoutput.html b/docs/articles/GGIRoutput.html
index 9202c64c7..13c1b7a43 100644
--- a/docs/articles/GGIRoutput.html
+++ b/docs/articles/GGIRoutput.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -466,27 +466,15 @@ <h4 id="circadian-rhythm-mxlx-iv-and-is">Circadian rhythm: MXLX, IV, and IS<a cl
 </tr>
 <tr class="odd">
 <td>IS</td>
-<td>inter daily stability. The movement count that is derived for this
-was an attempt to follow the original approach by Eus J. W. Van Someren
-(Chronobiology International. 1999. Volume 16, issue 4).</td>
+<td>Interdaily stability as dicussed in <a href="https://wadpac.github.io/GGIR/articles/chapter13_CircadianRhythm.html">chapter
+13</a>
+</td>
 </tr>
 <tr class="even">
 <td>IV</td>
-<td>intra daily variability. In contrast to the original paper, we
-ignore the epoch transitions between the end of a day and the beginning
-of the next day for the numerator of the equation, this to make it a
-true measure of intradaily variability. Same note as for IS: The
-movement count that is derived for this was an attempt to follow the
-original approach.</td>
-</tr>
-<tr class="odd">
-<td>IVIS_windowsize_minutes</td>
-<td>Sizes of the windows based on which IV and IS are calculated (note
-that this is modifiable)</td>
-</tr>
-<tr class="even">
-<td>IVIS_epochsize_seconds</td>
-<td>Argument has been deprecated</td>
+<td>Intradaily variability as dicussed in <a href="https://wadpac.github.io/GGIR/articles/chapter13_CircadianRhythm.html">chapter
+13</a>
+</td>
 </tr>
 <tr class="odd">
 <td>phi</td>
@@ -511,137 +499,146 @@ <h4 id="circadian-rhythm-mxlx-iv-and-is">Circadian rhythm: MXLX, IV, and IS<a cl
 <div class="section level4">
 <h4 id="circadian-rhythm-cosinor">Circadian rhythm: Cosinor<a class="anchor" aria-label="anchor" href="#circadian-rhythm-cosinor"></a>
 </h4>
+<p>The following variables in the GGIR output are derived by the ActCR
+package, the documentation has been copied from ActCR documentation.</p>
 <table class="table">
 <colgroup>
 <col width="36%">
 <col width="63%">
 </colgroup>
 <thead><tr class="header">
-<th>(Part of) variable name</th>
+<th>Variable name</th>
 <th>Description</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
-<td><code>cosinor_</code></td>
-<td>Cosinor analysis estimates such as mes, amp, acrophase, and
-acrotime, as documented in the <a href="https://CRAN.R-project.org/package=ActCR" class="external-link">ActCR</a> package.</td>
-</tr>
-<tr class="even">
-<td><code>cosinorExt_</code></td>
-<td>Extended Cosinor analysis estimates such as minimum, amp, alpha,
-beta, acrotime, UpMesor, DownMesor, MESOR, and F_pseudo, as documented
-in the <a href="https://CRAN.R-project.org/package=ActCR" class="external-link">ActCR</a>
-package.</td>
-</tr>
-<tr class="odd">
-<td><code>cosinorIV</code></td>
-<td>Cosinor analysis compatible estimate of the Intradaily Variability
-(IV)</td>
+<td>cosinor_mes</td>
+<td>MESOR which is short for midline statistics of rhythm, which is a
+rhythm adjusted mean. This represents mean activity level.</td>
 </tr>
 <tr class="even">
-<td><code>cosinorIS</code></td>
-<td>Cosinor analysis compatible estimate of Interdaily Stability
-(IS)</td>
+<td>cosinor_amp</td>
+<td>amplitude, a measure of half the extend of predictable variation
+within a cycle.</td>
 </tr>
 <tr class="odd">
-<td>cosinor_timeOffsetHours</td>
-<td>Offset between start time series as used and midnight</td>
+<td>This represents the highest activity one can achieve</td>
+<td></td>
 </tr>
 <tr class="even">
-<td>cosinor_mes</td>
-<td>The mean value of the cosinor function fitted to the log transformed
-acceleration signal. Higher values indicate more activity.</td>
+<td>cosinor_acrophase</td>
+<td>acrophase, a measure of the time of the overall high values
+recurring in each</td>
 </tr>
 <tr class="odd">
-<td>cosinor_amp</td>
-<td>The amplitude of the cosinor function fitted to the log transformed
-acceleration signal, corresponding to the peak of the function minus the
-mesor. Higher values indicate larger amplitude in the rhythm</td>
+<td>cycle. Here it has a unit of radian. This represents time to reach
+the peak.</td>
+<td></td>
 </tr>
 <tr class="even">
 <td>cosinor_acrotime</td>
-<td>Time at which the cosinor function fitted to the log transformed
-acceleration signal reaches its maximum</td>
+<td>acrotime represents time to reach the peak.</td>
 </tr>
 <tr class="odd">
-<td>cosinor_acrophase</td>
-<td>Acrotime expressed in radians</td>
-</tr>
-<tr class="even">
 <td>cosinor_ndays</td>
-<td>Number of days used for the cosinor analysis.</td>
-</tr>
-<tr class="odd">
-<td>cosinor_R2</td>
-<td>Measure of goodness of fit of the cosinor function to the log
-transformed acceleration signal. Higher values indicate better goodness
-of fit.</td>
+<td>Number of days modeled</td>
 </tr>
 <tr class="even">
 <td>cosinorExt_minimum</td>
-<td>(copied from ActCR documentation) Minimum value of the function</td>
+<td>Minimum value of the of the function.</td>
 </tr>
 <tr class="odd">
 <td>cosinorExt_amp</td>
-<td>(copied from ActCR documentation) Amplitude, a measure of half the
-extend of predictable variation within a cycle. This represents the
-highest activity one can achieve.</td>
+<td>amplitude, a measure of half the extend of predictable variation
+within a cycle. This represents the highest activity one can
+achieve.</td>
 </tr>
 <tr class="even">
 <td>cosinorExt_alpha</td>
-<td>(copied from ActCR documentation) It determines whether the peaks of
-the curve are wider than the troughs: when alpha is small, the troughs
-are narrow and the peaks are wide; when alpha is large, the troughs are
-wide and the peaks are narrow.</td>
+<td>It determines whether the peaks of the curve are wider than the
+troughs: when alpha is small, the troughs are narrow and the peaks are
+wide; when alpha is large, the troughs are wide and the peaks are
+narrow.</td>
 </tr>
 <tr class="odd">
 <td>cosinorExt_beta</td>
-<td>(copied from ActCR documentation) It dertermines whether the
-transformed function rises and falls more steeply than the cosine curve:
-large values of beta produce curves that are nearly square waves.</td>
+<td>It dertermines whether the transformed function rises and falls more
+steeply than the cosine curve: large values of beta produce curves that
+are nearly square waves.</td>
 </tr>
 <tr class="even">
 <td>cosinorExt_acrotime</td>
-<td>(copied from ActCR documentation) acrophase is the time of day of
-the peak in the unit of the time (hours)</td>
+<td>acrophase is the time of day of the peak in the unit of the time
+(hours)</td>
 </tr>
 <tr class="odd">
 <td>cosinorExt_UpMesor</td>
-<td>(copied from ActCR documentation) Time of day of switch from low to
-high activity. Represents the timing of the rest- activity rhythm. Lower
-(earlier) values indicate increase in activity earlier in the day and
-suggest a more advanced circadian phase.</td>
+<td>Time of day of switch from low to high activity. Represents the
+timing of the rest- activity rhythm. Lower (earlier) values indicate
+increase in activity earlier in the day and suggest a more advanced
+circadian phase.</td>
 </tr>
 <tr class="even">
 <td>cosinorExt_DownMesor</td>
-<td>(copied from ActCR documentation) Time of day of switch from high to
-low activity. Represents the timing of the rest-activity rhythm. Lower
-(earlier) values indicate decline in activity earlier in the day,
-suggesting a more advanced circadian phase.</td>
+<td>Time of day of switch from high to low activity. Represents the
+timing of the rest-activity rhythm. Lower (earlier) values indicate
+decline in activity earlier in the day, suggesting a more advanced
+circadian phase.</td>
 </tr>
 <tr class="odd">
 <td>cosinorExt_MESOR</td>
-<td>(copied from ActCR documentation) A measure analogous to the MESOR
-of the cosine model (or half the deflection of the curve) can be
-obtained from mes=min+amp/2. However, it goes through the middle of the
-peak, and is therefore not equal to the MESOR of the cosine model, which
-is the mean of the data</td>
+<td>A measure analogous to the MESOR of the cosine model (or half the
+deflection of the curve) can be obtained from mes=min+amp/2. However, it
+goes through the middle of the peak, and is therefore not equal to the
+MESOR of the cosine model, which is the mean of the data.</td>
 </tr>
 <tr class="even">
 <td>cosinorExt_ndays</td>
-<td>(copied from ActCR documentation) Number of days modeled.</td>
+<td>Number of days modeled.</td>
 </tr>
 <tr class="odd">
 <td>cosinorExt_F_pseudo</td>
-<td>(copied from ActCR documentation) Measure the improvement of the fit
-obtained by the non-linear estimation of the transformed cosine
-model</td>
+<td>Measure the improvement of the fit obtained by the non-linear
+estimation of the transformed cosine model</td>
+</tr>
+</tbody>
+</table>
+<p><strong>Additionally, GGIR derives:</strong></p>
+<table class="table">
+<colgroup>
+<col width="36%">
+<col width="63%">
+</colgroup>
+<thead><tr class="header">
+<th>Variable name</th>
+<th>Description</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td>cosinor_timeOffsetHours</td>
+<td>(Only used for code development purposes) Time offset in hours
+between midnight and the start of the time series used for cosinor
+analysis</td>
 </tr>
 <tr class="even">
+<td>cosinor_R2</td>
+<td>Squared correlated coefficient between fitted and log-transformed
+original time series.</td>
+</tr>
+<tr class="odd">
 <td>cosinorExt_R2</td>
-<td>Measure of goodness of fit of the extended cosinor function to the
-log transformed acceleration signal. Higher values indicate better
-goodness of fit.</td>
+<td>Squared correlated coefficient between fitted and log-transformed
+original time series.</td>
+</tr>
+<tr class="even">
+<td><code>cosinorIV</code></td>
+<td>Cosinor analysis compatible estimate of the Intradaily Variability
+(IV)</td>
+</tr>
+<tr class="odd">
+<td><code>cosinorIS</code></td>
+<td>Cosinor analysis compatible estimate of Interdaily Stability
+(IS)</td>
 </tr>
 </tbody>
 </table>
@@ -701,7 +698,8 @@ <h3 id="day-level-summary-csv">Day level summary (csv)<a class="anchor" aria-lab
 <tr class="odd">
 <td>L5hr_ENMO_mg_0-24h</td>
 <td>Hour on which L5 starts for these 24 hours (defined with metric
-ENMO)</td>
+ENMO), on a continuous scale relative to the preceding midnight such
+that 2am is 26 and 6am is 30</td>
 </tr>
 <tr class="even">
 <td>L5_ENMO_mg_0-24h</td>
@@ -1711,6 +1709,9 @@ <h2 id="ggir-part-6">GGIR Part 6<a class="anchor" aria-label="anchor" href="#ggi
 overlap with part 5, but are now derived based on the for the full time
 series only, whereas part 5 only presents these variables per day window
 which is then aggregated at person level.</p>
+<div class="section level4">
+<h4 id="mxlx-output-variables">MXLX output variables<a class="anchor" aria-label="anchor" href="#mxlx-output-variables"></a>
+</h4>
 <table class="table">
 <colgroup>
 <col width="36%">
@@ -1722,30 +1723,87 @@ <h2 id="ggir-part-6">GGIR Part 6<a class="anchor" aria-label="anchor" href="#ggi
 </tr></thead>
 <tbody>
 <tr class="odd">
-<td>L5TIME</td>
-<td>Timing of least active 5hrs, expressed as timestamp in the day</td>
+<td>L5VALUE, M5VALUE</td>
+<td>Acceleration value for least (L5) and most (M5) active consecutive
+5hrs per 24 hours</td>
 </tr>
 <tr class="even">
-<td>M5TIME</td>
-<td>Timing of most active 5hrs</td>
+<td>L5TIME_clock, M5TIME_clock</td>
+<td>Clock time of L5 and M5, e.g. 15:00</td>
 </tr>
 <tr class="odd">
 <td>L5TIME_num, M5TIME_num</td>
-<td>Timing of least/most active 5hrs, expressed as hours in the day.
-Note that L5/M5 timing variables are difficult to average across days
-because 23:00 and 1:00 would average to noon and not to midnight. So,
-caution is needed when interpreting person averages.</td>
+<td>Timing of L5 and M5, expressed as hours relative to previous
+midnight, meaning that 2am would be 26. This is done to ease averaging
+acros days/persons.</td>
+</tr>
+<tr class="even">
+<td>M5_mean_peakLUX</td>
+<td>Mean peak lux during M5</td>
+</tr>
+<tr class="odd">
+<td>M5_max_peakLUX</td>
+<td>Max peak lux during M5</td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="section level4">
+<h4 id="cosinor-output-variables">Cosinor output variables<a class="anchor" aria-label="anchor" href="#cosinor-output-variables"></a>
+</h4>
+<p>These variables names are consistent with the variable names <a href="https://wadpac.github.io/GGIR/articles/GGIRoutput.html#circadian-rhythm-cosinor">as
+discussed for GGIR part 2 output</a>.</p>
+</div>
+<div class="section level4">
+<h4 id="other-output-variables-in-part-6">Other output variables in part 6<a class="anchor" aria-label="anchor" href="#other-output-variables-in-part-6"></a>
+</h4>
+<table class="table">
+<colgroup>
+<col width="36%">
+<col width="63%">
+</colgroup>
+<thead><tr class="header">
+<th>Variable name</th>
+<th>Description</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td>IS</td>
+<td>Interdaily Stability</td>
 </tr>
 <tr class="even">
-<td>L5VALUE</td>
-<td>Acceleration value for least active 5hrs</td>
+<td>IV</td>
+<td>Intradaily Variability</td>
 </tr>
 <tr class="odd">
-<td>M5VALUE</td>
-<td>Acceleration value for most active 5hrs</td>
+<td>phi</td>
+<td>See <a href="https://wadpac.github.io/GGIR/articles/chapter13_CircadianRhythm.html#phi">section
+on phy in chapter 13</a>
+</td>
+</tr>
+<tr class="even">
+<td><code>FRAG_</code></td>
+<td>Fragmentation variables, as also <a href="https://wadpac.github.io/GGIR/articles/GGIRoutput.html#fragmentation">discussed
+for part 5 output</a>. Only difference now is that fragmentation
+variables name ending with <code>_day</code> are specific to the waking
+hours of a day, while variable names ending iwht <code>_spt</code> are
+specific to the SPT window.</td>
+</tr>
+<tr class="odd">
+<td>SSP</td>
+<td>See <a href="https://wadpac.github.io/GGIR/articles/chapter13_CircadianRhythm.html#self-similarity-paramerter-ssp">section
+on SSP in chapter 13</a>
+</td>
+</tr>
+<tr class="even">
+<td>ABI</td>
+<td>See <a href="https://wadpac.github.io/GGIR/articles/chapter13_CircadianRhythm.html#activity-balance-index-abi">section
+on ABI in chapter 13</a>
+</td>
 </tr>
 </tbody>
 </table>
+</div>
 </div>
   </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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diff --git a/docs/articles/HouseHoldCoanalysis.html b/docs/articles/HouseHoldCoanalysis.html
index 65117e652..d378a50eb 100644
--- a/docs/articles/HouseHoldCoanalysis.html
+++ b/docs/articles/HouseHoldCoanalysis.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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index 0271cb38f..ba3d53725 100644
--- a/docs/articles/TutorialDaySegmentAnalyses.html
+++ b/docs/articles/TutorialDaySegmentAnalyses.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
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diff --git a/docs/articles/chapter0_Contributing.html b/docs/articles/chapter0_Contributing.html
index 8b03b66dc..8bd40a5e5 100644
--- a/docs/articles/chapter0_Contributing.html
+++ b/docs/articles/chapter0_Contributing.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/chapter0_GetStarted.html b/docs/articles/chapter0_GetStarted.html
index 866606529..3c617a556 100644
--- a/docs/articles/chapter0_GetStarted.html
+++ b/docs/articles/chapter0_GetStarted.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/chapter0_Installation.html b/docs/articles/chapter0_Installation.html
index c93e2fcc0..6efdb2974 100644
--- a/docs/articles/chapter0_Installation.html
+++ b/docs/articles/chapter0_Installation.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/chapter0_Support.html b/docs/articles/chapter0_Support.html
index 851444f57..13711a24a 100644
--- a/docs/articles/chapter0_Support.html
+++ b/docs/articles/chapter0_Support.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/chapter10_SleepAnalysis.html b/docs/articles/chapter10_SleepAnalysis.html
index 34f108127..0a76f4987 100644
--- a/docs/articles/chapter10_SleepAnalysis.html
+++ b/docs/articles/chapter10_SleepAnalysis.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/chapter11_DescribingDataCutPoints.html b/docs/articles/chapter11_DescribingDataCutPoints.html
index e36709dbf..e322a21db 100644
--- a/docs/articles/chapter11_DescribingDataCutPoints.html
+++ b/docs/articles/chapter11_DescribingDataCutPoints.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/chapter12_TimeUseAnalysis.html b/docs/articles/chapter12_TimeUseAnalysis.html
index 44afe8d88..ccfa131f5 100644
--- a/docs/articles/chapter12_TimeUseAnalysis.html
+++ b/docs/articles/chapter12_TimeUseAnalysis.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/chapter13_CircadianRhythm.html b/docs/articles/chapter13_CircadianRhythm.html
index a2ed82feb..b4f8e62f7 100644
--- a/docs/articles/chapter13_CircadianRhythm.html
+++ b/docs/articles/chapter13_CircadianRhythm.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -110,6 +110,28 @@ <h1>13. Circadian Rhythm Analysis</h1>
 
     
     
+<p>Circadian rhythm are the physical, mental, and behavioral changes an
+organism experiences over a 24-hour cycle. GGIR facilitates a number of
+techniques to quantify the movement-component of circadian rhythms as
+discussed in this chapter.</p>
+<p>Most of the techniques are applied in both GGIR part 2 and 6. The
+main differences between these implementations are:</p>
+<ul>
+<li>In GGIR part 2 the circadian rhythm analysis tries to use every
+valid data point in the recording.</li>
+<li>In GGIR part 6 the circadian rhythm analysis uses a specific time
+window as defined by the user, e.g. from first wake-up time till before
+last wake-up time, and omits entire days that are considered invalid or
+incomplete.</li>
+</ul>
+<p>As a result, when your intention is to compare physical activity or
+sleep estimates derived in GGIR, part 6 estimates ensure that the same
+days and time points are used as in the other parts of GGIR. Whereas,
+part 2 does not attempt to do so.</p>
+<p>Further, part 6 offers a few additional circadian rhythm estimates
+such as DFA (discussed below), and when fragmentation analysis is turned
+on (see parameter <code>frag.metrics</code> as discussed in the <a href="https://wadpac.github.io/GGIR/articles/chapter14_BehaviouralFragmentation.html">next
+chapter</a>) activity and sleep fragmentation are also estimated.</p>
 <div class="section level2">
 <h2 id="mxlx">MXLX<a class="anchor" aria-label="anchor" href="#mxlx"></a>
 </h2>
diff --git a/docs/articles/chapter14_BehaviouralFragmentation.html b/docs/articles/chapter14_BehaviouralFragmentation.html
index e70481cb2..fe48a73d1 100644
--- a/docs/articles/chapter14_BehaviouralFragmentation.html
+++ b/docs/articles/chapter14_BehaviouralFragmentation.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
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diff --git a/docs/articles/chapter1_WhatIsGGIR.html b/docs/articles/chapter1_WhatIsGGIR.html
index a55a1bbe8..3eeba4a9e 100644
--- a/docs/articles/chapter1_WhatIsGGIR.html
+++ b/docs/articles/chapter1_WhatIsGGIR.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
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diff --git a/docs/articles/chapter2_Pipeline.html b/docs/articles/chapter2_Pipeline.html
index c0fe4cd00..9f3d718fe 100644
--- a/docs/articles/chapter2_Pipeline.html
+++ b/docs/articles/chapter2_Pipeline.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
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index 94f099cbf..141cc0819 100644
--- a/docs/articles/chapter3_QualityAssessment.html
+++ b/docs/articles/chapter3_QualityAssessment.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
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diff --git a/docs/articles/chapter4_AccMetrics.html b/docs/articles/chapter4_AccMetrics.html
index e9284bbeb..175d8999f 100644
--- a/docs/articles/chapter4_AccMetrics.html
+++ b/docs/articles/chapter4_AccMetrics.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
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diff --git a/docs/articles/chapter5_StudyProtocol.html b/docs/articles/chapter5_StudyProtocol.html
index f5d5ccd79..87482f6e3 100644
--- a/docs/articles/chapter5_StudyProtocol.html
+++ b/docs/articles/chapter5_StudyProtocol.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
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diff --git a/docs/articles/chapter6_DataImputation.html b/docs/articles/chapter6_DataImputation.html
index fd75fb4d1..28c372991 100644
--- a/docs/articles/chapter6_DataImputation.html
+++ b/docs/articles/chapter6_DataImputation.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
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diff --git a/docs/articles/chapter7_DescribingDataWithoutKnowingSleep.html b/docs/articles/chapter7_DescribingDataWithoutKnowingSleep.html
index f94fe6d97..2d9609bcc 100644
--- a/docs/articles/chapter7_DescribingDataWithoutKnowingSleep.html
+++ b/docs/articles/chapter7_DescribingDataWithoutKnowingSleep.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
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diff --git a/docs/articles/chapter8_SleepFundamentalsSibs.html b/docs/articles/chapter8_SleepFundamentalsSibs.html
index 66a6d761c..d019d77c9 100644
--- a/docs/articles/chapter8_SleepFundamentalsSibs.html
+++ b/docs/articles/chapter8_SleepFundamentalsSibs.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/chapter9_SleepFundamentalsGuiders.html b/docs/articles/chapter9_SleepFundamentalsGuiders.html
index b0bd0a0a1..ec86777b7 100644
--- a/docs/articles/chapter9_SleepFundamentalsGuiders.html
+++ b/docs/articles/chapter9_SleepFundamentalsGuiders.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -348,6 +348,13 @@ <h4 id="advanced-sleep-log">Advanced sleep log<a class="anchor" aria-label="anch
 If the date falls within 30 days of the start date of the accelerometer
 recording then the date format is assumed to be found. It starts with
 attempting “Y-m-d” (as in 2015-06-25).</p></li>
+<li><p>If a column name includes the word “impute” then this column is
+treated as a log of the diary imputation for outside GGIR for the
+preceding night. The value will be shown in the visualreport and is
+stored in the time series by GGIR part 5. The imputation codes we used
+for testing have the format C0000 to indicate no imputation for inbed,
+sleeponset, wakeup and outbed, respectively. If one of the numbers would
+be 1 then that would reflect imputation.</p></li>
 </ul>
 </div>
 </div>
diff --git a/docs/articles/chapterInProgress.html b/docs/articles/chapterInProgress.html
index c9c2b419e..4d8b9c86b 100644
--- a/docs/articles/chapterInProgress.html
+++ b/docs/articles/chapterInProgress.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/index.html b/docs/articles/index.html
index 925baa4fb..425c45b92 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/readmyacccsv.html b/docs/articles/readmyacccsv.html
index af55a1d53..1f4d42904 100644
--- a/docs/articles/readmyacccsv.html
+++ b/docs/articles/readmyacccsv.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/authors.html b/docs/authors.html
index 385bd2f01..25f596886 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -160,15 +160,15 @@ <h2>Authors</h2>
       <h2 id="citation">Citation</h2>
       <p><small class="dont-index">Source: <a href="https://github.com/wadpac/GGIR/blob/HEAD/inst/CITATION" class="external-link"><code>inst/CITATION</code></a></small></p>
 
-      <p>van Hees V, Migueles J, Fang Z, Zhao J, Heywood J, Mirkes E, Sabia S (2024).
+      <p>van Hees V, Migueles J, Fang Z, Zhao J, Heywood J, Mirkes E, Sabia S (2025).
 <em>GGIR: Raw Accelerometer Data Analysis</em>.
-<a href="https://doi.org/10.5281/zenodo.1051064" class="external-link">doi:10.5281/zenodo.1051064</a>, R package version 3.1-9, <a href="https://CRAN.R-project.org/package=GGIR" class="external-link">https://CRAN.R-project.org/package=GGIR</a>.
+<a href="https://doi.org/10.5281/zenodo.1051064" class="external-link">doi:10.5281/zenodo.1051064</a>, R package version 3.1-11, <a href="https://CRAN.R-project.org/package=GGIR" class="external-link">https://CRAN.R-project.org/package=GGIR</a>.
 </p>
       <pre>@Manual{,
   title = {{GGIR}: Raw Accelerometer Data Analysis},
   author = {Vincent T {van Hees} and Jairo H Migueles and Zhou Fang and Jing Hua Zhao and Joe Heywood and Evgeny Mirkes and Severine Sabia},
-  year = {2024},
-  note = {R package version 3.1-9},
+  year = {2025},
+  note = {R package version 3.1-11},
   doi = {10.5281/zenodo.1051064},
   url = {https://CRAN.R-project.org/package=GGIR},
 }</pre>
diff --git a/docs/index.html b/docs/index.html
index 56b770010..7e4b6192c 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -23,7 +23,7 @@
 
     <a class="navbar-brand me-2" href="index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/news/index.html b/docs/news/index.html
index d4cb85312..ef4fc05f6 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">GGIR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-9</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.1-11</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -76,6 +76,34 @@ <h1>Changelog</h1>
       <small>Source: <a href="https://github.com/wadpac/GGIR/blob/HEAD/NEWS.md" class="external-link"><code>NEWS.md</code></a></small>
     </div>
 
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="3.1-11" id="changes-in-ggir-version-31-11">CHANGES IN GGIR VERSION 3.1-11<a class="anchor" aria-label="anchor" href="#changes-in-ggir-version-31-11"></a></h2>
+<ul><li>
+<p>Part 2:</p>
+<ul><li><p>Fix bug in determining the number of files to be included in the part 2 report. It may affect the part 2 csv report which may have had less recordings than expected even though the missing recordings are just processed and visible in the output of subsequent parts. <a href="https://github.com/wadpac/GGIR/issues/1252" class="external-link">#1252</a></p></li>
+<li><p>Speed up activity diary extraction. <a href="https://github.com/wadpac/GGIR/issues/1250" class="external-link">#1250</a></p></li>
+<li><p>Make sure part 2 event report, e.g. based on step detection, is saved to csv format with intended sep and dec arguments rather than default.</p></li>
+<li><p>Simplify MVPA parameter specification in part 2 (mvpathreshold, boutcriter). When not specified GGIR copies the corresponding part 5 parameter values (threshold.mod and boutcriter.mvpa). <a href="https://github.com/wadpac/GGIR/issues/1247" class="external-link">#1247</a></p></li>
+</ul></li>
+<li><p>Part 2 and 4: Both activity diary and sleep diary are now always reloaded if the derived copy of it (in .RData) is older than the diary itself. <a href="https://github.com/wadpac/GGIR/issues/1250" class="external-link">#1250</a></p></li>
+<li><p>Visual report: Recently added visual report is now also able to handle epoch sizes not equal to 1 minutes, and now also able to handle recordings with only 1 valid day, these were previously skipped. <a href="https://github.com/wadpac/GGIR/issues/1246" class="external-link">#1246</a></p></li>
+<li>
+<p>Part 4:</p>
+<ul><li><p>Improvements to handling dates in sleeplog as a follow-up to work on <a href="https://github.com/wadpac/GGIR/issues/1243" class="external-link">#1243</a> in the previous release.</p></li>
+<li><p>Added warning when there are two accelerometer recordings with the same participant ID.</p></li>
+<li><p>Change extraction of imputation code from sleep diary, which is now assumed to correspond to preceding night. <a href="https://github.com/wadpac/GGIR/issues/1251" class="external-link">#1251</a></p></li>
+</ul></li>
+<li><p>Part 1, 5 and 6: Update code to be backward compatible with R 4.2.0</p></li>
+<li><p>Part 5: Default values for parameters <code>do.sibreport</code> and <code>save_ms5rawlevels</code> changed to TRUE, for <code>save_ms5raw_format</code> changed to “RData” in order to ease generating visual report, including our explorative nap detection, and working with part 6.</p></li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="3.1-10" id="changes-in-ggir-version-31-10">CHANGES IN GGIR VERSION 3.1-10<a class="anchor" aria-label="anchor" href="#changes-in-ggir-version-31-10"></a></h2>
+<ul><li>
+<p>Part 4:</p>
+<ul><li><p>Speed up the loading of advanced format sleeplog</p></li>
+<li><p>Improve automated recognition of date format in sleeplog</p></li>
+</ul></li>
+</ul></div>
     <div class="section level2">
 <h2 class="pkg-version" data-toc-text="3.1-9" id="changes-in-ggir-version-31-9">CHANGES IN GGIR VERSION 3.1-9<a class="anchor" aria-label="anchor" href="#changes-in-ggir-version-31-9"></a></h2>
 <ul><li>
@@ -83,7 +111,7 @@ <h2 class="pkg-version" data-toc-text="3.1-9" id="changes-in-ggir-version-31-9">
 <ul><li><p>Identification of daylight saving time days in the detection of the spt is improved as it had the risk to misclassify partial last days as daylight saving time.</p></li>
 <li><p>In days classified as daysleeper, the window over which the fraction_night_invalid is calculated now also shifts to 6pm-6pm, as it used to report the nonwear within 12pm-12pm.</p></li>
 </ul></li>
-<li><p>Vignette: Document that missing dates can be skipped i advanced format sleeplog</p></li>
+<li><p>Vignette: Now documented that missing dates can be skipped in advanced format sleeplog.</p></li>
 <li><p>Step detection: Include R script for Verisense step detection inside GitHub repository ‘user-scripts’ folder to ease future maintenance.</p></li>
 </ul></div>
     <div class="section level2">
@@ -97,7 +125,7 @@ <h2 class="pkg-version" data-toc-text="3.1-8" id="changes-in-ggir-version-31-8">
 <li><p>Visual report: Added new visualreport that is automatically generated when visualreport = TRUE and intended to eventually replace the problematic legacy report. Add parameter old_visualreport to turn off the old visualreport generation. <a href="https://github.com/wadpac/GGIR/issues/1173" class="external-link">#1173</a></p></li>
 <li><p>Part 4 + 5 + visualreport: If sleeplog has both reported sleeplog and time in bed process both. In part 4 we will still use one of the two but in the visualreport we display both. <a href="https://github.com/wadpac/GGIR/issues/967" class="external-link">#967</a></p></li>
 <li><p>Part 2: Add parameters nonwearFiltermaxHours and nonwearFilterWindow to give user the option to filter short lasting nighttime nonwear <a href="https://github.com/wadpac/GGIR/issues/1218" class="external-link">#1218</a>.</p></li>
-<li><p>Part 5 and 6: part6_threshold_combi when not specified now defaults to first threshold as specified for light, moderate and vigorous intensity respectively.</p></li>
+<li><p>Part 5 and 6: part6_threshold_combi when not specified now defaults to first threshold as specified for light, moderate and vigorous intensity, respectively.</p></li>
 </ul></div>
     <div class="section level2">
 <h2 class="pkg-version" data-toc-text="3.1-7" id="changes-in-ggir-version-31-7">CHANGES IN GGIR VERSION 3.1-7<a class="anchor" aria-label="anchor" href="#changes-in-ggir-version-31-7"></a></h2>
@@ -121,7 +149,7 @@ <h2 class="pkg-version" data-toc-text="3.1-6" id="changes-in-ggir-version-31-6">
 <li>
 <p>Part 5:</p>
 <ul><li><p>Moving LXMX from part 5 to part 6 as these are circadian rhythm analysis.</p></li>
-<li><p>Omitted fragmentation metrics for spt window as not meaningful given that part 5 only focusses on valid daytime behaviours.</p></li>
+<li><p>Omitted fragmentation metrics for spt window as not meaningful given that part 5 only focuses on valid daytime behaviours.</p></li>
 <li><p>includenightcrit.part5 added to allow for controlling inclusion of windows in part 5 based on their amount of valid data during spt window. Default remains the same.</p></li>
 <li><p>Fix incorrect usage of part 5 inclusion criteria testing, which used fraction as percentage.</p></li>
 </ul></li>
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 7343e1982..3774a111e 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -30,7 +30,7 @@ articles:
   HouseHoldCoanalysis: HouseHoldCoanalysis.html
   readmyacccsv: readmyacccsv.html
   TutorialDaySegmentAnalyses: TutorialDaySegmentAnalyses.html
-last_built: 2024-12-24T10:39Z
+last_built: 2025-01-23T11:51Z
 urls:
   reference: https://wadpac.github.io/GGIR/reference
   article: https://wadpac.github.io/GGIR/articles
diff --git a/docs/search.json b/docs/search.json
index 6e6bc6071..ad6078916 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1 +1 @@
-[{"path":"https://wadpac.github.io/GGIR/articles/chapter0_Contributing.html","id":"if-you-have-coding-skills","dir":"Articles","previous_headings":"","what":"If you have coding skills…","title":"Contributing","text":"welcome contributions development, maintenance, documentation GGIR. Please find GGIR’s contributing guidelines .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter0_Contributing.html","id":"if-you-do-not-have-coding-skills-","dir":"Articles","previous_headings":"","what":"If you do not have coding skills….","title":"Contributing","text":"might coding skills contribute code base GGIR, still contribution important us. example: Apply funding support development maintenance GGIR. GGIR free software entirely depend users applying funding sponsor efforts. Funding used support development new functionalities, support improvement existing GGIR software code, support development better open-access training materials instruction videos. Report issues questions GGIR google group. Proofread GGIR documentation inform us miss something found difficult follow. Take independent initiatives complement efforts. example, Prof. Stuart Fairclough created series GGIR video tutorials, Wei Guo colleagues created R package mMARCH.AC post-process GGIR output described supporting journal article, maybe initiatives aware . Please communicate initiatives via GGIR google group /GGIR maintainer Vincent van Hees avoid duplicated work support needed.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter0_GetStarted.html","id":"run-ggir-for-the-first-time","dir":"Articles","previous_headings":"","what":"Run GGIR for the first time","title":"Get started: the GGIR R package","text":"First, need place file(s) folder computer. Make sure folder contains accelerometer files. Use following command run GGIR. datadir refers directory located accelerometer files. outputdir refers directory want store GGIR’s output. minutes, able see output directory gets populated files, reports, visualizations. command let GGIR run default settings (parameters), analysis tailored yet study design research question. documentation chapters find website guide .","code":"library(GGIR)  GGIR(datadir=\"C:/mystudy/mydata\", outputdir=\"D:/myresults\")"},{"path":"https://wadpac.github.io/GGIR/articles/chapter0_GetStarted.html","id":"related-links","dir":"Articles","previous_headings":"","what":"Related links","title":"Get started: the GGIR R package","text":"Install R GGIR Get support Suitable file formats GGIR","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter0_Installation.html","id":"install-r-and-rstudio","dir":"Articles","previous_headings":"","what":"Install R and RStudio","title":"Installation of the GGIR R Package","text":"Download install R Download install RStudio","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter0_Installation.html","id":"install-ggir","dir":"Articles","previous_headings":"","what":"Install GGIR","title":"Installation of the GGIR R Package","text":"Install latest released version GGIR dependencies CRAN. can one command R command line: Alternatively, can install latest development version, might include additional bug fixes functionalities. get development version, please use:","code":"install.packages(\"GGIR\", dependencies = TRUE) library(GGIR) install.packages(\"remotes\", dependencies = TRUE) remotes::install_github(\"wadpac/GGIR\", dependencies = TRUE) library(GGIR)"},{"path":"https://wadpac.github.io/GGIR/articles/chapter0_Installation.html","id":"other-packages-you-may-need","dir":"Articles","previous_headings":"","what":"Other packages you may need","title":"Installation of the GGIR R Package","text":"Additionally, use-cases need install one multiple additional packages. Note packages installed default, please follow instructions : want derive Neishabouricounts (.neishabouricounts = TRUE), install actilifecounts package install.packages(\"actilifecounts\") want process Sensewear xlsx files (dataFormat = \"sensewear\"), install readxl package install.packages(\"readxl\")","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter0_Installation.html","id":"installing-older-versions-of-a-package","dir":"Articles","previous_headings":"","what":"Installing older versions of a package","title":"Installation of the GGIR R Package","text":"aiming reproduce historical analysis critical install correct package version. explain GGIR release 2.4-0 work release. Note GGIR archived CRAN (major releases ) GitHub (releases). CRAN archive: see releases available CRAN check : https://cran.r-project.org/src/contrib/Archive/GGIR/. GitHub: see releases available CRAN check : https://github.com/wadpac/GGIR/releases.","code":"require(remotes) install_version(\"GGIR\", version = \"2.4-0\", repos = \"http://cran.us.r-project.org\") require(remotes) install_github(\"wadpac/GGIR\", ref = \"2.4-0\")"},{"path":"https://wadpac.github.io/GGIR/articles/chapter0_Support.html","id":"questions-and-problems","dir":"Articles","previous_headings":"","what":"Questions and problems","title":"How can I get service and support?","text":"general questions issues please join GGIR google group create new thread. report problem group always try create minimal example someone else can use reproduce investigate problem. However, familiar GitHub, also welcome report issue via GitHub issue tracker. Please use message template displayed. Note support places based voluntary efforts encourage try help users questions. Questions valuable help us understand challenges run occasionally help us identify bug code. make practical, please AVOID sending questions personal messages. Instead, post public platforms. approach allows others benefit discussions, minimises need us respond inquiries repeatedly, enhances likelihood others can answer questions ’re unavailable, acknowledges volunteer effort invested responding queries.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter0_Support.html","id":"commercial-training-services","dir":"Articles","previous_headings":"","what":"Commercial training services","title":"How can I get service and support?","text":"Accelting provides online training options using GGIR, please find website. questions, please hesitate reach via: training@accelting.com.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter0_Support.html","id":"dedicated-support","dir":"Articles","previous_headings":"","what":"Dedicated support","title":"How can I get service and support?","text":"need dedicated support use GGIR, want GGIR modified enhanced needs, please contact Vincent van Hees.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter10_SleepAnalysis.html","id":"sleep-analysis","dir":"Articles","previous_headings":"","what":"Sleep analysis","title":"10. Sleep Analysis","text":"Sleep analysis GGIR comes three stages: discrimination sustained inactivity wakefulness periods, discussed chapter 8. Identification time windows guide eventual sleep detection, discussed chapter 9. Assess overlap windows identified step 1 2, use define Sleep Period Time window (SPT) time bed window (TimeInBed) discussed chapter. previous two chapters learnt first two steps chapter discuss last step.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter10_SleepAnalysis.html","id":"sleep-period-time-spt-window-or-time-in-bed","dir":"Articles","previous_headings":"Sleep analysis","what":"Sleep Period Time (SPT) window or Time in Bed","title":"10. Sleep Analysis","text":"two scenarios: guider reflects approximation Sleep Period Time window, window sleep onset waking end night, SIB fully partially overlaps guider considered sleep. guider reflects Time Bed SIB fully overlaps guider considered sleep. scenario sleep latency sleep efficiency can estimated included GGIR part 4 report. cases start first SIB considered sleep onset end last SIB considered waking . guiders, “HorAngle”, parameter sleepwindowType automatically set “SPT” corresponding scenario 1, attempt made estimate sleep latency sleep efficiency. use guider sleeplog reflects Time Bed need set parameter sleepwindowType = \"TimeInBed\" tell GGIR follow scenario 2. However, parameter sib_must_fully_overlap_with_TimeInBed suprres requirement sleepwindowType = \"TimeInBed\" SIB fully overlaps “TimeInBed”. example, want SIB counted sleep fully overlap TimeInBed prefer rely SIB define end night regardless overlap TimeInBed set sib_must_fully_overlap_with_TimeInBed = c(TRUE, FALSE).","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/chapter10_SleepAnalysis.html","id":"cleaningcode","dir":"Articles","previous_headings":"Sleep analysis > Quality assurance","what":"Cleaningcode","title":"10. Sleep Analysis","text":"monitor possible problems sleep detection, output variable cleaningcode stored per night. Cleaningcode per night (noon-noon 6pm-6pm described ) can one following values: 0: sleep log available SPT identified. 1: sleep log available, alternative guider used (HDCZA default) SPT identified . 2: enough valid accelerometer data present night, parameter includenightcrit used define many valid hours need. 3: accelerometer data available. 4: nights analysed person. 5: SPT estimated based guider , either SIB found entire guider window complicates defining start end SPT, user specified ID number recording night number data_cleaning_file, , tell GGIR rely guider rely accelerometer data particular night. 6: sleep log available also alternative guider (HDCZA/HorAngle) failed specific night use average guider estimates nights recording guider night. HDCZA/HorAngle estimates also available entire recording use L5+/-12 estimate night.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter10_SleepAnalysis.html","id":"visual-inspection-of-classifications","dir":"Articles","previous_headings":"Sleep analysis > Quality assurance","what":"Visual inspection of classifications","title":"10. Sleep Analysis","text":"overlap sib guiders difficult review quantitative way, GGIR offers option export visualisation, parameter .visual = TRUE. manage number visualisations generated possible tell GGIR show outliers. , outliers defined difference guider edge final classification sleep onset wakeup time larger parameter criterror. set parameter outliers.= TRUE nights considered outlier displayed. functionality useful reviewing classifications large data sets use sleep logs. Visual inspection outliers way can example help identify data entry errors sleep logs.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter10_SleepAnalysis.html","id":"data-cleaning-file","dir":"Articles","previous_headings":"Sleep analysis > Quality assurance","what":"Data cleaning file","title":"10. Sleep Analysis","text":"data quality check may observe adjustments needed. Parameter data_cleaning_file (path csv file create) allows specify individuals nights part4 entirely rely guider. first column csv file column name ID column relyonguider_part4 specify night. night_part4 allows tell GGIR night(s) omitted part 4.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter10_SleepAnalysis.html","id":"sleep-metrics-available-in-ggir","dir":"Articles","previous_headings":"Sleep analysis","what":"Sleep metrics available in GGIR","title":"10. Sleep Analysis","text":"full overview sleep variables part 4 see: https://cran.r-project.org/web/packages/GGIR/vignettes/GGIR.html#42_Output_part_4 Among assume intuitive: sleep onset wakeup Sleep duration SPT, accumulate sleep time (sustained inactivity bouts classified sleep) WASO, time spent wakefulness sleep onset. However, possible concepts need clarifications:","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter10_SleepAnalysis.html","id":"sleep-regularity-index-sri","dir":"Articles","previous_headings":"Sleep analysis > Sleep metrics available in GGIR","what":"Sleep Regularity Index (SRI)","title":"10. Sleep Analysis","text":"measure sleep regularity successive days, first described Phillips colleagues. SRI can value -100 100, 100 reflects perfect regularity (identical days), 0 reflects random pattern, -100 reflects perfect reversed regularity. SRI proposed calculated based seven, multitude seven, consecutive days data without missing values. avoid possible role imbalanced data final estimate. However, renders many datasets unsuitable analysis leads painful loss sample size statistical power.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter10_SleepAnalysis.html","id":"sleep-regularity-index-dealing-with-unbalanced-data","dir":"Articles","previous_headings":"Sleep analysis > Sleep metrics available in GGIR > Sleep Regularity Index (SRI)","what":"Sleep Regularity Index – Dealing with unbalanced data","title":"10. Sleep Analysis","text":"address , implemented SRI GGIR per day-pair. Per day-pair GGIR now stores SRI value fraction 30 second epoch-pairs days valid. fraction can found output variable name SriFractionValid. default, day-pairs excluded fraction 0.66. familiar GGIR threshold coupled 16-hour default value parameter “includenightcrit”. example, set parameter “includenightcrit = 12”, fraction threshold : 12 / 24 = 0.5. Note implemented SRI calculation accounts missing values denominator. result, SRI value interpretation remains unchanged. 30 second epoch setting automatically applied, even rest GGIR process works different epoch duration. day-pair level estimates stored variable SleepRegularityIndex GGIR part 4 .csv-report sleep. , GGIR also stores person-level aggregates : plain average valid days, average valid weekend days, average valid week days. GGIR input arguments needed invoke SRI calculation. calculation automatically performed updating GGIR processing data.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter10_SleepAnalysis.html","id":"sleep-regularity-index-benefits-of-the-revised-approach","dir":"Articles","previous_headings":"Sleep analysis > Sleep metrics available in GGIR > Sleep Regularity Index (SRI)","what":"Sleep Regularity Index – Benefits of the revised approach","title":"10. Sleep Analysis","text":"enables user study day-pair day-pair variation SRI, role day-pair inclusion criteria. access SRI day-pair level makes possible account imbalanced datasets via multilevel regression analysis applied output GGIR, day-pair one model levels.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter10_SleepAnalysis.html","id":"nap-detection","dir":"Articles","previous_headings":"Sleep analysis > Sleep metrics available in GGIR > Sleep Regularity Index (SRI)","what":"Nap detection","title":"10. Sleep Analysis","text":"references daytime nap detection GGIR based experimental functionality requires ongoing investigation. functionality matured expand documentation accordingly.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter10_SleepAnalysis.html","id":"key-parameters","dir":"Articles","previous_headings":"Sleep analysis","what":"Key parameters","title":"10. Sleep Analysis","text":"parameters part params_sleep category discussed section “Sleep parameters” https://cran.r-project.org/web/packages/GGIR/vignettes/GGIRParameters.html .visual, outliers., criterror. excludefirstlast. def.noc.sleep includenightcrit data_cleaning_file","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter10_SleepAnalysis.html","id":"related-output","dir":"Articles","previous_headings":"Sleep analysis","what":"Related output","title":"10. Sleep Analysis","text":"GGIR stores two type output: cleaned full output. cleaned output invalid nights removed, full output nights included. specifically, night excluded ‘cleaned’ results based following criteria: study proposed sleep log individuals, nights excluded sleep log used guider. words: nights cleaningcode equal 0 variable sleep log used equals FALSE). study propose sleep log individuals, nights removed cleaningcode higher 1. aware using full output working wrist accelerometer data, missing entries sleep log asks Time Bed replaced HDCZA estimates SPT. Therefore, extra caution taken working full output. Notice part 4 focused sleep research. chapters discuss analysis done part 5. , choice guider may considered less important, estimate time bed considered useful. , may see night excluded cleaned results part 4 still appears cleaned results part 5.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter11_DescribingDataCutPoints.html","id":"why-behavioural-classes","dir":"Articles","previous_headings":"","what":"Why behavioural classes?","title":"11. Physical Activity Fundamentals: Describing the data with cut-points","text":"Descriptive variables average acceleration per day recording discussed previous chapter powerful indicators physical activity. Extensive evidence exists association physical activity related energy expenditure measured indirect calorimetry. Similarly, scientific value MX metrics intensity gradient discussed chapter 7 shown several studies (example: Rowlands, 2019a; Rowlands 2019b). However, physical activity research community historically expressed strong interest measures physical activity can expressed time per behavioural class per day. chapter discuss GGIR facilitates .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter11_DescribingDataCutPoints.html","id":"construct-definition","dir":"Articles","previous_headings":"","what":"Construct definition","title":"11. Physical Activity Fundamentals: Describing the data with cut-points","text":"popular approach define behavioural classes physical activity research distinguish called intensity levels. , common distinguish sedentary behaviour (SB), light physical activity (LIPA), moderate, vigorous physical activity. latter two categories often combined moderate vigorous physical activity (MVPA). Inside GGIR refer sedentary behaviour inactivity emphasize methods quantify mainly lack activity rather sitting reclying posture. However, intensity levels behavioural classes lack feasible operational construct definition caused methodological discrepancies decades. elaborate reflection can found blog post. situation forced us pragmatic use operational construct definition intensity levels feasible accelerometer data.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter11_DescribingDataCutPoints.html","id":"classification-based-on-cut-points","dir":"Articles","previous_headings":"","what":"Classification based on cut-points","title":"11. Physical Activity Fundamentals: Describing the data with cut-points","text":"common classify intensity levels accelerometer data evaluating whether acceleration , , certain acceleration level(s). acceleration magnitude(s) use threshold(s), also known cut-points. use thresholds (cut-points) intended crude indicator time spent intensity levels sufficient rank individuals amount time spent behaviours. cut-point approach indisputably powerful method far drive physical activity research. See published cut-points use guidance choosing cut-points dataset. discussed detail , acceleration (intensity) level classification done GGIR parts 2 5.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter11_DescribingDataCutPoints.html","id":"epoch-length","dir":"Articles","previous_headings":"","what":"Epoch length","title":"11. Physical Activity Fundamentals: Describing the data with cut-points","text":"Although accelerometers collect data much higher sampling frequency, work aggregated values (e.g. 1 5 second epochs) following reasons: Accelerometers often used describe patterns metabolic energy expenditure. Metabolic energy expenditure typically defined per breath per minute (indirect calorimetry), per day (room calorimeter), per multiple days (doubly labelled water method). order evaluate methods reference standards, need work similar time resolution. Collapsing data epoch summary measures helps standardise output across data collected different sampling frequencies studies. little evidence raw data accurate representation body acceleration. scientific evidence validity accelerometer data far based epoch aggregates. Short epoch lengths, 1 5 seconds, sensitive sporadic behaviours often combined bout detection identify MVPA sustained behaviour. Longer epochs, 30 60 seconds, problem therefore easier use without bout detection. epoch length GGIR default 5 seconds, can set first value vector specified parameter windowsizes. Although discuss epoch length context MVPA, please note epoch length influences many outcomes GGIR sleep analysis. GGIR part 5 offers option aggregate time series 1 minute epoch length order physical activity research 1 minute resolution leaving sleep detection relies shorter epoch length untouched (see parameter part5_agg2_60seconds=TRUE).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter11_DescribingDataCutPoints.html","id":"bout-detection","dir":"Articles","previous_headings":"","what":"Bout detection","title":"11. Physical Activity Fundamentals: Describing the data with cut-points","text":"Behavioural bouts used frequently defined sustained time spent ina behavioural class adhering specific temporal criteria. GGIR facilitates processing data without accounting bouts. motivation look bouts can one following: idea behaviour certain minimum duration contributes certain physiological benefits. make classification behaviour consistent self-report data, sensitive duration specific duration. aid studying fragmentation behaviour. account sporadic nature behaviour working short epochs. define bout need answer series questions: cut-point ? epoch length ? minimum duration bout ? allow gaps bout breaks behaviour interest? yes 4, percentage bout duration, absolute minimum seconds, combination ? yes 4, bout gaps counted towards time spent bouts? first last epoch need meet threshold criteria? order bouts extracted? example, short MVPA bout part longer Inactivity bout two prevails? many bout categories ? GGIR facilitates following freedom bout detection: User decides : Acceleration thresholds light, moderate, vigorous intensity mvpathreshold part 2, threshold.lig, threshold.mod, threshold.vig part 5. Fraction time cut-point criteria need met (light, inactive, MVPA) boutcriter part 2 boutcriter.lig, boutcriter.mod, boutcriter.vig part 5. Bout duration range. part 2 mvpadur part 5 boutdur.lig, boutdur.mod, boutdur.mvpa. functionality slightly different part 2 part 5 discussed . Epoch length windowsizes part5_agg2_60second. User decide : Maximum bout gap 1 minute, fraction time cut-point criteria need met less 100% First last epoch need meet cut-point criteria. Number intensity levels, always: inactive, light MVPA. Order bouts calculated (1 MVPA; 2 inactive; 3 Light)","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter11_DescribingDataCutPoints.html","id":"controlling-the-time-window-of-analysis","dir":"Articles","previous_headings":"","what":"Controlling the time window of analysis","title":"11. Physical Activity Fundamentals: Describing the data with cut-points","text":"discussed chapter 7, possible tell GGIR part 2 part 5 extract variables per segment day. parameter qwindow can find detailed discussion Annex Day segment analysis.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter11_DescribingDataCutPoints.html","id":"key-parameters","dir":"Articles","previous_headings":"","what":"Key parameters","title":"11. Physical Activity Fundamentals: Describing the data with cut-points","text":"parameters needed MVPA estimates GGIR part 2 different parameters used estimating MVPA, LIPA Inactivity part 5.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter11_DescribingDataCutPoints.html","id":"physical-activity-cut-point-parameter-ggir-part-2","dir":"Articles","previous_headings":"Key parameters","what":"Physical activity cut-point parameter GGIR part 2","title":"11. Physical Activity Fundamentals: Describing the data with cut-points","text":"GGIR part 2, MVPA estimated since sleep classified point. threshold(s) MVPA used GGIR part 2 set parameter mvpathreshold. can specify single value vector multiple values, time spent MVPA derived . GGIR part 2 always provides six distinct approaches MVPA calculation controlled parameters mvpathreshold, boutcriter, mvpadur, first element vector windowsizes. , MVPA provides time spent MVPA based : 5 second, 1 minute 5 minute epochs bouts 5 second epochs 3 different minimum bout duration specified parameter mvpadur.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter11_DescribingDataCutPoints.html","id":"physical-activity-cut-point-parameter-ggir-part-5","dir":"Articles","previous_headings":"Key parameters","what":"Physical activity cut-point parameter GGIR part 5","title":"11. Physical Activity Fundamentals: Describing the data with cut-points","text":"bout durations used separate estimates used complimentary case part 5. example, specifying boutdur.mod = c(5, 10) part 5 result estimate time spent bouts lasting 5 till 10 minutes bouts lasting 10 minutes longer. example, simple scenario consider bouts minimum length 10 minutes, also possible subdivide bouts lasting [1, 5) [5, 10) [10, ∞) minutes. parameters related cut-points bout detection listed “Physical activity parameters”.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter11_DescribingDataCutPoints.html","id":"related-output","dir":"Articles","previous_headings":"","what":"Related output","title":"11. Physical Activity Fundamentals: Describing the data with cut-points","text":"GGIR part 2 csv reports find: Time spent MVPA GGIR part 5 csv reports find: Time spent MVPA Time spent LIPA Time spent inactivity (abbreviated ) chapter 7 discussed structure part 2 output. next chapter (chapter 12) provide detailed discussion part 5 output. overview output variables see GGIR output annex.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter12_TimeUseAnalysis.html","id":"creating-a-multi-variate-time-series-object","dir":"Articles","previous_headings":"","what":"Creating a multi-variate time series object","title":"12. Time-use Analysis","text":"first step need map happens recording. , GGIR code combines information derived parts 2, 3 4 multi-variate single time series object, including: Timestamp log data classified invalid. Average acceleration derived GGIR part 2, invalid epochs imputed acceleration metric used specified parameter acc.metric. Sleep classifications GGIR part 3 4. Behavioural class code, GGIR part 5 derives behavioural classes based magnitude acceleration sleep classification. exact number behavioural classes codified depends parameters set, constructed codified : sleep period time window: - Sleep - Wakefulness low acceleration - Wakefulness moderate acceleration - Wakefulness vigorous acceleration waking hours day: - Inactivity unbouted - Inactivity bouted, subdivided one multiple bout durations - Total inactivity time - LIPA unbouted - LIPA bouted, subdivided one multiple bout durations - Total LIPA time - Moderate activity unbouted - Vigorous activity unbouted - MVPA bouted, subdivided one multiple bout durations - Total MVPA time possible export time series generated, discussed towards end chapter.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter12_TimeUseAnalysis.html","id":"defining-the-time-windows","dir":"Articles","previous_headings":"","what":"Defining the time windows","title":"12. Time-use Analysis","text":"GGIR part 2 defined days midnight midnight, GGIR part 4 typically defined nights noon noon. access sleep timing, GGIR part 5 offers additional definitions day. However, given definitions day becoming different calendar day, refer windows data. GGIR part 5 facilitates following time window definitions, can selected parameter timewindow: “WW” “OO”, onset waking times guided estimates part 4, missing, part 5 attempt retrieve estimate guider method. Note parameter timewindow can consist one options beforementioned combination , example, default value timewindow = c(\"MM\", \"WW\"). recordings end night early morning sleep estimates night likely affected. example, recording ends 10am sure participant sleep 10am, recording ends 2am sure sleep onset time reliably estimated. handle ignore final window data, set parameter require_complete_lastnight_part5 = TRUE (default).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter12_TimeUseAnalysis.html","id":"defining-segments-within-the-mm-window","dir":"Articles","previous_headings":"Defining the time windows","what":"Defining segments within the MM window","title":"12. Time-use Analysis","text":"default GGIR segments window waking hours day (referred day) sleep period time window (referred spt). Additionally, timewindow set “MM”, day segment specific analysis performed based segments defined parameters qwindow . Please see annex day segmentation information.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter12_TimeUseAnalysis.html","id":"metrics-calculated-per-window-and-per-segment","dir":"Articles","previous_headings":"","what":"Metrics calculated per window and per segment","title":"12. Time-use Analysis","text":"GGIR provides following metrics time windows calculated, .e., full day, awake time, sleep period time, well (optionally) day segments might provided via parameter qwindow. Duration: Time spent minute per behavioural class. Acceleration: Average acceleration per behavioural class Number blocks: Number blocks per behavioural class, distinction made bouted unbouted, except total number blocks per intensity levels (Nblocks_day_total_IN, Nblocks_day_total_LIPA, Nblocks_day_total_MOD, Nblocks_day_total_VIG). Number bouts: Number bouts per behavioural class. Fragmentation: fragmentation metrics discussed previous chapter. distinction made bouted unbouted behavour. Note fragmentation classes sometimes group multiple intensity levels, e.g. fragmentation physical activity reflects fragmentation LIPA MVPA combined relative Inactive time. side note - multiply Acceleration Duration given class, combine information variables, arrive volume measure behaviour. similar construct calories time. think much richer way describing data opposed conventional approach looks either time spent per behavioural class average acceleration entire day.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter12_TimeUseAnalysis.html","id":"complementary-variables","dir":"Articles","previous_headings":"Metrics calculated per window and per segment","what":"Complementary variables","title":"12. Time-use Analysis","text":"primary interest sleep research recommend work GGIR part 4 reports. However, want look interactions behaviour sleep, GGIR part 5 reports include sleep estimates used part 5 analysis. Note part 5 criteria sleep estimate inclusion different part 4. part 5 happy estimate, even accelerometer worn night. Additionally, part 5 also come duration awake time, sleep period time, full-day windows, percentage non-wear (read invalid data, typically non-wear).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter12_TimeUseAnalysis.html","id":"seemingly-overlapping-variables-between-ggir-part-2-and-part-5-output","dir":"Articles","previous_headings":"Metrics calculated per window and per segment","what":"Seemingly overlapping variables between GGIR part 2 and part 5 output","title":"12. Time-use Analysis","text":"might noticed, variables reported GGIR part 2 part 5, average acceleration bouts moderate--vigorous physical activity. However, please note values necessarily identical following reasons: Times MVPA can happen . part 2 MVPA can happen time day, never overlap midnight. part 5 MVPA happens waking hours single day, can overlap midnight midnight part sleep period time window. use dayborder parameter value equal zero: midnight scenario actually midnight time set parameter dayborder, e.g. 2 equates 2am. . Difference epoch length. GGIR part 5 comes possibility aggregate epochs 60 seconds parameter part5_agg2_60seconds. parameter set TRUE, full time series aggregated 60 seconds, contrast default 5 second epoch length used part 2. Different time window definition. GGIR part 2 always uses “MM” definition days. GGIR part 5, option define days different ways (see ).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter12_TimeUseAnalysis.html","id":"exporting-time-series","dir":"Articles","previous_headings":"Metrics calculated per window and per segment","what":"Exporting time series","title":"12. Time-use Analysis","text":"export time series set parameter save_ms5rawlevels = TRUE. GGIR part 5 store subfolder meta/ms5.outraw subfolder named unique MVPA threshold combination used. behavioral classes included numbers, legend classes numbers stored separate legend file meta/ms5.outraw folder named “behavioralcodes2020-04-26.csv” date correspond date ran GGIR. Note time series exported GGIR part 5 includes acceleration metric specified parameter acc.metric (default = “ENMO”), angle metrics selected, angle metrics. want explore multiple acceleration metric values, please see documentation parameter epochvalues2csv discussed chapter 3. Additional input parameters may interest: save_ms5raw_format character string specify data stored: either “csv” (default) “RData”. used save_ms5rawlevels=TRUE. save_ms5raw_without_invalid Boolean indicate whether remove invalid days time series output files. used save_ms5rawlevels=TRUE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter12_TimeUseAnalysis.html","id":"key-arguments","dir":"Articles","previous_headings":"","what":"Key arguments","title":"12. Time-use Analysis","text":"threshold.lig, threshold.mod, threshold.vig boudur., boutdur.lig, boutdur.mvpa boutcriter., boutcriter.lig, boutcriter.mvpa frag.metrics timewindow part5_agg2_60seconds save_ms5rawlevels save_ms5raw_format save_ms5raw_without_invalid","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter12_TimeUseAnalysis.html","id":"related-output","dir":"Articles","previous_headings":"","what":"Related output","title":"12. Time-use Analysis","text":"https://cran.r-project.org/web/packages/GGIR/vignettes/GGIR.html#43_Output_part_5 find detailed discussion part 5 output. summary, part 5 produces following files. - Day level summary - Person level summary - Day level summary behaviour per segment day - analysis - Person level summary behaviour per segment day - Variable dictionary (see ) - Time series - Pdf reports vsiualisation","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter12_TimeUseAnalysis.html","id":"variables-dictionary","dir":"Articles","previous_headings":"Related output","what":"Variables dictionary","title":"12. Time-use Analysis","text":"Considering different time window segmentation options, number metrics calculated, different aggregation strategies (.e., plain averages, weighted averages, -optionally- weekday weekend-day averages), number variables exported Part 5 can high. help understanding interpretation variables, GGIR Part5 exports variable dictionary daysummary personsummary csv reports. dictionaries include list variable names calculated analyses together definition variables.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter13_CircadianRhythm.html","id":"mxlx","dir":"Articles","previous_headings":"","what":"MXLX","title":"13. Circadian Rhythm Analysis","text":"MXLX looks continuous least (LX) (MX) active X hour window day, X defined parameter winhr. LX MX, GGIR calculates average acceleration, start time, argument iglevels specified also intensity gradient. parameter winhr vector MX LX derived value vector. Within GGIR part 2 MXLX calculated per calendar day , argument qwindow specified, per segment day. Within GGIR part 5 MXLX calculated per window. used combination GENEActiv Axivity accelerometer brands, LUX estimates per LX MX also included GGIR part 5 csv reports. MX metric described confused MX metrics proposed Rowlands et al. looks accumulated active time may always continuous time. MX metrics Rowlands et al. discussed .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter13_CircadianRhythm.html","id":"extended-cosinor-analysis","dir":"Articles","previous_headings":"","what":"(Extended) Cosinor analysis","title":"13. Circadian Rhythm Analysis","text":"Cosinor analysis quantifies circadian 24 hour cycle. Cosinor analysis refers fitting cosine function log transformed time series, extended cosinor analysis refers fitting non-linear transformation traditional cosinor curve Marler et al. Statist. Med. 2006 (doi: 10.1002/sim.2466). Corinos analyssis run default, tell GGIR perform analyse specify parameter cosinor = TRUE. implementation follows: acceleration metric specified parameter acc.metric used. Acceleration metric values averaged per minute expressed mg input g, log transformed log(acceleration + 1). Invalid data points caused non-wear set missing (NA) want imputation used elsewhere GGIR influence Cosinor analysis. imputation technique generally come assumptions circadian rhythm. part 2 GGIR uses valid data recording part 6 use valid data interval defined parameter part6Window, e.g. first wake-time till last wake-time. GGIR looks first valid data point time series selects maximum integer number recording days following data point. Day Saving Time occurs time series duplicated timestamps clock moves backward ignored missing timestamps clock moves forward inserted missing values. Cosinor models fitted using functions ActCosinor ActExtendCosinor R package ActCR. , ActExtendCosinor uses anti-logistic function transformation. time offset start time series used following midnight used reverse offset ActCR results, ensure acrophase acrotime can interpreted relative midnight. Time series corresponding fitted models stored inside part 2 milestone data stored output subfolder meta/ms2.facilitate visual inspection. moment used GGIR visualisation, may want look try plot . stored object SUM$cosinor_ts.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter13_CircadianRhythm.html","id":"intradaily-variability-iv-and-interdaily-stability-is","dir":"Articles","previous_headings":"","what":"Intradaily Variability (IV) and Interdaily Stability (IS)","title":"13. Circadian Rhythm Analysis","text":"IV first proposed Witting W et al. 1990 van Someren EJ, et al. 1996. measures constant routine activity several days ranges 0 1, values close 1 indicate constant routine. IV measures variability activity hour hour throughout days. ranges 0 +∞\\infty, value close 2 indicates fragmented rhythm, >2 indicates ultradian rhythm (uncommon). GGIR implementation IV since GGIR release 3.1-6 described Danilevicz et al. 2024. implementation replaces experimental implementation IV present GGIR since release 1.5-1. experimental implementation sure go raw acceleration signal indicator active aspect documented original publications. Similarly, sure deal missing data. However, issues resolved release 3.1-6: active now defined mean acceleration metric value light physical activity threshold specified parameter threshold.lig. Missing values left missing imputed, algorithm now accounts . new implementation documented Danilevicz et al. 2024 compatible older experimental implementation. Parameters IVIS.activity.metric, IVIS_windowsize_minutes, IVIS_epochsize_seconds, IVIS_acc_threshold used longer needed deprecated. Cosinor analysis compatible IV sometimes used measure behavioural robustness conducting Cosinor analysis. However, work combination two outcomes seems important calculated time series. Therefore, cosinor = TRUE, IV calculated twice: part default IV analysis discussed , part Cosinor analysis using log transformed time series. Cosinor-compatible IV estimates stored output variables cosinorIV cosinorIS.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter13_CircadianRhythm.html","id":"phi","dir":"Articles","previous_headings":"","what":"phi","title":"13. Circadian Rhythm Analysis","text":"Phi indicates correlated multi-day acceleration time series hour shift, also known first-order auto-correlation first-order autoregressive model AR(1). higher phi value indicates higher autocorrelation, phi close zero even negative indicates fragmented behavior. detailed discussion phi see Dickey Fuller (1979) Danilevicz et al. 2024. Phi calculated default GGIR part 2 part 6 parameter part6CR set TRUE.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/chapter13_CircadianRhythm.html","id":"self-similarity-paramerter-ssp","dir":"Articles","previous_headings":"Detrended fluctionation analysis (DFA)","what":"Self-similarity paramerter (SSP)","title":"13. Circadian Rhythm Analysis","text":"self-similarity paramter (SSP) also known scaling exponent alpha. SSP real number zero two. Values range (0, 1) indicate stationary motion behaviour. Values int range (1, 2 indicate nonstationary motion behaviour. details see Mesquita et al 2020 Danilevicz et al. 2024.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter13_CircadianRhythm.html","id":"activity-balance-index-abi","dir":"Articles","previous_headings":"Detrended fluctionation analysis (DFA)","what":"Activity Balance Index (ABI)","title":"13. Circadian Rhythm Analysis","text":"Activity Balance Index (ABI) introduced Danilevicz et al. 2024 transformation SSP. ABI measures activity observed period balanced, higher values reflect balanced pattern activity. ABI real number zero one calculated acceleration metric time series directly without need cut-points.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter13_CircadianRhythm.html","id":"related-output","dir":"Articles","previous_headings":"","what":"Related output","title":"13. Circadian Rhythm Analysis","text":"MXLX derived per 24 hours (part 2) available per day part2_daysummary.csv, summarised per recording part2_summary.csv. MXLX derived per window (part6) stored recording summary part6_summary.csv. circadian rhythm variables derived recording level. IV, , phi, cosinor analysis variables stored part2_summary.csv part6_summary.csv, SSP ABI stored part6_summary.csv. detailed variable dictionary see annex GGIR output.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter14_BehaviouralFragmentation.html","id":"defining-fragments","dir":"Articles","previous_headings":"","what":"Defining fragments","title":"14. Behavioural fragmentation","text":"GGIR, fragment daytime defined sequence epochs belong one four categories: Inactivity Light Physical Activity (LIPA) Moderate Vigorous Physical Acitivty (MVPA) Physical activity (can either LIPA MVPA) categories represents combination bouted unbouted time respective categories. Inactivity physical activity add full day (outside SPT), well inactivity, LIPA MVPA. fragment SPT defined sequence epochs belong one four categories: Estimated sleep Estimated wakefulness Inactivity Physical activity (can either LIPA MVPA) parameter frag.metrics = \"\" can instruct GGIR part 5 derive behavioural fragmentation metrics. may want consider combining parameter part5_agg2_60seconds=TRUE aggregate time series 1 minute resolution common behavioural fragmentation literature. GGIR part 6 performs fragmentation analysis part6CR set TRUE. uses time series output generated part 5 discussed previous chapter. GGIR derives fragmentation metrics two ways: part 5 fragmentation quantified per waking hours day reported per day recording average daily estimates. part 6 fragmentation quantified based data recording within window specifed parameter part6Window. Calculation per day allows us explore possibly account behavioural differences days week. However, day level estimate considered less robust recording level estimates generated part 6. internal function g.fragmentation fragmentation metric calculation used part 5 6 ensuring calculation otherwise consistent.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter14_BehaviouralFragmentation.html","id":"fragmentation-metrics","dir":"Articles","previous_headings":"","what":"Fragmentation metrics","title":"14. Behavioural fragmentation","text":"Note fragmentation metrics discussed fragmentation metrics TP NFrag calculated SPT fragments. Coefficient Variance (CoV) calculated according Blikman et al. 2014, entails dividing standard deviation mean lognormal transformed fragment length (minutes). Transition probability (TP) Inactivity () Physical activity (IN2PA), Physical activity inactivity (PA2IN), LIPA MVPA calculated according Danilevicz et al. 2024. Gini index calculated function Gini ineq R package, ineq argument corr set TRUE. Power law exponent metrics: Alpha, x0.5, W0.5 calculated according Chastin et al. 2010. Note compared R package ActFrag described Junrui Di et al. 2017 use theoretical minimum fragment duration instead observed minimum fragment duration. Number fragment per minutes (NFragPM) calculated identical metric fragmentation index Chastin et al. 2012, renamed specific reflection calculation. term fragmentation index appears generic given fragmentation metrics inform us fragmentation. Please note close metrics transition probability, total number divided total sum duration equals 1 divided average duration. Although exact math slightly different.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter14_BehaviouralFragmentation.html","id":"conditions-for-calculation","dir":"Articles","previous_headings":"","what":"Conditions for calculation","title":"14. Behavioural fragmentation","text":"Metrics Gini CoV calculated least 10 fragments (e.g. 5 inactive 5 active). condition met metric value set missing. Metrics related power law exponent alpha also calculated least 10 fragments, additional condition standard deviation fragment duration zero. conditions met metric value set missing. metrics related binary fragmentation (mean_dur_PA mean_dur_IN), calculated least 2 fragments (1 inactive, 1 active). condition met value set zero. Metrics related TP calculated : least 1 inactivity fragment (1 LIPA 1 MVPA fragment). condition met TP metric value set zero. keep overview recording days met criteria non-zero standard deviation least ten fragments, GGIR part 5 stores variable Nvaliddays_AL10F person level (.e., number valid days least 10 fragments), SD_dur (.e., standard deviation fragment durations) day level well aggregated per person.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter14_BehaviouralFragmentation.html","id":"key-parameters","dir":"Articles","previous_headings":"","what":"Key parameters","title":"14. Behavioural fragmentation","text":"parameters related cut-points bout detection mainly parameters listed “Physical activity parameters”.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter14_BehaviouralFragmentation.html","id":"related-output","dir":"Articles","previous_headings":"","what":"Related output","title":"14. Behavioural fragmentation","text":"GGIR part 5 csv reports find: Fragmentation metrics day level per waking hours day GGIR part 6 csv report find: Fragmentation metrics overview output variables see GGIR output annex.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter1_WhatIsGGIR.html","id":"a-brief-overview","dir":"Articles","previous_headings":"","what":"A brief overview","title":"1. What is GGIR","text":"GGIR R-package primarily designed process multi-day raw accelerometer data physical activity, sleep, circadian rhythm research. term raw refers data expressed gravitational units (1 g equal gravitational acceleration, average 9.81 m/s2) opposed previous generation accelerometers stored data accelerometer brand-specific units epoch level, typically 5, 30, 60 seconds length. Despite focus raw data, GGIR also offers functionality process previous-generation accelerometer data. signal processing raw data includes many steps explained pages. example, automatic calibration gravity, detection abnormally high values, imputation raw-level time gaps (specific sensor brands), calculation orientation angle average magnitude acceleration based variety metrics. Next, signal processing raw previous-generation data continue detection non-wear epoch-level imputation. Finally, GGIR uses information describe data data quality data summary metrics interpreted estimates physical activity, inactivity, sleep, circadian rhythm. time resolutions GGIR output : Per recording, typically matches one participant ID. Calendar day option specify day border timing midnight default. Night period time participant likely main daily sleep period, initial focus time window defined noon noon next day, unless person wakes noon next day, case focus shifts 6pm 6pm next day. Day segment, can defined via code indicate timing segments within day standardised recordings days dataset via diary file, segment definition allowed vary per recording day. Window defined Waking-main sleep period Waking-next main sleep period. Window defined Sleep onset start main sleep period sleep onset start next main sleep period. Epoch-level time series, epoch length set user example 5 seconds. (Optionally, default) Per sequence recordings matching participant IDs. example, person tracked first one accelerometer accelerometer replaced different accelerometers. details use , see documentation GGIR parameter maxRecordingInterval documentation use GGIR parameters explained .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter1_WhatIsGGIR.html","id":"key-strengths","dir":"Articles","previous_headings":"","what":"Key strengths","title":"1. What is GGIR","text":"GGIR permissive open-source software license maximise re-use collaboration. GGIR applicable data multiple sensor brands file formats. GGIR facilitates sleep, physical activity, circadian rhythm research. GGIR extensive quantitative output designed use quantitative research. GGIR designed computationally efficient option store re-use intermediate milestone data option process multiple files parallel computer discussed Chapter 2. GGIR designed accessible new users without experience R programming. GGIR requires one function call comes elaborate open-access documentation. Additionally, paid training courses offered maximise opportunity users learn GGIR us learn users. GGIR dozen code contributors. GGIR available CRAN archive, meaning meets CRAN standards release gone series automated checks. public email list (google group) users reach maintainers. hundreds publications used GGIR, powerful way identify problems improve code provided us wide range reference values.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter1_WhatIsGGIR.html","id":"history","dir":"Articles","previous_headings":"","what":"History","title":"1. What is GGIR","text":"elaborate reflection GGIR’s first 10 years existence can found blog post. short, GGIR evolved series R scripts used research around 2010-2012 first release 2013. key factor growth GGIR adoption research community willingness variety researchers invest GGIR either terms time investment financially. GGIR without efforts.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/chapter1_WhatIsGGIR.html","id":"flexible-and-accessible","dir":"Articles","previous_headings":"Philosophy behind GGIR","what":"Flexible and accessible","title":"1. What is GGIR","text":"research field highly heterogeneous : choice sensor brand, data format, study protocols used, research questions tries answer. time many within field lack time skills write custom data processing software. GGIR aims flexible handle different scenarios time remain accessible lack time skills write software. , hope GGIR use without financial resources commercial software, although like stress charity depend paid unpaid contributions community.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter1_WhatIsGGIR.html","id":"algorithm-design","dir":"Articles","previous_headings":"Philosophy behind GGIR","what":"Algorithm design","title":"1. What is GGIR","text":"philosophy behind algorithms implemented GGIR biomechanical explainable (heuristic knowledge driven) approaches measurement science preferable purely data-driven approaches. Please note specification scientific context rather measurement general, e.g. consumer wearables. knowledge driven approach unrealistic can consider data-driven approach. idea knowledge driven approach order advance insight, essential understanding causal relation phenomena observed (e.g. acceleration one body part), way (acceleration) sensor works, data produced, interpret data. example, know body acceleration relates energy expenditure physics human physiology. abundance scientific publications reported positive correlation accelerometer data energy expenditure served confirm prior knowledge. contrast, data-driven methods focus optimal correlation sensor data reference labels values, much less concerned causal associations focus knowledge driven approaches, defined . Identical correlation necessarily equal causation health research, process measurement can also confounded. examples: may see differences body acceleration patterns correlate different activity types different levels energy expenditure, mean actually measure activity types energy expenditure levels. Ignoring distinction can easily lead overestimating value accelerometer measuring constructs (activity type, etc) underestimate value accelerometer capturing acceleration useful measure behaviour, appropriately used interpreted. second problem data-driven methods heavily depend availability reliable criterion methods. argue reliable criterion methods exist physical behaviour measurement: Indirect calorimetry indicators energy metabolism can derived unable account activity type specific role body weight energy metabolism. makes impossible make standardised comparison energy cost different activity types across individuals differ body weight. See also reflections blog post. Polysomnography (PSG) standard sleep research. PSG offers physiological definition sleep impossible capture directly movement sensor. Therefore, forced simplify definition ‘sleep’ towards definition can captured movement sensor. result, act evaluating accelerometer ability classify sleep PSG becomes somewhat meaningless already know measuring construct PSG. Activity types ambiguous define given high number ways can performed. introduces fundamental level uncertainty robustness models outside datasets context developed . result, essential put strong emphasis algorithms descriptive value regardless whether offer high correlation supposed criterion methods.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter1_WhatIsGGIR.html","id":"permissive-open-source-license","dir":"Articles","previous_headings":"Philosophy behind GGIR","what":"Permissive open-source license","title":"1. What is GGIR","text":"may sound obvious research software open-source, fields physical activity sleep research, far accepted approach. GGIR one research tools field permissive license aimed maximise potential re-use collaboration.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter1_WhatIsGGIR.html","id":"documentation-structure-and-origin","dir":"Articles","previous_headings":"","what":"Documentation structure and origin","title":"1. What is GGIR","text":"structured chapters line GGIR training course organising recent years. documentation existed collection ad-hoc written paragraphs, lacked clear overarching structure narrative. result, difficult use documentation training course. , also wanted provide good level documentation follow course want refresh understanding GGIR. documentation mainly written narrative style tried explain theory practice GGIR functionalities. mentioned , GGIR offers vast amount functionality. arrive expectation find quick instruction run use GGIR research disappoint . Learning use GGIR requires time investment. Everything need type R script highlighted like . documentation intended academic review: cite publications clarify origin algorithms discuss part GGIR. Finally, first version documentation sponsored Accelting commitment remain available free open-access documentation. However, open documentation much easier maintain community: grateful help improve documentation either giving feedback (e.g. via v.vanhees accelting dot com), pull requests (know ), financially. example, great funding creating high quality complementary info graphics videos.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"the-ggir-function","dir":"Articles","previous_headings":"","what":"The GGIR function","title":"2. The GGIR pipeline","text":"brief introduction unfamiliar R software, R packages constructed sub-components, named functions. single function typically allows specific task. example, may sum function sums numbers provide . function can one multiple parameters control functions behaviour. R parameters call function arguments. example, sum argument na.rm control needs done missing values. GGIR refer arguments parameters. GGIR comes large number functions parameters together form processing pipeline. ease interacting GGIR, one central function act interface functionality. function also named GGIR. need learn work function GGIR, important understand background function interacts functions GGIR. , important understand GGIR package structured two complementary ways: Parts: Reflecting computational components running GGIR. GGIR 6 parts numbered 1 6 reflect order executed. reason GGIR split parts avoids re-run preceding parts want make small change downstream parts. parts together form pipeline. Parameter themes: Reflecting themes around user can control GGIR, e.g. controlling sleep detected controlling output stored stored.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"parts-of-the-pipeline","dir":"Articles","previous_headings":"The GGIR function","what":"Parts of the pipeline","title":"2. The GGIR pipeline","text":"GGIR, computational structure six parts applied sequentially data: Part 1: Loads data, works data quality, stores derived summary measures per time interval, also known signal features metrics, needed following parts. Part 2: Basic data quality assessment based extract metrics description data per day, per file, optionally per day segment. Part 3: Estimation rest periods, needed input Part 4. Part 4: Labels rest periods derived Part 3 sleep per night per file. Part 5: Compiles time series classification sleep physical behaviour categories re-using information derived part 2, 3, 4. includes detection behavioural bouts, time segments behaviour sustained duration specified user. Next, Part 5 generates descriptive summary time spent average acceleration per behavioural category, also behavioural fragmentation. Part 6: Facilitates analyses span full recording household co-analysis circadian rhythm analysis. specific order content parts evolved time, Part 1 2 created 2011-2013, Part 3 4 created 2013-2015, Part 5 created 2017-2020, Part 6 created 2023-2024. , parts also reflect historical expansion GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"milestone-data","dir":"Articles","previous_headings":"The GGIR function > Parts of the pipeline","what":"Milestone data","title":"2. The GGIR pipeline","text":"part, run, stores output R-data file .RData extension, refer milestone data. user, unlikely ever need interact directly milestone data files relevant output stored csv pdf files output folder results. milestone files read next GGIR part. advantage design offers internal modularity. example, can run part 1 now continue part 2 another time without repeat part 1 . , design eases re-processing also helpful us developing testing GGIR code. milestone data files stored sub-folders output folder show . Note output folder named output_mystudy example exact name may differ . part 1: output_mystudy/meta/basic. part 2: output_mystudy/meta/ms2.. part 3: output_mystudy/meta/ms3.. part 4: output_mystudy/meta/ms4.. part 5: output_mystudy/meta/ms5.. part 6: output_mystudy/meta/ms6.. milestone files potentially useful following reasons: copying milestone files new computer, may continue analyses without access original data files. can example helpful process subsets study different computers, pooling resulting milestone data allows finalise analysis single computer. Remember preserve folder structure. run problem, milestone files may allow share problem reproducible example problem without share original large data file.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"parameter-themes","dir":"Articles","previous_headings":"The GGIR function","what":"Parameter themes","title":"2. The GGIR pipeline","text":"parameters GGIR package functions can used parameter GGIR function. parameters internally grouped thematically, independently six parts used : params_rawdata: parameters related handling raw data resampling calibrating. params_metrics: parameters related aggregating raw data epoch level summary measures (metrics). params_sleep: parameters related sleep detection. params_phyact: parameters related physical ()activity. params_247: parameters related 24/7 behaviours fall typical sleep physical ()activity research category measures circadian rhythm 24 hour data description techniques. params_output: parameters relating whether output stored. params_general: general parameters covered categories GGIR user need remember theme parameter belongs . However, may notice documentation structure parameter theme ease navigating parameters. couple ways inspect parameters parameter category default values:","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"ggir-function-documentation","dir":"Articles","previous_headings":"The GGIR function > Parameter themes","what":"GGIR function documentation:","title":"2. The GGIR pipeline","text":"","code":"?GGIR"},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"parameters-vignette","dir":"Articles","previous_headings":"The GGIR function > Parameter themes","what":"Parameters vignette:","title":"2. The GGIR pipeline","text":"Documentation meaning parameter, default value, expected value(s) can found vignette: GGIR configuration parameters.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"from-r-command-line","dir":"Articles","previous_headings":"The GGIR function > Parameter themes","what":"From R command line:","title":"2. The GGIR pipeline","text":"interested one specific category like sleep: interested e.g. parameter HASIB.algo sleep_params object: parameters accepted parameter function GGIR, GGIR like shell around GGIR functionality. However, params_ objects provided input GGIR.","code":"library(GGIR) print(load_params()) library(GGIR) print(load_params()$params_sleep) library(GGIR) print(load_params()$params_sleep[[\"HASIB.algo\"]])"},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"configuration-file-","dir":"Articles","previous_headings":"The GGIR function > Parameter themes","what":"Configuration file.","title":"2. The GGIR pipeline","text":"GGIR stores parameter values csv-file named config.csv. file stored run GGIR root output folder, overwriting existing config.csv file. , like add annotations file, e.g. fourth column, need store somewhere outside output folder specify path file parameter configfile. Note parameters datadir outputdirdiscussed always need specified directly part configfile. practical value eases replication analysis, instead share R script colleagues, sharing config.csv file sufficient. Please make sure GGIR R version installed using reproducibility. See guidance install older package versions.","code":"library(GGIR) GGIR(datadir = \"C:/mystudy/mydata\",      outputdir = \"D:/myresults\",       configfile = \"D:/myconfigfiles/config.csv\")"},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"parameter-extraction-order","dir":"Articles","previous_headings":"The GGIR function > Parameter themes","what":"Parameter extraction order","title":"2. The GGIR pipeline","text":"parameters provided GGIR call, GGIR always uses . parameters provided GGIR call, GGIR checks whether config.csv file either output folder specified via parameter configfile loads values. parameter neither specified GGIR function call available config.csv file, GGIR use default value’s can inspected discussed section . , important realise consequence logic GGIR revert default parameter values repeated run GGIR unless remove parameter function call delete config.csv file specify original (default) value parameter explicitly GGIR call. ensure clear example: GGIR used first time without specifying parameter mvpathreshold, use default value, 100. specify mvpathreshold = 120, GGIR use instead store config.csv file. run GGIR time delete mvpathreshold = 120 GGIR call, GGIR fall back value 120 now stored config.csv file. delete config.csv file run GGIR , value 100 used .","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"raw-data","dir":"Articles","previous_headings":"Input files","what":"Raw data","title":"2. The GGIR pipeline","text":"GGIR currently works following accelerometer brands formats: GENEActiv .bin Axivity AX3 AX6 .cwa ActiGraph .csv .gt3x (.gt3x newer format generated firmware versions 2.5.0. Serial numbers start “NEO” “MRA” firmware version 2.5.0 earlier use older format .gt3x file). want work .csv exports via commercial ActiLife software, note option export data timestamps, turned . cope absence timestamps GGIR calculate timestamps sample frequency, start time, start date presented file header. Movisens data stored folders. accelerometer brand generates csv output, see documentation functions read.myacc.csv parameter rmc.noise vignette Reading csv files raw data GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"externally-derived-epoch-level-data","dir":"Articles","previous_headings":"Input files","what":"Externally derived epoch-level data","title":"2. The GGIR pipeline","text":"default GGIR assumes data raw discussed chapter 1. However, studies raw data available epoch level aggregate. example, done external software done inside accelerometer device. Although can introduce severe limitations transparency flexibility analysis, GGIR makes attempt facilitate analysis externally performed aggregations raw data. Please find overview file format currently facilitated: Note: Actiwatch ActiGraph, physical activity description sleep classification needs tailored count-specific algorithms: .neishabouricounts = TRUE, acc.metric = \"NeishabouriCount_x\", HASPT.algo = \"NotWorn\" HASIB.algo = \"NotWorn\"; Note 2: UK Biobank csv epoch data, GGIR facilitate sleep analysis arm angle exported. See GGIR CookBook example recipes working external data. process files, GGIR loads content saves GGIR part 1 milestone data, essentially fooling rest GGIR think GGIR part 1 created based raw data input. discussed chapter 3, GGIR non-wear detection two steps: first step done part 1 second step done part 2. relation externally derived epoch data non-wear detected looking consecutive zeros one hour (Actiwatch, ActiGraph) derived file (UK Biobank csv). accelerometer data need analysed stored one folder subfolders folder. Make sure folder contain files accelerometer data. Choose appropriate name folder, preferable reference study project related rather just ‘data’, name folder used identifier dataset integrated name output folder GGIR creates.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"how-to-run-your-analysis","dir":"Articles","previous_headings":"","what":"How to run your analysis?","title":"2. The GGIR pipeline","text":"bare minimum input needed GGIR : Parameter datadir allows specify stored accelerometer data outputdir allows specify like output analyses stored. equal datadir. copy paste code new R script (file ending .R) Source R(Studio), dataset processed output stored specified output directory. GGIR refers file directories folders. unfamiliar term directory: folder. Next, can add parameter mode tell GGIR part(s) run, e.g. mode = 1:5 tells GGIR run five parts default. parameter overwrite, can tell GGIR whether overwrite previously produced milestone data . , parameter idloc tells GGIR find participant ID. default setting likely work data formats, important tailor value parameter study setting. example, files start participant ID followed underscore set idloc=2. See documentation parameter idloc examples. GGIR stores output csv files comma default column separator dot default decimal separator standard UK/US. However, computer configured different region world can modified parameters sep_reports dec_reports, respectively.","code":"library(GGIR) GGIR(datadir = \"C:/mystudy/mydata\",      outputdir = \"D:/myresults\")"},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"from-the-r-console-on-your-own-desktoplaptop","dir":"Articles","previous_headings":"How to run your analysis?","what":"From the R console on your own desktop/laptop","title":"2. The GGIR pipeline","text":"Create R-script put library(GGIR) next line GGIR call GGIR(datadir=\"yourdatapath\", outputdir=\"yourdatapath\"). Next, can source R-script source button RStudio: source(\"pathtoscript/myshellscript.R\") GGIR default supports multi-thread processing used process one input file per process, speeding processing data. can turned setting parameter .parallel = FALSE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"in-a-cluster","dir":"Articles","previous_headings":"How to run your analysis?","what":"In a cluster","title":"2. The GGIR pipeline","text":"processing data GGIR desktop/laptop fast enough, advise using GGIR computing cluster. way Sun Grid Engine cluster shown . Please note commands specific computing cluster working . Please consult local cluster specialist explore run GGIR cluster. , share . three files SGE setting: submit.sh run-mainscript.sh myshellscript.R need update ... last line parameters used GGIR. Note f0=f0,f1=f1 essential work. values f0 f1 passed bash script. setup, need call bash submit.sh command line. help computing clusters, GGIR successfully run world’s largest accelerometer datasets UK Biobank German NAKO study.","code":"for i in {1..707}; do     n=1     s=$(($(($n * $[$i-1]))+1))     e=$(($i * $n))     qsub /home/nvhv/WORKING_DATA/bashscripts/run-mainscript.sh $s $e done #! /bin/bash #$ -cwd -V #$ -l h_vmem=12G /usr/bin/R --vanilla --args f0=$1 f1=$2 < /home/nvhv/WORKING_DATA/test/myshellscript.R options(echo=TRUE) args = commandArgs(TRUE) if(length(args) > 0) {   for (i in 1:length(args)) {     eval(parse(text = args[[i]]))   } } GGIR(f0=f0,f1=f1,...)"},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"processing-time","dir":"Articles","previous_headings":"","what":"Processing time","title":"2. The GGIR pipeline","text":"time process typical seven-day recording anywhere 3 10 minutes depending sample frequency recording, sensor brand, data format, exact configuration GGIR, specifications computer. observing processing times 20 minutes longer seven-day recording probably slowed factors. tips may able address : Make sure data process machine GGIR run. Processing data located somewhere else computer network can substantially slow software. Make sure machine 8GB RAM memory. Using GGIR old machines 4GB known slow. However, total memory bottle neck. Also consider number processes (threads) CPU can run relative amount memory. Ending 2GB per process seems good target. can helpful turn parallel processing .parallel = FALSE. Avoid computational activities machine running GGIR. example, use DropBox OneDrive make sure sync running GGIR. probably best use machine using GGIR process large datasets. Make sure machine configured automatically turn X hours terminate GGIR. , may want configure machine fall asleep pauses GGIR. Lower value parameter maxNcores default uses number available cores derived command parallel::detectCores() minus 1. might cases demanding operating system. Reduce amount data GGIR loads memory parameter chunksize, can useful machines limited memory processing many files parallel. chunksize value 0.2 make GGIR load data chunks 20% size relative chunks loads default, approximately 12 hours data auto-calibration routine 24 hours data calculation signal metrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"ggir-output","dir":"Articles","previous_headings":"","what":"GGIR output","title":"2. The GGIR pipeline","text":"GGIR always creates output folder location specified parameter outputdir. output folder name constructed output_ followed name dataset derived distal folder name data directory specified datadir. recommend approach ensures output folder data directory matching names. way less likely confusion data folder output relates . However, possible use datadir specify vector paths individual files, may helpful want process set files position move new folder. scenario, need set parameter studyname tell GGIR dataset name . Inside output folder GGIR create two subfolders: meta results discussed earlier chapter. Inside results find folder named QC (Quality Checks). name QC (Quality Checks) possibly somewhat confusing. Data quality checks best started files stored results folder, files QC subfolder offer complementary information help quality check. GGIR generates reports parts 2, 4, 5, 6 pipeline. parameter .report can specify parts want reports generated. example, .report = c(2, 5) generate report parts 2 5. full GGIR analysis expect least following output files: Output files results subfolder: detailed discussion output can found chapters. Output files results/QC subfolder: detailed discussion output can found chapters. Output files meta/ms5.outraw subfolder: detailed discussion output can found chapters.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"time-gaps-identification-and-imputation","dir":"Articles","previous_headings":"","what":"Time gaps identification and imputation","title":"3. Data Quality Assurance","text":"Accelerometer data stored binary format (e.g. .bin .cwa) typically structured data blocks. data block header top constant number data points per block, usually equivalent seconds data. Axivity accelerometer data stored ‘.cwa’ file format blocks can, rare occasions, corrupted unreadable, therefore creating gap information recorded. ActiGraph accelerometer, also sensor brands export data ‘csv’ file format, possible recording stops certain time starts recording time, therefore creating time gap. GGIR developed efficiently identify manage time gaps.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"time-gaps-in-axivity-cwa-files","dir":"Articles","previous_headings":"Time gaps identification and imputation","what":"Time gaps in Axivity cwa files","title":"3. Data Quality Assurance","text":"Although expected occur often, Axivity cwa data can come faulty data blocks. ‘faulty’ mean data block interpretable. example, faulty blocks may occur device recording mode connected computer USB cable. address , R package GGIRread, used GGIR read cwa files, identifies faulty blocks imputes last recorded non-faulty acceleration value normalised 1 g (g unit gravitational acceleration). sampling rate accelerometer refers number data points recorded stored per second. Axivity devices expected design slightly variable sampling rate time, accounted interpolating data loaded R. example, data may collected 99.7 Hertz one block, interpolation technique interpolate data 100 Hertz. interpolation happens inside R package GGIRread. exact technique used interpolation set parameter interpolationType uses linear interpolation default (interpolationType = 1), can also set nearest neighbour interpolation (interpolationType = 2). quality assurance, GGIRread keeps track variation sampling rate per data block automatically imputes blocks (smallest segment data cwa file, typically seconds long) sampling rate deviates 10% expected sampling rate. imputation technique time gaps detailed earlier section. unhappy 10% threshold possibility changing percentage parameter frequency_tol. Biased sampling rates kind expected extremely rare expected affect normal research conditions, nonetheless like able account . Additionally, monitor process handling faulty blocks outliers sampling rate, GGIRread logs series file health statistics stored GGIR ‘data_quality_report.csv’ file located within ‘QC’ folder output directory ‘results’ (see previous chapter discussion GGIR output). data quality report, comes variable names prefixed ‘filehealth’, detailing number duration time gaps detected recording(s), well number epochs 5-10% 10% bias sampling rate.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"time-gaps-in-actigraph-gt3x-and-ad-hoc-csv-files","dir":"Articles","previous_headings":"Time gaps identification and imputation","what":"Time gaps in ActiGraph gt3x and ad-hoc csv files","title":"3. Data Quality Assurance","text":"manufacturers incorporate functionalities devices let stop recording long episode movement, aiming conserve battery life reduce data size. However, feature results recorded signals containing intermittent time gaps must accounted data processing. example, ActiGraph option called ‘idle sleep mode’ devices, pauses data collection movement detected sustained period time. ActiGraph’s idle sleep mode explained manufacturer’s website. time gaps data considered non-wear time GGIR. GGIR imputes gaps shorter 90 minutes raw data level, using last recorded value (meaning gap) normalised 1 g. approach assumes accelerometer kept orientation last observed. contrary, gaps longer 90 minutes imputed epoch level make data processing memory efficient faster. epoch level imputation discussed chapter 6. number duration time gaps found logged GGIR ‘data_quality_report.csv’ file located within ‘QC’ folder output directory ‘results’ (see previous chapter discussion GGIR output). Studies often forget clarify whether accelerometers configured pause data collection periods movement , , resulting time gaps accounted data processing. Especially, device firmware manufacturer software already imputes time gaps can cause significant bias GGIR estimates. generally speaking, advise : Report whether ‘idle sleep mode’ similar functionalities used. Disable functionality, possible, harms transparency reproducibility research. Indeed, mechanism exists replicate time gaps accelerometer brands, likely challenge accurate assessment sleep sedentary behaviour. data collected ‘idle sleep mode’ similar functionalities referred raw data accelerometry, data collection process involved proprietary pre-processing steps violate core principle raw data collection.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"specific-note-on-actigraph-idle-sleep-mode","dir":"Articles","previous_headings":"Time gaps identification and imputation","what":"Specific note on ActiGraph idle sleep mode","title":"3. Data Quality Assurance","text":"ActiGraph files might exported gt3x csv formats. idle sleep mode used, data files different. gt3x files, time gaps can found signal, imputation made ActiLife software. However, csv files exported ActiLife imputed values three axes periods movement. Note imputation ActiLife software changed point time. Initially imputation zeros recent versions ActiLife imputation uses last recorded value axis. Therefore, need aware GGIR take care time gap imputation relative idle sleep mode using gt3x files, using ActiGraph csv files (latter come time gaps already imputed).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"accelerometer-sensor-calibration","dir":"Articles","previous_headings":"","what":"Accelerometer sensor calibration","title":"3. Data Quality Assurance","text":"Many measurement tools require calibration ranging weighting scales Oxygen sensor accelerometers. Without good calibration risk error causes lack calibration undermines accurate reliable measurement. Confusingly accelerometers, field long time assumed accelerometer need calibrated relative energy expenditure. incorrect energy expenditure entirely different construct. true reference accelerometer sensors acceleration can calibrate gravitational acceleration reference. acceleration sensor works based principle acceleration captured mechanically converted electrical signal. relationship electrical signal acceleration usually assumed linear, involving offset gain factor. familiar terms, compare simple regression equation offset Beta0 (Y-intercept) gain Beta1 (slope). Therefore, offset number add signal adjust systematic error (bias) gain number multiply signal scale , order adjust relative error. shall refer establishment offset gain factor sensor calibration procedure. three types calibration: Factory calibration, done industry (always done, may need refinement afterward). Manual calibration, done researcher (advisable cases even possible. Auto-calibration, done algorithms real life study data (common scenario refine factory calibration). Accelerometers usually calibrated part manufacturing process non-movement conditions using local gravitational acceleration reference, referred factory calibration. manufacturer calibration can later evaluated holding sensor axis parallel () perpendicular direction gravity; readings axis ±1.000 0.000 g, respectively. one derive correction coefficients per axis. However, procedure can cumbersome studies high throughput. Furthermore, calibration check possible data collected past corresponding accelerometer device exist anymore. reason calibraton advisable cases possible. Finally, auto-calibration type calibration done algorithm using already collected real world data withoutb need additional experiments, explain detail.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"auto-calibration-algorithm","dir":"Articles","previous_headings":"Accelerometer sensor calibration","what":"Auto-calibration algorithm","title":"3. Data Quality Assurance","text":"general principle techniques recording acceleration screened non-movement periods. Next, rolling average non-movement periods taken three orthogonal sensor axes used generate three-dimensional ellipsoid representation ideally sphere radius 1 g. , deviations radius three-dimensional ellipsoid 1 g (ideal calibration) can used derive correction factors sensor axis-specific calibration error. auto-calibration performed GGIR uses technique detailed description demonstration can found published paper. success auto-calibration depends number non-movement periods variation accelerometer orientation periods available algorithm. result, auto-calibration expected perform less short recordings (e.g., less day) recordings participant wear accelerometer time. cases, can use recordings sensor -movement periods higher variation orientations derive calibration coefficients, apply coefficients recording interest. , use parameter backup.cal.coef. auto-calibration algorithm applied default can turned parameter .cal = FALSE. recommend turning auto-calibration unless strong reasons .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"non-wear-detection","dir":"Articles","previous_headings":"","what":"Non-wear detection","title":"3. Data Quality Assurance","text":"can happen study participant wear accelerometer. can happen variety reasons: getting tired wearing accelerometer, forgetting put accelerometer back short moment wearing , getting instructed researcher take . However, accelerometer worn, still collect data. accelerometer lying still, data collected looks like participant supposed wear moving. left undetected, wearing accelerometer bias estimates time spent inactive behaviours. Accelerometer non-wear time detected GGIR looking standard deviation range raw acceleration signals. time window statistical values calculated long enough, turn reliable indicators whether accelerometer worn . specifically, standard deviation value range (.e., maximum value minus minimum value) calculated per 60 minute windows start every 15 minutes (e.g. 14:00, 14:15, etc.) . overlapping nature time windows needed improve precision. time window labelled non-wear, least statistical values 2 3 axes meet brand specific thresholds. result, since multiple overlapping time windows classify 15 minutes, 15 minute window classified multiple times. non-wear criteria met windows overlap 15-minute window, labelled non-wear. brands 13.0 mg less 50 mg. size time window (60 minutes) size time intervals 15 minutes defined parameter windowsizes, three values. specifically, first value used non-wear detection discussed next chapter, second value defines mentioned intervals 15 minutes , third value mentioned 60 minutes time window. non-wear classification, discussed , default 2023. Prior , GGIR slightly different non-wear detection algorithm still available via parameter nonwear_approach recommend use .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"identifying-implausible-wear-time","dir":"Articles","previous_headings":"Non-wear detection","what":"Identifying implausible wear time","title":"3. Data Quality Assurance","text":"can happen time classified wear time implausible example accelerometer post moved around researcher ahead actually intended wear period. example, accelerometer post long periods non-wear briefly interrupted periods movement, interpreted algorithm monitor wear. Therefore, GGIR part 2, detected non-wear GGIR part 1 checked implausible wear periods relabelled non-wear. sure mean part 1 2 see chapter 1, gives overview. GGIR part 2 performs check follows: First , detected wear-periods last less six hours, duration less 30% combined duration bordering non-wear periods, relabelled non-wear. Second, remaining wear-periods less three hours, form less 80% bordering non-wear periods, classified non-wear. motivation selecting relatively high criterion (< 30%) combination long period (6 hrs) low criterion (< 80%) combination short period (3 hrs) long periods likely actually related monitor wear time. illustrate algorithm created visual model, see picture . , units time presented squares marked grey detected non-wear time. Period C detected wear-time borders non-wear periods B D. length C less six hours C divided sum B D less 0.3 first criteria met block C turned non-wear period.  Visual inspection >100 traces large observational study revealed applying stage three times iteratively allowed improved classification periods characterised intermittent periods non-wear apparent wear.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"implausible-wear-at-the-beginning-and-end-of-the-recording","dir":"Articles","previous_headings":"Non-wear detection","what":"Implausible wear at the beginning and end of the recording","title":"3. Data Quality Assurance","text":"Based experience, participants take accelerometer final 24 hours recording actual end. However, may hard detect accelerometer may still moved. Therefore, GGIR relabels wear-periods final 24 hrs recording shorter three hours preceded least one hour non-wear time non-wear. Finally, recording starts ends period less three hours wear followed preceded non-wear (length), period wear classified non-wear. additional criteria screening beginning end accelerometer file intended filter movements related attaching accelerometer start downloading data accelerometer end. final check can turned parameter nonWearEdgeCorrection, may relevant processing accelerometer data collected single-night polysomnography studies.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"clipping-detection","dir":"Articles","previous_headings":"","what":"Clipping detection","title":"3. Data Quality Assurance","text":"GGIR part 1 also screens acceleration signal “clipping”, .e., sustained unusual high (raw) acceleration values non compatible human movement. 30% data points 15-minute window (used non-wear) close maximal values (technical term dynamic range) sensor, corresponding time period considered potentially unreliable, may explained sensor getting stuck extreme value accelerometers used inappropriately (attached heavily accelerating object). example, dynamic range 8g, accelerations 7.5g marked “clipping”. window also classified clipping value window larger 150% dynamic range sensor. Given clipping rarely happens reported GGIR part non-wear time. clipping non-wear treated merging arrive single indicator amount invalid data. However, keep track occurrence clipping time, GGIR report fraction 15-minute windows recording clipping occurs, see section output .","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"time-gap-imputation","dir":"Articles","previous_headings":"Key parameters","what":"Time gap imputation","title":"3. Data Quality Assurance","text":"imputeTimegaps","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"auto-calibration","dir":"Articles","previous_headings":"Key parameters","what":"Auto-calibration","title":"3. Data Quality Assurance","text":".cal backup.cal.coef","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"non-wear-detection-1","dir":"Articles","previous_headings":"Key parameters","what":"Non-wear detection","title":"3. Data Quality Assurance","text":"windowsizes nonwear_approach nonWearEdgeCorrection","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"clipping-detection-1","dir":"Articles","previous_headings":"Key parameters","what":"Clipping detection","title":"3. Data Quality Assurance","text":"windowsizes dynrange","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"3. Data Quality Assurance","text":"van Hees VT, Fang Z, Langford J, Assah F, Mohammad , da Silva IC, Trenell MI, White T, Wareham NJ, Brage S. Autocalibration accelerometer data free-living physical activity assessment using local gravity temperature: evaluation four continents. J Appl Physiol (1985). 2014 Oct 1;117(7):738-44. PMID: 25103964 link van Hees VT, Gorzelniak L, Dean León EC, Eder M, Pias M, Taherian S, Ekelund U, Renström F, Franks PW, Horsch , Brage S. Separating movement gravity components acceleration signal implications assessment human daily physical activity. PLoS One. 2013 Apr 23","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter4_AccMetrics.html","id":"why-do-we-need-to-extract-metrics","dir":"Articles","previous_headings":"","what":"Why do we need to extract metrics","title":"4. From Raw Data to Acceleration Metrics","text":"Wearable accelerometers widely used health research study physical activity, sleep, behaviours. modern accelerometers can collect store least 30 values per second expressed units gravitational acceleration (g). data collected, important extract kinematically meaningful information . data processing, summary measures describe signal referred metrics signal features. knowledge accelerometer works typical approach metric calculation calculate possible statistical properties acceleration signal like mean, standard deviation, entropy, skewness. However, discussed chapter 1, favour approach try use knowledge sensor. knowledge sensor tells us acceleration signal comes three components need separated: acceleration related gravitational acceleration. absence movement three acceleration signals inform us orientation accelerometer relative gravity proxy posture. Accelerations decelerations related movement, can interpret proxy muscle contractions energy expenditure needed contractions . Measurement error bias. example, signal noise caused electronical components introducing minor variation acceleration signal even real acceleration constant. variation due noise typically small compared variation due movement. Another example calibration errors discussed chapter 3. Finding metric able separate three components provide informative value relation orientation magnitude acceleration proxies mentioned posture muscle contractions, respectively.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter4_AccMetrics.html","id":"metric-aggregation-per-epoch","dir":"Articles","previous_headings":"","what":"Metric aggregation per epoch","title":"4. From Raw Data to Acceleration Metrics","text":"metrics first calculated resolution raw data, reflects tiny fraction second. exact number data points per second also known sampling rate can vary studies. different sampling rate values directly comparable. However, aggregating metric values per larger time window, known epoch, can make values comparable. , GGIR aggregates values per epoch (e.g. 5 seconds). Aside harmonising data across studies, aggregation per epoch also advantages: Evidence value accelerometer data based epoch-level aggregates, reference values like Oxygen consumption sleep reliably derived sub-second resolution. Aggregating leads less data points makes lot practical work . GGIR, epoch length kept constant across GGIR parts allow consistent interpretation. epoch length set first value parameter windowsizes (default 5 seconds) used throughout steps GGIR, following exceptions: GGIR part 2, time spent MVPA variables (discussed chapter 11) done multiple epoch lengths, one output variable. However, per output variable epoch length held constant throughout recording, GGIR never mixes epoch lengths epoch length affects interpretation value. like reading overview article car speeds alternates unit speed every sentence (.e., miles per hour, meters per second, km per hour, etc). GGIR part 5, user option aggregate epochs 1 minute length parameter part5_agg2_60seconds. example, using 5 second epochs parts 1, 2, 3 4, can informative run part 5 1 minute epoch length.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter4_AccMetrics.html","id":"acceleration-metrics-available-in-ggir","dir":"Articles","previous_headings":"","what":"Acceleration metrics available in GGIR","title":"4. From Raw Data to Acceleration Metrics","text":"find list metrics GGIR can apply. Multiple metrics can derived GGIR run. acceleration metrics derived GGIR function g.applymetrics. Neishabouri counts GGIR relies R package actifelifecounts. Please see code respective package documentation information exact calculations. use metrics, add parameters GGIR call, e.g.:","code":"GGIR(do.enmo = TRUE, do.mad = TRUE, do.bfen = TRUE, …)"},{"path":"https://wadpac.github.io/GGIR/articles/chapter4_AccMetrics.html","id":"approach-to-removing-the-gravitational-signal-component","dir":"Articles","previous_headings":"Acceleration metrics available in GGIR","what":"Approach to removing the gravitational signal component","title":"4. From Raw Data to Acceleration Metrics","text":"table metrics overview indicates approach used separate gravitation component acceleration signal. two approaches design metrics: Magnitude, metric makes assumption magnitude gravitational acceleration component. Frequency, metric makes assumption frequency content gravitational acceleration component. assumptions known always true conditions, acceleration metric perfect.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter4_AccMetrics.html","id":"handling-high-frequency-components-in-the-signal","dir":"Articles","previous_headings":"Acceleration metrics available in GGIR","what":"Handling high frequency components in the signal","title":"4. From Raw Data to Acceleration Metrics","text":"argue high frequency components signal treated noise removed. However, likely represent harmonics low frequency movements thus part description movement. elaborate reflection , please see blog post. metrics, listed , letters LF BF name attempt suppress high frequency content signal. , GGIR user can decide whether prefer filter higher frequencies .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter4_AccMetrics.html","id":"why-ggir-uses-enmo-as-a-default-","dir":"Articles","previous_headings":"Acceleration metrics available in GGIR","what":"Why GGIR uses ENMO as a default.","title":"4. From Raw Data to Acceleration Metrics","text":"one metric can default. Acceleration metric ENMO (Euclidean Norm Minus One negative values rounded zero) default metric since GGIR created. 2013, investigated different ways summarising raw acceleration data (van Hees et al. PLoS ONE 2013). short, different metrics exist little literature support superiority metric time. long different studies use different metrics, findings comparable. Therefore, choice metric ENMO merely pragmatic. GGIR uses ENMO default : 1. ENMO demonstrated value describing variance daily energy expenditure, correlated questionnaire data, able describe patterns physical activity. 2. ENMO easy describe mathematically , therefore, improves reproducibility across studies software tools. 3. ENMO attempts quantify acceleration universal units collapse signal abstract scale. 4. 2013 paper showed ENMO used combination auto-calibration, similar validity filter-based metrics like HFEN BFEN, conceptually similar metrics proposed later MIMSunit, MAD, AI0. 5. Studies criticised ENMO consistently failed apply auto-calibration, attempted apply auto-calibration lab setting, ignoring fact auto-calibration designed short lab settings. needs free-living data work properly. , studies often clear problematic zero imputation idle sleep mode ActiGraph devices dealt .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter4_AccMetrics.html","id":"notes-on-implementation-of-zero-crossing-counts","dir":"Articles","previous_headings":"Acceleration metrics available in GGIR","what":"Notes on implementation of zero crossing counts","title":"4. From Raw Data to Acceleration Metrics","text":"implementation zero-crossing count GGIR attempt imitate zero-crossed counts previously described Sadeh, Cole, Kripke colleagues late 1980s 1990s. However, guaranteed exact copy original approach, used AMA-32 Motionlogger Actigraph Ambulatory-monitoring Inc. (“AMI”). complete publicly accessible description approach exists.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter4_AccMetrics.html","id":"missing-information","dir":"Articles","previous_headings":"Acceleration metrics available in GGIR > Notes on implementation of zero crossing counts","what":"Missing information","title":"4. From Raw Data to Acceleration Metrics","text":"missing information calculation : Sadeh specified calculations done based data Y-axis direction Y-axis clarified. Therefore, unclear whether Y-axis time corresponded Y-axis modern sensors. frequency filter used, properties filter missing. Sensitivity sensor: now guessing Motionlogger sensitivity 0.01 g without direct proof. Relationship piezo-electric acceleration signal used time modern piezo-capacitive acceleration signals. personal correspondence AMI, learnt technique kept proprietary never shared sold actigraphy manufacturers (time correspondence October 2021). Based correspondence AMI, can conclude even Actiwatch, ActiGraph, manufacturers, facilitated use 1990s sleep classification algorithms, guarantee exact replication original studies.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter4_AccMetrics.html","id":"our-guess-on-the-missing-information","dir":"Articles","previous_headings":"Acceleration metrics available in GGIR > Notes on implementation of zero crossing counts","what":"Our guess on the missing information","title":"4. From Raw Data to Acceleration Metrics","text":"Following challenges, implementation zero-crossing count GGIR based educated guess used information find literature product documentation. relation missing information listed : allow specify axis want use parameter Sadeh_axis choose default second axis. use 0.25 - 3 Hertz band-pass filter order 2, can modify parameters zc.lb, zc.hb, zc.order. use 0.01 g stop band, can change parameter zc.sb. assume band-passed signal comparable absence evidence contrary. evaluation, zero-crossing count value range looks plausible compared value range original publications. note ActiGraph users: decide compare GGIR Cole-Kripke estimates ActiLife’s Cole Kripke estimates, aware ActiLife may adopted different Cole-Kripke algorithm original publication presented four algorithms. potential source variation. , ActiLife may used different educated guesses Motionlogger counts calculated.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter4_AccMetrics.html","id":"embedding-your-own-metrics","dir":"Articles","previous_headings":"","what":"Embedding your own metrics","title":"4. From Raw Data to Acceleration Metrics","text":"GGIR users may like use metrics covered GGIR. facilitate , allow external function embedding discussed vignette Embedding external functions GGIR. fact, allows include entire algorithms step detection new sleep classification algorithm like test inside GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter4_AccMetrics.html","id":"related-output","dir":"Articles","previous_headings":"","what":"Related output","title":"4. From Raw Data to Acceleration Metrics","text":"GGIR part 2, output derived acceleration metric derived GGIR part 1 except metrics anglex, angley, anglez. GGIR part 4, output derived metrics used sleep detection, typically angle count (Neishabouri counts zero-crossing count). GGIR part 5, output derived single metric specified parameter acc.metric. reason constraint part 5 produces many variables creating multiple metrics computationally expensive substantially increase complexity underlying code.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter4_AccMetrics.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"4. From Raw Data to Acceleration Metrics","text":"compiled list related articles may find useful: Van Hees et al. 2011 Estimation Daily Energy Expenditure Pregnant Non-Pregnant Women Using Wrist-Worn Tri-Axial Accelerometer. van Hees et al. 2013 Separating Movement Gravity Components Acceleration Signal Implications Assessment Human Daily Physical Activity. Migueles et al. 2019 Comparability accelerometer signal aggregation metrics across placements dominant wrist cut points assessment physical activity adults. Aittasalo et al. 2015 Mean amplitude deviation calculated raw acceleration data: novel method classifying intensity adolescents’ physical activity irrespective accelerometer brand. Neishabouri et al. 2022 Quantification acceleration activity counts ActiGraph. Karas et al. 2022 Comparison accelerometry-based measures physical activity: retrospective observational data analysis study. van Hees 2019 Ten Misunderstandings surrounding Information Extraction Wearable Accelerometer data.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter5_StudyProtocol.html","id":"selectingmasking-the-data","dir":"Articles","previous_headings":"","what":"Selecting/Masking the data","title":"5. Accounting for Study Protocol","text":"important GGIR masks data outside time window participant instructed wear accelerometer. Study protocols differ duration expected wear period, GGIR offers variety ways account study protocol. main parameter data_masking_strategy. requires numeric value indicating one following strategies: data_masking_strategy = 1 indicate specific number hours masked start /end recording, specified parameters hrs.del.start hrs.del.end, respectively. data_masking_strategy = 2 indicate data first last midnight recording considered. data_masking_strategy = 3 indicate active X 24-h blocks starting time day used, X specified parameter ndayswindow. Note can combined aforementioned parameters hrs.del.start hrs.del.end, trim window start end recording. data_masking_strategy = 4 indicate data first midnight considered. data_masking_strategy = 5 similar data_masking_strategy = 3, yet selects X complete calendar days, X specified parameter ndayswindow. Additionally, can set maximum duration accelerometer worn recording starts. Use parameter maxdur specify duration number 24 hour blocks parameter max_calendar_daysfor number calendar days.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter5_StudyProtocol.html","id":"key-parameters","dir":"Articles","previous_headings":"","what":"Key parameters","title":"5. Accounting for Study Protocol","text":"data_masking_strategy hrs.del.start hrs.del.end ndayswindow maxdur max_calendar_days","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/chapter6_DataImputation.html","id":"imputation-of-invalid-epoch-data","dir":"Articles","previous_headings":"","what":"Imputation of invalid epoch data","title":"6. How GGIR Deals with Invalid Data","text":"time segments classified non-wear clipping (see Chapter 3) masked study protocol (see Chapter 5) treated invalid data. GGIR part 2, epoch level metric values imputed, log kept epochs imputed. subsequent analysis done GGIR, imputed time series used. time series without invalid segments used analyses: Weighted average full recording Cosinor analysis (see Chapter 10) specific non-default configuration sleep analysis (see Chapter 8) imputation epoch data done based mean metric value corresponding valid values time day days recording. However, time interval marked invalid across recorded days, value imputed zero, except metric EN imputed 1. example, imagine 5-day recording following ENMO metric data two specific epochs day across five days: imputed shown average 3, 4, 3 3 3.25:","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter6_DataImputation.html","id":"controlling-the-imputation","dir":"Articles","previous_headings":"","what":"Controlling the imputation","title":"6. How GGIR Deals with Invalid Data","text":"worth noting option disable imputation setting parameter .imp = FALSE. means values kept imputed omitted. Disabling imputation recommended use-cases, can relevant studies controlled sleep exercise laboratories sensor known worn throughout experiment. alternative way control imputation specify time segments invalid epochs imputed zeros (ones metric EN) instead following standard GGIR imputation method. , use parameter TimeSegments2ZeroFile.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter6_DataImputation.html","id":"key-parameters","dir":"Articles","previous_headings":"","what":"Key parameters","title":"6. How GGIR Deals with Invalid Data","text":".imp, TimeSegments2ZeroFile","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter6_DataImputation.html","id":"related-output","dir":"Articles","previous_headings":"","what":"Related output","title":"6. How GGIR Deals with Invalid Data","text":"GGIR part 2, plots check data quality highlight segments file considered invalid imputed. plots can found folder “results/QC/”. GGIR part 5, time series produced optionally stored within folder “meta/ms5out.raw/” either csv RData format. time series contain indicator epochs considered invalid imputed.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter7_DescribingDataWithoutKnowingSleep.html","id":"data-quality-indicators","dir":"Articles","previous_headings":"","what":"Data quality indicators","title":"7. Describing the Data Without Knowing Sleep","text":"GGIR part 2 summarises data quality checks done previous four chapters, ranging report successfulness auto-calibration procedure number valid days. way, GGIR part 2 ideal place start data quality assurance.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter7_DescribingDataWithoutKnowingSleep.html","id":"basic-descriptives","dir":"Articles","previous_headings":"","what":"Basic descriptives","title":"7. Describing the Data Without Knowing Sleep","text":"Descriptive variables calculated reported valid days , criteria valid day defined parameter includedaycrit.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter7_DescribingDataWithoutKnowingSleep.html","id":"average-acceleration","dir":"Articles","previous_headings":"Basic descriptives","what":"Average acceleration","title":"7. Describing the Data Without Knowing Sleep","text":"Average acceleration known correlated total activity-related energy expenditure. GGIR part 2 provide two types average acceleration: Average per day, stored day considered valid. Note descriptive descriptives also stored GGIR averages across days, weekend days, weekdays, discuss detail later . Weighted average valid data points recording, weighted timing day valid epochs, regardless whether come days whole classified valid .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter7_DescribingDataWithoutKnowingSleep.html","id":"acceleration-distribution","dir":"Articles","previous_headings":"Basic descriptives","what":"Acceleration distribution","title":"7. Describing the Data Without Knowing Sleep","text":"distribution acceleration values can informative . GGIR facilitates two ways: specifying quantiles distribution parameter qlevels, fed base R function quantile, GGIR gives us acceleration metric values corresponding quantiles (quantile multiplied 100 percentile). describing time spent acceleration ranges, defined parameter ilevels . distribution acceleration values often referred intensity distribution physical activity literature.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/chapter7_DescribingDataWithoutKnowingSleep.html","id":"sets-of-quantiles-mx-metrics-by-rowlands-et-al-","dir":"Articles","previous_headings":"Derived descriptives","what":"Sets of quantiles (MX metrics by Rowlands et al.)","title":"7. Describing the Data Without Knowing Sleep","text":"quantiles, discussed , can used describe accelerations participants exceed active “X” accumulated minutes day. specific approach, proposed Rowlands et al., quantiles referred MX metrics. MX metrics confused active continuous X hours, e.g. M10, used circadian rhythm research also can derived GGIR (see parameter winhr). use MX metrics proposed Rowlands et al., specify durations 24h day want identify accelerations values. example, generate minimum acceleration value active accumulated 30 minutes, can call qlevels = (1410/1440). parameter also accepts vector generate multiple MX metrics. example, call M60, M30, M10, can specify following: qlevels = c(c(1380/1440), c(1410/1440), c(1430/1440)). Note: time segments shorter 24 hours specified parameter qwindow, 8-hour school day (described Fairclough et al 2020), denominator qlevels change 1440 (24h) specific segment length. example, use 480 (8h). Accordingly, argument call M60, M30, M10 : qlevels = c(c(420/480), c(450/480), c(470/480)). moment, works one segment length GGIR facilitate generation MX metrics multiple unequal time segments within GGIR function call. output part 2 summary report file refer percentile day. Thus, 24-h day, M30 appear “p97.91666_ENMO_mg_0.24hr”. create radar plots MX metrics first described Rowlands et al., GitHub repository provides R code detailed instructions make radar plots using data.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter7_DescribingDataWithoutKnowingSleep.html","id":"intensity-gradient","dir":"Articles","previous_headings":"Derived descriptives","what":"Intensity gradient","title":"7. Describing the Data Without Knowing Sleep","text":"plot time spent equally spaced acceleration ranges, end asymptotic-shaped curve, indicating little time spent high intensities (acceleration levels) much time spent low intensities. shape distribution may informative hard quantify single number standard form. Therefore, new concept called intensity gradient proposed Rowlands colleagues. intensity gradient defines slope log-transformed axes intensity distribution. specifically, calculate time accumulated incremental acceleration bins (bin size = 25 mg) also keep track mid-point intensity bin, e.g. 62.5 mg bin ranging 50 75 mg. mid-point acceleration bin expressed mg time spent bin expressed minutes log-transformed. log-transformation expected change asymptotic-shaped curve straight line. Subsequently, linear regression fitted data points. slope regression line represents intensity gradient. , calculate correlation coefficient data points help verify degree form straight line (R^2). intensity gradient calculated default. include metric part 2 output, set iglevels = TRUE. , want methodological research , can use parameter define alternative acceleration bins, e.g. using bins 20 instead 25 mg iglevels = c(seq(0, 4000, = 20), 8000).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter7_DescribingDataWithoutKnowingSleep.html","id":"key-arguments","dir":"Articles","previous_headings":"","what":"Key arguments","title":"7. Describing the Data Without Knowing Sleep","text":"includedaycrit ilevels qlevels iglevels qwindow .report","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter7_DescribingDataWithoutKnowingSleep.html","id":"related-output","dir":"Articles","previous_headings":"","what":"Related output","title":"7. Describing the Data Without Knowing Sleep","text":"GGIR part 2 generates three csv reports: part2_daysummary.csv, part2_summary.csv, data_quality_report.csv. data_quality_report.csv discussed chapter 3, focus first two reports chapter. variables part2_summary.csv recording level aggregates variables part2_daysummary.csv. , variable names starting “AD_” refer average across days, “WD” refers average across weekdays, “” refers average across weekend days, “WWE” refers weighted weekend days ensure weekend days contribute equally, “WWD” refers weighted weekdays ensure weekdays contribute equally. , GGIR part 2 generates report named part2_daysummary_longformat.csv, generated GGIR used day segment analysis, see documentation parameter qwindow. report contains exact information part2_daysummary.csv, long format instead wide format. part2_daysummary_longformat.csv, row represents one segment one day one recording, part2_daysummary.csv, row contains one day one recording segments day organised different columns.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter7_DescribingDataWithoutKnowingSleep.html","id":"descriptive-variables","dir":"Articles","previous_headings":"Related output","what":"Descriptive variables","title":"7. Describing the Data Without Knowing Sleep","text":"clarify b refers part2_summary.csv part2_daysummary.csv, r refers part2_summary.csv .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter8_SleepFundamentalsSibs.html","id":"sib-vanhees2015","dir":"Articles","previous_headings":"","what":"SIB: vanHees2015","title":"8. Sleep Fundamentals: Sustained Inactivity Bout Detection (SIB)","text":"algorithm looks periods time z-angle change 5 degrees least 5 minutes. algorithm proposed 2015 article. idea behind algorithm interpretable heuristic compared conventional approaches use magnitude acceleration distinguish sustained inactivity bouts. reason assume vanHees2015 better worse reflection sleep, advancement purely intended terms interpretability. vanHees2015 algorithm default. values 5 5 algorithm can modified parameters anglethreshold timethreshold, currently see basis recommend advise sticking default values.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter8_SleepFundamentalsSibs.html","id":"sib-notworn-experimental","dir":"Articles","previous_headings":"","what":"SIB: NotWorn (EXPERIMENTAL)","title":"8. Sleep Fundamentals: Sustained Inactivity Bout Detection (SIB)","text":"Disclaimer: status SIB algorithm experimental described evaluated peer-reviewed publication yet. means revisions algorithm can expected algorithm matures. algorithms named “NotWorn” sib guider (next chapter) designed studies instruction wear accelerometer night. obvious facilitate meaningful sleep analysis. Nonetheless need crude estimate night time versus day time order GGIR part 5 characterise day time behaviours. case dataset use guider setting HASPT.algo = \"NotWorn\" discussed next chapter. , recommend combining using “NotWorn” : .imp = FALSE, HASPT.ignore.invalid = NA, ignorenonwear = FALSE. detection sib periods based acceleration metric defined parameter acc.metric.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter8_SleepFundamentalsSibs.html","id":"combined-with-count-acceleration-metrics","dir":"Articles","previous_headings":"SIB: NotWorn (EXPERIMENTAL)","what":"Combined with count acceleration metrics","title":"8. Sleep Fundamentals: Sustained Inactivity Bout Detection (SIB)","text":"using count accelereration metric set HASIB.algo = \"NotWorn\". part 4 sib set equal detected guider window. , effectively guider sib algorithm identical case.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter8_SleepFundamentalsSibs.html","id":"combined-with-gravitational-unit-acceleration-metrics","dir":"Articles","previous_headings":"SIB: NotWorn (EXPERIMENTAL)","what":"Combined with gravitational unit acceleration metrics","title":"8. Sleep Fundamentals: Sustained Inactivity Bout Detection (SIB)","text":"using acceleration metrics expresses acceleration gravitational units set HASIB.algo accelerometer expected worn specify second guider parameter HASPT.algo discussed int next chapter, e.g. HASPT.algo = c(\"NotWorn\", \"HDCZA)\". way GGIR first search long non-wear periods indicator sleep use define sleep window found fall back sib-algortihm specified HASIB.algo, e.g. \"vanHees2015\".","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter8_SleepFundamentalsSibs.html","id":"sib-count-based-algorithms-experimental","dir":"Articles","previous_headings":"","what":"SIB: Count based algorithms (EXPERIMENTAL)","title":"8. Sleep Fundamentals: Sustained Inactivity Bout Detection (SIB)","text":"Disclaimer: status SIB algorithm experimental described evaluated peer-reviewed publication yet. means revisions algorithm can expected algorithm matures. Accelerometers used sleep research since 1990s. However, initial accelerometers store data gravitational units sub-second level use nowadays stored data 30 60 second epoch aggregates. Although aggregates referred counts many manufacturers calculation counts differs manufacturer. attempted facilitate several sleep detection algorithms literature period “Sadeh1994”, “ColeKripke1992”, “Galland2012”. problem algorithms preprocessing done generate counts insufficiently described literature. zero-crossing count used Sadeh1994 ColeKripke1992 attempt made collect much information found made educated guess missing information. zero-crossing counts discussed chapter 4 acceleration metrics. counts calculated can use following SIB algorithms. uncertain whether Y-axis direction modern accelerometers matches direction Y-axis literature old studies direction Y-axis knowledge never clarified.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter8_SleepFundamentalsSibs.html","id":"sadeh1994","dir":"Articles","previous_headings":"SIB: Count based algorithms (EXPERIMENTAL)","what":"Sadeh1994","title":"8. Sleep Fundamentals: Sustained Inactivity Bout Detection (SIB)","text":"algorithm proposed Sadeh et al. link. use set parameter HASIB.algo = \"Sadeh1994\" argument Sadeh_axis = \"Y\" indicate algorithm use Y-axis sensor.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter8_SleepFundamentalsSibs.html","id":"galland2012","dir":"Articles","previous_headings":"SIB: Count based algorithms (EXPERIMENTAL)","what":"Galland2012","title":"8. Sleep Fundamentals: Sustained Inactivity Bout Detection (SIB)","text":"algorithm proposed Galland et al. link. use implementation Galland2012 algorithm specify parameter HASIB.algo = \"Galland2012\". , set Sadeh_axis = \"Y\" specify algorithm use Y-axis.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter8_SleepFundamentalsSibs.html","id":"colekripke1992","dir":"Articles","previous_headings":"SIB: Count based algorithms (EXPERIMENTAL)","what":"ColeKripke1992","title":"8. Sleep Fundamentals: Sustained Inactivity Bout Detection (SIB)","text":"algorithm proposed Cole et al. link, specifically GGIR uses algortihm proposed paper 10-second non-overlapping epochs counts expressed average per minute. skip re-scoring steps paper showed marginal added value added complexity. use GGIR implementation algortihm, specify parameters HASIB.algo = \"ColeKripke1992\" Sadeh_axis = \"Y\" indicate algorithm use Y-axis sensor.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter8_SleepFundamentalsSibs.html","id":"dealing-with-expected-or-detected-nonwear-time-segments","dir":"Articles","previous_headings":"","what":"Dealing with expected or detected nonwear time segments","title":"8. Sleep Fundamentals: Sustained Inactivity Bout Detection (SIB)","text":"Depending study protocol may want interpret invalid data (typically non-wear) differently. set parameter ignorenonwear=TRUE (default) ignore non-wear period SIB detection. useful prevent nonwear episodes going bed waking contributing sleep.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter9_SleepFundamentalsGuiders.html","id":"time-window-used-for-sleep-analyses","dir":"Articles","previous_headings":"","what":"Time window used for sleep analyses","title":"9. Sleep Fundamentals: Guiding sleep detection","text":"default sleep analysis considers window noon-noon, ideal shift workers may go bed early day wake noon. address , GGIR changes window analysis seems case: sleep log indicates person woke noon, sleep analysis part 4 done window 6pm-6pm. Similarly, guider indicates person woke 11 , sleep analysis part 3 4 done window 6pm-6pm. way method sensitive individuals main sleep period starting noon ending noon, referred daysleepers output. example case shift workers. Note guider L5+/-12 (discussed ) able , consider noon-noon time window.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/chapter9_SleepFundamentalsGuiders.html","id":"guider-sleeplog","dir":"Articles","previous_headings":"Guiders","what":"Guider: Sleeplog","title":"9. Sleep Fundamentals: Guiding sleep detection","text":"sleep log (diary) already used studies. way GGIR uses sleeplog first described 2015 article. Two sleeplog file structures supported: -called basic advanced sleeplog. use guider set location sleeplog value parameter loglocation. General notes GGIR uses sleeplogs guider: GGIR expects start end sleep window specified. one missing sleeplog data assumed missing entire night. GGIR impute sleeplog data. feel imputation desirable running GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter9_SleepFundamentalsGuiders.html","id":"basic-sleep-log","dir":"Articles","previous_headings":"Guiders > Guider: Sleeplog","what":"Basic sleep log","title":"9. Sleep Fundamentals: Guiding sleep detection","text":"Example basic sleeplog: One column participant id, first column. Specify column argument colid. Alternatingly one column onset time one column waking time. Specify column column first night argument coln1, example coln1=2. Timestamps stored without date hh:mm:ss hour values ranging 0 23 (24). onset corresponds lights intention fall asleep, specify sleepwindowType = \"TimeInBed\". can multiple sleeplogs spreadsheet. row representing single recording. First row: first row spreadsheet needs filled column names. basic sleep log format matter column names . first night basic sleeplog assumed correspond first recorded night accelerometer recording. know sleep log start later day make sure add columns labels without timestamps. Note recorded night mean data regardless whether data valid. , participant wear accelerometer first night still first night recording.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter9_SleepFundamentalsGuiders.html","id":"advanced-sleep-log","dir":"Articles","previous_headings":"Guiders > Guider: Sleeplog","what":"Advanced sleep log","title":"9. Sleep Fundamentals: Guiding sleep detection","text":"Example advanced sleeplog two recordings: Relative basic sleeplog format advanced sleep log format comes following changes: Recording stored rows, information per days stored columns. Information per day preceded one columns holds calendar date. GGIR designed recognise handle date format assumes used date format consistently sleeplog. Per calendar date column wakeup time followed column onset -bed time. Note different basic sleep log, wakeup time follows column onset -bed time. , advanced sleep log calendar date oriented: asking participant woke fell asleep certain date. However, sleep onset time 2am, still fill 02:00:00, even though 02:00:00 next calendar date. timestamps known certain date, can skip date sleep log. Note different basic format sleep log columns left empty missing night(s). can add columns relating self-reported napping time nonwear time. used sleep analysis g.part3 g.part4, used g.part5 facilitate napping analysis, see argument .sibreport paragraph naps. Multiple naps multiple nonwear periods can entered per day. Leave cells missing values blank. Column names critical advanced sleeplog format: Date columns recognised GGIR column name word “date” . advanced sleep log format recognised GGIR looking occurrence least two column names word “date” name. Wakeup times recognised words “wakeup” column name. Sleeponset times recognised columns word “onset” column name. Time going bed recognised column names “lightsout”, “inbed”, “tobed”, “bedstart”. Time getting recognised column names “lightson”, “outbed”, “bedend”. Napping times recognised columns word “nap” name. Nonwear times recognised columns word “nonwear” name. GGIR guesses data format looping common date formats. date falls within 30 days start date accelerometer recording date format assumed found. starts attempting “Y-m-d” (2015-06-25).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter9_SleepFundamentalsGuiders.html","id":"guider-hdcza","dir":"Articles","previous_headings":"Guiders","what":"Guider: HDCZA","title":"9. Sleep Fundamentals: Guiding sleep detection","text":"HDCZA algorithm designed studies wrist-worn accelerometer (raw) data sleep log available. algorithm first described 2018 article, modified slightly: Step 6 Figure 1 replaced single threshold (0.2 default). short, step 1-6 attempt classify time periods limited change posture. Next, step 7 extracts time blocks longer 30 minutes, step 8 includes intermittent time periods shorter 60 minutes, step 9 looks longest resulting block day, step 10 represents guider window. Note step 10 Figure 1 paper gives false impression step represents final classification SPT window. way guider used identify SPT window described chapter 10. time segment HDCZA derived default noon noon. However, ends 11am noon applied 6pm-6pm time segment. use guider set parameter HASPT.algo = \"HorAngle\".","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter9_SleepFundamentalsGuiders.html","id":"guider-l5-12-legacy-algorithm","dir":"Articles","previous_headings":"Guiders","what":"Guider: L5+/-12 (LEGACY ALGORITHM)","title":"9. Sleep Fundamentals: Guiding sleep detection","text":"Disclaimer: legacy algorithm used publications therefore kept inside GGIR. performance expected less available algorithm, recommend using . guider reflects twelve hour window centred around least active 5 hours day. crude approach likely inferior guiders, easy describe. first presented 2018 article. use guider set parameter def.noc.sleep = c().","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter9_SleepFundamentalsGuiders.html","id":"guider-setwindow-legacy-algorithm","dir":"Articles","previous_headings":"Guiders","what":"Guider: setwindow (LEGACY ALGORITHM)","title":"9. Sleep Fundamentals: Guiding sleep detection","text":"Disclaimer: legacy algorithm used publications therefore kept inside GGIR. performance expected less available algorithm, recommend using . guider uses set window day recording. Start end time specified argument def.noc.sleep. example, use guider window 10pm 8am set parameter def.noc.sleep = c(22, 8).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter9_SleepFundamentalsGuiders.html","id":"guider-horangle-experimental","dir":"Articles","previous_headings":"Guiders","what":"Guider: HorAngle (EXPERIMENTAL)","title":"9. Sleep Fundamentals: Guiding sleep detection","text":"Disclaimer: status guider experimental described evaluated peer-reviewed publication yet. means revisions algorithm can expected algorithm matures. guider designed hip-worn accelerometer (raw) data, looking longest period horizontal trunk. needs GGIR part 1 2 derived angle longitudinal axis. Setting parameter sensor.location=\"hip\" triggers identification longitudinal axis looking angle strongest 24-hour lagged correlation. can also force GGIR use specific axis longitudinal axis parameter longitudinal_axis. Next, algorithm identifies horizontal axis -45 45 degrees considers horizontal posture. Next, used identify largest time bed period, considering horizontal time segments least 30 minutes, looking longest horizontal period day gaps less 60 minutes ignored. Therefore, last 4 steps algorithm identical last four steps HDCZA algorithm. use guider set parameter HASPT.algo = \"HorAngle\"","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter9_SleepFundamentalsGuiders.html","id":"guider-notworn-experimental","dir":"Articles","previous_headings":"Guiders","what":"Guider: NotWorn (EXPERIMENTAL)","title":"9. Sleep Fundamentals: Guiding sleep detection","text":"Disclaimer: status guider experimental described evaluated peer-reviewed publication yet. means revisions algorithm can expected algorithm matures. already referenced previous chapter NotWorn guider designed studies instruction wear accelerometer night. obvious facilitate meaningful sleep analysis. Nonetheless need crude estimate night time versus day time order GGIR part 5 characterise day time behaviours. First NotWorn algorithm calculates 5 minute rolling average acceleration metric values (.e., acceleration metric defined parameter acc.metric) applies threshold 5% standard deviation resulting signal. However, threshold less minimum value signal threshold set equal 10th percentile distribution. Next, used identify largest non-movement period, considering segments least 30 minutes, looking longest segment day gaps less 60 minutes ignored. Therefore, last 4 steps algorithm identical last four steps HDCZA HorAngle algorithms. algorithm expected work acceleration metric, count-type metrics metrics gravitational units. use guider set parameter HASPT.algo = \"NotWorn\". , recommend combining using “NotWorn” : .imp = FALSE ignorenonwear = FALSE. Internally HASPT.ignore.invalid always set NA “NotWorn” used. used also define resulting window SIB period ignore identified SIB window ensure entire window treated sleep. , SIB periods detected ignored. However, know experience participants occasionally wear accelerometer night even told . GGIR offers solution working count data accelerometer metrics gravitational units. case, possible specify second guider use accelerometer worn less 25% time detection window (noon-noon 6pm-6pm). happens check whether parameter HASPT.algo two guiders specified. use second one. example, HASPT.algo = c(\"NotWorn\", \"HDCZA\") HASPT.algo = c(\"NotWorn\", \"HorAngle\").","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter9_SleepFundamentalsGuiders.html","id":"dealing-with-expected-or-detect-nonwear-time-segments","dir":"Articles","previous_headings":"","what":"Dealing with expected or detect nonwear time segments","title":"9. Sleep Fundamentals: Guiding sleep detection","text":"Depending study protocol may want interpret invalid data (typically non-wear) differently: want rely available time series invalid time segments imputed leave parameter HASPT.ignore.invalid = FALSE default. want guider ignore invalid segment despite efforts impute , see HASPT.ignore.invalid = TRUE. approach may helpful studies accelerometer often worn waking hour day. want guider consider invalid segments movement period set parameter HASPT.ignore.invalid = NA. approach may helpful studies accelerometer often worn night. used, guider name output shown “+invalid” end, e.g. “HDCZA+invalid”, reflect guider enhanced.","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/Cookbook.html","id":"in--awd-format","dir":"Articles","previous_headings":"Handling externally derived data > Actiwatch data","what":"in .AWD format","title":"Cookbook","text":"","code":"GGIR(datadir = \"/media/actiwatch_awd\", # folder with epoch level .AWD file   outputdir = \"/media/myoutput\",   dataFormat = \"actiwatch_awd\",   extEpochData_timeformat = \"\\%m/\\%d/\\%Y \\%H:\\%M:\\%S\",   windowsizes = c(60, 900, 3600), # 60 is the expected epoch length   HASIB.algo = \"Sadeh1994\",   def.noc.sleep = c()) # <= because we cannot use HDCZA for ZCY"},{"path":"https://wadpac.github.io/GGIR/articles/Cookbook.html","id":"in--csv-format","dir":"Articles","previous_headings":"Handling externally derived data > Actiwatch data","what":"in .CSV format","title":"Cookbook","text":"","code":"GGIR(datadir = \"/media/actiwatch_csv\", # folder with epoch level .AWD file   outputdir = \"/media/myoutput\",   dataFormat = \"actiwatch_csv\",   extEpochData_timeformat = \"\\%m/\\%d/\\%Y \\%H:\\%M:\\%S\",   windowsizes = c(15, 900, 3600), # 15 is the expected epoch length   HASIB.algo = \"Sadeh1994\",   def.noc.sleep = c()) # <= because we cannot use HDCZA for ZCY"},{"path":"https://wadpac.github.io/GGIR/articles/Cookbook.html","id":"uk-biobank-data-in--csv-format","dir":"Articles","previous_headings":"Handling externally derived data","what":"UK Biobank data in .CSV format","title":"Cookbook","text":"","code":"GGIR(datadir = \"/media/ukbiobank\",    outputdir = \"/media/myoutput\",    dataFormat = \"ukbiobank_csv\",    extEpochData_timeformat = \"\\%m/\\%d/\\%Y \\%H:\\%M:\\%S\",    windowsizes = c(5, 900, 3600), # We know that data was stored in 5 second epoch    desiredtz = \"Europe/London\") # We know that data was collected in the UK"},{"path":"https://wadpac.github.io/GGIR/articles/Cookbook.html","id":"actigraph-count-data-in--csv-format","dir":"Articles","previous_headings":"Handling externally derived data","what":"ActiGraph count data in .CSV format","title":"Cookbook","text":"","code":"GGIR(datadir = \"/examplefiles\",    outputdir = \"\",    dataFormat = \"actigraph_csv\",    windowsizes = c(5, 900, 3600),    threshold.in = round(100 * (5/60), digits = 2),    threshold.mod = round(2500 * (5/60), digits = 2),    threshold.vig = round(10000 * (5/60), digits = 2),    extEpochData_timeformat = \"\\%m/\\%d/\\%Y \\%H:\\%M:\\%S\",    do.neishabouricounts = TRUE,    acc.metric = \"NeishabouriCount_x\")"},{"path":"https://wadpac.github.io/GGIR/articles/Cookbook.html","id":"senwear-data-in--xls-format","dir":"Articles","previous_headings":"Handling externally derived data","what":"Senwear data in .xls format","title":"Cookbook","text":"","code":"GGIR(datadir = \"C:/yoursenseweardatafolder\",     outputdir = \"D:/youroutputfolder\",     windowsizes = c(60, 900, 3600),     threshold.in = 1.5,     threshold.mod = 3,     threshold.vig = 6,     dataFormat = \"sensewear_xls\",     extEpochData_timeformat = \"\\%d-\\%b-\\%Y \\%H:\\%M:\\%S\",     HASPT.algo = \"NotWorn\")"},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/Cookbook.html","id":"not-worn-during-night","dir":"Articles","previous_headings":"Handling study protocol","what":"Not worn during night","title":"Cookbook","text":"Data type: Study protocol: Worn day, taken night Wear location: “NotWorn” specified second guider can supplied parameter shown . second guider used accelerometer worn 75 percent night. example shows HDCZA.","code":"GGIR(HASPT.algo = c(\"NotWorn\", \"HDCZA\"),     HASIB.algo = \"vanHees2015\",     do.imp = FALSE, # Do not impute nonwear because sensor was never worn 24/7     HASPT.ignore.invalid = NA, # Treat nonwear as potential part of guider window     ignorenonwear = FALSE, # Consider nonwear as potential sleep     includenightcrit = 8,     includedaycrit = 8)"},{"path":"https://wadpac.github.io/GGIR/articles/CutPoints.html","id":"considerations","dir":"Articles","previous_headings":"","what":"Considerations","title":"Published cut-points and how to use them in GGIR","text":"physical activity research field used called cut-points segment accelerometer time series based level intensity. vignette compiled list published cut-points instructions use GGIR. Please note GGIR refers cut-points thresholds, referring thing: value set values help split levels movement intensity. newer cut-points frequently published list may date. Please let us know aware published cut-points missed!","code":""},{"path":"https://wadpac.github.io/GGIR/articles/CutPoints.html","id":"cut-points-expressed-in-gravitational-units-this-vignette","dir":"Articles","previous_headings":"Considerations","what":"Cut-points expressed in gravitational units (this vignette)","title":"Published cut-points and how to use them in GGIR","text":"vignette focuses cut-points metrics attempt quantify average acceleration per epoch gravitational units. strength metrics values affected sampling rate epoch length improving comparability across studies.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/CutPoints.html","id":"cut-points-not-expressed-in-gravitational-units-not-in-this-vignette","dir":"Articles","previous_headings":"Considerations","what":"Cut-points NOT expressed in gravitational units (not in this vignette)","title":"Published cut-points and how to use them in GGIR","text":"However, GGIR also facilitates metrics whose values expressed gravitational units historically used. example, metric described Neishabouri (see GGIR argument .neishabouricounts) reflects indicator accumulated body movement time, referred counts, calculated ActiLife software ActiGraph accelerometer brand. Cut-points counts corresponding ActiGraph brand recurrently proposed literature, example, see systematic review stratification age group. Note cut-points ActiGraph counts proposed introduction multiday raw data collection likely hardware-based calculations may perfectly align ActiGraph software-based (Actilife) calculations counts Neishabouri described. result, older cut-points may need used caution. cut-points find literature ActiGraph counts applied Neishabouri counts directly epoch length specific. cut-points literature need corrected conversion factor. conversion factor calculated epoch length new study (e.g. 5 seconds) divided epoch length original study (e.g. 60 seconds). Note correction differences sampling rate needed Neishabouri counts already account via -sampling. want use cut-point “100 counts per minute” literature 5 second epoch data, GGIR function call look like :","code":"GGIR([...],      mode = 1:5,      windowsizes = c(5, 900, 3600),      do.neishabouricounts = TRUE,      acc.metric = \"NeishabouriCount_y\",      threshold.in = 100 * (5/60),      [...])"},{"path":"https://wadpac.github.io/GGIR/articles/CutPoints.html","id":"relevant-arguments-to-use-cut-points-in-ggir","dir":"Articles","previous_headings":"","what":"Relevant arguments to use cut-points in GGIR","title":"Published cut-points and how to use them in GGIR","text":"argument mvpathreshold used part 2 quantify time accumulated user-specified threshold moderate--vigorous intensity expected occur. mvpathreshold applied metrics extracted part 1 arguments .metric (e.g., .enmo, .mad, .neishabouricounts). part 5, threshold.lig, threshold.mod, threshold.vig used indicate thresholds separate inactivity light, light moderate, moderate vigorous, respectively.thresholds applied metric defined acc.metric (default = “ENMO”). summary table parameters definition calculate acceleration metrics previously used calibration cut-points define used physical activity intensity classification cut-points.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/CutPoints.html","id":"cut-points-for-preschoolers","dir":"Articles","previous_headings":"Summary of published cut-points","what":"Cut-points for preschoolers","title":"Published cut-points and how to use them in GGIR","text":"*publications used acceleration metrics sum values per epoch rather average per epoch like GGIR . , use cut-point GGIR, provide scaled version cut-points presented paper : (CutPointFromPaper_in_gsecs/85.7) * 1000. Note sample frequency 87.5 reported publication incorrect based correspondence authors replaced 85.7.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/CutPoints.html","id":"cut-points-for-childrenadolescents","dir":"Articles","previous_headings":"Summary of published cut-points","what":"Cut-points for children/adolescents","title":"Published cut-points and how to use them in GGIR","text":"*publications used acceleration metrics sum values per epoch rather average per epoch like GGIR . , use cut-point GGIR, provide scaled version cut-points presented paper : (CutPointFromPaper_in_gmins/(sampleRateFromPaper * EpochLengthInSecondsPaper)) * 1000 ** publication used acceleration metrics expressed cut-points g units. , use cut-point GGIR, provide cut-point multiplied 1000.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/CutPoints.html","id":"cut-points-for-adults","dir":"Articles","previous_headings":"Summary of published cut-points","what":"Cut-points for adults","title":"Published cut-points and how to use them in GGIR","text":"*publications used acceleration metrics sum values per epoch rather average per epoch like GGIR . , use cut-point GGIR, provide scaled version cut-points presented paper : (CutPointFromPaper_in_gmins/(sampleRateFromPaper * EpochLengthInSecondsPaper)) * 1000 † publication, cut-point based data sampled 30 Hz 100 Hz. scaling cut-points specified (*), resulting thresholds virtually (ones presented table).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/CutPoints.html","id":"cut-points-for-older-adults","dir":"Articles","previous_headings":"Summary of published cut-points","what":"Cut-points for older adults","title":"Published cut-points and how to use them in GGIR","text":"*Cut-points derived applying Youden index ROC curves. ** Cut-points derived increasing Sensitivity Specificity light vice versa moderate ROC curves (see paper details).† publications used acceleration metrics sum values per epoch rather average per epoch like GGIR . , use cut-point GGIR, provide scaled version cut-points presented paper : (CutPointFromPaper_in_gmins/(sampleRateFromPaper * EpochLengthInSecondsPaper)) * 1000 ‡ cut-points excluding data aided walking washing activities can found publication.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/CutPoints.html","id":"notes-on-cut-point-validity","dir":"Articles","previous_headings":"","what":"Notes on cut-point validity","title":"Published cut-points and how to use them in GGIR","text":"Sensor calibration studies , excluding Hildebrand et al. 2016, effort made calibrate acceleration sensors relative gravitational acceleration prior cut-point development. Theoretically can expected cause bias cut-point estimates proportional calibration error device, especially cut-points based acceleration metrics rely assumption accurate calibration metrics: ENMO, EN, ENMOa, also metric SVMgs used studies Esliger 2011, Phillips 2013, Dibben 2020. Idle sleep mode ActiGraph discussed main package vignette, studies using ActiGraph sensor often forget clarify whether idle sleep mode used , accounted data processing. criticism towards cut-point methods? elaborate reflection limitations cut-points motivation cut-points still value GGIR see: https://www.accelting.com/updates/--ggir-facilitate-cut-points/","code":""},{"path":"https://wadpac.github.io/GGIR/articles/CutPoints.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"Published cut-points and how to use them in GGIR","text":"Aittasalo 2015: https://doi.org/10.1186/s13102-015-0010-0 Bammann 2021: https://doi.org/10.1371/journal.pone.0252615 Dibben 2020: https://doi.org/10.1186/s13102-020-00196-7 Dillon 2016: https://doi.org/10.1371%2Fjournal.pone.0109913 Esliger 2011: https://doi.org/10.1249/mss.0b013e31820513be Fraysse 2020: https://doi.org/10.3389%2Ffspor.2020.579278 Hildebrand 2014: https://doi.org/10.1249/mss.0000000000000289 Hildebrand 2016: https://doi.org/10.1111/sms.12795 Migueles 2021: https://doi.org/10.3390%2Fs21103326 Phillips 2013: https://doi.org/10.1016/j.jsams.2012.05.013 Sanders 2018: https://doi.org/10.1080/02640414.2018.1555904 Schaefer 2014: https://doi.org/10.1249%2FMSS.0000000000000150 Roscoe 2017: https://doi.org/10.1007/s00431-017-2948-2 Vähä-Ypyä 2015: https://doi.org/10.1371/journal.pone.0134813","code":""},{"path":"https://wadpac.github.io/GGIR/articles/ExternalFunction.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Embedding external functions in GGIR","text":"like GGIR want use algorithms raw data included GGIR external function embedding feature can solution. example, may want pilot new machine learned classifiction algorithm want write data cleaning aggregation steps needed analysis real life ‘lab’ acceleormeter data. works: Internally GGIR loads raw accelerometer data memory blocks 24 hours. data memory, corrected calibration error, resampled sample rate required function, GGIR applies default algorithms well external function provided (Python R). external function expected take input: three-column matrix acceleration data corresponding three acceleration axes, optional parameters argument can R format (character, list, vector, data.frame, etc). output external function expected produce matrix data.frame one multiple columns corresponding output external function.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/ExternalFunction.html","id":"example-with-external-r-function","dir":"Articles","previous_headings":"","what":"Example with external R function","title":"Embedding external functions in GGIR","text":"example apply function counts() R package activityCounts raw data, produces estimate Actigraph counts per second.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/ExternalFunction.html","id":"write-external-function","dir":"Articles","previous_headings":"Example with external R function","what":"Write external function","title":"Embedding external functions in GGIR","text":"Create file calculateCounts.R insert following code:","code":"calculateCounts = function(data=c(), parameters=c()) {   # data: 3 column matrix with acc data   # parameters: the sample rate of data   library(\"activityCounts\")   if (ncol(data) == 4) data= data[,2:4]   mycounts = counts(data=data, hertz=parameters,                      x_axis=1, y_axis=2, z_axis=3,                     start_time = Sys.time())   mycounts = mycounts[,2:4] #Note: do not provide timestamps to GGIR   return(mycounts) }"},{"path":"https://wadpac.github.io/GGIR/articles/ExternalFunction.html","id":"provide-external-function-to-ggir","dir":"Articles","previous_headings":"Example with external R function","what":"Provide external function to GGIR","title":"Embedding external functions in GGIR","text":"Create new .R file running GGIR analysis, e.g. named myscript.R, insert following code. forget update filepath first line point calculateCounts.R file. code creates object myfun type list expected come following elements: FUN character string specifying location external function want apply. parameters parameters used function, can stored format (vector, matrix, list, data.frame). user make sure external function can handle object. expected_sample_rate Expected sample rate, inputdata difference sample rate, data resampled. expected_unit Expected unit acceleration external function: “mg”, “g” “ms2”. input data different converted. colnames Character vector names columns produced external function. outputres resolution (seconds) output produced external function. Note, needs equal multitude short epoch size g.part1 output (5 seconds) short epoch size multitude resolution. way GGIR can aggregate repeat external function output used inside GGIR. minlength minimum length (seconds) input data needed, typically window per output provided. outputtype Character indicate type external function output. Set “numeric” data stored numbers (numeric format), “character” character string. aggfunction data needs aggregated match short epoch size g.part1 output (5 seconds) element specifies function used aggregation, e.g. mean, sum, median. timestamp Boolean indicated whether timestamps (seconds since 1-1-1970) passed external function first columm data matrix.. reporttype Character indicate type reporting GGIR: “scalar” averaged per day, “event” summed per day, “type” categorical variable can aggregated per day tabulating . name Character indicate name entity detected cases event detection, name used output filename, e.g. part2_stepsummary.csv name “step”. moment name use event detection algorithms Next, add call GGIR function GGIR myfun provided one arguments: Please see  information function GGIR.","code":"source(\"~/calculateCounts.R\") myfun =  list(FUN=calculateCounts,               parameters= 30,               expected_sample_rate= 30,               expected_unit=\"g\",               colnames = c(\"countsX\",\"countsY\",\"countsZ\"),               outputres = 1,               minlength = 1,               outputtype=\"numeric\",               aggfunction = sum,               timestamp=F,               reporttype=\"scalar\") library(GGIR) GGIR(datadir=\"~/myaccelerometerdata\",              outputdir=\"~/myresults\",              mode=1:2,              epochvalues2csv = TRUE,              do.report=2,              myfun=myfun) #<= this is where object myfun is provided to GGIR"},{"path":"https://wadpac.github.io/GGIR/articles/ExternalFunction.html","id":"example-with-external-python-function","dir":"Articles","previous_headings":"","what":"Example with external Python function","title":"Embedding external functions in GGIR","text":"example use external Python function estimate dominant signal frequency per acceleration axis. Note can also done R, shows even Python functions can provided.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/ExternalFunction.html","id":"write-external-function-1","dir":"Articles","previous_headings":"Example with external Python function","what":"Write external function","title":"Embedding external functions in GGIR","text":"Create dominant_frequency.py insert code shown : Create dominant_frequency.R calls python function insert following code:","code":"import numpy  def dominant_frequency(x, sf):   # x: vector with data values   # sf: sample frequency   fourier = numpy.fft.fft(x)   frequencies = numpy.fft.fftfreq(len(x), 1/sf)   magnitudes = abs(fourier[numpy.where(frequencies > 0)])   peak_frequency = frequencies[numpy.argmax(magnitudes)]   return peak_frequency dominant_frequency = function(data=c(), parameters=c()) {   # data: 3 column matrix with acc data   # parameters: the sample rate of data   source_python(\"dominant_frequency.py\")   sf=parameters   N = nrow(data)   ws = 5 # windowsize   if (ncol(data) == 4) data= data[,2:4]   data = data.frame(t= floor(seq(0,(N-1)/sf,by=1/sf)/ws),                     x=data[,1], y=data[,2], z=data[,3])   df = aggregate(data, by = list(data$t),                   FUN=function(x) {return(dominant_frequency(x,sf))})   df = df[,-c(1:2)]   return(df) } }"},{"path":"https://wadpac.github.io/GGIR/articles/ExternalFunction.html","id":"provide-external-function-to-ggir-1","dir":"Articles","previous_headings":"Example with external Python function","what":"Provide external function to GGIR","title":"Embedding external functions in GGIR","text":"Create new .R file running GGIR analysis, e.g. named myscript.R, insert following blocks code. Specification Python environment use, can also conda environment docker container (see documentation R package  details). Make sure Python environment required dependencies external function, need numpy. Specify myfun object explained R example. forget update filepath \"~/dominant_frequency.R\" file. Add call function GGIR myfun provided argument. Note , .parallel set FALSE. Unfortunately Python embedding R package reticulate multi-threading R package foreach used GGIR combine well.","code":"library(\"reticulate\")   use_virtualenv(\"~/myvenv\", required = TRUE) # Local Python environment   py_install(\"numpy\", pip = TRUE) source(\"~/dominant_frequency.R\") myfun =  list(FUN=dominant_frequency,               parameters= 30,               expected_sample_rate= 30,               expected_unit=\"g\",               colnames = c(\"domfreqX\", \"domfreqY\", \"domfreqZ\"),               minlength = 5,               outputres = 5,               outputtype=\"numeric\",               aggfunction = median               timestamp=F,               reporttype=\"scalar\") library(GGIR) GGIR(datadir=\"~/myaccelerometerdata\",              outputdir=\"~/myresults\",              mode=1:2,              epochvalues2csv = TRUE,              do.report=2,              myfun=myfun,              do.parallel = FALSE)"},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/ExternalFunction.html","id":"part-1","dir":"Articles","previous_headings":"Integration in GGIR output","what":"Part 1","title":"Embedding external functions in GGIR","text":"external function output included time series produced function GGIR function g.part1 stored RData-file /output_nameofstudy/meta/basic. resolution output GGIR set GGIR argument windowsizes, c(5,900,3600) default. , first element 5 specifies short epoch size seconds. output external function less resolution aggregated function specificied aggfunction myfun object. count example used sum dominant frequency example used median.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/ExternalFunction.html","id":"part-2","dir":"Articles","previous_headings":"Integration in GGIR output","what":"Part 2","title":"Embedding external functions in GGIR","text":"Next, part2 GGIR aims detect non-wear periods imputes . impute time series can found part 2 milestone data folder: /output_nameofstudy/meta/ms2.. want directly stored csv file set argument epochvalues2csv = TRUE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/ExternalFunction.html","id":"external-functions-released-by-ggir-collaborators","dir":"Articles","previous_headings":"","what":"External functions released by GGIR collaborators:","title":"Embedding external functions in GGIR","text":"Wrist-based step detection algorithm: https://github.com/ShimmerEngineering/Verisense-Toolbox/tree/master/Verisense_step_algorithm Wrist-based sleep classification described Sundararajan et al. 2021 link paper, corresponding code : https://github.com/wadpac/Sundararajan-SleepClassification-2021/tree/master/ggir_ext","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"what-is-ggir","dir":"Articles","previous_headings":"Introduction","what":"What is GGIR?","title":"Accelerometer data processing with GGIR","text":"GGIR R-package process multi-day raw accelerometer data physical activity sleep research. term raw refers data expressed m/s2 gravitational acceleration opposed previous generation accelerometers stored data accelerometer brand specific units. signal processing includes automatic calibration, detection sustained abnormally high values, detection non-wear calculation average magnitude dynamic acceleration based variety metrics. Next, GGIR uses information describe data per recording, per day measurement, (optionally) per segment day measurement, including estimates physical activity, inactivity sleep. published overview paper GGIR 2019 link. vignette provides general introduction use GGIR interpret output, additionally can find introduction video mini-tutorial YouTube. want use algorithms raw data GGIR facilitates ’s external function embedding feature, documented separate vignette: Embedding external functions GGIR. GGIR increasingly used research groups across world. non-exhaustive overview academic publications related GGIR can found . R package GGIR possible without support contributors listed author list GGIR, specific code contributions time since April 2016 (GGIR development moved GitHub) shown . Cite GGIR: use GGIR publications forget cite properly makes research reproducible gives credit ’s developers. See paragraph Citing GGIR details.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"contributing-support-and-keeping-up-to-date","dir":"Articles","previous_headings":"Introduction","what":"Contributing, Support, and Keeping up to date","title":"Accelerometer data processing with GGIR","text":"contribute code? development version GGIR can found github, also find guidance contribute. can get service support? GGIR open source software come service support guarantees. However, user-community can help via GGIR google group GitHub issue tracker. Please use public platform rather private e-mails users can learn conversations. need dedicated support use GGIR need someone adapt GGIR needs Vincent van Hees available independent consultant. Training R essentials GGIR offer frequent online GGIR training courses. Check dedicated training website details option book training. questions training booking process? hesitate contact us via: training@accelting.com. Also interest may brief free R introduction tutorial. Change log log main changes GGIR time can found .","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"install-r-and-rstudio","dir":"Articles","previous_headings":"Setting up your work environment","what":"Install R and RStudio","title":"Accelerometer data processing with GGIR","text":"Download install R Download install RStudio Install GGIR dependencies CRAN. can one command console command line: Alternatively, install latest development version latest bug fixes use instead: Additionally, use-cases need install one multiple additional packages: working Axivity, GENEActiv, GENEA files, install GGIRread package install.packages(\"GGIRread\") working ActiGraph gt3x files, install read.gt3x package install.packages(\"read.gt3x\") want derive Neishabouricounts (.neishabouricounts = TRUE), install actilifecounts package install.packages(\"actilifecounts\") want derive circadian rhythm indicators using [Cosinor analysis Extended Cosinor analysis] (cosinor = TRUE part 2, part 6 always performed), install ActCR package install.packages(\"ActCR\")","code":"install.packages(\"GGIR\", dependencies = TRUE) install.packages(\"remotes\") remotes::install_github(\"wadpac/GGIR\")"},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"prepare-folder-structure","dir":"Articles","previous_headings":"Setting up your work environment","what":"Prepare folder structure","title":"Accelerometer data processing with GGIR","text":"GENEActiv .bin Axivity AX3 AX6 .cwa ActiGraph .csv .gt3x (.gt3x newer format generated firmware versions 2.5.0. Serial numbers start “NEO” “MRA” firmware version 2.5.0 earlier use older format .gt3x file). Note Actigraph users: want work .csv exports via commercial ActiLife software note option export data timestamps. Please causes memory issues GGIR. cope absence timestamps GGIR calculate timestamps sample frequency, start time start date presented file header. Movisens .bin files data stored folders. GGIR expects participant’s folder contains least file named acc.bin. accelerometer brand generates csv output, see documentation functions read.myacc.csv argument rmc.noise GGIR function documentation (pdf). Note functionality following file formats part GGIR deprecated required significant maintenance effort without clear use case community support: (1) .bin Genea monitor Unilever Discover, accelerometer used studies 2007 2012) .bin, (2) .wav files can exported Axivity Ltd OMGUI software. Please contact us think data formats facilitated GGIR interested supporting ongoing maintenance. accelerometer data needs analysed stored one folder, subfolders folder. Give folder appropriate name, preferable reference study project related rather just ‘data’, name folder used later identifier dataset.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"ggir-shell-function","dir":"Articles","previous_headings":"Setting up your work environment","what":"GGIR shell function","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"key-general-arguments","dir":"Articles","previous_headings":"Setting up your work environment > GGIR shell function","what":"Key general arguments","title":"Accelerometer data processing with GGIR","text":"probably never need think arguments listed , lot arguments included facilitate methodological studies researchers want control every little detail. See previous paragraph links documentation find default value parameter. bare minimum input needed GGIR : Argument datadir allows specify stored accelerometer data outputdir allows specify like output analyses stored. equal datadir. copy paste code new R script (file ending .R) Source R(Studio) dataset processed output stored specified output directory. highlighted key arguments may want aware . giving detailed explanation, please see package manual . mode - part GGIR run, GGIR constructed five parts sixth part development. overwrite - whether overwrite previously produced milestone output. GGIR part, GGIR stores milestone output ease re-running parts pipeline. idloc - tells GGIR find participant ID (default: inside file header) data_masking_strategy set value 1, check arguments hrs.del.start hrs.del.end. data_masking_strategy set value 3 5, check arguments ndayswindow, hrs.del.start hrs.del.end. maxdur - maximum number days expect data file based study protocol. desiredtz - time zone experiment. chunksize - way tell GGIR use less memory, can useful machines limited memory. includedaycrit - tell GGIR many hours valid data per day (midnight-midnight) acceptable. includenightcrit - tell GGIR many hours valid night (noon-noon) acceptable. qwindow - argument tell GGIR whether segment day day-segment specific analysis. mvpathreshold boutcriter - acceleration threshold bout criteria used calculating time spent MVPA (used GGIR part2). epochvalues2csv - export epoch level magnitude acceleration csv files (addition already stored RData file) dayborder - decide whether edge day midnight. iglevels - argument related intensity gradient method proposed . Rowlands. .report - specify reports need generated. viewingwindow visualreport - create visual report, works five parts GGIR successfully run. Note visual report initially developed provide something show study participants data quality checking purposes. time improved visual report also useful QC-ing data. however, scorings shown visual report created visual report may reflect scorings main GGIR analyses reported quantitative csv-reports. effort past 10 years gone making sure csv-report correct, visualreport mostly side project. unfortunate hope find funding future design new report specifically purpose QC-ing anlayses done GGIR. maxRecordingInterval - specified controls whether neighboring overlapping recordings participant ID brand appended epoch level. can useful intention monitor behaviour larger periods time accelerometers allow weeks data collection. GGIR never append alter raw input file, operation preformed derived data. study_dates_file - specified trims recorded data first last date study took place. relevant studies started recording several days accelerometers actually worn participants. used top data_masking_strategy, may combined strategies GGIR.","code":"library(GGIR) GGIR(datadir=\"C:/mystudy/mydata\",  outputdir=\"D:/myresults\")"},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"key-arguments-related-to-sleep-analysis","dir":"Articles","previous_headings":"Setting up your work environment > GGIR shell function","what":"Key arguments related to sleep analysis","title":"Accelerometer data processing with GGIR","text":"explanation sleep detected specific role various function arguments see section Sleep analysis. Arguments related configuring sleep detection algorithm: anglethreshold, timethreshold, HASPT.algo, HASIB.algo, Sadeh_axis, HASPT.ignore.invalid. ignorenonwear set TRUE ignore detected monitor non-wear periods detection sustained inactivity bouts avoid confusion monitor non-wear time. want create visualisation sleep period time sustained inactivity bouts match throughout day consider arguments .visual, outliers., criterror. want exclude first last night sleep analysis used excludefirstlast. def.noc.sleep specifies sleep period time window estimated sleeplog used. includenightcrit Minimum number valid hours per night (24 hour window noon noon 6pm-6pm). data_cleaning_file ginore specific nights specific individuals, see also section Data cleaning file. want sleep analysis guided sleeplog (diary) check arguments loglocation specifies location spreadsheet (csv) sleep log information. , use arguments colid coln1 specify details sleep log structure. GGIR facilitates two possible sleeplog file structures:","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"basic-sleep-log","dir":"Articles","previous_headings":"Setting up your work environment > GGIR shell function > Key arguments related to sleep analysis","what":"Basic sleep log","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"advanced-sleep-log","dir":"Articles","previous_headings":"Setting up your work environment > GGIR shell function > Key arguments related to sleep analysis","what":"Advanced sleep log","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"key-arguments-related-to-time-use-analysis","dir":"Articles","previous_headings":"Setting up your work environment > GGIR shell function","what":"Key arguments related to time use analysis","title":"Accelerometer data processing with GGIR","text":"explanation time use analysis performed see section Waking-waking 24 hour time-use analysis. excludefirstlast.part5 - whether ignore last first day. includedaycrit.part5 - tell GGIR fraction waking hours day (value 1) acceptable. minimum_MM_length.part5 - tell GGIR minimum length (hours) MM window part 5. Configure thresholds acceleration levels (may want interpret intensity levels): threshold.lig, threshold.mod, threshold.vig. single interruption can last < 1 min Repeated interruptions allowed provided total time exceed 20% bout duration time spent interruptions included duration MVPA bout. example: 25-minute bout can two 1 minute interruption, single 2-minute interruption. , full 25 minutes count towards duration MVPA bout. timewindow specify whether days defined midnight midnight \"MM\", waking-waking-\"WW\", sleep onset sleep onset \"OO\", combination . Configure durations bouts: boutdur.mvpa, boutdur., boutdur.lig. Note can vector multiple values indicating minimum maximum duration subsequent bout types, e.g. 1-5 minutes MVPA, 5-10 minutes MVPA, longer 10 minutes MVPA.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"published-cut-points-and-how-to-use-them","dir":"Articles","previous_headings":"Setting up your work environment > GGIR shell function","what":"Published cut-points and how to use them","title":"Accelerometer data processing with GGIR","text":"section rewritten moved. Please, visit vignette Published cut-points use GGIR details cut-points available, use , additional reflections use cut-points GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"example-call","dir":"Articles","previous_headings":"Setting up your work environment > GGIR shell function","what":"Example call","title":"Accelerometer data processing with GGIR","text":"consider arguments may end call GGIR look follows. used GGIR output directory (outputdir) filled milestone data results.","code":"library(GGIR) GGIR(mode=c(1,2,3,4,5),       datadir=\"C:/mystudy/mydata\",       outputdir=\"D:/myresults\",       do.report=c(2,4,5),       #=====================       # Part 2       #=====================       data_masking_strategy = 1,       hrs.del.start = 0,          hrs.del.end = 0,       maxdur = 9,                 includedaycrit = 16,       qwindow=c(0,24),       mvpathreshold =c(100),       excludefirstlast = FALSE,       includenightcrit = 16,       #=====================       # Part 3 + 4       #=====================       def.noc.sleep = 1,       outliers.only = TRUE,       criterror = 4,       do.visual = TRUE,       #=====================       # Part 5       #=====================       threshold.lig = c(30), threshold.mod = c(100),  threshold.vig = c(400),       boutcriter = 0.8,      boutcriter.in = 0.9,     boutcriter.lig = 0.8,       boutcriter.mvpa = 0.8, boutdur.in = c(1,10,30), boutdur.lig = c(1,10),       boutdur.mvpa = c(1),       includedaycrit.part5 = 2/3,       #=====================       # Visual report       #=====================       timewindow = c(\"WW\"),       visualreport=TRUE)"},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"configuration-file","dir":"Articles","previous_headings":"Setting up your work environment > GGIR shell function","what":"Configuration file","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"from-the-r-console-on-your-own-desktoplaptop","dir":"Articles","previous_headings":"Time for action: How to run your analysis?","what":"From the R console on your own desktop/laptop","title":"Accelerometer data processing with GGIR","text":"Create R-script put GGIR call . Next, can source R-script source function R: source(\"pathtoscript/myshellscript.R\") use Source button RStudio use RStudio.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"in-a-cluster","dir":"Articles","previous_headings":"Time for action: How to run your analysis?","what":"In a cluster","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"processing-time","dir":"Articles","previous_headings":"Time for action: How to run your analysis?","what":"Processing time","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"inspecting-the-results","dir":"Articles","previous_headings":"","what":"Inspecting the results","title":"Accelerometer data processing with GGIR","text":"GGIR generates following types output. - csv-spreadsheets variables need physical activity, sleep circadian rhythm research - Pdfs page low resolution plot data per file quality indicators - R objects milestone data - Pdfs visual summary physical activity sleep patterns identified (see example )","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"output-part-2","dir":"Articles","previous_headings":"Inspecting the results","what":"Output part 2","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"person-level-summary","dir":"Articles","previous_headings":"Inspecting the results > Output part 2","what":"Person level summary","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"day-level-summary","dir":"Articles","previous_headings":"Inspecting the results > Output part 2","what":"Day level summary","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"data_quality_report","dir":"Articles","previous_headings":"Inspecting the results > Output part 2","what":"Data_quality_report","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"output-part-4","dir":"Articles","previous_headings":"Inspecting the results","what":"Output part 4","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"night-level-summaries","dir":"Articles","previous_headings":"Inspecting the results > Output part 4","what":"Night level summaries","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"non-default-variables-in-part-4-csv-report","dir":"Articles","previous_headings":"Inspecting the results > Output part 4 > Night level summaries","what":"Non-default variables in part 4 csv report","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"visualisation_sleep-pdf","dir":"Articles","previous_headings":"Inspecting the results > Output part 4","what":"visualisation_sleep.pdf","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"output-part-5","dir":"Articles","previous_headings":"Inspecting the results","what":"Output part 5","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"output5","dir":"Articles","previous_headings":"Inspecting the results > Output part 5","what":"Day level summary","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"person-level-summary-1","dir":"Articles","previous_headings":"Inspecting the results > Output part 5","what":"Person level summary","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"output-part-6","dir":"Articles","previous_headings":"Inspecting the results","what":"Output part 6","title":"Accelerometer data processing with GGIR","text":"part6CR = TRUE vector specified paramter .report includes number 6, csv report stored following variables:","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"motivation-and-clarification","dir":"Articles","previous_headings":"","what":"Motivation and clarification","title":"Accelerometer data processing with GGIR","text":"chapter try collect motivations clarification behind GGIR may clear existing publications.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"reproducibilty-of-ggir-analyses","dir":"Articles","previous_headings":"Motivation and clarification","what":"Reproducibilty of GGIR analyses","title":"Accelerometer data processing with GGIR","text":"tips increase reproducibility findings: publish findings, please remember add GGIR package version number. GGIR archived CRAN available archive section package website. GGIR evolved years. get better understanding versions differ check NEWS sections package website Report configured accelerometer Report study protocol wear instructions given participants Report GGIR version Report GGIR used: Share config.csv file R script Report post-processed / cleaned GGIR output Report reported outcomes relate specific variable names GGIR","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"auto-calibration","dir":"Articles","previous_headings":"Motivation and clarification","what":"Auto-calibration","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"non-wear-detection","dir":"Articles","previous_headings":"Motivation and clarification","what":"Non-wear detection","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"clipping-score","dir":"Articles","previous_headings":"Motivation and clarification","what":"Clipping score","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"why-collapse-information-to-epoch-level","dir":"Articles","previous_headings":"Motivation and clarification","what":"Why collapse information to epoch level?","title":"Accelerometer data processing with GGIR","text":"Although many data points collected decide work aggregated values (e.g. 1 5 second epochs) following reasons: Accelerometers often used describe patterns metabolic energy expenditure. Metabolic energy expenditure typically defined per breath per minute (indirect calorimetry), per day (room calorimeter), per multiple days (doubly labelled water method). order validate methods reference standards need work similar time resolution. Collapsing data epoch summary measures helps standardise differences sample frequency studies. little evidence raw data accurate representation body acceleration. scientific evidence validity accelerometer data far based epoch averages. Collapsing data epoch summary measures may help average different noise levels make sensor brands comparable.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"why-does-the-first-epoch-not-allign-with-the-original-start-of-the-recording","dir":"Articles","previous_headings":"Motivation and clarification > Why collapse information to epoch level?","what":"Why does the first epoch not allign with the original start of the recording","title":"Accelerometer data processing with GGIR","text":"GGIR uses short (default 5 seconds) long epochs (default 15 minutes). epochs aligned hour day, . example, recording starts 9:52:00 GGIR work epochs derived 10:00:00 onward. recording starts 10:12 GGIR work epochs derived 10:15:00 onward. Motivation: allows us standardised time grid across recordings describe behaviour. allows us calculate behaviour exactly per day per specified time interval day. first 15 minute epochs start 9:52 next one start 10:07, makes impossible make statement behaviour 10:00 13:00.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"Sleep_analysis","dir":"Articles","previous_headings":"Motivation and clarification","what":"Sleep analysis","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"replication-of-the-movement-counts-needed","dir":"Articles","previous_headings":"Motivation and clarification > Sleep analysis > Notes on sleep classification algorithms designed for count data","what":"Replication of the movement counts needed","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"missing-information-for-replicating-movement-counts","dir":"Articles","previous_headings":"Motivation and clarification > Sleep analysis > Notes on sleep classification algorithms designed for count data","what":"Missing information for replicating movement counts","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"an-educated-guess-and-how-you-can-to-help-optimise-the-implementation","dir":"Articles","previous_headings":"Motivation and clarification > Sleep analysis > Notes on sleep classification algorithms designed for count data","what":"An educated guess and how you can to help optimise the implementation","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"guiders","dir":"Articles","previous_headings":"Motivation and clarification > Sleep analysis","what":"Guiders","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"daysleepers-nights-workers","dir":"Articles","previous_headings":"Motivation and clarification > Sleep analysis","what":"Daysleepers (nights workers)","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"Cleaningcode","dir":"Articles","previous_headings":"Motivation and clarification > Sleep analysis","what":"Cleaningcode","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"difference-between-cleaned-and-full-output","dir":"Articles","previous_headings":"Motivation and clarification > Sleep analysis","what":"Difference between cleaned and full output","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"Data_cleaning_file","dir":"Articles","previous_headings":"Motivation and clarification > Sleep analysis","what":"Data cleaning file","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"Waking-waking_or_24_hour","dir":"Articles","previous_headings":"Motivation and clarification","what":"Waking-waking or 24 hour time-use analysis","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"time-series-output-files","dir":"Articles","previous_headings":"Motivation and clarification > Waking-waking or 24 hour time-use analysis","what":"Time series output files","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"day-inclusion-criteria","dir":"Articles","previous_headings":"Motivation and clarification > Waking-waking or 24 hour time-use analysis","what":"Day inclusion criteria","title":"Accelerometer data processing with GGIR","text":"full part 5 output stored results/QC folder. default inclusion criteria days cleaned output part 5 (stored results folder) : MM WW defined days: valid (sensor worn) time fraction day needs fraction specified argument includedaycrit.part5 (default 2/3). MM defined days : length day needs least number hours specified minimum_MM_length.part5 (default 23). Note experiment started ended middle day default setting exclude incomplete first last days. think including days still meaningful work adjust argument minimum_MM_length.part5. Important notes: criteria set amount valid data SPT window, interested part 5 knowing borders night trust sufficiently estimated part 4. disagree please notice days included full report available results/QC folder. means argument includenightcrit used part 4 used part 5. data_cleaning_file argument discussed Data_cleaning_file also allows tell GGIR person(s) day(s) omitted part 5. day numbers excluded listed column day_part5 header.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"fragmentation-metrics","dir":"Articles","previous_headings":"Motivation and clarification > Waking-waking or 24 hour time-use analysis","what":"Fragmentation metrics","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR. Difference fragments blocks: Elsewhere part5 use term block. block sequence epochs belong behavioural class. may sound similar definition fragment, blocks distinguish every behavioural class, includes subcategories bouted unbouted behaviour. means variables Nblock_day_total_IN Nblock_day_total_LIG identical Nfrag_IN_day Nfrag_LIPA_day, respectively. contrast, fragments may group LIPA MVPA together refering fragmentation PA. Differences R package ActFrag: section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"why-use-data-metric-enmo-as-default","dir":"Articles","previous_headings":"Motivation and clarification","what":"Why use data metric ENMO as default?","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"what-does-ggir-stand-for","dir":"Articles","previous_headings":"Motivation and clarification","what":"What does GGIR stand for?","title":"Accelerometer data processing with GGIR","text":"wanted short name spend much time finding . abbreviation lost functional meaning, now use GGIR name.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"circadian-rhythm-analyses","dir":"Articles","previous_headings":"Motivation and clarification","what":"Circadian Rhythm analyses","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"actigraphs-idle-sleep-mode","dir":"Articles","previous_headings":"Motivation and clarification","what":"ActiGraph’s idle sleep mode","title":"Accelerometer data processing with GGIR","text":"idle sleep mode explained manufacturer’s website. short, idle sleep mode setting can turned user. turned device fall asleep periods movement, resulting time gaps data. functionality probably introduced safe battery life minimize data size. However, also means end time gaps need accounted .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"time-gap-imputation","dir":"Articles","previous_headings":"Motivation and clarification > ActiGraph’s idle sleep mode","what":"Time gap imputation","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"the-importance-of-reporting-idle-sleep-mode-usage","dir":"Articles","previous_headings":"Motivation and clarification > ActiGraph’s idle sleep mode","what":"The importance of reporting idle.sleep.mode usage","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"mx-metrics-minimum-intensity-of-most-active-x-minutes","dir":"Articles","previous_headings":"Motivation and clarification","what":"MX metrics (minimum intensity of most active X minutes)","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"Minimum_recording_duration","dir":"Articles","previous_headings":"Motivation and clarification","what":"Minimum recording duration","title":"Accelerometer data processing with GGIR","text":"GGIR designed process multi-day recordings. minimum recording duration considered GGIR depends type analysis: Running part 1 2 File size; least 2MB, 2MB can adjusted argument minimumFileSizeMB. changed unless good reason believe smaller file size also acceptable. Recording duration: least two long epoch windows (default 60 minutes) g.readaccfile. size epoch can altered second third value vector argument windowsizes, third smaller second. example, short lasting lab-experiments may find easier set windowsizes = c(5, 600, 600) non-wear detection usually necessary lab studies. Running part 3 4 least one night data expected, night expected least timestamp midnight. midnight found sleep detection skipped. Running part 5 Ideally two valid consecutive nights waking hours .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"lux-sensor-data-processing","dir":"Articles","previous_headings":"Motivation and clarification","what":"LUX sensor data processing","title":"Accelerometer data processing with GGIR","text":"Although GGIR focuses accelerometer data brands come LUX data. part 1 GGIR calculates peak lux per long epoch default resolution 15 minutes, can modified argument windowsizes. Peak light offers reliable estimate light exposure per time window compared taking average. , LUX used auto-calibration. GGIR part 2 visualise LUX values qc plot. part 3 4 LUX used sleep classification relation light exposure sleep weak. part 5 calculate mean maximum peak LUX per epoch across waking hours day. , mean (peak per epoch) LUX indicate average light exposure per time segment, max peak indicate maximum light exposure per day. , calculate max mean peak LUX per active consecutive X hour day. intended offer alternative LUX exposure waking hours relies correct sleep classification. LUX exposure M10 may seen alternative unsure whether can trust sleep classification data set.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"other-resources","dir":"Articles","previous_headings":"","what":"Other Resources","title":"Accelerometer data processing with GGIR","text":"GGIR package manual provides documentation individual functions. general questions use GGIR join google group/mailing list. bug reports please post .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"CitingGGIR","dir":"Articles","previous_headings":"","what":"Citing GGIR","title":"Accelerometer data processing with GGIR","text":"correct citation research software important make research reproducible acknowledge effort goes development open-source software. , please report GGIR version used text. Additionally, please also cite: Migueles JH, Rowlands AV, et al. GGIR: Research Community–Driven Open Source R Package Generating Physical Activity Sleep Outcomes Multi-Day Raw Accelerometer Data. Journal Measurement Physical Behaviour. 2(3) 2019. doi: 10.1123/jmpb.2018-0063. work depends quantification physical activity also cite: van Hees VT, Gorzelniak L, et al. Separating Movement Gravity Components Acceleration Signal Implications Assessment Human Daily Physical Activity. PLoS ONE 8(4) 2013. link Sabia S, van Hees VT, Shipley MJ, Trenell MI, Hagger-Johnson G, Elbaz , Kivimaki M, Singh-Manoux . Association questionnaire- accelerometer-assessed physical activity: role sociodemographic factors. J Epidemiol. 2014 Mar 15;179(6):781-90. doi: 10.1093/aje/kwt330. Epub 2014 Feb 4. PMID: 24500862 link used auto-calibration functionality also cite: van Hees VT, Fang Z, et al. Auto-calibration accelerometer data free-living physical activity assessment using local gravity temperature: evaluation four continents. J Appl Physiol 2014. link used sleep detection also cite: van Hees VT, Sabia S, et al. novel, open access method assess sleep duration using wrist-worn accelerometer, PLoS ONE, 2015 link used sleep detection without relying sleep diary also cite: van Hees VT, Sabia S, et al. Estimating sleep parameters using accelerometer without sleep diary. Scientific Reports 2018. doi: 10.1038/s41598-018-31266-z. link used sleep regularity index also cite: Andrew J. K. Phillips, William M. Clerx, et al. Irregular sleep/wake patterns associated poorer academic performance delayed circadian sleep/wake timing. Scientific Reports. 2017 June 12 link.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"copyright-for-ggir-logo","dir":"Articles","previous_headings":"Citing GGIR","what":"Copyright for GGIR logo","title":"Accelerometer data processing with GGIR","text":"copyright GGIR logo lies Accelting (Almere, Netherlands), please contact v.vanhees@acceleting.com ask permission use logo.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"ggir-part-1","dir":"Articles","previous_headings":"","what":"GGIR Part 1","title":"GGIR output","text":"GGIR part 1, outputs RData files used GGIR part 2. RData files intended direct use GGIR user.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"ggir-part-2","dir":"Articles","previous_headings":"","what":"GGIR Part 2","title":"GGIR output","text":"Part 2 generates following output: part2_summary.csv: Person level summary (see ) part2_daysummary.csv: Day level summary (see ) QC/data_quality_report.csv: Overview calibration results whether file corrupt short processed, QC/plots check data quality 1.pdf: pdf visualisation acceleration time series 15 minute resolution invalid data segments highlighted colours (yellow: non-wear based standard deviation threshold, brown: non-wear extra filtering step (introduced 2013), purple: clipping)","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":[]},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"day-level-summary-csv","dir":"Articles","previous_headings":"GGIR Part 2","what":"Day level summary (csv)","title":"GGIR output","text":"non-exhaustive list, concepts explained summary.csv","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"data_quality_report-csv","dir":"Articles","previous_headings":"GGIR Part 2","what":"Data_quality_report (csv)","title":"GGIR output","text":"data_quality_report.csv stored subfolder folder results/QC.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"ggir-part-3","dir":"Articles","previous_headings":"","what":"GGIR Part 3","title":"GGIR output","text":"GGIR part 3, outputs RData files used GGIR part 4 5. RData files intended direct use GGIR user.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"ggir-part-4","dir":"Articles","previous_headings":"","what":"GGIR Part 4","title":"GGIR output","text":"Part 4 generates following output:","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"night-level-summaries-csv","dir":"Articles","previous_headings":"GGIR Part 4","what":"Night level summaries (csv)","title":"GGIR output","text":"part4_nightsummary_sleep_cleaned.csv QC/part4_nightsummary_sleep_full.csv latter ‘_full’ name intended aid clarifying nights () excluded cleaned summary report. Although, nights accelerometer worn excluded . , 30 day recording accelerometer worn day 7 onward find last 22 nights either csv-report. csv. files contain variables shown . Non-default variables part 4 csv report additional stored used sleeplog captures time bed, using guider HorAngle hip-worn accelerometer data. either applies set argument sleepwindowType “TimeInBed”.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"person-level-summaries-csv","dir":"Articles","previous_headings":"GGIR Part 4","what":"Person level summaries (csv)","title":"GGIR output","text":"part4_summary_sleep_cleaned.csv QC/part4_summary_sleep_full.csv person level report variables derived variables night level summary. Minor extensions variable names explain variables aggregated across days. Please find extra clarification variable names meaning may obvious:","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"visualisation-pdf","dir":"Articles","previous_headings":"GGIR Part 4","what":"Visualisation (pdf)","title":"GGIR output","text":"Visualisation support data quality checks: - visualisation_sleep.pdf (optional) input argument .visual set TRUE GGIR can show following visual comparison time window asleep (bed) according sleeplog detected sustained inactivity bouts according accelerometer data. visualisation stored results folder visualisation_sleep.pdf. Explanation image: line represents one night. Colours used distinguish definitions sustained inactivity bouts (2 definitions case) indicate existence absence overlap sleeplog. argument outliers.set FALSE visualise available nights dataset. outliers.set TRUE visualise nights difference onset waking time sleeplog sustained inactivity bouts larger value argument criterror. visualisation outliers.set TRUE critererror set 4 powerful identify entry errors sleeplog data van Hees et al PLoSONE 2015. 25 thousand nights data, visualisation allowed us quickly zoom problematic nights investigate possible mistakes GGIR mistakes data entry.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"ggir-part-5","dir":"Articles","previous_headings":"","what":"GGIR Part 5","title":"GGIR output","text":"output part 5 dependent parameter configuration, generate many output files unique combination three thresholds provided. example, following files generated threshold configuration 30 light activity, 100 moderate 400 vigorous activity: part5_daysummary_MM_L30M100V400_T5A5.csv part5_daysummary_WW_L30M100V400_T5A5.csv part5_personsummary_MM_L30M100V400_T5A5.csv part5_personsummary_WW_L30M100V400_T5A5.csv file summary reports/Report_nameofdatafile.pdf","code":""},{"path":[]},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"old-napping-behaviour-variables-to-be-deprecated","dir":"Articles","previous_headings":"GGIR Part 5 > Day level summary (csv)","what":"Old napping behaviour variables (to be deprecated)","title":"GGIR output","text":"variables deprecated napping, detection turned , now behavioural category just like behaviours. Special note working compositional data analysis: duration dur_ variables _total_ name add total length waking hours day. Similarly, duration dur_ variables excluding variables _total_ name excluding variable dur_day_min, dur_spt_min, dur_day_spt_min also add length full day. Motivation default boutcriter.= 0.9: idea allow bouts 30 minutes make sense allow breaks 20 percent (6 minutes!) used stringent criteria highest category. Please note can change criteria via arguments boutcriter.mvpa, boutcriter., boutcriter.lig.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"person-level-summary-csv-1","dir":"Articles","previous_headings":"GGIR Part 5","what":"Person level summary (csv)","title":"GGIR output","text":"variables person level summary derived day level summary, extended _pla indicate variable calculated plain average across valid days. Variables extended _wei represent weighted average across days weekend days always weighted 2/5 relative contribution week days.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"ggir-part-6","dir":"Articles","previous_headings":"","what":"GGIR Part 6","title":"GGIR output","text":"Part 6 stores person level summary (csv). column names overlap part 5, now derived based full time series , whereas part 5 presents variables per day window aggregated person level.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"default-argument-values","dir":"Articles","previous_headings":"","what":"Arguments/parameters description","title":"GGIR configuration parameters","text":"information shown auto-generated identical information provided GGIR package pdf manual.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"mode","dir":"Articles","previous_headings":"Arguments/parameters description > GGIR function input arguments","what":"mode","title":"GGIR configuration parameters","text":"Numeric (default = 1:5). Specify five parts need run, e.g., mode = 1 makes g.part1 run; mode = 1:5 makes whole GGIR pipeline run, g.part1 g.part5. Optionally mode can also include number 6 tell GGIR run g.part6 currently development.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"datadir","dir":"Articles","previous_headings":"Arguments/parameters description > GGIR function input arguments","what":"datadir","title":"GGIR configuration parameters","text":"Character (default = c()). Directory accelerometer files stored, e.g., “C:/mydata”, list accelerometer filenames directories, e.g. c(“C:/mydata/myfile1.bin”, “C:/mydata/myfile2.bin”).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"outputdir","dir":"Articles","previous_headings":"Arguments/parameters description > GGIR function input arguments","what":"outputdir","title":"GGIR configuration parameters","text":"Character (default = c()). Directory output needs stored. Note function attempt create folders directory uses folder keep output.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"studyname","dir":"Articles","previous_headings":"Arguments/parameters description > GGIR function input arguments","what":"studyname","title":"GGIR configuration parameters","text":"Character (default = c()). datadir folder, study given name data directory. datadir list filenames studyname specified input argument used name study.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"f0","dir":"Articles","previous_headings":"Arguments/parameters description > GGIR function input arguments","what":"f0","title":"GGIR configuration parameters","text":"Numeric (default = 1). File index start (default = 1). Index refers filenames sorted alphabetical order.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"f1","dir":"Articles","previous_headings":"Arguments/parameters description > GGIR function input arguments","what":"f1","title":"GGIR configuration parameters","text":"Numeric (default = 0). File index finish (defaults number files available).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-report","dir":"Articles","previous_headings":"Arguments/parameters description > GGIR function input arguments","what":"do.report","title":"GGIR configuration parameters","text":"Numeric (default = c(2, 4, 5, 6)). parts generate summary spreadsheet: 2, 4, 5, /6. Default c(2, 4, 5, 6). report generated based available milestone data. creating milestone data multiple machines advisable turn report generation generating milestone data, value = c(), merge milestone data turn report generation back setting overwrite FALSE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"configfile","dir":"Articles","previous_headings":"Arguments/parameters description > GGIR function input arguments","what":"configfile","title":"GGIR configuration parameters","text":"Character (default = c()). Configuration file previously generated function GGIR. See details.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"myfun","dir":"Articles","previous_headings":"Arguments/parameters description > GGIR function input arguments","what":"myfun","title":"GGIR configuration parameters","text":"List (default = c()). External function object applied raw data. See package vignette detailed tutorial examples use function embedding: https://cran.r-project.org/package=GGIR/vignettes/ExternalFunction.html","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"overwrite","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"overwrite","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). want overwrite analysis milestone data exists? overwrite = FALSE, milestone data previous analysis used available visual reports created .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"acc-metric","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"acc.metric","title":"GGIR configuration parameters","text":"Character (default = “ENMO”). one acceleration metrics want use acceleration magnitude analyses GGIR part 5 visual report? example: “ENMO”, “LFENMO”, “MAD”, “NeishabouriCount_y”, “NeishabouriCount_vm”. one acceleration metric can specified selected metric needs calculated part 1 (see g.part1) via arguments .enmo = TRUE .mad = TRUE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"maxncores","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"maxNcores","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Maximum number cores use argument .parallel set true. GGIR default uses either maximum number available cores number files process (whichever lower), argument allows set lower maximum.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"print-filename","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"print.filename","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). Whether print filename analysing (case .parallel = FALSE). Printing filename can useful investigate problems (e.g., verify file read).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-parallel","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"do.parallel","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). Whether use multi-core processing (works least 4 CPU cores available).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"windowsizes","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"windowsizes","title":"GGIR configuration parameters","text":"Numeric vector, three values (default = c(5, 900, 3600)). indicate lengths windows c(window1, window2, window3): window1 short epoch length seconds, default 5, time window acceleration angle metrics calculated; window2 long epoch length seconds non-wear signal clipping defined, default 900 (expected multitude 60 seconds); window3 window length data used non-wear detection default 3600 seconds. , window3 larger window2 use overlapping windows, window2 equals window3 non-wear periods assessed non-overlapping windows.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"desiredtz","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"desiredtz","title":"GGIR configuration parameters","text":"Character (default = ““, .e., system timezone). Timezone device configured experiments took place. experiments took place different timezone, use argument timezone experiments took place argument configtz specify device configured. Use ”TZ identifier” specified ://en.wikipedia.org/wiki/Zone.tabhttps://en.wikipedia.org/wiki/Zone.tab set desiredtz, e.g., “Europe/London”.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"configtz","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"configtz","title":"GGIR configuration parameters","text":"Character (default = ““, .e., system timezone). moment functional GENEActiv .bin, AX3 cwa, ActiGraph .gt3x, ad-hoc csv file format. Timezone accelerometer configured. use argument timezone configuration timezone recording took place different. Use ”TZ identifier” specified ://en.wikipedia.org/wiki/Zone.tabhttps://en.wikipedia.org/wiki/Zone.tab set configtz, e.g., “Europe/London”.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"idloc","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"idloc","title":"GGIR configuration parameters","text":"Numeric (default = 1). idloc = 1 code assumes ID number stored obvious header field. Note ActiGraph data ID never stored file header. value set 2, 5, 6, 7, GGIR looks filename extracts character string preceding first occurance “_” (idloc = 2), ” ” (space, idloc = 5), “.” (dot, idloc = 6), “-” (idloc = 7), respectively. may noticed idloc 3 4 skipped, used one study 2012, actively maintained anymore, legacy code omitted.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"dayborder","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"dayborder","title":"GGIR configuration parameters","text":"Numeric (default = 0). Hour days start end (dayborder = 4 mean 4 ).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"part5_agg2_60seconds","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"part5_agg2_60seconds","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). Whether use aggregate epochs 60 seconds part GGIR g.part5 analysis. Aggregation doen averaging. Note working count metrics Neishabouri counts means threshold can stay part 2, threshold expressed relative original epoch size, even averaged per minute. example want use cut-point 100 count per minute specify mvpathreshold = 100 * (5/60) well `threshold.mod = 100 * (5/60) regardless whether set part5_agg2_60seconds TRUE FALSE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"sensor-location","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"sensor.location","title":"GGIR configuration parameters","text":"Character (default = “wrist”). indicate sensor location, default wrist. hip, HDCZA algorithm sleep detection also requires longitudinal axis sensor -45 +45 degrees.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"expand_tail_max_hours","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"expand_tail_max_hours","title":"GGIR configuration parameters","text":"Numeric (default = NULL). parameter replaced recordingEndSleepHour.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"recordingendsleephour","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"recordingEndSleepHour","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Time (hours) recording end (later) expand g.part1 output synthetic data trigger sleep detection last night. Using argument recordingEndSleepHour implies assumption participant fell asleep end recording recording ended recordingEndSleepHour hour last day. assumption may always hold true used caution. synthetic data metashort entails: timestamps continuing regularly, zeros acceleration metrics EN, one EN. Angle columns created way triggers sleep detection using equation: round(sin((1:length_expansion) / (900/epochsize))) * 15. keep track tail expansion g.part1 stores length expansion RData files, passed via g.part2, g.part3, g.part4 g.part5. g.part5 tail expansion size included additional variable csv-reports. g.part4 csv-report last night omitted, know sleep estimates last night trustworthy. Similarly, g.part5 output columns related sleep assessment omitted last window avoid biasing averages. , synthetic data also ignored visualizations time series output avoid biased output.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"dataformat","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"dataFormat","title":"GGIR configuration parameters","text":"Character (default = “raw”). indicate format data datadir. Alternatives: ukbiobank_csv, actiwatch_csv, actiwatch_awd, actigraph_csv, sensewear_xls, correspond epoch level data files , respecitively, UK Biobank csv format, Actiwatch csv format, Actiwatch awd format, ActiGraph csv format, Sensewear xls format (also works xlsx). , assumed epoch size UK Biobank csvdata 5 seconds. epoch size non-raw data formats flexible, make sure set first value argument windowsizes accordingly. Also working non-raw data formats specify argument extEpochData_timeformat documented . ukbiobank_csv nonwear column data , actiwatch_csv, actiwatch_awd, actigraph_csv, sensewear_xls non-wear detected 60 minute rolling zeros. length window can modified third value argument windowsizes expressed seconds.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"maxrecordinginterval","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"maxRecordingInterval","title":"GGIR configuration parameters","text":"Numeric (default = NULL). indicate maximum gap hours repeated measurements ID recordings appended. , assumption ID can matched, make sure argument idloc set correctly. argument maxRecordingInterval set NULL (default) recordings appended. recordings overlap GGIR use data latest recording. recordings separated timegap recordings filled data points resemble monitor worn. maximum value maxFile gap 504 (21 days). recordings accelerometer brand appended. part 2 csv report show number appended recordings, sampling rate , time overlap gap names filenames respective recording.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"extepochdata_timeformat","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"extEpochData_timeformat","title":"GGIR configuration parameters","text":"Character (default = “%d-%m-%Y %H:%M:%S”). specify time format used external epoch level data argument dataFormat set “actiwatch_csv”, “actiwatch_awd”, “actigraph_csv” “sensewear_xls”. example “%Y-%m-%d %:%M:%S %p” “2023-07-11 01:24:01 PM” “%m/%d/%Y %H:%M:%S” “2023-07-11 13:24:01”","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"chunksize","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"chunksize","title":"GGIR configuration parameters","text":"Numeric (default = 1). Value specify size chunks loaded fraction approximately 12 hour period auto-calibration procedure fraction 24 hour period metric calculation, e.g., 0.5 equals 6 12 hour chunks, respectively. machines less 4Gb RAM memory < 2GB memory per process using .parallel = TRUE value 1 recommended. value constrained GGIR lower 0.05. Please note setting 0.05 produce output 3rd value parameter windowsizes 3600.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"spherecrit","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"spherecrit","title":"GGIR configuration parameters","text":"Numeric (default = 0.3). minimum required acceleration value (g) sides 0 g axis. Used judge whether sphere sufficiently populated","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"minloadcrit","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"minloadcrit","title":"GGIR configuration parameters","text":"Numeric (default = 168). minimum number hours code needs read autocalibration procedure effective (sensitive multitudes 12 hrs, values ceiled). loading hours extra data loaded calibration error reduced 0.01 g.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"printsummary","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"printsummary","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE print summary calibration procedure console done.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-cal","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"do.cal","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). Whether apply auto-calibration g.calibrate. Recommended setting TRUE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"backup-cal-coef","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"backup.cal.coef","title":"GGIR configuration parameters","text":"Character (default = “retrieve”). Option use backed-calibration coefficient instead deriving calibration coefficients analysing file twice. Argument backup.cal.coef two usecase. Use case 1: auto-calibration fails user option provide back-calibration coefficients via argument. value argument needs name directory csv-spreadsheet following column names subsequent values: “filename” names accelerometer files calibration coefficients need applied case auto-calibration fails; “scale.x”, “scale.y”, “scale.z” scaling coefficients; “offset.x”, “offset.y”, “offset.z” offset coefficients, ; “temperature.offset.x”, “temperature.offset.y”, “temperature.offset.z” temperature offset coefficients. can useful analysing short lasting laboratory experiments insufficient sphere data perform auto-calibration, calibration coefficients can derived alternative way. users responsibility compile csv-spreadsheet. Instead building file user can also Use case 2: user wants avoid performing auto-calibration repeatedly file. backup.cal.coef value set “retrieve” (default) GGIR look “data_quality_report.csv” file outputfolder QC, holds previously generated calibration coefficients. want happen, deleted data_quality_report.csv QC folder set value “redo”.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"dynrange","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"dynrange","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Provide dynamic range 8 gravity.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"minimumfilesizemb","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"minimumFileSizeMB","title":"GGIR configuration parameters","text":"Numeric (default = 2). Minimum File size MB required enter processing. argument can help avoid short uninformative files enter analyses. Given typical accelerometer collects several MBs per hour, default setting skip tiny files.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-dec","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.dec","title":"GGIR configuration parameters","text":"Character (default = “.”). Decimal used numbers, dec argument [utils]read.csv [data.table]fread.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-firstrow-acc","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.firstrow.acc","title":"GGIR configuration parameters","text":"Numeric (default = NULL). First row (number) acceleration data.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-firstrow-header","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.firstrow.header","title":"GGIR configuration parameters","text":"Numeric (default = NULL). First row (number) header. Leave blank file header.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-header-length","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.header.length","title":"GGIR configuration parameters","text":"Numeric (default = NULL). file header, specify header length (number rows).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-col-acc","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.col.acc","title":"GGIR configuration parameters","text":"Numeric, three values (default = c(1, 2, 3)). Vector three column (numbers) acceleration signals stored.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-col-temp","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.col.temp","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Scalar column (number) temperature stored. Leave default setting temperature available. temperature used g.calibrate.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-col-time","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.col.time","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Scalar column (number) timestamps stored. Leave default setting timestamps stored.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-unit-acc","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.unit.acc","title":"GGIR configuration parameters","text":"Character (default = “g”). Character unit acceleration values: “g”, “mg”, “bit”.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-unit-temp","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.unit.temp","title":"GGIR configuration parameters","text":"Character (default = “C”). Character unit temperature values: (K)elvin, (C)elsius, (F)ahrenheit.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-unit-time","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.unit.time","title":"GGIR configuration parameters","text":"Character (default = “POSIX”). Character unit timestamps: “POSIX”, “UNIXsec” (seconds since origin, see argument rmc.origin), “UNIXmsec” (UNIXsec milliseconds), “character”, “ActivPAL” (exotic timestamp format used ActivPAL activity monitor).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-format-time","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.format.time","title":"GGIR configuration parameters","text":"Character (default = “%Y-%m-%d %H:%M:%OS”). Character giving date-time format used [base]strptime. used rmc.unit.time: character POSIX.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-bitrate","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.bitrate","title":"GGIR configuration parameters","text":"Numeric (default = NULL). unit acceleration bit provide bit rate, e.g., 12 bit.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-dynamic_range","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.dynamic_range","title":"GGIR configuration parameters","text":"Numeric character (default = NULL). unit acceleration bit provide dynamic range deviation g zero, e.g., +/-6g mean argument needs 6. give argument character value code search file header elements name equal character value use corresponding numeric value next dynamic range.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-unsignedbit","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.unsignedbit","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). unsignedbit = TRUE means bits positive numbers. unsignedbit = FALSE bits positive negative.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-origin","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.origin","title":"GGIR configuration parameters","text":"Character (default = “1970-01-01”). Origin time unit time UNIXsec, e.g., 1970-1-1.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-desiredtz","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.desiredtz","title":"GGIR configuration parameters","text":"Character (default = NULL). Timezone experiments took place. argument scheduled deprecated now used overwrite desiredtz provided.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-configtz","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.configtz","title":"GGIR configuration parameters","text":"Character (default = NULL). Timezone device configured. argument scheduled deprecated now used overwrite configtz provided.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-sf","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.sf","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Sample rate Hertz, stored file header used instead (see argument rmc.headername.sf).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-headername-sf","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.headername.sf","title":"GGIR configuration parameters","text":"Character (default = NULL). file header: Row name sample frequency can found.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-headername-sn","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.headername.sn","title":"GGIR configuration parameters","text":"Character (default = NULL). file header: Row name serial number can found.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-headername-recordingid","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.headername.recordingid","title":"GGIR configuration parameters","text":"Character (default = NULL). file header: Row name recording ID can found.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-header-structure","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.header.structure","title":"GGIR configuration parameters","text":"Character (default = NULL). Used split header name header value, e.g., “:” ” “.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-check4timegaps","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.check4timegaps","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). indicate whether gaps time imputed zeros. sensing equipment provides accelerometer gaps time. rest GGIR designed , setting argument TRUE gaps time filled zeros.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-noise","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.noise","title":"GGIR configuration parameters","text":"Numeric (default = 13). Noise level acceleration signal m-units, used working ad-hoc .csv data formats using read.myacc.csv. read.myacc.csv take rmc.noise argument, interacting GGIR g.part1 rmc.noise used.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"nonwear_range_threshold","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"nonwear_range_threshold","title":"GGIR configuration parameters","text":"Numeric (default 150) used define maximum value range per axis non-wear detection, used combination brand specific standard deviation per axis.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-col-wear","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.col.wear","title":"GGIR configuration parameters","text":"Numeric (default = NULL). external wear detection outcome stored part data can used GGIR. argument specifies column wear detection (Boolean) stored.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-doresample","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.doresample","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). indicate whether resample data based available timestamps extracted sample rate file header.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"interpolationtype","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"interpolationType","title":"GGIR configuration parameters","text":"Integer (default = 1). indicate type interpolation used resampling time series (mainly relevant Axivity sensors), 1=linear, 2=nearest neighbour.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"imputetimegaps","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"imputeTimegaps","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). indicate whether timegaps larger 1 sample imputed. Currently used .gt3x data ActiGraph .csv format, timegaps can expected result Actigraph’s idle sleep.mode configuration.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"frequency_tol","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"frequency_tol","title":"GGIR configuration parameters","text":"Number (default = 0.1) passed readAxivity GGIRread package. Represents frequency tolerance fraction 0 1. relative bias per data block larger fraction data block imputed lack movement gravitational oriationed guessed recent valid data block. applicable Axivity .cwa data.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-scalefactor-acc","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.scalefactor.acc","title":"GGIR configuration parameters","text":"Numeric value (default 1) scale acceleration signals via multiplication. example, data provided m/s2 setting 1/9.81 derive gravitational units.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-anglex","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.anglex","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates angle X axis relative horizontal: = (^-1_rollmedian(x)(acc_rollmedian(y))^2 + (acc_rollmedian(z))^2) * 180/","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-angley","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.angley","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates angle Y axis relative horizontal: = (^-1_rollmedian(y)(acc_rollmedian(x))^2 + (acc_rollmedian(z))^2) * 180/","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-anglez","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.anglez","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). TRUE, calculates angle Z axis relative horizontal: = (^-1_rollmedian(z)(acc_rollmedian(x))^2 + (acc_rollmedian(y))^2) * 180/","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-zcx","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.zcx","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric zero-crossing count x-axis. computation specifics see source code function g.applymetrics","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-zcy","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.zcy","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric zero-crossing count y-axis. computation specifics see source code function g.applymetrics","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-zcz","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.zcz","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric zero-crossing count z-axis. computation specifics see source code function g.applymetrics","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-enmo","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.enmo","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). TRUE, calculates metric: = _x^2 + acc_y^2 + acc_z^2 - 1 (ENMO < 0, ENMO = 0).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-lfenmo","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.lfenmo","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric ENMO low-pass filtered accelerations (computation specifics see source code function g.applymetrics). filter bound defined parameter hb.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-en","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.en","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates Euclidean Norm raw accelerations: = _x^2 + acc_y^2 + acc_z^2","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-mad","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.mad","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates Mean Amplitude Deviation: = 1n|r_i - |","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-enmoa","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.enmoa","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric: = _x^2 + acc_y^2 + acc_z^2 - 1 (ENMOa < 0, ENMOa = |ENMOa|).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-roll_med_acc_x","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.roll_med_acc_x","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-roll_med_acc_y","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.roll_med_acc_y","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-roll_med_acc_z","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.roll_med_acc_z","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-dev_roll_med_acc_x","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.dev_roll_med_acc_x","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-dev_roll_med_acc_y","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.dev_roll_med_acc_y","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-dev_roll_med_acc_z","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.dev_roll_med_acc_z","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-bfen","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.bfen","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-hfen","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.hfen","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-hfenplus","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.hfenplus","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-lfen","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.lfen","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-lfx","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.lfx","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-lfy","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.lfy","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-lfz","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.lfz","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-hfx","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.hfx","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-hfy","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.hfy","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-hfz","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.hfz","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-bfx","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.bfx","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-bfy","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.bfy","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-bfz","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.bfz","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-brondcounts","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.brondcounts","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). option deprecated (October 2022) due issues activityCounts package used dependency. TRUE, calculated metric via R package activityCounts. called BrondCounts large number activity counts physical activity sleep research field. calling brondcounts clarify counts proposed Jan Brønd implemented R Ruben Brondeel. brondcounts intended imitation counts produced one closed source ActiLife software ActiGraph.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-neishabouricounts","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.neishabouricounts","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric via R package actilifecounts, implementation algorithm used closed-source software ActiLife ActiGraph (methods published doi: 10.1038/s41598-022-16003-x). use name first author (instead ActiLifeCounts) paper call NeishabouriCount uncertainty ActiLife implement algorithm time. use Neishabouri counts physical activity intensity classification part 5 (.e., metric threshold.lig, threshold.mod, threshold.vig applied), acc.metric argument needs set one following: “NeishabouriCount_x”, “NeishabouriCount_y”, “NeishabouriCount_z”, “NeishabouriCount_vm” use counts x-, y-, z-axis vector magnitude, respectively.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"hb","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"hb","title":"GGIR configuration parameters","text":"Numeric (default = 15). Higher boundary frequency filter (Hertz) used filter-based metrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"lb","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"lb","title":"GGIR configuration parameters","text":"Numeric (default = 0.2). Lower boundary frequency filter (Hertz) used filter-based metrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"n","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"n","title":"GGIR configuration parameters","text":"Numeric (default = n). Order frequency filter used filter-based metrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"zc-lb","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"zc.lb","title":"GGIR configuration parameters","text":"Numeric (default = 0.25). Used zero-crossing counts . Lower boundary cut-frequency filter.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"zc-hb","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"zc.hb","title":"GGIR configuration parameters","text":"Numeric (default = 3). Used zero-crossing counts . Higher boundary cut-frequencies filter.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"zc-sb","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"zc.sb","title":"GGIR configuration parameters","text":"Numeric (default = 0.01). Stop band used calculation zero crossing counts. Value acceleration threshold g units acceleration rounded zero.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"zc-order","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"zc.order","title":"GGIR configuration parameters","text":"Numeric (default = 2). Used zero-crossing counts . Order frequency filter.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"zc-scale","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"zc.scale","title":"GGIR configuration parameters","text":"Numeric (default = 1) Used zero-crossing counts . Scaling factor applied counts calculated (GGIR part 3).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"actilife_lfe","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"actilife_LFE","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates NeishabouriCount metric low-frequency extension filter proposed closed source ActiLife software ActiGraph. applicable metric NeishabouriCount.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"includedaycrit","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"includedaycrit","title":"GGIR configuration parameters","text":"Numeric (default = 16). Minimum required number valid hours calendar day specific analysis part 2. specify two values c(16, 16) first value used part 2 second value used part 5 applied criterion full part 5 window. Note applied addition parameter includedaycrit.part5 looks valid data waking hours.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"ndayswindow","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"ndayswindow","title":"GGIR configuration parameters","text":"Numeric (default = 7). data_masking_strategy set 3 5, size window number days. data_masking_strategy 3 value can fractional, e.g. 7.5, data_masking_strategy 5 needs integer.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"strategy","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"strategy","title":"GGIR configuration parameters","text":"Deprecated replaced data_masking_strategy. strategy specified value passed used data_masking_strategy.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"data_masking_strategy","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"data_masking_strategy","title":"GGIR configuration parameters","text":"Numeric (default = 1). deal knowledge study protocol. data_masking_strategy = 1 means select data based hrs.del.start hrs.del.end. data_masking_strategy = 2 makes data first midnight last midnight used. data_masking_strategy = 3 selects active X days file X specified argument ndayswindow, days series 24-h blocks starting time day (X hours beginning end period can deleted arguments hrs.del.start hrs.del.end) data_masking_strategy = 4 use data first midnight. data_masking_strategy = 5 similar data_masking_strategy = 3, selects X complete calendar days X specified argument ndayswindow (X hours beginning end period can deleted arguments hrs.del.start hrs.del.end).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"maxdur","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"maxdur","title":"GGIR configuration parameters","text":"Numeric (default = 0). many DAYS start experiment experiment definitely stop? (set zero unknown).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"hrs-del-start","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"hrs.del.start","title":"GGIR configuration parameters","text":"Numeric (default = 0). many HOURS start experiment wearing monitor start? Used GGIR g.part2 data_masking_strategy = 1.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"hrs-del-end","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"hrs.del.end","title":"GGIR configuration parameters","text":"Numeric (default = 0). many HOURS end experiment wearing monitor definitely end? Used GGIR g.part2 data_masking_strategy = 1.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"includedaycrit-part5","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"includedaycrit.part5","title":"GGIR configuration parameters","text":"Numeric (default = 2/3). Inclusion criteria used part 5 number valid hours waking hours day, value smaller equal 1 used fraction waking hours, value 1 used absolute number valid hours required. confuse argument argument includedaycrit used GGIR part 2 applies entire day.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"excludefirstlast-part5","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"excludefirstlast.part5","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE first last window (waking-waking, midnight-midnight, sleep onset-onset) ignored g.part5.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"timesegments2zerofile","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"TimeSegments2ZeroFile","title":"GGIR configuration parameters","text":"Character (default = NULL). Takes path csv file columns “windowstart” “windowend” refer start end time time windows format “2024-10-12 20:00:00”, “filename” GGIR milestone data file without “meta_” segment name. GGIR part 2 uses set acceleration values zero non-wear classification zero (meaning sensor worn). Motivation: accelerometer worn night GGIR automatically labels invalid, user may like treat zero movement. Disclaimer: functionality developed 2019. hindsight generic enough need revision. Please contact GGIR maintainers like us invest time improving functionality.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-imp","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"do.imp","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). Whether impute missing values (e.g., suspected monitor non-wear clippling) g.impute GGIR g.part2. Recommended setting TRUE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"data_cleaning_file","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"data_cleaning_file","title":"GGIR configuration parameters","text":"Character (default = NULL). Optional path csv file create holds four columns: ID, day_part5, relyonguider_part4, night_part4. ID hold participant ID. Columns day_part5 night_part4 allow specify day(s) night(s) need excluded g.part5 g.part4, respectively. including multiple day(s)/night(s) create new line day/night. , done regardless whether rest GGIR thinks day(s)/night(s) valid. Column relyonguider_part4 allows specify nights g.part4 fully rely guider. See also package vignette.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"minimum_mm_length-part5","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"minimum_MM_length.part5","title":"GGIR configuration parameters","text":"Numeric (default = 23). Minimum length hours MM day included cleaned g.part5 results.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"excludefirstlast","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"excludefirstlast","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE first last night measurement ignored sleep assessment g.part4.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"includenightcrit","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"includenightcrit","title":"GGIR configuration parameters","text":"Numeric (default = 16). Minimum number valid hours per night (24 hour window noon noon), used sleep assessment g.part4.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"excludefirst-part4","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"excludefirst.part4","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE first night measurement ignored sleep assessment g.part4.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"excludelast-part4","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"excludelast.part4","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE last night measurement ignored sleep assessment g.part4.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"max_calendar_days","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"max_calendar_days","title":"GGIR configuration parameters","text":"Numeric (default = 0). maximum number calendar days include (set zero unknown).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"nonwearedgecorrection","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"nonWearEdgeCorrection","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). TRUE non-wear detection around edges recording (first last 3 hours) corrected following description vanHees2013 default since . functionality advisable working sleep clinic exercise lab data typically lasting less day.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"nonwear_approach","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"nonwear_approach","title":"GGIR configuration parameters","text":"Character (default = “2023”). Whether use traditional version non-wear detection algorithm (nonwear_approach = “2013”) new version (nonwear_approach = “2023”). 2013 version use longsize window (windowsizes[3], one hour default) check conditions nonwear identification flag nonwear mediumsize window (windowsizes[2], 15 min default) middle. 2023 version differs flag nonwear full longsize window. 2013 method longsize window centered centre mediumsize window, 2023 method longsizewindow aligned left edge left edge mediumsize window.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"segmentwearcrit-part5","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"segmentWEARcrit.part5","title":"GGIR configuration parameters","text":"Numeric (default = 0.5). Fraction qwindow segment expected valid part 5, 0.3 indicates least 30 percent time valid.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"segmentdaysptcrit-part5","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"segmentDAYSPTcrit.part5","title":"GGIR configuration parameters","text":"Numeric vector length 2 (default = c(0.9, 0)). Inclusion criteria proportion segment classified day (awake) spt (sleep period time) considered valid. interested comparing time spent behaviour better set one two numbers 0, defines proportion segment classified day spt, respectively. default setting focus waking hour segments includes segments overlap least 90 percent waking hours. order shift focus SPT use c(0, 0.9) ensures segments overlap least 90 percent SPT included. Setting zero problematic comparing time spent behaviours days individuals: complete segment averaged incomplete segments (someone going bed waking middle segment) longer clear whether person less active sleeps segment. Similarly clear whether person wakefulness SPT segment woke went bed segment.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"study_dates_file","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"study_dates_file","title":"GGIR configuration parameters","text":"Character (default = c()). Full path csv file containing first last date expected wear period every study participant (dates provided per individual). Expected format activity diary : First column headers followed one row per recording. three columns: first column recording ID, needs match ID GGIR extracts accelerometer file; second column contain first date study; third column last date study. Date columns default format “23-04-2017”, date format specified argument study_dates_dateformat (). specified (default), GGIR use first last day recording beginning end study. Note dates used top data_masking_strategy selected.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"study_dates_dateformat","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"study_dates_dateformat","title":"GGIR configuration parameters","text":"Character (default = “%d-%m-%Y”). specify date format used study_dates_file used [base]strptime.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"includecrit-part6","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"includecrit.part6","title":"GGIR configuration parameters","text":"Numeric (default = c(2/3, 2/3)) Vector two minimum fraction valid data required day spt time, respectively. criteria used circadian rhythm analysis.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"includenightcrit-part5","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"includenightcrit.part5","title":"GGIR configuration parameters","text":"Numeric (default = 0). Inclusion criteria used part 5 number valid hours sleep period hours day (night), value smaller equal 1 used fraction sleep period hours, value 1 used absolute number valid hours required. confuse argument argument includenightcrit used GGIR part 4 applies entire 24 hour window noon noon 6pm 6pm.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"nonwearfiltermaxhours","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"nonwearFiltermaxHours","title":"GGIR configuration parameters","text":"Numeric (default = NULL). NULL, ignore detected nonwear periods last shorter nonwearFiltermaxHours window defined parameter NonwearFilterWindow. NonwearFilterWindow provided (set NULL default) check whether qwindow defined activity diary file whether timestamps allow defining time bed, SPT lightsoff window. code looks column names diary terms inbed, sleeponset, lightsout define start window. , define end window code looks columns outbed, wakeup, lightsoff. yes, uses largest windows can defined timestamps. , use midnight-6am fall back setting. may realise columns advanced format sleelog accepted GGIR part 4. , already advanced format sleeplog part 4 can provide value qwindow parameter. want use qwindow guide nonwear filtering described want GGIR use day segment analysis part 2 5 make sure diary filename includes word “onlyfilter” “filteronly” tell GGIR use qwindow nonwear filtering. main purpose functionality offer option ignore short lasting nonwear episodes night suspicion falsely detect, e.g. individuals extended motionless sleep periods caused medication.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"nonwearfilterwindow","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"nonwearFilterWindow","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Vector length 2 specify start end hour night used functionality described NonwearFiltermaxHours.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"anglethreshold","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"anglethreshold","title":"GGIR configuration parameters","text":"Numeric (default = 5). Angle threshold (degrees) sustained inactivity periods detection. algorithm look periods time (timethreshold) angle variability lower anglethreshold. can specified multiple thresholds, implemented, e.g., anglethreshold = c(5,10).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"timethreshold","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"timethreshold","title":"GGIR configuration parameters","text":"Numeric (default = 5). Time threshold (minutes) sustained inactivity periods detection. algorithm look periods time (timethreshold) angle variability lower anglethreshold. can specified multiple thresholds, implemented, e.g., timethreshold = c(5,10).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"ignorenonwear","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"ignorenonwear","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). TRUE ignore detected monitor non-wear periods avoid confusion monitor non-wear time sustained inactivity.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"haspt-algo","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"HASPT.algo","title":"GGIR configuration parameters","text":"Character (default = “HDCZA”). indicate algorithm used sleep period time detection. Default “HDCZA” Heuristic algorithm looking Distribution Change Z-Angle described van Hees et al. 2018. options included: “HorAngle”, based HDCZA replaces non-movement detection HDCZA algorithm looking time segments angle longitudinal sensor axis angle relative horizontal plane -45 +45 degrees. “NotWorn” also HDCZA looks time segments rolling average acceleration magnitude 5 per cent standard deviation, see Cookbook vignette Annexes https://wadpac.github.io/GGIR/ detailed guidance use “NotWorn”.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"hasib-algo","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"HASIB.algo","title":"GGIR configuration parameters","text":"Character (default = “vanHees2015”). indicate algorithm used define sustained inactivity bouts (.e., likely sleep). Options: “vanHees2015”, “Sadeh1994”, “Galland2012”, “NotWorn”. See vignette ://wadpac.github.io/GGIR/articles/chapter8_SleepFundamentalsSibs.htmlchapter 8 details.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"sadeh_axis","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"Sadeh_axis","title":"GGIR configuration parameters","text":"Character (default = “Y”). indicate axis use Sadeh1994 algorithm, algortihms relied count-based Actigraphy Galland2012.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"longitudinal_axis","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"longitudinal_axis","title":"GGIR configuration parameters","text":"Integer (default = NULL). indicate axis longitudinal axis. provided, function estimate longitudinal axis axis highest 24 hour lagged autocorrelation. used sensor.location = “hip” HASPT.algo = “HorAngle”.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"haspt-ignore-invalid","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"HASPT.ignore.invalid","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). indicate whether invalid time segments ignored heuristic guiders. FALSE (default), imputed angle activity metric invalid time segments used. TRUE, invalid time segments ignored (.e., contribute guider). NA, invalid time segments considered movement segments can contribute guider. , guider name output shown “+invalid” end, e.g. “HDCZA+invalid”, reflect NA setting. HASPT.algo “NotWorn”, HASPT.ignore.invalid automatically set NA.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"loglocation","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"loglocation","title":"GGIR configuration parameters","text":"Character (default = NULL). Path csv file sleep log information. See package vignette format file.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"colid","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"colid","title":"GGIR configuration parameters","text":"Numeric (default = 1). Column number sleep log spreadsheet participant ID code stored.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"coln1","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"coln1","title":"GGIR configuration parameters","text":"Numeric (default = 2). Column number sleep log spreadsheet onset first night starts.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"nnights","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"nnights","title":"GGIR configuration parameters","text":"Numeric (default = NULL). argument deprecated.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"relyonguider","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"relyonguider","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). Sustained inactivity bouts (sib) overlap guider labelled sleep. relyonguider = FALSE sib overlaps partially guider sib defines edge SPT window guider. relyonguider = TRUE sib overlaps partially guider guider defines edge SPT window sib. participants instructed wear accelerometer waking hours ignorenonware=FALSE set relyonguider=TRUE, scenarios set FALSE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"def-noc-sleep","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"def.noc.sleep","title":"GGIR configuration parameters","text":"Numeric (default = 1). time window sustained inactivity assumed represent sleep, e.g., def.noc.sleep = c(21, 9). used sleep log entry available. left blank def.noc.sleep = c() 12 hour window centred least active 5 hours 24 hour period used instead. , L5 hardcoded change changing argument winhr function g.part2. def.noc.sleep filled single integer, e.g., def.noc.sleep=c(1) window detected based built algorithms. See argument HASPT.algo HASPT specifying algorithms use.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"sleeplogsep","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"sleeplogsep","title":"GGIR configuration parameters","text":"Character (default = NULL). argument deprecated.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"sleepwindowtype","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"sleepwindowType","title":"GGIR configuration parameters","text":"Character (default = “SPT”). indicate type information sleeplog, “SPT” sleep period time. Set “TimeInBed” sleep log recorded time bed enable calculation sleep latency sleep efficiency.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"possible_nap_window","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"possible_nap_window","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Numeric vector length two range clock hours naps assumed take place, e.g., possible_nap_window = c(9, 18). nap classification applied possible_nap_window possible_nap_dur specified. documentation follow 2025 development work completed.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"possible_nap_dur","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"possible_nap_dur","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Numeric vector length two range duration (minutes) nap, e.g., possible_nap_dur = c(15, 240). nap classification applied possible_nap_window possible_nap_dur specified. documentation follow 2025 development work completed.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"possible_nap_gap","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"possible_nap_gap","title":"GGIR configuration parameters","text":"Numeric (default = 0). Time gap expressed seconds allowed sustained inactivity bouts form naps.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"possible_nap_edge_acc","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"possible_nap_edge_acc","title":"GGIR configuration parameters","text":"Numeric (default = Inf). Maximum acceleration SIB nap considered. default allow possible naps.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"nap_model","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"nap_model","title":"GGIR configuration parameters","text":"Character (default = NULL). specify classification model. Currently option “hip3yr”, corresponds model trained hip data 3-3.5 olds trained parent diary data. functionality currently superseded nap detection triggered possible_nap_window possible_nap_dur. Leave nap_model NULL using new functionality. documentation follow 2025 development work completed.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"sleepefficiency-metric","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"sleepefficiency.metric","title":"GGIR configuration parameters","text":"Numeric (default = 1). 1 (default), sleep efficiency calculated detected sleep time SPT window divided log-derived time bed. 2, sleep efficiency calculated detected sleep time SPT window divided detected duration sleep period time plus sleep latency (sleep latency refers difference time bed sleep onset). sleepefficiency.metric considered parameter sleepwindowType = “TimeInBed”","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"hdcza_threshold","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"HDCZA_threshold","title":"GGIR configuration parameters","text":"Numeric (default = c()) HASPT.algo set “HDCZA” HDCZA_threshold NULL, (e.g., HDCZA_threshold = 0.2), value used threshold 6th step diagram Figure 1 van Hees et al. 2018 Scientific Report (doi: 10.1038/s41598-018-31266-z). However, supported research yet intended facilitate methodological research, advise sticking default line publication. , HDCZA_threshold set numeric vector length 2, e.g. c(10, 15), used percentile multiplier mentioned 6th step.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"sib_must_fully_overlap_with_timeinbed","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"sib_must_fully_overlap_with_TimeInBed","title":"GGIR configuration parameters","text":"Boolean (default = c(TRUE, TRUE)). indicate whether sib must fully overlap TimeInBed considered sleep start end time bed, respectively. considered parameter sleepwindowType = “TimeInBed”. Note negative sleep latency, , reported night summary report (part4_nightsummary_sleep csv files) negative sleep latency corresponding sleep efficiency values calculating person summary aggregate stored person level report (part4_summary_sleep csv files).","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"mvpathreshold","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"mvpathreshold","title":"GGIR configuration parameters","text":"Numeric (default = 100). Acceleration threshold MVPA estimation GGIR g.part2. can single number vector numbers, e.g., mvpathreshold = c(100, 120). latter case code estimate MVPA separately threshold. variable left blank, e.g., mvpathreshold = c(), MVPA estimated.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"boutcriter","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"boutcriter","title":"GGIR configuration parameters","text":"Numeric (default = 0.8). number 0 1, defines fraction bout needs mvpathreshold, used GGIR g.part2.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"mvpadur","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"mvpadur","title":"GGIR configuration parameters","text":"Numeric (default = 10). bout duration(s) MVPA calculated. used GGIR g.part2.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"boutcriter-in","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"boutcriter.in","title":"GGIR configuration parameters","text":"Numeric (default = 0.9). number 0 1, defines fraction bout needs threshold.lig.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"boutcriter-lig","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"boutcriter.lig","title":"GGIR configuration parameters","text":"Numeric (default = 0.8). number 0 1, defines fraction bout needs threshold.lig threshold.mod.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"boutcriter-mvpa","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"boutcriter.mvpa","title":"GGIR configuration parameters","text":"Numeric (default = 0.8). number 0 1, defines fraction bout needs threshold.mod.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"threshold-lig","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"threshold.lig","title":"GGIR configuration parameters","text":"Numeric (default = 40). g.part5: Threshold light physical activity separate inactivity light. Value can one number vector multiple numbers, e.g., threshold.lig =c(30,40). multiple numbers entered analysis repeated combination threshold values. Threshold applied first metric milestone data, specified .enmo = TRUE applied ENMO.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"threshold-mod","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"threshold.mod","title":"GGIR configuration parameters","text":"Numeric (default = 100). g.part5: Threshold moderate physical activity separate light moderate. Value can one number vector multiple numbers, e.g., threshold.mod = c(100, 120). multiple numbers entered analysis repeated combination threshold values. Threshold applied first metric milestone data, specified .enmo = TRUE applied ENMO.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"threshold-vig","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"threshold.vig","title":"GGIR configuration parameters","text":"Numeric (default = 400). g.part5: Threshold vigorous physical activity separate moderate vigorous. Value can one number vector multiple numbers, e.g., threshold.vig =c(400,500). multiple numbers entered analysis repeated combination threshold values. Threshold applied first metric milestone data, specified .enmo = TRUE applied ENMO.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"boutdur-mvpa","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"boutdur.mvpa","title":"GGIR configuration parameters","text":"Numeric (default = c(1, 5, 10)). Duration(s) MVPA bouts minutes extracted. start identification longest shortest duration. default setting, start 10 minute bouts, followed 5 minute bouts rest data, followed 1 minute bouts rest data.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"boutdur-in","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"boutdur.in","title":"GGIR configuration parameters","text":"Numeric (default = c(10, 20, 30)). Duration(s) inactivity bouts minutes extracted. Inactivity bouts detected segments data labelled sleep MVPA bouts. start identification longest shortest duration. default setting, start identification 30 minute bouts, followed 20 minute bouts rest data, followed 10 minute bouts rest data. Note use term inactivity instead sedentary behaviour lowest intensity level behaviour. reason GGIR attempt classifying activity type sitting moment, feel using term sedentary behaviour fail communicate .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"boutdur-lig","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"boutdur.lig","title":"GGIR configuration parameters","text":"Numeric (default = c(1, 5, 10)). Duration(s) light activity bouts minutes extracted. Light activity bouts detected segments data labelled sleep, MVPA, inactivity bouts. start identification longest shortest duration. default setting, start identification 10 minute bouts, followed 5 minute bouts rest data, followed 1 minute bouts rest data.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"frag-metrics","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"frag.metrics","title":"GGIR configuration parameters","text":"Character (default = NULL). Fragmentation metric extract. Can “mean”, “TP”, “Gini”, “power”, “CoV”, “NFragPM”, metrics “”. See package vignette description fragmentation metrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"part6_threshold_combi","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"part6_threshold_combi","title":"GGIR configuration parameters","text":"Character (default = NULL) indicate threshold combination derived part 5 used part 6. example, “40_100_120”. left default value GGIR use first threshold value parameters threshold.lig, threshold.mod, threshold.vig.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"qwindow","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"qwindow","title":"GGIR configuration parameters","text":"Numeric character (default = c(0, 24)). specify windows variables calculated, e.g., acceleration distribution, number valid hours, LXMX analysis, MVPA. numeric, qwindow length two, e.g., qwindow = c(0, 24), variables calculated full 24 hours day. qwindow = c(8, 24) variables calculated window 0-8, 8-24 0-24. days recording segmented based values. want use day specific segmentation day can set qwindow full path activity diary file (character). Expected format activity diary : First column headers followed one row per recording, first column recording ID, needs match ID GGIR extracts accelerometer file. Followed date column format “23-04-2017”, date format specified parameter qwindow_dateformat (). Use character combination date, Date DATE column name. followed one multiple columns start times activity types day format hours:minutes:seconds. header column used label activity type. Insert new date column continuing activity types next day. Leave missing values empty. activity log used individuals appear activity log still processed value qwindow = c(0, 24). Dates activity log data can skipped, need column date followed column next date. times activity diary multiple short window size (epoch length), next epoch considered (e.g., epoch 5 seconds, 8:00:02 redefined 8:00:05 activity log). using qwindow functionality combination GGIR part 5 make sure check parameters segmentWEARcrit.part5 segmentDAYSPTcrit.part5 specified research needs. using activity diary aware column name including words “impute” “uncertain” ignored. means can, convenience, add columns log timestamps manually imputed considered uncertain.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"qlevels","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"qlevels","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Vector percentiles value needs extracted. need expressed fraction 1, e.g., c(0.1, 0.5, 0.75). limit number percentiles. left empty percentiles extracted. Distribution derived short epoch metric data. Parameter qlevels can example used MX-metrics (e.g. Rowlands et al) discussed ://cran.r-project.org/package=GGIR/vignettes/GGIR.htmlmain package vignette","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"qwindow_dateformat","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"qwindow_dateformat","title":"GGIR configuration parameters","text":"Character (default = “%d-%m-%Y”). specify date format used activity log used [base]strptime.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"ilevels","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"ilevels","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Levels acceleration value frequency distribution m, e.g., ilevels = c(0,100,200). limit number levels. left empty intensity levels extracted. Distribution derived short epoch metric data.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"ivis_windowsize_minutes","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"IVIS_windowsize_minutes","title":"GGIR configuration parameters","text":"argument deprecated.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"ivis_epochsize_seconds","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"IVIS_epochsize_seconds","title":"GGIR configuration parameters","text":"Numeric (default = NULL). parameter deprecated.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"ivis-activity-metric","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"IVIS.activity.metric","title":"GGIR configuration parameters","text":"argument deprecated.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"ivis_acc_threshold","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"IVIS_acc_threshold","title":"GGIR configuration parameters","text":"argument deprecated.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"qm5l5","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"qM5L5","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Percentiles (quantiles) calculated L5 M5 window.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"mx-ig-min-dur","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"MX.ig.min.dur","title":"GGIR configuration parameters","text":"Numeric (default = 10). Minimum MX duration needed order intensity gradient calculated.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"m5l5res","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"M5L5res","title":"GGIR configuration parameters","text":"Numeric (default = 10). Resolution L5 M5 analysis minutes.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"winhr","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"winhr","title":"GGIR configuration parameters","text":"Numeric (default = 5). Vector window size(s) (unit: hours) LX MX analysis, look least active consecutive number X hours.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"iglevels","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"iglevels","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Levels acceleration value frequency distribution mused intensity gradient calculation (according method Rowlands 2018). default parameter empty intensity gradient calculation done. user can either provide single value () make intensity gradient use bins iglevels = c(seq(0,4000,=25), 8000) user specify distribution. constriction number levels.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"luxthresholds","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"LUXthresholds","title":"GGIR configuration parameters","text":"Numeric (default = c(0, 100, 500, 1000, 3000, 5000, 10000)). Vector numeric sequence corresponding thresholds used calculate time spent LUX ranges.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"lux_cal_constant","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"LUX_cal_constant","title":"GGIR configuration parameters","text":"Numeric (default = NULL). LUX_cal_constant LUX_cal_exponent provided LUX values converted based formula y = constant * exp(x * exponent)","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"lux_cal_exponent","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"LUX_cal_exponent","title":"GGIR configuration parameters","text":"Numeric (default = NULL). LUX_cal_constant LUX_cal_exponent provided LUX LUX values converted based formula y = constant * exp(x * exponent)","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"lux_day_segments","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"LUX_day_segments","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Vector hours day segmented LUX analysis.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"l5m5window","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"L5M5window","title":"GGIR configuration parameters","text":"deprecated version 1.5-24. parameter used define start end time, 24 hour clock hours, L5M5 needs calculated. Now done parameter qwindow.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"cosinor","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"cosinor","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). Whether apply cosinor analysis ActCR package part 2. part 6 cosinor analysis applied default turned .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"part6cr","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"part6CR","title":"GGIR configuration parameters","text":"Boolean (default = FALSE) indicate whether circadian rhythm analysis run part 6, includes: cosinor analysis, extended cosinor analysis, , IV, phi. Optionally can expanded detrended fluctutation analysis controlled parameter part6DFA.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"part6hca","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"part6HCA","title":"GGIR configuration parameters","text":"Boolean (default = FALSE) indicate whether Household Co Analysis run part 6.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"part6window","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"part6Window","title":"GGIR configuration parameters","text":"Character vector length two (default = c(“start”, “end”)) indicate start end time series used circadian rhythm analysis part 6. words, parameters used Household co-analysis. Alternative values : “Wx”, “Ox”, “Hx”, “x” number indicat xth wakeup, onset hour recording. Negative values “x” also possible count relative end recording. example, c(“W1”, “W-1”) goes first till last wakeup, c(“H5”, “H-5”) ignores first last 5 hours, c(“O2”, “W10”) goes second onset till 10th wakeup time.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"part6dfa","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"part6DFA","title":"GGIR configuration parameters","text":"Boolean (default = FALSE) indicate whether perform Detrended Fluctuation Analysis. Turned default can time consuming.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"clevels","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"clevels","title":"GGIR configuration parameters","text":"fully operational yet, actived 2025. Numeric vector (default = c(30, 150)) length 2 indicate cadence ranges used summarising step counts, available. set NULL, step counts summarised part 5 output.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"epochvalues2csv","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"epochvalues2csv","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). g.part2: TRUE epoch values exported csv file. , non-wear time imputed possible.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"save_ms5rawlevels","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"save_ms5rawlevels","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). g.part5: Whether save time series classification (levels) csv RData files (defined save_ms5raw_format). Note time stamps stored column timenum UTC format (.e., seconds 1970-01-01). convert timenum time stamp format, need specify desired time zone, e.g., .POSIXct(mdat$timenum, tz = “Europe/London”).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"save_ms5raw_format","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"save_ms5raw_format","title":"GGIR configuration parameters","text":"Character (default = “csv”). g.part5: specify data stored: either “csv” “RData”. used save_ms5rawlevels = TRUE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"save_ms5raw_without_invalid","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"save_ms5raw_without_invalid","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). g.part5: indicate whether remove invalid days time series output files. used save_ms5rawlevels = TRUE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"storefolderstructure","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"storefolderstructure","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). Store folder structure accelerometer data.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"timewindow","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"timewindow","title":"GGIR configuration parameters","text":"Character (default = c(“MM”, “WW”)). g.part5: Timewindow summary statistics derived. Value can “MM” (midnight midnight), “WW” (waking time waking time), “OO” (sleep onset sleep onset), combination .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"viewingwindow","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"viewingwindow","title":"GGIR configuration parameters","text":"Numeric (default = 1). Centre day displayed around noon (viewingwindow = 1) around midnight (viewingwindow = 2) “Report_” visual report generated visualreport = TRUE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"dofirstpage","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"dofirstpage","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). indicate whether first page histograms summarizing whole measurement added file summary reports named “Report_” generated visualreport = TRUE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"visualreport","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"visualreport","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). TRUE, store two visualreports outputfolder ‘results/file summary reports’ names starting “Report_…” “Time_report…”. report “Report_…” GGIR years based combined output GGIR part 2 4. initially quick development attempt something show study participants. report designed data quality checking purposes makes attempt exactly visualise classifications made part 5. Given number inaccuracies report eventually deprecated GGIR. turn old report use old_visualreport = FALSE. “Time_report”, new report GGIR 3.1-8 specifically aimed supporting data quality checks shows time series data generated used GGIR part 5. .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"week_weekend_aggregate-part5","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"week_weekend_aggregate.part5","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). g.part5: indicate whether week weekend-days aggregates stored. turned default generates large number extra columns output report.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-part3-pdf","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"do.part3.pdf","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). g.part3: Whether generate pdf g.part3.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"outliers-only","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"outliers.only","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). g.part4: used .visual = TRUE. FALSE, available nights included visual representation data sleeplog. TRUE, nights difference onset waking time larger variable parameter criterror included.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"criterror","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"criterror","title":"GGIR configuration parameters","text":"Numeric (default = 3). g.part4: used .visual = TRUE outliers.= TRUE. criterror specifies number minimum number hours difference sleep log accelerometer estimate night included visualisation.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-visual","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"do.visual","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). g.part4: TRUE, function generate pdf visual representation overlap sleeplog entries accelerometer detections. can used visually verify sleeplog entries come obvious mistakes.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-sibreport","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"do.sibreport","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). g.part4: indicate whether generate report sustained inactivity bouts (SIB). set TRUE advanced sleep diary available part 4 part 5 use generate summary statistics overlap self-reported nonwear napping SIB. , SIB can filter based parameter possible_nap_edge_acc first value possible_nap_dur","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-part2-pdf","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"do.part2.pdf","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). g.part2: Whether generate pdf g.part2.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"sep_reports","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"sep_reports","title":"GGIR configuration parameters","text":"Character (default = “,”). Value used sep parameter [data.table]fwrite writing csv reports.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"sep_config","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"sep_config","title":"GGIR configuration parameters","text":"Character (default = “,”). Value used sep parameter [data.table]fwrite writing csv config file.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"dec_reports","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"dec_reports","title":"GGIR configuration parameters","text":"Character (default = “.”). Value used dec parameter [data.table]fwrite writing csv reports.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"dec_config","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"dec_config","title":"GGIR configuration parameters","text":"Character (default = “.”). Value used dec parameter [data.table]fwrite writing csv config file.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"visualreport_without_invalid","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"visualreport_without_invalid","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). TRUE, reports generated visualreport = TRUE named “Report_” show windows sufficiently valid data according includedaycrit viewingwindow = 1 includenightcrit viewingwindow = 2","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"old_visualreport","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"old_visualreport","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). TRUE, generate old visual report addition new visualreport. old visualreport eventually deprecated","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"visualreport_hrsperrow","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"visualreport_hrsPerRow","title":"GGIR configuration parameters","text":"Numeric (default = 36). Width plots new visualreport (named “Time_report…”) expressed hours. Expected range 24-48. 24 extra time overlaps beginning next plot.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"visualreport_focus","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"visualreport_focus","title":"GGIR configuration parameters","text":"Character (default = “day”) Whether new visual report (names start “Time_report…”) focuss day night. set ot “night” focus night.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"visualreport_validcrit","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"visualreport_validcrit","title":"GGIR configuration parameters","text":"Numeric (default = 0). Value 0 1 representing fraction data plot expected valid. criteria met row skipped. used new visual report (names start “Time_report…”).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"require_complete_lastnight_part5","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"require_complete_lastnight_part5","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). set TRUE: last WW window excluded recording ends midnight 3pm, starts date one day recording end date; last OO MM window excluded recording ends midnight 9am, starts date one day recording end date. avoid risk recording end biases sleep estimates last night.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"method_research_vars","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"method_research_vars","title":"GGIR configuration parameters","text":"Character (default = NULL). Vector names methodological variable categories store csv output files. Currently “nap” available affects part5 output. variables intended methodological research default turned .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/HouseHoldCoanalysis.html","id":"accelerometer-file-names","dir":"Articles","previous_headings":"","what":"Accelerometer file names","title":"Household Co-Analysis","text":"household co-analysis requires households family member can recognised. assume following logic file names: StudyNumber-HouseholdID-MemberID_anyotherinformation.bin example .bin file, applies .cwa .csv files. example files: 001-002-001_12345-2023.bin 001-002-002_23456-2023.bin 001-002-M_23456-2023.bin recognised household ID 002 member IDs 001, 002, M.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/HouseHoldCoanalysis.html","id":"configuring-ggir","dir":"Articles","previous_headings":"","what":"Configuring GGIR","title":"Household Co-Analysis","text":"following input arguments needed run Household co-analysis: Household co-analysis integrated GGIR part 6, need run GGIR part 1 6, mode = 1:6. windowsizes = c(5, 60, 3600). Setting second value 60 ensures light temperature, available, aggregated per minute. part5_agg2_60seconds = TRUE. ensures GGIR part 5 stores time series 1 minute resolution. part6HCA = TRUE tell GGIR perform Household Co-Analysis. part6_threshold_combi = \"30_100_400\" 30, 100 400 need correspond accelerometer threshold combination used part 5 want use part 6. GGIR part 5 facilitates multiple threshold combinations part 6 need select one. GGIR arguments can set according needs. example:","code":"datadir = \"C:/projects/studyZ/binfiles\" outputdir = \"C:/projects/studyZ\" library(GGIR) GGIR(mode = 1:5,       datadir = datadir,      idloc = 2,       outputdir = outputdir,      do.report = c(2, 4, 5),      do.parallel = TRUE,      overwrite = FALSE,      printsummary = TRUE,      desiredtz = \"America/Halifax\",      windowsizes = c(5, 60, 3600),      threshold.lig = 30,      threshold.mod = 100,      threshold.vig = 400,      part6_threshold_combi = \"30_100_400\",      boutcriter.in = 1, boutcriter.lig = 1,  boutcriter.mvpa = 0.9,      boutdur.in = 30,      boutdur.lig = 10,      boutdur.mvpa = 5,      part6HCA = TRUE,      save_ms5rawlevels = TRUE, # Not necessary because GGIR will set this to TRUE when part6HCA is TRUE.      save_ms5raw_without_invalid = FALSE, # <= Needed for household co-analysis      part5_agg2_60seconds = TRUE,      visualreport = FALSE)"},{"path":"https://wadpac.github.io/GGIR/articles/HouseHoldCoanalysis.html","id":"household-co-analysis","dir":"Articles","previous_headings":"","what":"Household co-analysis","title":"Household Co-Analysis","text":"GGIR part 1, 2, 3, 4, 5 recording processed individually without considering relations recordings. Next, part 6 subdivided alligning time series produced part 1 5 per household, pairwise analysis data.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/HouseHoldCoanalysis.html","id":"align-individuals","dir":"Articles","previous_headings":"Household co-analysis","what":"Align individuals","title":"Household Co-Analysis","text":"Household members one member ignored. Next, per household per household member code loads merges time series produced GGIR part 1 part 5. Days, defined waking-waking-, removed less 20% valid data waking hours, sleep period time window, day whole. Next, time series completed indicates valid household member pairs time points. Finally, store: aligned time series per household separate csv files GGIR output directory (.../results/part6HouseholdCoAnalysis/alignedTimeseries). columns file documented . pdf file names timeseriesPlot.pdf plots aligned time series facilitate visual inspection data completeness per household.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/HouseHoldCoanalysis.html","id":"pairwise-analysis","dir":"Articles","previous_headings":"Household co-analysis","what":"Pairwise analysis","title":"Household Co-Analysis","text":"Per household identify possible member pairs loop pairs. Per member pair code identify wake-time pairs. , wake-times occur within last 15 minutes time series ignored need least recording time quantify behaviour waking . Per wake-pair assess woke first second, time difference, corresponding calendar dates waking . Next, code quantifies: Activity person first woke minute second person woke Activity second person wake woke LUX person first woke tminute second person woke LUX second person wake woke . Describe matching waking hours pairs: Correlation continuous acceleration values (ENMO metric) Derive binary class inactivity/active (ENMO metric, threshold < 50) ICC based binary scores (irr package, model=twoway, type=agreement, unit=single) Cohen’s Kappa (psych package) Similarity binary scores (calculation line Sleep Regularity Index) Describe noon-noon window stronger focus sleep: Describe binary class sleep/wakefulness (note: attempt classify daytime naps) ICC based binary scores (irr package, model=twoway, type=agreement, unit=single) Cohen’s Kappa (psych package) Similarity binary scores (calculation line Sleep Regularity Index) Describe wakefulness dynamics SPT prior wakeup: Look indices spt prior wakeup individuals SPT. Assess fraction data valid Identify wake times night wake-time: Assess whether persons woke time, person wake within 5 minutes, person wake within 5 minutes. Store output csv one row per unique household pair, columns clarify household members pair household .","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/HouseHoldCoanalysis.html","id":"alignedtimesieres","dir":"Articles","previous_headings":"Output variables","what":"alignedTimesieres","title":"Household Co-Analysis","text":"GGIR output folder .../results/part6HouseholdCoAnalysis/alignedTimeseries find csv files time series per household. data dictionary shows column names get household two members: X Y. columns copied time series output files, documented . Therefore, column documented .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/HouseHoldCoanalysis.html","id":"pairwise-summary-report","dir":"Articles","previous_headings":"Output variables","what":"Pairwise summary report","title":"Household Co-Analysis","text":"stored inside pairwise_summary_all_housholds.csv","code":""},{"path":"https://wadpac.github.io/GGIR/articles/readmyacccsv.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Reading csv files with raw data in GGIR","text":"GGIR can automatically read data -frequently used accelerometer brands field: GENEActiv .bin Axivity AX3 AX6 .wav, .csv .cwa ActiGraph .csv .gt3x (.gt3x newer format generated firmware versions 2.5.0). Note Actigraph users: want work .csv exports via ActiLife note option export data timestamps. Please causes memory issues GGIR. cope absence timestamps GGIR re-caculate timestamps sample frequency start time date presented file header Movisens data stored folders Genea (accelerometer commercially available anymore, used studies 2007 2012) .bin .csv However, accelerometer manufacturers proliferating increasing number brands market. reason, GGIR includes read.myacc.csv function, able read accelerometer raw triaxial data stored csv files, independently brand. vignette provides general introduction use GGIR read accelerometer raw data stored csv files. works: Internally GGIR loads csv files accelerometer data standardises output format make data compatible GGIR functions. Data format standardisation includes unit measurement, timestamp, file header format, column locations.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/readmyacccsv.html","id":"the-read-myacc-csv-function","dir":"Articles","previous_headings":"","what":"The read.myacc.csv function","title":"Reading csv files with raw data in GGIR","text":"rest GGIR functions, read.myacc.csv intended used within function GGIR. arguments read.myacc.csv can easily recognized start “rmc”. GGIR checks whether argument rmc.firstrow.acc provided user; case, GGIR attempt read data function read.myacc.csv. words always need specify rmc.firstrow.acc use read.myacc.csv. , recommend always first test function argument settings first trying use function read.myacc.csv . works copy arguments GGIR call.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/readmyacccsv.html","id":"input-arguments","dir":"Articles","previous_headings":"The read.myacc.csv function","what":"Input arguments","title":"Reading csv files with raw data in GGIR","text":"read.myacc.csv function tries read csv files wide variety formats, key arguments specify depend characteristics csv file process. Overall, argument relevant, left default setting (e.g., csv file contain temperature data, arguments related temperature settings left default values). present summary available input arguments. Please see parameters vignette elaborate description input arguments. , arguments also covered function documentation read.myacc.csv function.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/readmyacccsv.html","id":"general-arguments","dir":"Articles","previous_headings":"The read.myacc.csv function > Input arguments","what":"General arguments","title":"Reading csv files with raw data in GGIR","text":"rmc.file - Filename file read working directory, full path file otherwise. rmc.nrow - Number rows read, nrow argument nrows . whole file read default (.e., rmc.nrow = Inf). rmc.skip - Number rows skip, skip argument . rmc.dec - Decimal separator used numbers, dec argument data.table::. “.” (default) usually “,”. rmc.firstrow.acc - First row (number) acceleration data. rmc.unit.acc - Character unit acceleration values: “g”, “mg”, “bit”. desiredtz - Timezone device worn. confgitz - Timezone device configured. rmc.sf - Sample rate Hertz, stored file header used instead.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/readmyacccsv.html","id":"header","dir":"Articles","previous_headings":"The read.myacc.csv function > Input arguments","what":"Arguments for files containing a header","title":"Reading csv files with raw data in GGIR","text":"rmc.firstrow.header - First row (number) file header. Leave blank (default) file file header. confused one row column header, file header typically takes several rows one two columns. header two columns, first column assumed header item names second column assumed header item values. header one column assumed value contains name value item. rmc.header.length - file header, specify header length (numeric). rmc.headername.sf - file header, row name (character) sample frequency can found, e.g. “sample_rate”. rmc.headername.sn - file header, row name (character) serial number can found, e.g. “serial_number”. rmc.headername.recordingid - file header, row name (character) recording ID can found, e.g. “ID”. rmc.header.structure - Character used split header name header value, e.g. “:” header value look like “ID: 123” ” ” ehader value like “ID 123”.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/readmyacccsv.html","id":"arguments-for-files-including-timestamps","dir":"Articles","previous_headings":"The read.myacc.csv function > Input arguments","what":"Arguments for files including timestamps","title":"Reading csv files with raw data in GGIR","text":"rmc.col.time - Scalar column (number) timestamps stored. Leave default setting timestamps stored. rmc.unit.time - Character unit timestamps: “POSIX”, “UNIXsec” (seconds since origin, see argument rmc.origin), “UNIXmsec” (milliseconds since origin, see argument rmc.origin), “character”, “ActivPAL” (exotic timestamp format used ActivPAL activity monitor). rmc.format.time - Character string giving date-time format used . used rmc.unit.time: character POSIX. rmc.origin - Origin time unit time UNIXsec, e.g. 1970-1-1.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/readmyacccsv.html","id":"arguments-for-files-with-acceleration-stored-in-bits","dir":"Articles","previous_headings":"The read.myacc.csv function > Input arguments","what":"Arguments for files with acceleration stored in bits","title":"Reading csv files with raw data in GGIR","text":"rmc.bitrate - Numeric: unit acceleration bit provide bit rate, e.g. 12 bit. rmc.dynamic_range - Numeric, unit acceleration bit provide dynamic range deviation g zero, e.g. +/-6g mean argument needs 6. give argument character value code search file header elements name equal character value use corresponding numeric value next dynamic range. rmc.unsignedbit - Boolean, unsignedbit = TRUE means bits positive numbers. unsignedbit = FALSE bits positive negative.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/readmyacccsv.html","id":"arguments-for-files-including-temperature","dir":"Articles","previous_headings":"The read.myacc.csv function > Input arguments","what":"Arguments for files including temperature","title":"Reading csv files with raw data in GGIR","text":"rmc.col.temp - Scalar column (number) temperature stored. Leave default setting temperature avaible. temperature used . rmc.unit.temp - Character unit temperature values: (K)elvin, (C)elsius, (F)ahrenheit.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/readmyacccsv.html","id":"arguments-for-files-including-wear-time-information","dir":"Articles","previous_headings":"The read.myacc.csv function > Input arguments","what":"Arguments for files including wear time information","title":"Reading csv files with raw data in GGIR","text":"rmc.col.wear - external wear detection outcome stored part data can used GGIR. argument specifies column wear detection (Boolean) stored.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/readmyacccsv.html","id":"arguments-to-find-time-gaps-and-resampling","dir":"Articles","previous_headings":"The read.myacc.csv function > Input arguments","what":"Arguments to find time gaps and resampling","title":"Reading csv files with raw data in GGIR","text":"rmc.check4timegaps - Boolean indicate whether gaps time imputed zeros. rmc.doresample - Boolean indicate whether resample data based available timestamps extracted sample rate file header interpolationType - Integer indicate type interpolation used resampling time series (mainly relevant Axivity sensors), 1=linear, 2=nearest neighbour.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/readmyacccsv.html","id":"usage-of-the-read-myacc-csv-function","dir":"Articles","previous_headings":"","what":"Usage of the read.myacc.csv function","title":"Reading csv files with raw data in GGIR","text":"section shows example real case read.myacc.csv function can used. csv file read following structure: file contains timestamps column 1 (formatted “%d/%m/%Y %H:%M:%OS”), acceleration signals (g’s) x, y, z axis columns 2, 3, 4, respectively, temperature information Celsius column 5. Also, file file header. can use GGIR, first test read file using read.myacc.csv function directly. object data list data.frame name data header. time column data.frame represents timestamps expressed seconds since 1-1-1970.","code":"library(GGIR) data = read.myacc.csv(rmc.file = \"C:/mystudy/mydata/datafile.csv\",                rmc.nrow = Inf,                rmc.skip = 0,                rmc.dec = \".\",                rmc.firstrow.acc = 2,                rmc.col.acc = 2:4,                rmc.col.temp = 5,                rmc.col.time=1,                rmc.unit.acc = \"g\",                rmc.unit.temp = \"C\",                rmc.unit.time = \"POSIX\",                rmc.format.time = \"%d/%m/%Y %H:%M:%OS\",                desiredtz = \"Europe/London\",                rmc.sf = 100)"},{"path":"https://wadpac.github.io/GGIR/articles/readmyacccsv.html","id":"example-using-the-shell-function","dir":"Articles","previous_headings":"Usage of the read.myacc.csv function","what":"Example using the shell function","title":"Reading csv files with raw data in GGIR","text":"rmc.firstrow.acc argument defined within GGIR function, data read read.myacc.csv. GGIR needs user specify row starts accelerometer data within csv, argument must always explicitly specified user. Thus, call GGIR including rmc arguments look follows (note rmc.file, rmc.nrow, rmc.skip arguments used GGIR arguments already defined datadir, strategy, header arguments, respectively).","code":"library(GGIR) GGIR(              mode=c(1,2,3,4,5),              datadir=\"C:/mystudy/mydata/datafile.csv\",              outputdir=\"D:/myresults\",              do.report=c(2,4,5),              #=====================              # read.myacc.csv arguments              #=====================              rmc.nrow = Inf,               rmc.dec = \".\",              rmc.firstrow.acc = 2,               rmc.col.acc = 2:4,               rmc.col.temp = 5,               rmc.col.time=1,              rmc.unit.acc = \"g\",               rmc.unit.temp = \"C\",               rmc.unit.time = \"POSIX\",              rmc.format.time = \"%d/%m/%Y %H:%M:%OS\",              desiredtz = \"Europe/London\",              rmc.sf = 100,              rmc.noise = 0.013 )"},{"path":"https://wadpac.github.io/GGIR/articles/TutorialDaySegmentAnalyses.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Day segment analyses with GGIR","text":"specific person active morning afternoon? children active work hours leisure time? much inactivity occurs work office workers? Questions like can answered GGIR first specify parameters. main input argument specified qwindow, can used following ways: specify clock hours day based segmented day analyses take place. specify activity log (diary) used guide segmentation per individual per day recording. following sections discuss scenarios.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/TutorialDaySegmentAnalyses.html","id":"clock-hour-based-segmentation","dir":"Articles","previous_headings":"","what":"Clock hour-based segmentation","title":"Day segment analyses with GGIR","text":"perform clock hour segmentation, need provide function GGIR argument qwindow assign numeric vector hours segmentation. start end day, explicitly provided vector GGIR add . Please find example values qwindow. number values used qwindow unlimited, aware analyses MX-metrics impossible small windows produce empty results. Day Saving Time (DST) taken account identifying start day, identifying day segments. words, 23 hour days processed 24 hours first midnight. ensure segment length identical across days week, needed ease comparison outcome variables across days.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/TutorialDaySegmentAnalyses.html","id":"segmentation-guided-by-activity-log","dir":"Articles","previous_headings":"","what":"Segmentation guided by activity log","title":"Day segment analyses with GGIR","text":"perform activity-log based segmentation, need provide function GGIR argument qwindow assign full path activity log .csv format, e.g. qwindow=\"C:/myactivitylog.csv\". activity log expected .csv-file following structure: Rows: First row represents column headers row represents one accelerometer recording. ID-column: first column expected hold recording ID, needs match ID GGIR extracts accelerometer file. unsure format ID values, apply GGIR sample accelerometer files using default argument settings. ID column generated part 2 .csv reports show participant ID extracted GGIR. ID extracted, see documentation argument idloc, helps specify location participant file name file header. ID extraction fails accelerometer files matched corresponding activity log entries. Date-column: ID column followed date column first log day. ensure GGIR recognises date correctly, specify argument qwindow_dateformat. default format \"\\%d-\\%m-\\%Y\" 23-2-2021 indicate 23rd February 2021. date formatted 2-23-21 specify\"\\%m-\\%d-\\%y\". column name date column needs include character combination “date” “Date” “DATE”. Use date format consistently throughout activity diary. Start-times: date column followed one multiple columns start times activity types day format hours:minutes:seconds. provide dates cells. header column used label activity type. Insert new date column continuing activity types next day. Leave missing values empty. Missing values: values missing preceding succeeding time point used edges segment. example means define segment -C ID 1234, ID 6789 defined segments -B B-C, segment -C derived . Notes: - activity log collected individuals processed qwindow value c(0,24). - Dates activity log data can skipped, need column date followed column next date. - end time one activity assumed start time next activity. currently facilitate overlapping time segments.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/TutorialDaySegmentAnalyses.html","id":"implementation-in-ggir","dir":"Articles","previous_headings":"","what":"Implementation in GGIR","title":"Day segment analyses with GGIR","text":"approaches implemented GGIR part 2 part 5. Therefore specific output variables calculated part 2 5 available per day, per person, per segment day based argument qwindow Note qwindow used part 5 timewindow includes \"MM\" (see specific documentation timewindow} parameters vignette) moment, specifying argument qwindow triggers calculation qwindow segments part 2 part 5, may result longer time finish analysis. interested segments either part 2 part 5, option might run GGIR parts 1:2 argument qwindow interest, set qwindow = NULL run GGIR parts 3:5 (vice versa: qwindow = NULL GGIR parts 1:2, desired qwindow segments running GGIR parts 3:5). information output variables calculated part pipeline, see main GGIR vignette.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/TutorialDaySegmentAnalyses.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"Day segment analyses with GGIR","text":"information use GGIR function call see explanation main GGIR vignette.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/TutorialDaySegmentAnalyses.html","id":"clock-hour-based-segmentation-1","dir":"Articles","previous_headings":"Examples","what":"Clock-hour based segmentation:","title":"Day segment analyses with GGIR","text":"","code":"library(\"GGIR\") GGIR(datadir = \"/your/data/directory\",              outputdir = \"/your/output/directory\",              mode = 1:5, # <= run GGIR parts 1 to 5              do.report = c(2, 5), # <= generate csv-report for GGIR part 2 and part 5              qwindow = c(0, 6, 12, 18, 24),              timewindow = \"MM\")"},{"path":"https://wadpac.github.io/GGIR/articles/TutorialDaySegmentAnalyses.html","id":"activity-log-based-segmentation","dir":"Articles","previous_headings":"Examples","what":"Activity log based segmentation:","title":"Day segment analyses with GGIR","text":"running code GGIR creates output folder output directory specified argument outputdir. subfolder results find csv files reports generated part 2 part 5 pipeline: Part 2 part2_summary.csv recording level summary, 1 row per recording recording level aggregates day segments columns. part2_daysummary.csv day level summary, 1 row per day day segment specific outcomes columns. part2_daysummary_longformat.csv day level summary long format, row represents one segment one day one recording. part2_summary.csv part2_daysummary.csv column names tell day segment correspond . example, column names ending _18-24hr refer time segment 18:00-24:00. part2_daysummary_longformat.csv time segment clarified via columns qwindow_timestamps qwindow_name. Part 5 part 5, information segments days exported different csv reports person-level day-level summaries. files include word “Segments” filename provided long format aggregated per day per person: part5_daysummary_Segments[...].csv day level summary long format, row represents one segment one day one recording. part5_personsummary_Segments[...].csv recording level summary long format, row represents average outcome one specific segments across days segment available per participant.","code":"library(\"GGIR\") GGIR(datadir = \"/your/data/directory\",              outputdir = \"/your/output/directory\",              mode = 1:5, # <= run GGIR parts 1 to 5              do.report = c(2, 5), # <= generate csv-report for GGIR part 2 and part 5              qwindow = \"/path/to/your/activity/log.csv\",              timewindow = \"MM\")"},{"path":"https://wadpac.github.io/GGIR/articles/TutorialDaySegmentAnalyses.html","id":"cleaning-parameters-for-day-segments-in-part-5","dir":"Articles","previous_headings":"","what":"Cleaning parameters for day segments (in part 5):","title":"Day segment analyses with GGIR","text":"part 5, analyses performed per segment day come possibility clean reports based information available segments. users can select include segments given amount wear time segment (segmentWEARcrit.part5), well given awake time sleep period time segment (segmentDAYSPTcrit.part5). arguments likely critical meaningful analysis data. presence sleep segment physical activity bias physical inactivity estimates presence physical activity segment sleep bias sleep estimates. become impossible quantify whether lack one presence behaviour drives association example health outcome.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/TutorialDaySegmentAnalyses.html","id":"analyses-performed-per-day-segment","dir":"Articles","previous_headings":"","what":"Analyses performed per day segment","title":"Day segment analyses with GGIR","text":"analyses GGIR per segment day, include: Acceleration distribution (part 2): Derived argument ilevels specified. find variable names [0,36)_ENMO_mg means time spent 0 36 mg defined acceleration metric ENMO. Number valid hours data (part 2): recognise N_valid_hours_in_window tells number valid hours per time window, N_valid_hours number valid hours per day. Non-wear time percentage (part 5): nonwear_day_perc, nonwear_spt_perc, nonwear_day_spt_perc tell proportion segment classified non-wear awake time (day) sleep period time (spt). LXMX analysis (part 2 part5): LXMX analysis, stands least active X hours segment. recognise variable names like L5hr_ENMO_mg start time least active five hours defined metric ENMO, L5_ENMO_mg average acceleration hours. Intensity gradient analysis (part 2 part 5): find variables start ig_gradient_ See description GGIR part 2 output main GGIR vignette details. Time spent Moderate Vigorous Physical Activity (MVPA) (part 2 part 5): find variables MVPA_E5S_T201_ENMO MVPA_E5S_B1M80%_T201_ENMO. See description GGIR part 2 output main GGIR vignette details. Time spent sleeping, inactivity physical activity intensities (part 5): find variables part 5 reports, bouted, unbouted, total time version variables. See description GGIR part 5 output main GGIR vignette details.","code":""},{"path":"https://wadpac.github.io/GGIR/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Vincent T van Hees. Author, maintainer. Jairo H Migueles. Author. Severine Sabia. Contributor. Matthew R Patterson. Contributor. Zhou Fang. Contributor. Joe Heywood. Contributor. Joan Capdevila Pujol. Contributor. Lena Kushleyeva. Contributor. Mathilde Chen. Contributor. Manasa Yerramalla. Contributor. Patrick Bos. Contributor. Taren Sanders. Contributor. Chenxuan Zhao. Contributor. Ian Meneghel Danilevicz. Contributor. Victor Barreto Mesquita. Contributor. Gaia Segantin. Contributor. Medical Research Council UK. Copyright holder, funder. Accelting. Copyright holder, funder. French National Research Agency. Copyright holder, funder.","code":""},{"path":"https://wadpac.github.io/GGIR/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"van Hees V, Migueles J, Fang Z, Zhao J, Heywood J, Mirkes E, Sabia S (2024). GGIR: Raw Accelerometer Data Analysis. doi:10.5281/zenodo.1051064, R package version 3.1-9, https://CRAN.R-project.org/package=GGIR. van Hees V, Fang Z, Langford J, Assah F, MohammadMirkes , da Silva , Trenell M, White T, Wareham N, Brage S (2014). “Autocalibration accelerometer data free-living physical activity assessment using local gravity temperature: evaluation four continents.” Journal Applied Physiology, 117(7), 738–744. https://doi.org/10.1152/japplphysiol.00421.2014. van Hees V, Sabia S, Anderson K, Denton S, Oliver J, Catt M, Abell J, Kivimaki M, Trenell M, Singh-Manoux (2015). “Novel, Open Access Method Assess Sleep Duration Using Wrist-Worn Accelerometer.” PLoS One, 10(11). doi:10.1371/journal.pone.0142533, https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0142533. van Hees V, Sabia S, Jones S, Wood , Anderson K, Kivimaki M, Frayling T, Pack , Bucan M, Trenell M, Mazzotti D, Gehrman P, Singh-Manoux , Weedon M (2018). “Estimating sleep parameters using accelerometer without sleep diary.” Scientific Reports, 8(1). doi:10.1038/s41598-018-31266-z, https://www.nature.com/articles/s41598-018-31266-z. Migueles J, Rowlands , Huber F, Sabia S, van Hees V (2019). “GGIR: Research Community-Driven Open Source R Package Generating Physical Activity Sleep Outcomes Multi-Day Raw Accelerometer Data.” Journal Measurement Physical Behavior, 2(3). doi:10.1123/jmpb.2018-0063, https://doi.org/10.1123/jmpb.2018-0063.","code":"@Manual{,   title = {{GGIR}: Raw Accelerometer Data Analysis},   author = {Vincent T {van Hees} and Jairo H Migueles and Zhou Fang and Jing Hua Zhao and Joe Heywood and Evgeny Mirkes and Severine Sabia},   year = {2024},   note = {R package version 3.1-9},   doi = {10.5281/zenodo.1051064},   url = {https://CRAN.R-project.org/package=GGIR}, } @Article{,   title = {Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents},   author = {Vincent T {van Hees} and Zhou Fang and Joss Langford and Felix Assah and A MohammadMirkes and Inacio C {da Silva} and Michael I Trenell and Tom White and Nicholas J Wareham and Soren Brage},   journal = {Journal of Applied Physiology},   volume = {117},   number = {7},   pages = {738--744},   year = {2014},   url = {https://doi.org/10.1152/japplphysiol.00421.2014}, } @Article{,   title = {A Novel, Open Access Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer},   author = {Vincent T {van Hees} and Severine Sabia and Kirstie N Anderson and Sarah J Denton and James Oliver and Michael Catt and Jesica G Abell and Mika Kivimaki and Michael I Trenell and Archana Singh-Manoux},   doi = {10.1371/journal.pone.0142533},   journal = {PLoS One},   volume = {10},   number = {11},   year = {2015},   url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0142533}, } @Article{,   title = {Estimating sleep parameters using an accelerometer without sleep diary},   author = {Vincent T {van Hees} and Severine Sabia and Samuel E Jones and Andrew R Wood and Kirstie N Anderson and Mika Kivimaki and Tim M Frayling and Allan I Pack and Maja Bucan and Michael I Trenell and Diego R Mazzotti and Philip R Gehrman and Archana Singh-Manoux and Michael N Weedon},   doi = {10.1038/s41598-018-31266-z},   journal = {Scientific Reports},   volume = {8},   number = {1},   year = {2018},   url = {https://www.nature.com/articles/s41598-018-31266-z}, } @Article{,   title = {GGIR: A Research Community-Driven Open Source R Package for Generating Physical Activity and Sleep Outcomes From Multi-Day Raw Accelerometer Data},   author = {Jairo H Migueles and Alex V Rowlands and Florian Huber and Severine Sabia and Vincent T {van Hees}},   doi = {10.1123/jmpb.2018-0063},   journal = {Journal for the Measurement of Physical Behavior},   volume = {2},   number = {3},   year = {2019},   url = {https://doi.org/10.1123/jmpb.2018-0063}, }"},{"path":[]},{"path":"https://wadpac.github.io/GGIR/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"interest fostering open welcoming environment, contributors maintainers pledge making participation project community harassment-free experience everyone, regardless age, body size, disability, ethnicity, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, religion, sexual identity orientation.","code":""},{"path":"https://wadpac.github.io/GGIR/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes creating positive environment include: Using welcoming inclusive language respectful differing viewpoints experiences Gracefully accepting constructive criticism Focusing best community Showing empathy towards community members Examples unacceptable behavior participants include: use sexualized language imagery unwelcome sexual attention advances Trolling, insulting/derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical electronic address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://wadpac.github.io/GGIR/CODE_OF_CONDUCT.html","id":"our-responsibilities","dir":"","previous_headings":"","what":"Our Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Project maintainers responsible clarifying standards acceptable behavior expected take appropriate fair corrective action response instances unacceptable behavior. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, ban temporarily permanently contributor behaviors deem inappropriate, threatening, offensive, harmful.","code":""},{"path":"https://wadpac.github.io/GGIR/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within project spaces public spaces individual representing project community. Examples representing project community include using official project e-mail address, posting via official social media account, acting appointed representative online offline event. Representation project may defined clarified project maintainers.","code":""},{"path":"https://wadpac.github.io/GGIR/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported contacting main project maintainer v.vanhees@accelting.com. complaints reviewed investigated result response deemed necessary appropriate circumstances. project team obligated maintain confidentiality regard reporter incident. details specific enforcement policies may posted separately.","code":""},{"path":"https://wadpac.github.io/GGIR/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 1.4, available https://www.contributor-covenant.org/version/1/4/code--conduct.html","code":""},{"path":"https://wadpac.github.io/GGIR/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing guidelines","title":"Contributing guidelines","text":"welcome kind contribution software, simple comment question full fledged pull request. Please read follow Code Conduct. contribution can one following cases: question; think may found bug (including unexpected behavior); want make kind change code base (e.g. fix bug, add new feature, update documentation); want make new release code base. sections outline steps case.","code":""},{"path":"https://wadpac.github.io/GGIR/CONTRIBUTING.html","id":"questions","dir":"","previous_headings":"","what":"Questions","title":"Contributing guidelines","text":"use search functionality see someone already experienced issue; search yield relevant results, start new conversation.","code":""},{"path":"https://wadpac.github.io/GGIR/CONTRIBUTING.html","id":"bugs","dir":"","previous_headings":"","what":"Bugs","title":"Contributing guidelines","text":"use search functionality see someone already filed issue; issue search yield relevant results, make new issue, choose Bug report type. includes checklist make sure provide enough information rest community understand cause context problem.","code":""},{"path":"https://wadpac.github.io/GGIR/CONTRIBUTING.html","id":"changes-or-additions","dir":"","previous_headings":"","what":"Changes or additions","title":"Contributing guidelines","text":"(important) announce plan rest community start working. announcement form (new) issue. Choose Feature request type, includes checklist things consider get discussion going; (important) wait kind consensus reached idea good idea; needed, fork repository Github profile create feature branch latest master commit. working feature branch, make sure stay date master branch pulling changes, possibly ‘upstream’ repository (follow instructions ); make sure existing tests still work running test suite RStudio; add tests (necessary); update expand documentation, see package documentation guidelines; make sure release notes inst/NEWS.Rd updated; add name contributors lists DESCRIPTION file; push feature branch (fork ) GGIR repository GitHub; create pull request, e.g. following instructions . pull request template includes checklist items. case feel like ’ve made valuable contribution, don’t know write run tests , generate documentation: don’t let discourage making pull request; can help ! Just go ahead submit pull request, keep mind might asked append additional commits pull request.","code":""},{"path":"https://wadpac.github.io/GGIR/CONTRIBUTING.html","id":"coding-style","dir":"","previous_headings":"Changes or additions","what":"Coding style","title":"Contributing guidelines","text":"loosely follow tidyverse style guide, enforce every rule strictly. instance, prefer = instead <- default assignment operator. doubt style use, don’t hesitate get touch. general guidelines try adhere : Use standard R much possible, keep dependencies minimum. Keep loops minimum. Don’t make lines long. first time contributor, don’t worry coding style much. help get things shape.","code":""},{"path":"https://wadpac.github.io/GGIR/CONTRIBUTING.html","id":"package-documentation","dir":"","previous_headings":"Changes or additions","what":"Package documentation","title":"Contributing guidelines","text":"currently three sources documenting package: reference manual, including package basic information functions documentation files. package vignettes. github.io website (built pkgdown package).","code":""},{"path":"https://wadpac.github.io/GGIR/CONTRIBUTING.html","id":"reference-manual","dir":"","previous_headings":"Changes or additions > Package documentation","what":"Reference manual","title":"Contributing guidelines","text":"reference manual gets information .Rd documents within man folder package repository. Therefore, updating information files automatically update reference manual. Note GGIR functions intended direct interaction user, , documentation arguments centralized details section man/GGIR.Rd. example want add extra parameter params_247 documented . , forget include new argument functions  .","code":""},{"path":"https://wadpac.github.io/GGIR/CONTRIBUTING.html","id":"package-vignettes","dir":"","previous_headings":"Changes or additions > Package documentation","what":"Package vignettes","title":"Contributing guidelines","text":"folder vignettes GGIR repository contains .Rmd files. .Rmd files start word ‘chapter’ used traditional package vignettes hosted CRAN. Use files edit existing vignette, use structure vignettes build new one. .Rmd files name starts word ‘chapter’ ignored. chapter-vignettes used github.io website (see next section). create new vignette CRAN create new package vignette CRAN, please use usethis::use_vignette() make sure name vignette file start “chapter”. example, want create new vignette sleep CRAN, may following: create new “sleep.Rmd” file within vignettes folder GGIR repository. can edit file build vignette. remove vignette CRAN two ways remove vignette CRAN: Removing Rmd file corresponding vignette vignettes folder, note file information lost. Adding path vignette .Rbuildignore file available GGIR repository. example, remove GGIRParameters vignette CRAN, can add:","code":"usethis::use_vignette(name = \"sleep\",                       title = \"How to analyse your sleep data in GGIR\") ^vignettes/GGIRParameters.Rmd"},{"path":"https://wadpac.github.io/GGIR/CONTRIBUTING.html","id":"githubio-website","dir":"","previous_headings":"Changes or additions > Package documentation","what":"github.io website","title":"Contributing guidelines","text":"updating adding information github.io website, need use pkgdown configuration file can found repositories root directory, well chapter vignettes discussed . edit information existing chapter Open vignette corresponding chapter wish edit (see _pkgdown.yml) file chapter vignette path (href). Make changes vignette. Run pkgdown::build_site() function. add new chapter Create Rmd file vignette via usethis::use_vignette() make sure name vignette starts “chapter”, example: Open _pkgdown.yml file fill name reference new chapter menu. Make sure follow coding structure rest chapters. Run pkgdown::build_site() function. remove chapter Remove lines corresponding chapter _pkgdown.yml file line 42 onwards. Optionally may remove Rmd file corresponding chapter, step 1, chapter appear github.io website. Run pkgdown::build_site() function. edit name chapter Chapter names defined twice, _pkgdown.yml file vignette file . need make sure titles match first used drop-list github.io website specific page chapter.","code":"usethis::use_vignette(name = \"chapterSleep\",                       title = \"10. How to analyse your sleep data in GGIR\")"},{"path":"https://wadpac.github.io/GGIR/CONTRIBUTING.html","id":"adding-the-changes-to-the-master-branch","dir":"","previous_headings":"Changes or additions > Package documentation","what":"Adding the changes to the master branch","title":"Contributing guidelines","text":"last step committing pushing changes github making pull request contribution package. Note , running pkgdown::build_site() function edit files within docs folder, probably add new files. applies editing information github.io website. important changes files docs folder also part pull requests, otherwise website updated.","code":""},{"path":"https://wadpac.github.io/GGIR/CONTRIBUTING.html","id":"new-release","dir":"","previous_headings":"","what":"New release","title":"Contributing guidelines","text":"GGIR follows release cycle process described document.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/ABI.html","id":null,"dir":"Reference","previous_headings":"","what":"Activity balance index (ABI) — ABI","title":"Activity balance index (ABI) — ABI","text":"function estimates Activity balance index (ABI), transformation self-similarity parameter (SSP), also known scaling exponent alpha.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/ABI.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Activity balance index (ABI) — ABI","text":"","code":"ABI(x)"},{"path":"https://wadpac.github.io/GGIR/reference/ABI.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Activity balance index (ABI) — ABI","text":"x estimated self-similarity parameter (SSP)","code":""},{"path":"https://wadpac.github.io/GGIR/reference/ABI.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Activity balance index (ABI) — ABI","text":"estimated Activity balance index (ABI) real number zero one.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/ABI.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Activity balance index (ABI) — ABI","text":"ABI = exp(-abs(SSP-1)/exp(-2))","code":""},{"path":"https://wadpac.github.io/GGIR/reference/ABI.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Activity balance index (ABI) — ABI","text":"C.-K. Peng, S.V. Buldyrev, S. Havlin, M. Simons, H.E. Stanley, .L. Goldberger Phys. Rev. E, 49 (1994), p. 1685   Mesquita, Victor & Filho, Florencio & Rodrigues, Paulo. (2020). Detection crossover points detrended fluctuation analysis: application EEG signals patients epilepsy. Bioinformatics. 10.1093/bioinformatics/btaa955.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/ABI.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Activity balance index (ABI) — ABI","text":"Ian Meneghel Danilevicz <ian.meneghel-danilevicz@inserm.fr>","code":""},{"path":"https://wadpac.github.io/GGIR/reference/ABI.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Activity balance index (ABI) — ABI","text":"","code":"# Estimate Activity balance index of a very known time series   # available on R base: the sunspot.year.   if (FALSE) { # \\dontrun{       ssp = SSP(sunspot.year)     abi = ABI(ssp)   } # }"},{"path":"https://wadpac.github.io/GGIR/reference/aggregateEvent.html","id":null,"dir":"Reference","previous_headings":"","what":"Aggregates event data as produced by external function — aggregateEvent","title":"Aggregates event data as produced by external function — aggregateEvent","text":"Aggregates event data produced external function","code":""},{"path":"https://wadpac.github.io/GGIR/reference/aggregateEvent.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aggregates event data as produced by external function — aggregateEvent","text":"","code":"aggregateEvent(metric_name, epochsize, daysummary, ds_names,                           fi, di, vari, segmentInfo, myfun = NULL,                           params_247)"},{"path":"https://wadpac.github.io/GGIR/reference/aggregateEvent.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Aggregates event data as produced by external function — aggregateEvent","text":"metric_name Character name metric epochsize Numeric epochsize seconds timeseries daysummary Matrix prelimenary day summary ds_names Daysummary column names fi Column index daysummary di Row index daysummary representing recording day vari Time series derived metashort segmentInfo List passed g.analyse.perday time series indices     segment analyse segment name segment number. myfun See GGIR params_247 See GGIR","code":""},{"path":"https://wadpac.github.io/GGIR/reference/aggregateEvent.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Aggregates event data as produced by external function — aggregateEvent","text":"Vincent T van Hees <v.vanhees@accelting.com>","code":""},{"path":"https://wadpac.github.io/GGIR/reference/apply_cosinor_IS_IV_Analyses.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply Cosinor Analyses to time series — apply_cosinor_IS_IV_Analyses","title":"Apply Cosinor Analyses to time series — apply_cosinor_IS_IV_Analyses","text":"Wrapper function around cosinor_IS_IV_Analyses first prepares time series   applying cosinorAnlayses","code":""},{"path":"https://wadpac.github.io/GGIR/reference/apply_cosinor_IS_IV_Analyses.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply Cosinor Analyses to time series — apply_cosinor_IS_IV_Analyses","text":"","code":"apply_cosinor_IS_IV_Analyses(ts, qcheck, midnightsi, epochsizes, threshold = NULL)"},{"path":"https://wadpac.github.io/GGIR/reference/apply_cosinor_IS_IV_Analyses.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply Cosinor Analyses to time series — apply_cosinor_IS_IV_Analyses","text":"ts Data.frame timestamps acceleration metric. qcheck Vector equal length number rows ts value 1 invalid     timestamps, 0 otherwise. midnightsi Indices midnights time series epochsizes Epoch size ts qcheck respectively threshold See cosinor_IS_IV_Analyses","code":""},{"path":"https://wadpac.github.io/GGIR/reference/apply_cosinor_IS_IV_Analyses.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Apply Cosinor Analyses to time series — apply_cosinor_IS_IV_Analyses","text":"Vincent T van Hees <v.vanhees@accelting.com>","code":""},{"path":"https://wadpac.github.io/GGIR/reference/cosinor_IS_IV_Analyses.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply cosinor anlaysis and extended cosinor analysis — cosinor_IS_IV_Analyses","title":"Apply cosinor anlaysis and extended cosinor analysis — cosinor_IS_IV_Analyses","text":"Applies cosinor anlaysis ActCR package time series, well   IV, phi estimates.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/cosinor_IS_IV_Analyses.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply cosinor anlaysis and extended cosinor analysis — cosinor_IS_IV_Analyses","text":"","code":"cosinor_IS_IV_Analyses(Xi, epochsize = 60, timeOffsetHours = 0, threshold = NULL)"},{"path":"https://wadpac.github.io/GGIR/reference/cosinor_IS_IV_Analyses.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply cosinor anlaysis and extended cosinor analysis — cosinor_IS_IV_Analyses","text":"Xi Vector time series movement indicators maximum < 8 mean < 1     input assumed g-units multiplied 1000. epochsize Numeric epochsize seconds timeOffsetHours Numeric time hours relative next midnight threshold Numeric value use threshold distinguish inactivity active behaviour     IV analysis. GGIR uses parameter threshold.lig set threshold.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/cosinor_IS_IV_Analyses.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Apply cosinor anlaysis and extended cosinor analysis — cosinor_IS_IV_Analyses","text":"Vincent T van Hees <v.vanhees@accelting.com>","code":""},{"path":"https://wadpac.github.io/GGIR/reference/DFA.html","id":null,"dir":"Reference","previous_headings":"","what":"Detrended Fluctuation Analysis — DFA","title":"Detrended Fluctuation Analysis — DFA","text":"Detrended Fluctuation Analysis (DFA)","code":""},{"path":"https://wadpac.github.io/GGIR/reference/DFA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Detrended Fluctuation Analysis — DFA","text":"","code":"DFA(data, scale = 2^(1/8), box_size = 4, m = 1)"},{"path":"https://wadpac.github.io/GGIR/reference/DFA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Detrended Fluctuation Analysis — DFA","text":"data Univariate time series (must vector data frame) scale Specifies ratio successive box sizes (default scale = 2^(1/8)) box_size Vector box sizes (must used conjunction scale = \"F\") m integer polynomial order detrending (default m=1)","code":""},{"path":"https://wadpac.github.io/GGIR/reference/DFA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Detrended Fluctuation Analysis — DFA","text":"Estimated alpha real number zero two.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/DFA.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Detrended Fluctuation Analysis — DFA","text":"DFA fluctuation can computed geometric scale different choices boxes sizes.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/DFA.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Detrended Fluctuation Analysis — DFA","text":"possible estimating alpha multiple time series .","code":""},{"path":"https://wadpac.github.io/GGIR/reference/DFA.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Detrended Fluctuation Analysis — DFA","text":"C.-K. Peng, S.V. Buldyrev, S. Havlin, M. Simons, H.E. Stanley, .L. Goldberger Phys. Rev. E, 49 (1994), p. 1685   Mesquita, Victor & Filho, Florencio & Rodrigues, Paulo. (2020). Detection crossover points detrended fluctuation analysis: application EEG signals patients epilepsy. Bioinformatics. 10.1093/bioinformatics/btaa955.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/DFA.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Detrended Fluctuation Analysis — DFA","text":"Ian Meneghel Danilevicz <ian.meneghel-danilevicz@inserm.fr>   Victor Barreto Mesquita <victormesquita40@hotmail.com>","code":""},{"path":"https://wadpac.github.io/GGIR/reference/DFA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Detrended Fluctuation Analysis — DFA","text":"","code":"# Estimate self-similarity of a very known time series available   # on R base: the sunspot.year.   # Then the spend time with each method is compared.   if (FALSE) { # \\dontrun{     dfa = DFA(sunspot.year)   } # }"},{"path":"https://wadpac.github.io/GGIR/reference/filterNonwearNight.html","id":null,"dir":"Reference","previous_headings":"","what":"Filters short lasting nonwear during the night — filterNonwearNight","title":"Filters short lasting nonwear during the night — filterNonwearNight","text":"Filters short lasting nonwear night. intended   direct use GGIR user.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/filterNonwearNight.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filters short lasting nonwear during the night — filterNonwearNight","text":"","code":"filterNonwearNight(r1, metalong, qwindowImp, desiredtz, params_cleaning, ws2)"},{"path":"https://wadpac.github.io/GGIR/reference/filterNonwearNight.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filters short lasting nonwear during the night — filterNonwearNight","text":"r1 Object r1 created inside function g.weardec vector     zeros ones derived metalong 1 indicates nonwear 0 wear. metalong Object metalong created g.part1 qwindowImp See g.impute desiredtz See GGIR params_cleaning Parameters object cleaning paramete, see GGIR. ws2 Object ws2 equals second value vector parameter windowsizes","code":""},{"path":"https://wadpac.github.io/GGIR/reference/filterNonwearNight.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filters short lasting nonwear during the night — filterNonwearNight","text":"r1 object short lasting nonwear night removed","code":""},{"path":"https://wadpac.github.io/GGIR/reference/filterNonwearNight.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Filters short lasting nonwear during the night — filterNonwearNight","text":"Vincent T van Hees <v.vanhees@accelting.com>","code":""},{"path":"https://wadpac.github.io/GGIR/reference/MXLX.html","id":null,"dir":"Reference","previous_headings":"","what":"MX LX analysis — MXLX","title":"MX LX analysis — MXLX","text":"Identifies least active X hours","code":""},{"path":"https://wadpac.github.io/GGIR/reference/MXLX.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MX LX analysis — MXLX","text":"","code":"MXLX(Y = NULL, X = 5, epochSize = 1, tseg = c(0, 24), resolutionMin = 10)"},{"path":"https://wadpac.github.io/GGIR/reference/MXLX.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MX LX analysis — MXLX","text":"Y Vector numeric time series X Single numeric value X hours epochSize Numeric epoch size seconds Y tseg Numeric vector length two reflecting time windownof Y real clock hours.     example, Y represent noon-midnight     set tseg c(12, 24). resolutionMin Numeric value indicate resolution minutes MX LX search","code":""},{"path":"https://wadpac.github.io/GGIR/reference/MXLX.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MX LX analysis — MXLX","text":"data.frame : LX average value Y LX LXhr hour day LX starts start_LX index Y LX starts end_LX index Y LX ends MX average value Y MX MXhr hour day MX starts start_MX index Y MX starts end_MX index Y MX ends","code":""},{"path":"https://wadpac.github.io/GGIR/reference/MXLX.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"MX LX analysis — MXLX","text":"Vincent T van Hees <v.vanhees@accelting.com>","code":""},{"path":"https://wadpac.github.io/GGIR/reference/SSP.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimated self-similarity parameter — SSP","title":"Estimated self-similarity parameter — SSP","text":"function estimates self-similarity parameter (SSP), also known scaling exponent alpha.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/SSP.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimated self-similarity parameter — SSP","text":"","code":"SSP(data,scale = 2^(1/8),box_size = 4,m=1)"},{"path":"https://wadpac.github.io/GGIR/reference/SSP.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimated self-similarity parameter — SSP","text":"data Univariate time series (must vector data frame) scale Specifies ratio successive box sizes (default scale = 2^(1/8)) box_size Vector box sizes (must used conjunction scale = \"F\") m integer polynomial order detrending (default m=1)","code":""},{"path":"https://wadpac.github.io/GGIR/reference/SSP.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimated self-similarity parameter — SSP","text":"Estimated alpha real number zero two.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/SSP.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimated self-similarity parameter — SSP","text":"DFA fluctuation can computed geometric scale different choices boxes sizes.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/SSP.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Estimated self-similarity parameter — SSP","text":"possible estimating alpha multiple time series .","code":""},{"path":"https://wadpac.github.io/GGIR/reference/SSP.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Estimated self-similarity parameter — SSP","text":"C.-K. Peng, S.V. Buldyrev, S. Havlin, M. Simons, H.E. Stanley, .L. Goldberger Phys. Rev. E, 49 (1994), p. 1685   Mesquita, Victor & Filho, Florencio & Rodrigues, Paulo. (2020). Detection crossover points detrended fluctuation analysis: application EEG signals patients epilepsy. Bioinformatics. 10.1093/bioinformatics/btaa955.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/SSP.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimated self-similarity parameter — SSP","text":"Ian Meneghel Danilevicz <ian.meneghel-danilevicz@inserm.fr>   Victor Barreto Mesquita <victormesquita40@hotmail.com>","code":""},{"path":"https://wadpac.github.io/GGIR/reference/SSP.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimated self-similarity parameter — SSP","text":"","code":"# Estimate self-similarity of a very known time series available on R base: the sunspot.year.   # Then the spend time with each method is compared.   if (FALSE) { # \\dontrun{     ssp = SSP(sunspot.year)   } # }"},{"path":"https://wadpac.github.io/GGIR/reference/visualReport.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate visualisation of time series produced by part 5. — visualReport","title":"Generate visualisation of time series produced by part 5. — visualReport","text":"Function called GGIR. intended   direct use user","code":""},{"path":"https://wadpac.github.io/GGIR/reference/visualReport.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate visualisation of time series produced by part 5. — visualReport","text":"","code":"visualReport(metadatadir = c(),                f0 = c(), f1 = c(), overwrite = FALSE,               desiredtz = \"\",               verbose = TRUE,               part6_threshold_combi = NULL, GGIRversion = NULL,               params_sleep = NULL,               params_output = NULL)"},{"path":"https://wadpac.github.io/GGIR/reference/visualReport.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate visualisation of time series produced by part 5. — visualReport","text":"metadatadir Directory holds folder 'meta' inside folder 'basic'     contains milestone data produced g.part1. folderstructure     normally created g.part1 GGIR recognise     value metadatadir . f0 File index start (default = 1). Index refers filenames sorted     alphabetical order f1 File index finish (defaults number files available, .e., f1 = 0) overwrite See GGIR desiredtz See GGIR verbose See GGIR part6_threshold_combi See GGIR GGIRversion Character GGIR version number params_sleep See GGIR params_output See GGIR","code":""},{"path":"https://wadpac.github.io/GGIR/reference/visualReport.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate visualisation of time series produced by part 5. — visualReport","text":"values, function generates plot","code":""},{"path":"https://wadpac.github.io/GGIR/reference/visualReport.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate visualisation of time series produced by part 5. — visualReport","text":"Vincent T van Hees <v.vanhees@accelting.com>","code":""},{"path":"https://wadpac.github.io/GGIR/RELEASE_CYCLE.html","id":null,"dir":"","previous_headings":"","what":"Version numbering","title":"Version numbering","text":"use version encoding .B-C: increases major changes affect backward compatibility previous releases like changes function names, function arguments file format. B increases every CRAN release. aim avoid four CRAN releases per year. C increases every GitHub release. aim avoid one GitHub release per month.","code":""},{"path":"https://wadpac.github.io/GGIR/RELEASE_CYCLE.html","id":"github-releases","dir":"","previous_headings":"","what":"GitHub releases","title":"Version numbering","text":"releasing, please make sure check following: Create GitHub issue least 1 weeks intended release announce release indicate release. Make sure change log inst/NEWS.Rd date says “GitHub--release date” rather “release date” Make sure third (last) digit version number incremented one relative master branch date present date. applies files DESCRIPTION, GGIR-package.Rd NEWS.Rd file. Use function prepareNewRelease.R root GGIR double check version number date consistent files. Update package contributor list new people contributed. Run R CMD check ---cran make sure tests checks pass. Note GitHub releases require release name. typically choose random name city town South America. Whatever choose easy read remember word.","code":""},{"path":"https://wadpac.github.io/GGIR/RELEASE_CYCLE.html","id":"cran-releases","dir":"","previous_headings":"","what":"CRAN releases","title":"Version numbering","text":"CRAN release, follow following steps: Create GitHub issue least 4 weeks intended CRAN release announcing release indicating release list. CRAN release come major changes covered GitHub-releases. change log now say “release date” rather “GitHub--release date”. Second digit version number incremented 1 relative current CRAN version. Check whether new R version released coming make sure GGIR also tested version. Run RStudio devtools::check( manual = TRUE, remote = TRUE, incoming = TRUE) help check urls Ask Vincent (GitHub tag: vincentvanhees) submit release CRAN needs come e-mail address.","code":""}]
+[{"path":"https://wadpac.github.io/GGIR/articles/chapter0_Contributing.html","id":"if-you-have-coding-skills","dir":"Articles","previous_headings":"","what":"If you have coding skills…","title":"Contributing","text":"welcome contributions development, maintenance, documentation GGIR. Please find GGIR’s contributing guidelines .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter0_Contributing.html","id":"if-you-do-not-have-coding-skills-","dir":"Articles","previous_headings":"","what":"If you do not have coding skills….","title":"Contributing","text":"might coding skills contribute code base GGIR, still contribution important us. example: Apply funding support development maintenance GGIR. GGIR free software entirely depend users applying funding sponsor efforts. Funding used support development new functionalities, support improvement existing GGIR software code, support development better open-access training materials instruction videos. Report issues questions GGIR google group. Proofread GGIR documentation inform us miss something found difficult follow. Take independent initiatives complement efforts. example, Prof. Stuart Fairclough created series GGIR video tutorials, Wei Guo colleagues created R package mMARCH.AC post-process GGIR output described supporting journal article, maybe initiatives aware . Please communicate initiatives via GGIR google group /GGIR maintainer Vincent van Hees avoid duplicated work support needed.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter0_GetStarted.html","id":"run-ggir-for-the-first-time","dir":"Articles","previous_headings":"","what":"Run GGIR for the first time","title":"Get started: the GGIR R package","text":"First, need place file(s) folder computer. Make sure folder contains accelerometer files. Use following command run GGIR. datadir refers directory located accelerometer files. outputdir refers directory want store GGIR’s output. minutes, able see output directory gets populated files, reports, visualizations. command let GGIR run default settings (parameters), analysis tailored yet study design research question. documentation chapters find website guide .","code":"library(GGIR)  GGIR(datadir=\"C:/mystudy/mydata\", outputdir=\"D:/myresults\")"},{"path":"https://wadpac.github.io/GGIR/articles/chapter0_GetStarted.html","id":"related-links","dir":"Articles","previous_headings":"","what":"Related links","title":"Get started: the GGIR R package","text":"Install R GGIR Get support Suitable file formats GGIR","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter0_Installation.html","id":"install-r-and-rstudio","dir":"Articles","previous_headings":"","what":"Install R and RStudio","title":"Installation of the GGIR R Package","text":"Download install R Download install RStudio","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter0_Installation.html","id":"install-ggir","dir":"Articles","previous_headings":"","what":"Install GGIR","title":"Installation of the GGIR R Package","text":"Install latest released version GGIR dependencies CRAN. can one command R command line: Alternatively, can install latest development version, might include additional bug fixes functionalities. get development version, please use:","code":"install.packages(\"GGIR\", dependencies = TRUE) library(GGIR) install.packages(\"remotes\", dependencies = TRUE) remotes::install_github(\"wadpac/GGIR\", dependencies = TRUE) library(GGIR)"},{"path":"https://wadpac.github.io/GGIR/articles/chapter0_Installation.html","id":"other-packages-you-may-need","dir":"Articles","previous_headings":"","what":"Other packages you may need","title":"Installation of the GGIR R Package","text":"Additionally, use-cases need install one multiple additional packages. Note packages installed default, please follow instructions : want derive Neishabouricounts (.neishabouricounts = TRUE), install actilifecounts package install.packages(\"actilifecounts\") want process Sensewear xlsx files (dataFormat = \"sensewear\"), install readxl package install.packages(\"readxl\")","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter0_Installation.html","id":"installing-older-versions-of-a-package","dir":"Articles","previous_headings":"","what":"Installing older versions of a package","title":"Installation of the GGIR R Package","text":"aiming reproduce historical analysis critical install correct package version. explain GGIR release 2.4-0 work release. Note GGIR archived CRAN (major releases ) GitHub (releases). CRAN archive: see releases available CRAN check : https://cran.r-project.org/src/contrib/Archive/GGIR/. GitHub: see releases available CRAN check : https://github.com/wadpac/GGIR/releases.","code":"require(remotes) install_version(\"GGIR\", version = \"2.4-0\", repos = \"http://cran.us.r-project.org\") require(remotes) install_github(\"wadpac/GGIR\", ref = \"2.4-0\")"},{"path":"https://wadpac.github.io/GGIR/articles/chapter0_Support.html","id":"questions-and-problems","dir":"Articles","previous_headings":"","what":"Questions and problems","title":"How can I get service and support?","text":"general questions issues please join GGIR google group create new thread. report problem group always try create minimal example someone else can use reproduce investigate problem. However, familiar GitHub, also welcome report issue via GitHub issue tracker. Please use message template displayed. Note support places based voluntary efforts encourage try help users questions. Questions valuable help us understand challenges run occasionally help us identify bug code. make practical, please AVOID sending questions personal messages. Instead, post public platforms. approach allows others benefit discussions, minimises need us respond inquiries repeatedly, enhances likelihood others can answer questions ’re unavailable, acknowledges volunteer effort invested responding queries.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter0_Support.html","id":"commercial-training-services","dir":"Articles","previous_headings":"","what":"Commercial training services","title":"How can I get service and support?","text":"Accelting provides online training options using GGIR, please find website. questions, please hesitate reach via: training@accelting.com.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter0_Support.html","id":"dedicated-support","dir":"Articles","previous_headings":"","what":"Dedicated support","title":"How can I get service and support?","text":"need dedicated support use GGIR, want GGIR modified enhanced needs, please contact Vincent van Hees.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter10_SleepAnalysis.html","id":"sleep-analysis","dir":"Articles","previous_headings":"","what":"Sleep analysis","title":"10. Sleep Analysis","text":"Sleep analysis GGIR comes three stages: discrimination sustained inactivity wakefulness periods, discussed chapter 8. Identification time windows guide eventual sleep detection, discussed chapter 9. Assess overlap windows identified step 1 2, use define Sleep Period Time window (SPT) time bed window (TimeInBed) discussed chapter. previous two chapters learnt first two steps chapter discuss last step.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter10_SleepAnalysis.html","id":"sleep-period-time-spt-window-or-time-in-bed","dir":"Articles","previous_headings":"Sleep analysis","what":"Sleep Period Time (SPT) window or Time in Bed","title":"10. Sleep Analysis","text":"two scenarios: guider reflects approximation Sleep Period Time window, window sleep onset waking end night, SIB fully partially overlaps guider considered sleep. guider reflects Time Bed SIB fully overlaps guider considered sleep. scenario sleep latency sleep efficiency can estimated included GGIR part 4 report. cases start first SIB considered sleep onset end last SIB considered waking . guiders, “HorAngle”, parameter sleepwindowType automatically set “SPT” corresponding scenario 1, attempt made estimate sleep latency sleep efficiency. use guider sleeplog reflects Time Bed need set parameter sleepwindowType = \"TimeInBed\" tell GGIR follow scenario 2. However, parameter sib_must_fully_overlap_with_TimeInBed suprres requirement sleepwindowType = \"TimeInBed\" SIB fully overlaps “TimeInBed”. example, want SIB counted sleep fully overlap TimeInBed prefer rely SIB define end night regardless overlap TimeInBed set sib_must_fully_overlap_with_TimeInBed = c(TRUE, FALSE).","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/chapter10_SleepAnalysis.html","id":"cleaningcode","dir":"Articles","previous_headings":"Sleep analysis > Quality assurance","what":"Cleaningcode","title":"10. Sleep Analysis","text":"monitor possible problems sleep detection, output variable cleaningcode stored per night. Cleaningcode per night (noon-noon 6pm-6pm described ) can one following values: 0: sleep log available SPT identified. 1: sleep log available, alternative guider used (HDCZA default) SPT identified . 2: enough valid accelerometer data present night, parameter includenightcrit used define many valid hours need. 3: accelerometer data available. 4: nights analysed person. 5: SPT estimated based guider , either SIB found entire guider window complicates defining start end SPT, user specified ID number recording night number data_cleaning_file, , tell GGIR rely guider rely accelerometer data particular night. 6: sleep log available also alternative guider (HDCZA/HorAngle) failed specific night use average guider estimates nights recording guider night. HDCZA/HorAngle estimates also available entire recording use L5+/-12 estimate night.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter10_SleepAnalysis.html","id":"visual-inspection-of-classifications","dir":"Articles","previous_headings":"Sleep analysis > Quality assurance","what":"Visual inspection of classifications","title":"10. Sleep Analysis","text":"overlap sib guiders difficult review quantitative way, GGIR offers option export visualisation, parameter .visual = TRUE. manage number visualisations generated possible tell GGIR show outliers. , outliers defined difference guider edge final classification sleep onset wakeup time larger parameter criterror. set parameter outliers.= TRUE nights considered outlier displayed. functionality useful reviewing classifications large data sets use sleep logs. Visual inspection outliers way can example help identify data entry errors sleep logs.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter10_SleepAnalysis.html","id":"data-cleaning-file","dir":"Articles","previous_headings":"Sleep analysis > Quality assurance","what":"Data cleaning file","title":"10. Sleep Analysis","text":"data quality check may observe adjustments needed. Parameter data_cleaning_file (path csv file create) allows specify individuals nights part4 entirely rely guider. first column csv file column name ID column relyonguider_part4 specify night. night_part4 allows tell GGIR night(s) omitted part 4.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter10_SleepAnalysis.html","id":"sleep-metrics-available-in-ggir","dir":"Articles","previous_headings":"Sleep analysis","what":"Sleep metrics available in GGIR","title":"10. Sleep Analysis","text":"full overview sleep variables part 4 see: https://cran.r-project.org/web/packages/GGIR/vignettes/GGIR.html#42_Output_part_4 Among assume intuitive: sleep onset wakeup Sleep duration SPT, accumulate sleep time (sustained inactivity bouts classified sleep) WASO, time spent wakefulness sleep onset. However, possible concepts need clarifications:","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter10_SleepAnalysis.html","id":"sleep-regularity-index-sri","dir":"Articles","previous_headings":"Sleep analysis > Sleep metrics available in GGIR","what":"Sleep Regularity Index (SRI)","title":"10. Sleep Analysis","text":"measure sleep regularity successive days, first described Phillips colleagues. SRI can value -100 100, 100 reflects perfect regularity (identical days), 0 reflects random pattern, -100 reflects perfect reversed regularity. SRI proposed calculated based seven, multitude seven, consecutive days data without missing values. avoid possible role imbalanced data final estimate. However, renders many datasets unsuitable analysis leads painful loss sample size statistical power.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter10_SleepAnalysis.html","id":"sleep-regularity-index-dealing-with-unbalanced-data","dir":"Articles","previous_headings":"Sleep analysis > Sleep metrics available in GGIR > Sleep Regularity Index (SRI)","what":"Sleep Regularity Index – Dealing with unbalanced data","title":"10. Sleep Analysis","text":"address , implemented SRI GGIR per day-pair. Per day-pair GGIR now stores SRI value fraction 30 second epoch-pairs days valid. fraction can found output variable name SriFractionValid. default, day-pairs excluded fraction 0.66. familiar GGIR threshold coupled 16-hour default value parameter “includenightcrit”. example, set parameter “includenightcrit = 12”, fraction threshold : 12 / 24 = 0.5. Note implemented SRI calculation accounts missing values denominator. result, SRI value interpretation remains unchanged. 30 second epoch setting automatically applied, even rest GGIR process works different epoch duration. day-pair level estimates stored variable SleepRegularityIndex GGIR part 4 .csv-report sleep. , GGIR also stores person-level aggregates : plain average valid days, average valid weekend days, average valid week days. GGIR input arguments needed invoke SRI calculation. calculation automatically performed updating GGIR processing data.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter10_SleepAnalysis.html","id":"sleep-regularity-index-benefits-of-the-revised-approach","dir":"Articles","previous_headings":"Sleep analysis > Sleep metrics available in GGIR > Sleep Regularity Index (SRI)","what":"Sleep Regularity Index – Benefits of the revised approach","title":"10. Sleep Analysis","text":"enables user study day-pair day-pair variation SRI, role day-pair inclusion criteria. access SRI day-pair level makes possible account imbalanced datasets via multilevel regression analysis applied output GGIR, day-pair one model levels.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter10_SleepAnalysis.html","id":"nap-detection","dir":"Articles","previous_headings":"Sleep analysis > Sleep metrics available in GGIR > Sleep Regularity Index (SRI)","what":"Nap detection","title":"10. Sleep Analysis","text":"references daytime nap detection GGIR based experimental functionality requires ongoing investigation. functionality matured expand documentation accordingly.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter10_SleepAnalysis.html","id":"key-parameters","dir":"Articles","previous_headings":"Sleep analysis","what":"Key parameters","title":"10. Sleep Analysis","text":"parameters part params_sleep category discussed section “Sleep parameters” https://cran.r-project.org/web/packages/GGIR/vignettes/GGIRParameters.html .visual, outliers., criterror. excludefirstlast. def.noc.sleep includenightcrit data_cleaning_file","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter10_SleepAnalysis.html","id":"related-output","dir":"Articles","previous_headings":"Sleep analysis","what":"Related output","title":"10. Sleep Analysis","text":"GGIR stores two type output: cleaned full output. cleaned output invalid nights removed, full output nights included. specifically, night excluded ‘cleaned’ results based following criteria: study proposed sleep log individuals, nights excluded sleep log used guider. words: nights cleaningcode equal 0 variable sleep log used equals FALSE). study propose sleep log individuals, nights removed cleaningcode higher 1. aware using full output working wrist accelerometer data, missing entries sleep log asks Time Bed replaced HDCZA estimates SPT. Therefore, extra caution taken working full output. Notice part 4 focused sleep research. chapters discuss analysis done part 5. , choice guider may considered less important, estimate time bed considered useful. , may see night excluded cleaned results part 4 still appears cleaned results part 5.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter11_DescribingDataCutPoints.html","id":"why-behavioural-classes","dir":"Articles","previous_headings":"","what":"Why behavioural classes?","title":"11. Physical Activity Fundamentals: Describing the data with cut-points","text":"Descriptive variables average acceleration per day recording discussed previous chapter powerful indicators physical activity. Extensive evidence exists association physical activity related energy expenditure measured indirect calorimetry. Similarly, scientific value MX metrics intensity gradient discussed chapter 7 shown several studies (example: Rowlands, 2019a; Rowlands 2019b). However, physical activity research community historically expressed strong interest measures physical activity can expressed time per behavioural class per day. chapter discuss GGIR facilitates .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter11_DescribingDataCutPoints.html","id":"construct-definition","dir":"Articles","previous_headings":"","what":"Construct definition","title":"11. Physical Activity Fundamentals: Describing the data with cut-points","text":"popular approach define behavioural classes physical activity research distinguish called intensity levels. , common distinguish sedentary behaviour (SB), light physical activity (LIPA), moderate, vigorous physical activity. latter two categories often combined moderate vigorous physical activity (MVPA). Inside GGIR refer sedentary behaviour inactivity emphasize methods quantify mainly lack activity rather sitting reclying posture. However, intensity levels behavioural classes lack feasible operational construct definition caused methodological discrepancies decades. elaborate reflection can found blog post. situation forced us pragmatic use operational construct definition intensity levels feasible accelerometer data.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter11_DescribingDataCutPoints.html","id":"classification-based-on-cut-points","dir":"Articles","previous_headings":"","what":"Classification based on cut-points","title":"11. Physical Activity Fundamentals: Describing the data with cut-points","text":"common classify intensity levels accelerometer data evaluating whether acceleration , , certain acceleration level(s). acceleration magnitude(s) use threshold(s), also known cut-points. use thresholds (cut-points) intended crude indicator time spent intensity levels sufficient rank individuals amount time spent behaviours. cut-point approach indisputably powerful method far drive physical activity research. See published cut-points use guidance choosing cut-points dataset. discussed detail , acceleration (intensity) level classification done GGIR parts 2 5.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter11_DescribingDataCutPoints.html","id":"epoch-length","dir":"Articles","previous_headings":"","what":"Epoch length","title":"11. Physical Activity Fundamentals: Describing the data with cut-points","text":"Although accelerometers collect data much higher sampling frequency, work aggregated values (e.g. 1 5 second epochs) following reasons: Accelerometers often used describe patterns metabolic energy expenditure. Metabolic energy expenditure typically defined per breath per minute (indirect calorimetry), per day (room calorimeter), per multiple days (doubly labelled water method). order evaluate methods reference standards, need work similar time resolution. Collapsing data epoch summary measures helps standardise output across data collected different sampling frequencies studies. little evidence raw data accurate representation body acceleration. scientific evidence validity accelerometer data far based epoch aggregates. Short epoch lengths, 1 5 seconds, sensitive sporadic behaviours often combined bout detection identify MVPA sustained behaviour. Longer epochs, 30 60 seconds, problem therefore easier use without bout detection. epoch length GGIR default 5 seconds, can set first value vector specified parameter windowsizes. Although discuss epoch length context MVPA, please note epoch length influences many outcomes GGIR sleep analysis. GGIR part 5 offers option aggregate time series 1 minute epoch length order physical activity research 1 minute resolution leaving sleep detection relies shorter epoch length untouched (see parameter part5_agg2_60seconds=TRUE).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter11_DescribingDataCutPoints.html","id":"bout-detection","dir":"Articles","previous_headings":"","what":"Bout detection","title":"11. Physical Activity Fundamentals: Describing the data with cut-points","text":"Behavioural bouts used frequently defined sustained time spent ina behavioural class adhering specific temporal criteria. GGIR facilitates processing data without accounting bouts. motivation look bouts can one following: idea behaviour certain minimum duration contributes certain physiological benefits. make classification behaviour consistent self-report data, sensitive duration specific duration. aid studying fragmentation behaviour. account sporadic nature behaviour working short epochs. define bout need answer series questions: cut-point ? epoch length ? minimum duration bout ? allow gaps bout breaks behaviour interest? yes 4, percentage bout duration, absolute minimum seconds, combination ? yes 4, bout gaps counted towards time spent bouts? first last epoch need meet threshold criteria? order bouts extracted? example, short MVPA bout part longer Inactivity bout two prevails? many bout categories ? GGIR facilitates following freedom bout detection: User decides : Acceleration thresholds light, moderate, vigorous intensity mvpathreshold part 2, threshold.lig, threshold.mod, threshold.vig part 5. Fraction time cut-point criteria need met (light, inactive, MVPA) boutcriter part 2 boutcriter.lig, boutcriter.mod, boutcriter.vig part 5. Bout duration range. part 2 mvpadur part 5 boutdur.lig, boutdur.mod, boutdur.mvpa. functionality slightly different part 2 part 5 discussed . Epoch length windowsizes part5_agg2_60second. User decide : Maximum bout gap 1 minute, fraction time cut-point criteria need met less 100% First last epoch need meet cut-point criteria. Number intensity levels, always: inactive, light MVPA. Order bouts calculated (1 MVPA; 2 inactive; 3 Light)","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter11_DescribingDataCutPoints.html","id":"controlling-the-time-window-of-analysis","dir":"Articles","previous_headings":"","what":"Controlling the time window of analysis","title":"11. Physical Activity Fundamentals: Describing the data with cut-points","text":"discussed chapter 7, possible tell GGIR part 2 part 5 extract variables per segment day. parameter qwindow can find detailed discussion Annex Day segment analysis.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter11_DescribingDataCutPoints.html","id":"key-parameters","dir":"Articles","previous_headings":"","what":"Key parameters","title":"11. Physical Activity Fundamentals: Describing the data with cut-points","text":"parameters needed MVPA estimates GGIR part 2 different parameters used estimating MVPA, LIPA Inactivity part 5.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter11_DescribingDataCutPoints.html","id":"physical-activity-cut-point-parameter-ggir-part-2","dir":"Articles","previous_headings":"Key parameters","what":"Physical activity cut-point parameter GGIR part 2","title":"11. Physical Activity Fundamentals: Describing the data with cut-points","text":"GGIR part 2, MVPA estimated since sleep classified point. threshold(s) MVPA used GGIR part 2 set parameter mvpathreshold. can specify single value vector multiple values, time spent MVPA derived . GGIR part 2 always provides six distinct approaches MVPA calculation controlled parameters mvpathreshold, boutcriter, mvpadur, first element vector windowsizes. , MVPA provides time spent MVPA based : 5 second, 1 minute 5 minute epochs bouts 5 second epochs 3 different minimum bout duration specified parameter mvpadur.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter11_DescribingDataCutPoints.html","id":"physical-activity-cut-point-parameter-ggir-part-5","dir":"Articles","previous_headings":"Key parameters","what":"Physical activity cut-point parameter GGIR part 5","title":"11. Physical Activity Fundamentals: Describing the data with cut-points","text":"bout durations used separate estimates used complimentary case part 5. example, specifying boutdur.mod = c(5, 10) part 5 result estimate time spent bouts lasting 5 till 10 minutes bouts lasting 10 minutes longer. example, simple scenario consider bouts minimum length 10 minutes, also possible subdivide bouts lasting [1, 5) [5, 10) [10, ∞) minutes. parameters related cut-points bout detection listed “Physical activity parameters”.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter11_DescribingDataCutPoints.html","id":"related-output","dir":"Articles","previous_headings":"","what":"Related output","title":"11. Physical Activity Fundamentals: Describing the data with cut-points","text":"GGIR part 2 csv reports find: Time spent MVPA GGIR part 5 csv reports find: Time spent MVPA Time spent LIPA Time spent inactivity (abbreviated ) chapter 7 discussed structure part 2 output. next chapter (chapter 12) provide detailed discussion part 5 output. overview output variables see GGIR output annex.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter12_TimeUseAnalysis.html","id":"creating-a-multi-variate-time-series-object","dir":"Articles","previous_headings":"","what":"Creating a multi-variate time series object","title":"12. Time-use Analysis","text":"first step need map happens recording. , GGIR code combines information derived parts 2, 3 4 multi-variate single time series object, including: Timestamp log data classified invalid. Average acceleration derived GGIR part 2, invalid epochs imputed acceleration metric used specified parameter acc.metric. Sleep classifications GGIR part 3 4. Behavioural class code, GGIR part 5 derives behavioural classes based magnitude acceleration sleep classification. exact number behavioural classes codified depends parameters set, constructed codified : sleep period time window: - Sleep - Wakefulness low acceleration - Wakefulness moderate acceleration - Wakefulness vigorous acceleration waking hours day: - Inactivity unbouted - Inactivity bouted, subdivided one multiple bout durations - Total inactivity time - LIPA unbouted - LIPA bouted, subdivided one multiple bout durations - Total LIPA time - Moderate activity unbouted - Vigorous activity unbouted - MVPA bouted, subdivided one multiple bout durations - Total MVPA time possible export time series generated, discussed towards end chapter.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter12_TimeUseAnalysis.html","id":"defining-the-time-windows","dir":"Articles","previous_headings":"","what":"Defining the time windows","title":"12. Time-use Analysis","text":"GGIR part 2 defined days midnight midnight, GGIR part 4 typically defined nights noon noon. access sleep timing, GGIR part 5 offers additional definitions day. However, given definitions day becoming different calendar day, refer windows data. GGIR part 5 facilitates following time window definitions, can selected parameter timewindow: “WW” “OO”, onset waking times guided estimates part 4, missing, part 5 attempt retrieve estimate guider method. Note parameter timewindow can consist one options beforementioned combination , example, default value timewindow = c(\"MM\", \"WW\"). recordings end night early morning sleep estimates night likely affected. example, recording ends 10am sure participant sleep 10am, recording ends 2am sure sleep onset time reliably estimated. handle ignore final window data, set parameter require_complete_lastnight_part5 = TRUE (default).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter12_TimeUseAnalysis.html","id":"defining-segments-within-the-mm-window","dir":"Articles","previous_headings":"Defining the time windows","what":"Defining segments within the MM window","title":"12. Time-use Analysis","text":"default GGIR segments window waking hours day (referred day) sleep period time window (referred spt). Additionally, timewindow set “MM”, day segment specific analysis performed based segments defined parameters qwindow . Please see annex day segmentation information.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter12_TimeUseAnalysis.html","id":"metrics-calculated-per-window-and-per-segment","dir":"Articles","previous_headings":"","what":"Metrics calculated per window and per segment","title":"12. Time-use Analysis","text":"GGIR provides following metrics time windows calculated, .e., full day, awake time, sleep period time, well (optionally) day segments might provided via parameter qwindow. Duration: Time spent minute per behavioural class. Acceleration: Average acceleration per behavioural class Number blocks: Number blocks per behavioural class, distinction made bouted unbouted, except total number blocks per intensity levels (Nblocks_day_total_IN, Nblocks_day_total_LIPA, Nblocks_day_total_MOD, Nblocks_day_total_VIG). Number bouts: Number bouts per behavioural class. Fragmentation: fragmentation metrics discussed previous chapter. distinction made bouted unbouted behavour. Note fragmentation classes sometimes group multiple intensity levels, e.g. fragmentation physical activity reflects fragmentation LIPA MVPA combined relative Inactive time. side note - multiply Acceleration Duration given class, combine information variables, arrive volume measure behaviour. similar construct calories time. think much richer way describing data opposed conventional approach looks either time spent per behavioural class average acceleration entire day.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter12_TimeUseAnalysis.html","id":"complementary-variables","dir":"Articles","previous_headings":"Metrics calculated per window and per segment","what":"Complementary variables","title":"12. Time-use Analysis","text":"primary interest sleep research recommend work GGIR part 4 reports. However, want look interactions behaviour sleep, GGIR part 5 reports include sleep estimates used part 5 analysis. Note part 5 criteria sleep estimate inclusion different part 4. part 5 happy estimate, even accelerometer worn night. Additionally, part 5 also come duration awake time, sleep period time, full-day windows, percentage non-wear (read invalid data, typically non-wear).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter12_TimeUseAnalysis.html","id":"seemingly-overlapping-variables-between-ggir-part-2-and-part-5-output","dir":"Articles","previous_headings":"Metrics calculated per window and per segment","what":"Seemingly overlapping variables between GGIR part 2 and part 5 output","title":"12. Time-use Analysis","text":"might noticed, variables reported GGIR part 2 part 5, average acceleration bouts moderate--vigorous physical activity. However, please note values necessarily identical following reasons: Times MVPA can happen . part 2 MVPA can happen time day, never overlap midnight. part 5 MVPA happens waking hours single day, can overlap midnight midnight part sleep period time window. use dayborder parameter value equal zero: midnight scenario actually midnight time set parameter dayborder, e.g. 2 equates 2am. . Difference epoch length. GGIR part 5 comes possibility aggregate epochs 60 seconds parameter part5_agg2_60seconds. parameter set TRUE, full time series aggregated 60 seconds, contrast default 5 second epoch length used part 2. Different time window definition. GGIR part 2 always uses “MM” definition days. GGIR part 5, option define days different ways (see ).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter12_TimeUseAnalysis.html","id":"exporting-time-series","dir":"Articles","previous_headings":"Metrics calculated per window and per segment","what":"Exporting time series","title":"12. Time-use Analysis","text":"export time series set parameter save_ms5rawlevels = TRUE. GGIR part 5 store subfolder meta/ms5.outraw subfolder named unique MVPA threshold combination used. behavioral classes included numbers, legend classes numbers stored separate legend file meta/ms5.outraw folder named “behavioralcodes2020-04-26.csv” date correspond date ran GGIR. Note time series exported GGIR part 5 includes acceleration metric specified parameter acc.metric (default = “ENMO”), angle metrics selected, angle metrics. want explore multiple acceleration metric values, please see documentation parameter epochvalues2csv discussed chapter 3. Additional input parameters may interest: save_ms5raw_format character string specify data stored: either “csv” (default) “RData”. used save_ms5rawlevels=TRUE. save_ms5raw_without_invalid Boolean indicate whether remove invalid days time series output files. used save_ms5rawlevels=TRUE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter12_TimeUseAnalysis.html","id":"key-arguments","dir":"Articles","previous_headings":"","what":"Key arguments","title":"12. Time-use Analysis","text":"threshold.lig, threshold.mod, threshold.vig boudur., boutdur.lig, boutdur.mvpa boutcriter., boutcriter.lig, boutcriter.mvpa frag.metrics timewindow part5_agg2_60seconds save_ms5rawlevels save_ms5raw_format save_ms5raw_without_invalid","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter12_TimeUseAnalysis.html","id":"related-output","dir":"Articles","previous_headings":"","what":"Related output","title":"12. Time-use Analysis","text":"https://cran.r-project.org/web/packages/GGIR/vignettes/GGIR.html#43_Output_part_5 find detailed discussion part 5 output. summary, part 5 produces following files. - Day level summary - Person level summary - Day level summary behaviour per segment day - analysis - Person level summary behaviour per segment day - Variable dictionary (see ) - Time series - Pdf reports vsiualisation","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter12_TimeUseAnalysis.html","id":"variables-dictionary","dir":"Articles","previous_headings":"Related output","what":"Variables dictionary","title":"12. Time-use Analysis","text":"Considering different time window segmentation options, number metrics calculated, different aggregation strategies (.e., plain averages, weighted averages, -optionally- weekday weekend-day averages), number variables exported Part 5 can high. help understanding interpretation variables, GGIR Part5 exports variable dictionary daysummary personsummary csv reports. dictionaries include list variable names calculated analyses together definition variables.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter13_CircadianRhythm.html","id":"mxlx","dir":"Articles","previous_headings":"","what":"MXLX","title":"13. Circadian Rhythm Analysis","text":"MXLX looks continuous least (LX) (MX) active X hour window day, X defined parameter winhr. LX MX, GGIR calculates average acceleration, start time, argument iglevels specified also intensity gradient. parameter winhr vector MX LX derived value vector. Within GGIR part 2 MXLX calculated per calendar day , argument qwindow specified, per segment day. Within GGIR part 5 MXLX calculated per window. used combination GENEActiv Axivity accelerometer brands, LUX estimates per LX MX also included GGIR part 5 csv reports. MX metric described confused MX metrics proposed Rowlands et al. looks accumulated active time may always continuous time. MX metrics Rowlands et al. discussed .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter13_CircadianRhythm.html","id":"extended-cosinor-analysis","dir":"Articles","previous_headings":"","what":"(Extended) Cosinor analysis","title":"13. Circadian Rhythm Analysis","text":"Cosinor analysis quantifies circadian 24 hour cycle. Cosinor analysis refers fitting cosine function log transformed time series, extended cosinor analysis refers fitting non-linear transformation traditional cosinor curve Marler et al. Statist. Med. 2006 (doi: 10.1002/sim.2466). Corinos analyssis run default, tell GGIR perform analyse specify parameter cosinor = TRUE. implementation follows: acceleration metric specified parameter acc.metric used. Acceleration metric values averaged per minute expressed mg input g, log transformed log(acceleration + 1). Invalid data points caused non-wear set missing (NA) want imputation used elsewhere GGIR influence Cosinor analysis. imputation technique generally come assumptions circadian rhythm. part 2 GGIR uses valid data recording part 6 use valid data interval defined parameter part6Window, e.g. first wake-time till last wake-time. GGIR looks first valid data point time series selects maximum integer number recording days following data point. Day Saving Time occurs time series duplicated timestamps clock moves backward ignored missing timestamps clock moves forward inserted missing values. Cosinor models fitted using functions ActCosinor ActExtendCosinor R package ActCR. , ActExtendCosinor uses anti-logistic function transformation. time offset start time series used following midnight used reverse offset ActCR results, ensure acrophase acrotime can interpreted relative midnight. Time series corresponding fitted models stored inside part 2 milestone data stored output subfolder meta/ms2.facilitate visual inspection. moment used GGIR visualisation, may want look try plot . stored object SUM$cosinor_ts.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter13_CircadianRhythm.html","id":"intradaily-variability-iv-and-interdaily-stability-is","dir":"Articles","previous_headings":"","what":"Intradaily Variability (IV) and Interdaily Stability (IS)","title":"13. Circadian Rhythm Analysis","text":"IV first proposed Witting W et al. 1990 van Someren EJ, et al. 1996. measures constant routine activity several days ranges 0 1, values close 1 indicate constant routine. IV measures variability activity hour hour throughout days. ranges 0 +∞\\infty, value close 2 indicates fragmented rhythm, >2 indicates ultradian rhythm (uncommon). GGIR implementation IV since GGIR release 3.1-6 described Danilevicz et al. 2024. implementation replaces experimental implementation IV present GGIR since release 1.5-1. experimental implementation sure go raw acceleration signal indicator active aspect documented original publications. Similarly, sure deal missing data. However, issues resolved release 3.1-6: active now defined mean acceleration metric value light physical activity threshold specified parameter threshold.lig. Missing values left missing imputed, algorithm now accounts . new implementation documented Danilevicz et al. 2024 compatible older experimental implementation. Parameters IVIS.activity.metric, IVIS_windowsize_minutes, IVIS_epochsize_seconds, IVIS_acc_threshold used longer needed deprecated. Cosinor analysis compatible IV sometimes used measure behavioural robustness conducting Cosinor analysis. However, work combination two outcomes seems important calculated time series. Therefore, cosinor = TRUE, IV calculated twice: part default IV analysis discussed , part Cosinor analysis using log transformed time series. Cosinor-compatible IV estimates stored output variables cosinorIV cosinorIS.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter13_CircadianRhythm.html","id":"phi","dir":"Articles","previous_headings":"","what":"phi","title":"13. Circadian Rhythm Analysis","text":"Phi indicates correlated multi-day acceleration time series hour shift, also known first-order auto-correlation first-order autoregressive model AR(1). higher phi value indicates higher autocorrelation, phi close zero even negative indicates fragmented behavior. detailed discussion phi see Dickey Fuller (1979) Danilevicz et al. 2024. Phi calculated default GGIR part 2 part 6 parameter part6CR set TRUE.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/chapter13_CircadianRhythm.html","id":"self-similarity-paramerter-ssp","dir":"Articles","previous_headings":"Detrended fluctionation analysis (DFA)","what":"Self-similarity paramerter (SSP)","title":"13. Circadian Rhythm Analysis","text":"self-similarity paramter (SSP) also known scaling exponent alpha. SSP real number zero two. Values range (0, 1) indicate stationary motion behaviour. Values int range (1, 2 indicate nonstationary motion behaviour. details see Mesquita et al 2020 Danilevicz et al. 2024.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter13_CircadianRhythm.html","id":"activity-balance-index-abi","dir":"Articles","previous_headings":"Detrended fluctionation analysis (DFA)","what":"Activity Balance Index (ABI)","title":"13. Circadian Rhythm Analysis","text":"Activity Balance Index (ABI) introduced Danilevicz et al. 2024 transformation SSP. ABI measures activity observed period balanced, higher values reflect balanced pattern activity. ABI real number zero one calculated acceleration metric time series directly without need cut-points.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter13_CircadianRhythm.html","id":"related-output","dir":"Articles","previous_headings":"","what":"Related output","title":"13. Circadian Rhythm Analysis","text":"MXLX derived per 24 hours (part 2) available per day part2_daysummary.csv, summarised per recording part2_summary.csv. MXLX derived per window (part6) stored recording summary part6_summary.csv. circadian rhythm variables derived recording level. IV, , phi, cosinor analysis variables stored part2_summary.csv part6_summary.csv, SSP ABI stored part6_summary.csv. detailed variable dictionary see annex GGIR output.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter14_BehaviouralFragmentation.html","id":"defining-fragments","dir":"Articles","previous_headings":"","what":"Defining fragments","title":"14. Behavioural fragmentation","text":"GGIR, fragment daytime defined sequence epochs belong one four categories: Inactivity Light Physical Activity (LIPA) Moderate Vigorous Physical Acitivty (MVPA) Physical activity (can either LIPA MVPA) categories represents combination bouted unbouted time respective categories. Inactivity physical activity add full day (outside SPT), well inactivity, LIPA MVPA. fragment SPT defined sequence epochs belong one four categories: Estimated sleep Estimated wakefulness Inactivity Physical activity (can either LIPA MVPA) parameter frag.metrics = \"\" can instruct GGIR part 5 derive behavioural fragmentation metrics. may want consider combining parameter part5_agg2_60seconds=TRUE aggregate time series 1 minute resolution common behavioural fragmentation literature. GGIR part 6 performs fragmentation analysis part6CR set TRUE. uses time series output generated part 5 discussed previous chapter. GGIR derives fragmentation metrics two ways: part 5 fragmentation quantified per waking hours day reported per day recording average daily estimates. part 6 fragmentation quantified based data recording within window specifed parameter part6Window. Calculation per day allows us explore possibly account behavioural differences days week. However, day level estimate considered less robust recording level estimates generated part 6. internal function g.fragmentation fragmentation metric calculation used part 5 6 ensuring calculation otherwise consistent.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter14_BehaviouralFragmentation.html","id":"fragmentation-metrics","dir":"Articles","previous_headings":"","what":"Fragmentation metrics","title":"14. Behavioural fragmentation","text":"Note fragmentation metrics discussed fragmentation metrics TP NFrag calculated SPT fragments. Coefficient Variance (CoV) calculated according Blikman et al. 2014, entails dividing standard deviation mean lognormal transformed fragment length (minutes). Transition probability (TP) Inactivity () Physical activity (IN2PA), Physical activity inactivity (PA2IN), LIPA MVPA calculated according Danilevicz et al. 2024. Gini index calculated function Gini ineq R package, ineq argument corr set TRUE. Power law exponent metrics: Alpha, x0.5, W0.5 calculated according Chastin et al. 2010. Note compared R package ActFrag described Junrui Di et al. 2017 use theoretical minimum fragment duration instead observed minimum fragment duration. Number fragment per minutes (NFragPM) calculated identical metric fragmentation index Chastin et al. 2012, renamed specific reflection calculation. term fragmentation index appears generic given fragmentation metrics inform us fragmentation. Please note close metrics transition probability, total number divided total sum duration equals 1 divided average duration. Although exact math slightly different.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter14_BehaviouralFragmentation.html","id":"conditions-for-calculation","dir":"Articles","previous_headings":"","what":"Conditions for calculation","title":"14. Behavioural fragmentation","text":"Metrics Gini CoV calculated least 10 fragments (e.g. 5 inactive 5 active). condition met metric value set missing. Metrics related power law exponent alpha also calculated least 10 fragments, additional condition standard deviation fragment duration zero. conditions met metric value set missing. metrics related binary fragmentation (mean_dur_PA mean_dur_IN), calculated least 2 fragments (1 inactive, 1 active). condition met value set zero. Metrics related TP calculated : least 1 inactivity fragment (1 LIPA 1 MVPA fragment). condition met TP metric value set zero. keep overview recording days met criteria non-zero standard deviation least ten fragments, GGIR part 5 stores variable Nvaliddays_AL10F person level (.e., number valid days least 10 fragments), SD_dur (.e., standard deviation fragment durations) day level well aggregated per person.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter14_BehaviouralFragmentation.html","id":"key-parameters","dir":"Articles","previous_headings":"","what":"Key parameters","title":"14. Behavioural fragmentation","text":"parameters related cut-points bout detection mainly parameters listed “Physical activity parameters”.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter14_BehaviouralFragmentation.html","id":"related-output","dir":"Articles","previous_headings":"","what":"Related output","title":"14. Behavioural fragmentation","text":"GGIR part 5 csv reports find: Fragmentation metrics day level per waking hours day GGIR part 6 csv report find: Fragmentation metrics overview output variables see GGIR output annex.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter1_WhatIsGGIR.html","id":"a-brief-overview","dir":"Articles","previous_headings":"","what":"A brief overview","title":"1. What is GGIR","text":"GGIR R-package primarily designed process multi-day raw accelerometer data physical activity, sleep, circadian rhythm research. term raw refers data expressed gravitational units (1 g equal gravitational acceleration, average 9.81 m/s2) opposed previous generation accelerometers stored data accelerometer brand-specific units epoch level, typically 5, 30, 60 seconds length. Despite focus raw data, GGIR also offers functionality process previous-generation accelerometer data. signal processing raw data includes many steps explained pages. example, automatic calibration gravity, detection abnormally high values, imputation raw-level time gaps (specific sensor brands), calculation orientation angle average magnitude acceleration based variety metrics. Next, signal processing raw previous-generation data continue detection non-wear epoch-level imputation. Finally, GGIR uses information describe data data quality data summary metrics interpreted estimates physical activity, inactivity, sleep, circadian rhythm. time resolutions GGIR output : Per recording, typically matches one participant ID. Calendar day option specify day border timing midnight default. Night period time participant likely main daily sleep period, initial focus time window defined noon noon next day, unless person wakes noon next day, case focus shifts 6pm 6pm next day. Day segment, can defined via code indicate timing segments within day standardised recordings days dataset via diary file, segment definition allowed vary per recording day. Window defined Waking-main sleep period Waking-next main sleep period. Window defined Sleep onset start main sleep period sleep onset start next main sleep period. Epoch-level time series, epoch length set user example 5 seconds. (Optionally, default) Per sequence recordings matching participant IDs. example, person tracked first one accelerometer accelerometer replaced different accelerometers. details use , see documentation GGIR parameter maxRecordingInterval documentation use GGIR parameters explained .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter1_WhatIsGGIR.html","id":"key-strengths","dir":"Articles","previous_headings":"","what":"Key strengths","title":"1. What is GGIR","text":"GGIR permissive open-source software license maximise re-use collaboration. GGIR applicable data multiple sensor brands file formats. GGIR facilitates sleep, physical activity, circadian rhythm research. GGIR extensive quantitative output designed use quantitative research. GGIR designed computationally efficient option store re-use intermediate milestone data option process multiple files parallel computer discussed Chapter 2. GGIR designed accessible new users without experience R programming. GGIR requires one function call comes elaborate open-access documentation. Additionally, paid training courses offered maximise opportunity users learn GGIR us learn users. GGIR dozen code contributors. GGIR available CRAN archive, meaning meets CRAN standards release gone series automated checks. public email list (google group) users reach maintainers. hundreds publications used GGIR, powerful way identify problems improve code provided us wide range reference values.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter1_WhatIsGGIR.html","id":"history","dir":"Articles","previous_headings":"","what":"History","title":"1. What is GGIR","text":"elaborate reflection GGIR’s first 10 years existence can found blog post. short, GGIR evolved series R scripts used research around 2010-2012 first release 2013. key factor growth GGIR adoption research community willingness variety researchers invest GGIR either terms time investment financially. GGIR without efforts.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/chapter1_WhatIsGGIR.html","id":"flexible-and-accessible","dir":"Articles","previous_headings":"Philosophy behind GGIR","what":"Flexible and accessible","title":"1. What is GGIR","text":"research field highly heterogeneous : choice sensor brand, data format, study protocols used, research questions tries answer. time many within field lack time skills write custom data processing software. GGIR aims flexible handle different scenarios time remain accessible lack time skills write software. , hope GGIR use without financial resources commercial software, although like stress charity depend paid unpaid contributions community.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter1_WhatIsGGIR.html","id":"algorithm-design","dir":"Articles","previous_headings":"Philosophy behind GGIR","what":"Algorithm design","title":"1. What is GGIR","text":"philosophy behind algorithms implemented GGIR biomechanical explainable (heuristic knowledge driven) approaches measurement science preferable purely data-driven approaches. Please note specification scientific context rather measurement general, e.g. consumer wearables. knowledge driven approach unrealistic can consider data-driven approach. idea knowledge driven approach order advance insight, essential understanding causal relation phenomena observed (e.g. acceleration one body part), way (acceleration) sensor works, data produced, interpret data. example, know body acceleration relates energy expenditure physics human physiology. abundance scientific publications reported positive correlation accelerometer data energy expenditure served confirm prior knowledge. contrast, data-driven methods focus optimal correlation sensor data reference labels values, much less concerned causal associations focus knowledge driven approaches, defined . Identical correlation necessarily equal causation health research, process measurement can also confounded. examples: may see differences body acceleration patterns correlate different activity types different levels energy expenditure, mean actually measure activity types energy expenditure levels. Ignoring distinction can easily lead overestimating value accelerometer measuring constructs (activity type, etc) underestimate value accelerometer capturing acceleration useful measure behaviour, appropriately used interpreted. second problem data-driven methods heavily depend availability reliable criterion methods. argue reliable criterion methods exist physical behaviour measurement: Indirect calorimetry indicators energy metabolism can derived unable account activity type specific role body weight energy metabolism. makes impossible make standardised comparison energy cost different activity types across individuals differ body weight. See also reflections blog post. Polysomnography (PSG) standard sleep research. PSG offers physiological definition sleep impossible capture directly movement sensor. Therefore, forced simplify definition ‘sleep’ towards definition can captured movement sensor. result, act evaluating accelerometer ability classify sleep PSG becomes somewhat meaningless already know measuring construct PSG. Activity types ambiguous define given high number ways can performed. introduces fundamental level uncertainty robustness models outside datasets context developed . result, essential put strong emphasis algorithms descriptive value regardless whether offer high correlation supposed criterion methods.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter1_WhatIsGGIR.html","id":"permissive-open-source-license","dir":"Articles","previous_headings":"Philosophy behind GGIR","what":"Permissive open-source license","title":"1. What is GGIR","text":"may sound obvious research software open-source, fields physical activity sleep research, far accepted approach. GGIR one research tools field permissive license aimed maximise potential re-use collaboration.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter1_WhatIsGGIR.html","id":"documentation-structure-and-origin","dir":"Articles","previous_headings":"","what":"Documentation structure and origin","title":"1. What is GGIR","text":"structured chapters line GGIR training course organising recent years. documentation existed collection ad-hoc written paragraphs, lacked clear overarching structure narrative. result, difficult use documentation training course. , also wanted provide good level documentation follow course want refresh understanding GGIR. documentation mainly written narrative style tried explain theory practice GGIR functionalities. mentioned , GGIR offers vast amount functionality. arrive expectation find quick instruction run use GGIR research disappoint . Learning use GGIR requires time investment. Everything need type R script highlighted like . documentation intended academic review: cite publications clarify origin algorithms discuss part GGIR. Finally, first version documentation sponsored Accelting commitment remain available free open-access documentation. However, open documentation much easier maintain community: grateful help improve documentation either giving feedback (e.g. via v.vanhees accelting dot com), pull requests (know ), financially. example, great funding creating high quality complementary info graphics videos.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"the-ggir-function","dir":"Articles","previous_headings":"","what":"The GGIR function","title":"2. The GGIR pipeline","text":"brief introduction unfamiliar R software, R packages constructed sub-components, named functions. single function typically allows specific task. example, may sum function sums numbers provide . function can one multiple parameters control functions behaviour. R parameters call function arguments. example, sum argument na.rm control needs done missing values. GGIR refer arguments parameters. GGIR comes large number functions parameters together form processing pipeline. ease interacting GGIR, one central function act interface functionality. function also named GGIR. need learn work function GGIR, important understand background function interacts functions GGIR. , important understand GGIR package structured two complementary ways: Parts: Reflecting computational components running GGIR. GGIR 6 parts numbered 1 6 reflect order executed. reason GGIR split parts avoids re-run preceding parts want make small change downstream parts. parts together form pipeline. Parameter themes: Reflecting themes around user can control GGIR, e.g. controlling sleep detected controlling output stored stored.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"parts-of-the-pipeline","dir":"Articles","previous_headings":"The GGIR function","what":"Parts of the pipeline","title":"2. The GGIR pipeline","text":"GGIR, computational structure six parts applied sequentially data: Part 1: Loads data, works data quality, stores derived summary measures per time interval, also known signal features metrics, needed following parts. Part 2: Basic data quality assessment based extract metrics description data per day, per file, optionally per day segment. Part 3: Estimation rest periods, needed input Part 4. Part 4: Labels rest periods derived Part 3 sleep per night per file. Part 5: Compiles time series classification sleep physical behaviour categories re-using information derived part 2, 3, 4. includes detection behavioural bouts, time segments behaviour sustained duration specified user. Next, Part 5 generates descriptive summary time spent average acceleration per behavioural category, also behavioural fragmentation. Part 6: Facilitates analyses span full recording household co-analysis circadian rhythm analysis. specific order content parts evolved time, Part 1 2 created 2011-2013, Part 3 4 created 2013-2015, Part 5 created 2017-2020, Part 6 created 2023-2024. , parts also reflect historical expansion GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"milestone-data","dir":"Articles","previous_headings":"The GGIR function > Parts of the pipeline","what":"Milestone data","title":"2. The GGIR pipeline","text":"part, run, stores output R-data file .RData extension, refer milestone data. user, unlikely ever need interact directly milestone data files relevant output stored csv pdf files output folder results. milestone files read next GGIR part. advantage design offers internal modularity. example, can run part 1 now continue part 2 another time without repeat part 1 . , design eases re-processing also helpful us developing testing GGIR code. milestone data files stored sub-folders output folder show . Note output folder named output_mystudy example exact name may differ . part 1: output_mystudy/meta/basic. part 2: output_mystudy/meta/ms2.. part 3: output_mystudy/meta/ms3.. part 4: output_mystudy/meta/ms4.. part 5: output_mystudy/meta/ms5.. part 6: output_mystudy/meta/ms6.. milestone files potentially useful following reasons: copying milestone files new computer, may continue analyses without access original data files. can example helpful process subsets study different computers, pooling resulting milestone data allows finalise analysis single computer. Remember preserve folder structure. run problem, milestone files may allow share problem reproducible example problem without share original large data file.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"parameter-themes","dir":"Articles","previous_headings":"The GGIR function","what":"Parameter themes","title":"2. The GGIR pipeline","text":"parameters GGIR package functions can used parameter GGIR function. parameters internally grouped thematically, independently six parts used : params_rawdata: parameters related handling raw data resampling calibrating. params_metrics: parameters related aggregating raw data epoch level summary measures (metrics). params_sleep: parameters related sleep detection. params_phyact: parameters related physical ()activity. params_247: parameters related 24/7 behaviours fall typical sleep physical ()activity research category measures circadian rhythm 24 hour data description techniques. params_output: parameters relating whether output stored. params_general: general parameters covered categories GGIR user need remember theme parameter belongs . However, may notice documentation structure parameter theme ease navigating parameters. couple ways inspect parameters parameter category default values:","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"ggir-function-documentation","dir":"Articles","previous_headings":"The GGIR function > Parameter themes","what":"GGIR function documentation:","title":"2. The GGIR pipeline","text":"","code":"?GGIR"},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"parameters-vignette","dir":"Articles","previous_headings":"The GGIR function > Parameter themes","what":"Parameters vignette:","title":"2. The GGIR pipeline","text":"Documentation meaning parameter, default value, expected value(s) can found vignette: GGIR configuration parameters.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"from-r-command-line","dir":"Articles","previous_headings":"The GGIR function > Parameter themes","what":"From R command line:","title":"2. The GGIR pipeline","text":"interested one specific category like sleep: interested e.g. parameter HASIB.algo sleep_params object: parameters accepted parameter function GGIR, GGIR like shell around GGIR functionality. However, params_ objects provided input GGIR.","code":"library(GGIR) print(load_params()) library(GGIR) print(load_params()$params_sleep) library(GGIR) print(load_params()$params_sleep[[\"HASIB.algo\"]])"},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"configuration-file-","dir":"Articles","previous_headings":"The GGIR function > Parameter themes","what":"Configuration file.","title":"2. The GGIR pipeline","text":"GGIR stores parameter values csv-file named config.csv. file stored run GGIR root output folder, overwriting existing config.csv file. , like add annotations file, e.g. fourth column, need store somewhere outside output folder specify path file parameter configfile. Note parameters datadir outputdirdiscussed always need specified directly part configfile. practical value eases replication analysis, instead share R script colleagues, sharing config.csv file sufficient. Please make sure GGIR R version installed using reproducibility. See guidance install older package versions.","code":"library(GGIR) GGIR(datadir = \"C:/mystudy/mydata\",      outputdir = \"D:/myresults\",       configfile = \"D:/myconfigfiles/config.csv\")"},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"parameter-extraction-order","dir":"Articles","previous_headings":"The GGIR function > Parameter themes","what":"Parameter extraction order","title":"2. The GGIR pipeline","text":"parameters provided GGIR call, GGIR always uses . parameters provided GGIR call, GGIR checks whether config.csv file either output folder specified via parameter configfile loads values. parameter neither specified GGIR function call available config.csv file, GGIR use default value’s can inspected discussed section . , important realise consequence logic GGIR revert default parameter values repeated run GGIR unless remove parameter function call delete config.csv file specify original (default) value parameter explicitly GGIR call. ensure clear example: GGIR used first time without specifying parameter mvpathreshold, use default value, 100. specify mvpathreshold = 120, GGIR use instead store config.csv file. run GGIR time delete mvpathreshold = 120 GGIR call, GGIR fall back value 120 now stored config.csv file. delete config.csv file run GGIR , value 100 used .","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"raw-data","dir":"Articles","previous_headings":"Input files","what":"Raw data","title":"2. The GGIR pipeline","text":"GGIR currently works following accelerometer brands formats: GENEActiv .bin Axivity AX3 AX6 .cwa ActiGraph .csv .gt3x (.gt3x newer format generated firmware versions 2.5.0. Serial numbers start “NEO” “MRA” firmware version 2.5.0 earlier use older format .gt3x file). want work .csv exports via commercial ActiLife software, note option export data timestamps, turned . cope absence timestamps GGIR calculate timestamps sample frequency, start time, start date presented file header. Movisens data stored folders. accelerometer brand generates csv output, see documentation functions read.myacc.csv parameter rmc.noise vignette Reading csv files raw data GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"externally-derived-epoch-level-data","dir":"Articles","previous_headings":"Input files","what":"Externally derived epoch-level data","title":"2. The GGIR pipeline","text":"default GGIR assumes data raw discussed chapter 1. However, studies raw data available epoch level aggregate. example, done external software done inside accelerometer device. Although can introduce severe limitations transparency flexibility analysis, GGIR makes attempt facilitate analysis externally performed aggregations raw data. Please find overview file format currently facilitated: Note: Actiwatch ActiGraph, physical activity description sleep classification needs tailored count-specific algorithms: .neishabouricounts = TRUE, acc.metric = \"NeishabouriCount_x\", HASPT.algo = \"NotWorn\" HASIB.algo = \"NotWorn\"; Note 2: UK Biobank csv epoch data, GGIR facilitate sleep analysis arm angle exported. See GGIR CookBook example recipes working external data. process files, GGIR loads content saves GGIR part 1 milestone data, essentially fooling rest GGIR think GGIR part 1 created based raw data input. discussed chapter 3, GGIR non-wear detection two steps: first step done part 1 second step done part 2. relation externally derived epoch data non-wear detected looking consecutive zeros one hour (Actiwatch, ActiGraph) derived file (UK Biobank csv). accelerometer data need analysed stored one folder subfolders folder. Make sure folder contain files accelerometer data. Choose appropriate name folder, preferable reference study project related rather just ‘data’, name folder used identifier dataset integrated name output folder GGIR creates.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"how-to-run-your-analysis","dir":"Articles","previous_headings":"","what":"How to run your analysis?","title":"2. The GGIR pipeline","text":"bare minimum input needed GGIR : Parameter datadir allows specify stored accelerometer data outputdir allows specify like output analyses stored. equal datadir. copy paste code new R script (file ending .R) Source R(Studio), dataset processed output stored specified output directory. GGIR refers file directories folders. unfamiliar term directory: folder. Next, can add parameter mode tell GGIR part(s) run, e.g. mode = 1:5 tells GGIR run five parts default. parameter overwrite, can tell GGIR whether overwrite previously produced milestone data . , parameter idloc tells GGIR find participant ID. default setting likely work data formats, important tailor value parameter study setting. example, files start participant ID followed underscore set idloc=2. See documentation parameter idloc examples. GGIR stores output csv files comma default column separator dot default decimal separator standard UK/US. However, computer configured different region world can modified parameters sep_reports dec_reports, respectively.","code":"library(GGIR) GGIR(datadir = \"C:/mystudy/mydata\",      outputdir = \"D:/myresults\")"},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"from-the-r-console-on-your-own-desktoplaptop","dir":"Articles","previous_headings":"How to run your analysis?","what":"From the R console on your own desktop/laptop","title":"2. The GGIR pipeline","text":"Create R-script put library(GGIR) next line GGIR call GGIR(datadir=\"yourdatapath\", outputdir=\"yourdatapath\"). Next, can source R-script source button RStudio: source(\"pathtoscript/myshellscript.R\") GGIR default supports multi-thread processing used process one input file per process, speeding processing data. can turned setting parameter .parallel = FALSE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"in-a-cluster","dir":"Articles","previous_headings":"How to run your analysis?","what":"In a cluster","title":"2. The GGIR pipeline","text":"processing data GGIR desktop/laptop fast enough, advise using GGIR computing cluster. way Sun Grid Engine cluster shown . Please note commands specific computing cluster working . Please consult local cluster specialist explore run GGIR cluster. , share . three files SGE setting: submit.sh run-mainscript.sh myshellscript.R need update ... last line parameters used GGIR. Note f0=f0,f1=f1 essential work. values f0 f1 passed bash script. setup, need call bash submit.sh command line. help computing clusters, GGIR successfully run world’s largest accelerometer datasets UK Biobank German NAKO study.","code":"for i in {1..707}; do     n=1     s=$(($(($n * $[$i-1]))+1))     e=$(($i * $n))     qsub /home/nvhv/WORKING_DATA/bashscripts/run-mainscript.sh $s $e done #! /bin/bash #$ -cwd -V #$ -l h_vmem=12G /usr/bin/R --vanilla --args f0=$1 f1=$2 < /home/nvhv/WORKING_DATA/test/myshellscript.R options(echo=TRUE) args = commandArgs(TRUE) if(length(args) > 0) {   for (i in 1:length(args)) {     eval(parse(text = args[[i]]))   } } GGIR(f0=f0,f1=f1,...)"},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"processing-time","dir":"Articles","previous_headings":"","what":"Processing time","title":"2. The GGIR pipeline","text":"time process typical seven-day recording anywhere 3 10 minutes depending sample frequency recording, sensor brand, data format, exact configuration GGIR, specifications computer. observing processing times 20 minutes longer seven-day recording probably slowed factors. tips may able address : Make sure data process machine GGIR run. Processing data located somewhere else computer network can substantially slow software. Make sure machine 8GB RAM memory. Using GGIR old machines 4GB known slow. However, total memory bottle neck. Also consider number processes (threads) CPU can run relative amount memory. Ending 2GB per process seems good target. can helpful turn parallel processing .parallel = FALSE. Avoid computational activities machine running GGIR. example, use DropBox OneDrive make sure sync running GGIR. probably best use machine using GGIR process large datasets. Make sure machine configured automatically turn X hours terminate GGIR. , may want configure machine fall asleep pauses GGIR. Lower value parameter maxNcores default uses number available cores derived command parallel::detectCores() minus 1. might cases demanding operating system. Reduce amount data GGIR loads memory parameter chunksize, can useful machines limited memory processing many files parallel. chunksize value 0.2 make GGIR load data chunks 20% size relative chunks loads default, approximately 12 hours data auto-calibration routine 24 hours data calculation signal metrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter2_Pipeline.html","id":"ggir-output","dir":"Articles","previous_headings":"","what":"GGIR output","title":"2. The GGIR pipeline","text":"GGIR always creates output folder location specified parameter outputdir. output folder name constructed output_ followed name dataset derived distal folder name data directory specified datadir. recommend approach ensures output folder data directory matching names. way less likely confusion data folder output relates . However, possible use datadir specify vector paths individual files, may helpful want process set files position move new folder. scenario, need set parameter studyname tell GGIR dataset name . Inside output folder GGIR create two subfolders: meta results discussed earlier chapter. Inside results find folder named QC (Quality Checks). name QC (Quality Checks) possibly somewhat confusing. Data quality checks best started files stored results folder, files QC subfolder offer complementary information help quality check. GGIR generates reports parts 2, 4, 5, 6 pipeline. parameter .report can specify parts want reports generated. example, .report = c(2, 5) generate report parts 2 5. full GGIR analysis expect least following output files: Output files results subfolder: detailed discussion output can found chapters. Output files results/QC subfolder: detailed discussion output can found chapters. Output files meta/ms5.outraw subfolder: detailed discussion output can found chapters.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"time-gaps-identification-and-imputation","dir":"Articles","previous_headings":"","what":"Time gaps identification and imputation","title":"3. Data Quality Assurance","text":"Accelerometer data stored binary format (e.g. .bin .cwa) typically structured data blocks. data block header top constant number data points per block, usually equivalent seconds data. Axivity accelerometer data stored ‘.cwa’ file format blocks can, rare occasions, corrupted unreadable, therefore creating gap information recorded. ActiGraph accelerometer, also sensor brands export data ‘csv’ file format, possible recording stops certain time starts recording time, therefore creating time gap. GGIR developed efficiently identify manage time gaps.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"time-gaps-in-axivity-cwa-files","dir":"Articles","previous_headings":"Time gaps identification and imputation","what":"Time gaps in Axivity cwa files","title":"3. Data Quality Assurance","text":"Although expected occur often, Axivity cwa data can come faulty data blocks. ‘faulty’ mean data block interpretable. example, faulty blocks may occur device recording mode connected computer USB cable. address , R package GGIRread, used GGIR read cwa files, identifies faulty blocks imputes last recorded non-faulty acceleration value normalised 1 g (g unit gravitational acceleration). sampling rate accelerometer refers number data points recorded stored per second. Axivity devices expected design slightly variable sampling rate time, accounted interpolating data loaded R. example, data may collected 99.7 Hertz one block, interpolation technique interpolate data 100 Hertz. interpolation happens inside R package GGIRread. exact technique used interpolation set parameter interpolationType uses linear interpolation default (interpolationType = 1), can also set nearest neighbour interpolation (interpolationType = 2). quality assurance, GGIRread keeps track variation sampling rate per data block automatically imputes blocks (smallest segment data cwa file, typically seconds long) sampling rate deviates 10% expected sampling rate. imputation technique time gaps detailed earlier section. unhappy 10% threshold possibility changing percentage parameter frequency_tol. Biased sampling rates kind expected extremely rare expected affect normal research conditions, nonetheless like able account . Additionally, monitor process handling faulty blocks outliers sampling rate, GGIRread logs series file health statistics stored GGIR ‘data_quality_report.csv’ file located within ‘QC’ folder output directory ‘results’ (see previous chapter discussion GGIR output). data quality report, comes variable names prefixed ‘filehealth’, detailing number duration time gaps detected recording(s), well number epochs 5-10% 10% bias sampling rate.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"time-gaps-in-actigraph-gt3x-and-ad-hoc-csv-files","dir":"Articles","previous_headings":"Time gaps identification and imputation","what":"Time gaps in ActiGraph gt3x and ad-hoc csv files","title":"3. Data Quality Assurance","text":"manufacturers incorporate functionalities devices let stop recording long episode movement, aiming conserve battery life reduce data size. However, feature results recorded signals containing intermittent time gaps must accounted data processing. example, ActiGraph option called ‘idle sleep mode’ devices, pauses data collection movement detected sustained period time. ActiGraph’s idle sleep mode explained manufacturer’s website. time gaps data considered non-wear time GGIR. GGIR imputes gaps shorter 90 minutes raw data level, using last recorded value (meaning gap) normalised 1 g. approach assumes accelerometer kept orientation last observed. contrary, gaps longer 90 minutes imputed epoch level make data processing memory efficient faster. epoch level imputation discussed chapter 6. number duration time gaps found logged GGIR ‘data_quality_report.csv’ file located within ‘QC’ folder output directory ‘results’ (see previous chapter discussion GGIR output). Studies often forget clarify whether accelerometers configured pause data collection periods movement , , resulting time gaps accounted data processing. Especially, device firmware manufacturer software already imputes time gaps can cause significant bias GGIR estimates. generally speaking, advise : Report whether ‘idle sleep mode’ similar functionalities used. Disable functionality, possible, harms transparency reproducibility research. Indeed, mechanism exists replicate time gaps accelerometer brands, likely challenge accurate assessment sleep sedentary behaviour. data collected ‘idle sleep mode’ similar functionalities referred raw data accelerometry, data collection process involved proprietary pre-processing steps violate core principle raw data collection.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"specific-note-on-actigraph-idle-sleep-mode","dir":"Articles","previous_headings":"Time gaps identification and imputation","what":"Specific note on ActiGraph idle sleep mode","title":"3. Data Quality Assurance","text":"ActiGraph files might exported gt3x csv formats. idle sleep mode used, data files different. gt3x files, time gaps can found signal, imputation made ActiLife software. However, csv files exported ActiLife imputed values three axes periods movement. Note imputation ActiLife software changed point time. Initially imputation zeros recent versions ActiLife imputation uses last recorded value axis. Therefore, need aware GGIR take care time gap imputation relative idle sleep mode using gt3x files, using ActiGraph csv files (latter come time gaps already imputed).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"accelerometer-sensor-calibration","dir":"Articles","previous_headings":"","what":"Accelerometer sensor calibration","title":"3. Data Quality Assurance","text":"Many measurement tools require calibration ranging weighting scales Oxygen sensor accelerometers. Without good calibration risk error causes lack calibration undermines accurate reliable measurement. Confusingly accelerometers, field long time assumed accelerometer need calibrated relative energy expenditure. incorrect energy expenditure entirely different construct. true reference accelerometer sensors acceleration can calibrate gravitational acceleration reference. acceleration sensor works based principle acceleration captured mechanically converted electrical signal. relationship electrical signal acceleration usually assumed linear, involving offset gain factor. familiar terms, compare simple regression equation offset Beta0 (Y-intercept) gain Beta1 (slope). Therefore, offset number add signal adjust systematic error (bias) gain number multiply signal scale , order adjust relative error. shall refer establishment offset gain factor sensor calibration procedure. three types calibration: Factory calibration, done industry (always done, may need refinement afterward). Manual calibration, done researcher (advisable cases even possible. Auto-calibration, done algorithms real life study data (common scenario refine factory calibration). Accelerometers usually calibrated part manufacturing process non-movement conditions using local gravitational acceleration reference, referred factory calibration. manufacturer calibration can later evaluated holding sensor axis parallel () perpendicular direction gravity; readings axis ±1.000 0.000 g, respectively. one derive correction coefficients per axis. However, procedure can cumbersome studies high throughput. Furthermore, calibration check possible data collected past corresponding accelerometer device exist anymore. reason calibraton advisable cases possible. Finally, auto-calibration type calibration done algorithm using already collected real world data withoutb need additional experiments, explain detail.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"auto-calibration-algorithm","dir":"Articles","previous_headings":"Accelerometer sensor calibration","what":"Auto-calibration algorithm","title":"3. Data Quality Assurance","text":"general principle techniques recording acceleration screened non-movement periods. Next, rolling average non-movement periods taken three orthogonal sensor axes used generate three-dimensional ellipsoid representation ideally sphere radius 1 g. , deviations radius three-dimensional ellipsoid 1 g (ideal calibration) can used derive correction factors sensor axis-specific calibration error. auto-calibration performed GGIR uses technique detailed description demonstration can found published paper. success auto-calibration depends number non-movement periods variation accelerometer orientation periods available algorithm. result, auto-calibration expected perform less short recordings (e.g., less day) recordings participant wear accelerometer time. cases, can use recordings sensor -movement periods higher variation orientations derive calibration coefficients, apply coefficients recording interest. , use parameter backup.cal.coef. auto-calibration algorithm applied default can turned parameter .cal = FALSE. recommend turning auto-calibration unless strong reasons .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"non-wear-detection","dir":"Articles","previous_headings":"","what":"Non-wear detection","title":"3. Data Quality Assurance","text":"can happen study participant wear accelerometer. can happen variety reasons: getting tired wearing accelerometer, forgetting put accelerometer back short moment wearing , getting instructed researcher take . However, accelerometer worn, still collect data. accelerometer lying still, data collected looks like participant supposed wear moving. left undetected, wearing accelerometer bias estimates time spent inactive behaviours. Accelerometer non-wear time detected GGIR looking standard deviation range raw acceleration signals. time window statistical values calculated long enough, turn reliable indicators whether accelerometer worn . specifically, standard deviation value range (.e., maximum value minus minimum value) calculated per 60 minute windows start every 15 minutes (e.g. 14:00, 14:15, etc.) . overlapping nature time windows needed improve precision. time window labelled non-wear, least statistical values 2 3 axes meet brand specific thresholds. result, since multiple overlapping time windows classify 15 minutes, 15 minute window classified multiple times. non-wear criteria met windows overlap 15-minute window, labelled non-wear. brands 13.0 mg less 50 mg. size time window (60 minutes) size time intervals 15 minutes defined parameter windowsizes, three values. specifically, first value used non-wear detection discussed next chapter, second value defines mentioned intervals 15 minutes , third value mentioned 60 minutes time window. non-wear classification, discussed , default 2023. Prior , GGIR slightly different non-wear detection algorithm still available via parameter nonwear_approach recommend use .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"identifying-implausible-wear-time","dir":"Articles","previous_headings":"Non-wear detection","what":"Identifying implausible wear time","title":"3. Data Quality Assurance","text":"can happen time classified wear time implausible example accelerometer post moved around researcher ahead actually intended wear period. example, accelerometer post long periods non-wear briefly interrupted periods movement, interpreted algorithm monitor wear. Therefore, GGIR part 2, detected non-wear GGIR part 1 checked implausible wear periods relabelled non-wear. sure mean part 1 2 see chapter 1, gives overview. GGIR part 2 performs check follows: First , detected wear-periods last less six hours, duration less 30% combined duration bordering non-wear periods, relabelled non-wear. Second, remaining wear-periods less three hours, form less 80% bordering non-wear periods, classified non-wear. motivation selecting relatively high criterion (< 30%) combination long period (6 hrs) low criterion (< 80%) combination short period (3 hrs) long periods likely actually related monitor wear time. illustrate algorithm created visual model, see picture . , units time presented squares marked grey detected non-wear time. Period C detected wear-time borders non-wear periods B D. length C less six hours C divided sum B D less 0.3 first criteria met block C turned non-wear period.  Visual inspection >100 traces large observational study revealed applying stage three times iteratively allowed improved classification periods characterised intermittent periods non-wear apparent wear.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"implausible-wear-at-the-beginning-and-end-of-the-recording","dir":"Articles","previous_headings":"Non-wear detection","what":"Implausible wear at the beginning and end of the recording","title":"3. Data Quality Assurance","text":"Based experience, participants take accelerometer final 24 hours recording actual end. However, may hard detect accelerometer may still moved. Therefore, GGIR relabels wear-periods final 24 hrs recording shorter three hours preceded least one hour non-wear time non-wear. Finally, recording starts ends period less three hours wear followed preceded non-wear (length), period wear classified non-wear. additional criteria screening beginning end accelerometer file intended filter movements related attaching accelerometer start downloading data accelerometer end. final check can turned parameter nonWearEdgeCorrection, may relevant processing accelerometer data collected single-night polysomnography studies.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"clipping-detection","dir":"Articles","previous_headings":"","what":"Clipping detection","title":"3. Data Quality Assurance","text":"GGIR part 1 also screens acceleration signal “clipping”, .e., sustained unusual high (raw) acceleration values non compatible human movement. 30% data points 15-minute window (used non-wear) close maximal values (technical term dynamic range) sensor, corresponding time period considered potentially unreliable, may explained sensor getting stuck extreme value accelerometers used inappropriately (attached heavily accelerating object). example, dynamic range 8g, accelerations 7.5g marked “clipping”. window also classified clipping value window larger 150% dynamic range sensor. Given clipping rarely happens reported GGIR part non-wear time. clipping non-wear treated merging arrive single indicator amount invalid data. However, keep track occurrence clipping time, GGIR report fraction 15-minute windows recording clipping occurs, see section output .","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"time-gap-imputation","dir":"Articles","previous_headings":"Key parameters","what":"Time gap imputation","title":"3. Data Quality Assurance","text":"imputeTimegaps","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"auto-calibration","dir":"Articles","previous_headings":"Key parameters","what":"Auto-calibration","title":"3. Data Quality Assurance","text":".cal backup.cal.coef","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"non-wear-detection-1","dir":"Articles","previous_headings":"Key parameters","what":"Non-wear detection","title":"3. Data Quality Assurance","text":"windowsizes nonwear_approach nonWearEdgeCorrection","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"clipping-detection-1","dir":"Articles","previous_headings":"Key parameters","what":"Clipping detection","title":"3. Data Quality Assurance","text":"windowsizes dynrange","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/chapter3_QualityAssessment.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"3. Data Quality Assurance","text":"van Hees VT, Fang Z, Langford J, Assah F, Mohammad , da Silva IC, Trenell MI, White T, Wareham NJ, Brage S. Autocalibration accelerometer data free-living physical activity assessment using local gravity temperature: evaluation four continents. J Appl Physiol (1985). 2014 Oct 1;117(7):738-44. PMID: 25103964 link van Hees VT, Gorzelniak L, Dean León EC, Eder M, Pias M, Taherian S, Ekelund U, Renström F, Franks PW, Horsch , Brage S. Separating movement gravity components acceleration signal implications assessment human daily physical activity. PLoS One. 2013 Apr 23","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter4_AccMetrics.html","id":"why-do-we-need-to-extract-metrics","dir":"Articles","previous_headings":"","what":"Why do we need to extract metrics","title":"4. From Raw Data to Acceleration Metrics","text":"Wearable accelerometers widely used health research study physical activity, sleep, behaviours. modern accelerometers can collect store least 30 values per second expressed units gravitational acceleration (g). data collected, important extract kinematically meaningful information . data processing, summary measures describe signal referred metrics signal features. knowledge accelerometer works typical approach metric calculation calculate possible statistical properties acceleration signal like mean, standard deviation, entropy, skewness. However, discussed chapter 1, favour approach try use knowledge sensor. knowledge sensor tells us acceleration signal comes three components need separated: acceleration related gravitational acceleration. absence movement three acceleration signals inform us orientation accelerometer relative gravity proxy posture. Accelerations decelerations related movement, can interpret proxy muscle contractions energy expenditure needed contractions . Measurement error bias. example, signal noise caused electronical components introducing minor variation acceleration signal even real acceleration constant. variation due noise typically small compared variation due movement. Another example calibration errors discussed chapter 3. Finding metric able separate three components provide informative value relation orientation magnitude acceleration proxies mentioned posture muscle contractions, respectively.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter4_AccMetrics.html","id":"metric-aggregation-per-epoch","dir":"Articles","previous_headings":"","what":"Metric aggregation per epoch","title":"4. From Raw Data to Acceleration Metrics","text":"metrics first calculated resolution raw data, reflects tiny fraction second. exact number data points per second also known sampling rate can vary studies. different sampling rate values directly comparable. However, aggregating metric values per larger time window, known epoch, can make values comparable. , GGIR aggregates values per epoch (e.g. 5 seconds). Aside harmonising data across studies, aggregation per epoch also advantages: Evidence value accelerometer data based epoch-level aggregates, reference values like Oxygen consumption sleep reliably derived sub-second resolution. Aggregating leads less data points makes lot practical work . GGIR, epoch length kept constant across GGIR parts allow consistent interpretation. epoch length set first value parameter windowsizes (default 5 seconds) used throughout steps GGIR, following exceptions: GGIR part 2, time spent MVPA variables (discussed chapter 11) done multiple epoch lengths, one output variable. However, per output variable epoch length held constant throughout recording, GGIR never mixes epoch lengths epoch length affects interpretation value. like reading overview article car speeds alternates unit speed every sentence (.e., miles per hour, meters per second, km per hour, etc). GGIR part 5, user option aggregate epochs 1 minute length parameter part5_agg2_60seconds. example, using 5 second epochs parts 1, 2, 3 4, can informative run part 5 1 minute epoch length.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter4_AccMetrics.html","id":"acceleration-metrics-available-in-ggir","dir":"Articles","previous_headings":"","what":"Acceleration metrics available in GGIR","title":"4. From Raw Data to Acceleration Metrics","text":"find list metrics GGIR can apply. Multiple metrics can derived GGIR run. acceleration metrics derived GGIR function g.applymetrics. Neishabouri counts GGIR relies R package actifelifecounts. Please see code respective package documentation information exact calculations. use metrics, add parameters GGIR call, e.g.:","code":"GGIR(do.enmo = TRUE, do.mad = TRUE, do.bfen = TRUE, …)"},{"path":"https://wadpac.github.io/GGIR/articles/chapter4_AccMetrics.html","id":"approach-to-removing-the-gravitational-signal-component","dir":"Articles","previous_headings":"Acceleration metrics available in GGIR","what":"Approach to removing the gravitational signal component","title":"4. From Raw Data to Acceleration Metrics","text":"table metrics overview indicates approach used separate gravitation component acceleration signal. two approaches design metrics: Magnitude, metric makes assumption magnitude gravitational acceleration component. Frequency, metric makes assumption frequency content gravitational acceleration component. assumptions known always true conditions, acceleration metric perfect.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter4_AccMetrics.html","id":"handling-high-frequency-components-in-the-signal","dir":"Articles","previous_headings":"Acceleration metrics available in GGIR","what":"Handling high frequency components in the signal","title":"4. From Raw Data to Acceleration Metrics","text":"argue high frequency components signal treated noise removed. However, likely represent harmonics low frequency movements thus part description movement. elaborate reflection , please see blog post. metrics, listed , letters LF BF name attempt suppress high frequency content signal. , GGIR user can decide whether prefer filter higher frequencies .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter4_AccMetrics.html","id":"why-ggir-uses-enmo-as-a-default-","dir":"Articles","previous_headings":"Acceleration metrics available in GGIR","what":"Why GGIR uses ENMO as a default.","title":"4. From Raw Data to Acceleration Metrics","text":"one metric can default. Acceleration metric ENMO (Euclidean Norm Minus One negative values rounded zero) default metric since GGIR created. 2013, investigated different ways summarising raw acceleration data (van Hees et al. PLoS ONE 2013). short, different metrics exist little literature support superiority metric time. long different studies use different metrics, findings comparable. Therefore, choice metric ENMO merely pragmatic. GGIR uses ENMO default : 1. ENMO demonstrated value describing variance daily energy expenditure, correlated questionnaire data, able describe patterns physical activity. 2. ENMO easy describe mathematically , therefore, improves reproducibility across studies software tools. 3. ENMO attempts quantify acceleration universal units collapse signal abstract scale. 4. 2013 paper showed ENMO used combination auto-calibration, similar validity filter-based metrics like HFEN BFEN, conceptually similar metrics proposed later MIMSunit, MAD, AI0. 5. Studies criticised ENMO consistently failed apply auto-calibration, attempted apply auto-calibration lab setting, ignoring fact auto-calibration designed short lab settings. needs free-living data work properly. , studies often clear problematic zero imputation idle sleep mode ActiGraph devices dealt .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter4_AccMetrics.html","id":"notes-on-implementation-of-zero-crossing-counts","dir":"Articles","previous_headings":"Acceleration metrics available in GGIR","what":"Notes on implementation of zero crossing counts","title":"4. From Raw Data to Acceleration Metrics","text":"implementation zero-crossing count GGIR attempt imitate zero-crossed counts previously described Sadeh, Cole, Kripke colleagues late 1980s 1990s. However, guaranteed exact copy original approach, used AMA-32 Motionlogger Actigraph Ambulatory-monitoring Inc. (“AMI”). complete publicly accessible description approach exists.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter4_AccMetrics.html","id":"missing-information","dir":"Articles","previous_headings":"Acceleration metrics available in GGIR > Notes on implementation of zero crossing counts","what":"Missing information","title":"4. From Raw Data to Acceleration Metrics","text":"missing information calculation : Sadeh specified calculations done based data Y-axis direction Y-axis clarified. Therefore, unclear whether Y-axis time corresponded Y-axis modern sensors. frequency filter used, properties filter missing. Sensitivity sensor: now guessing Motionlogger sensitivity 0.01 g without direct proof. Relationship piezo-electric acceleration signal used time modern piezo-capacitive acceleration signals. personal correspondence AMI, learnt technique kept proprietary never shared sold actigraphy manufacturers (time correspondence October 2021). Based correspondence AMI, can conclude even Actiwatch, ActiGraph, manufacturers, facilitated use 1990s sleep classification algorithms, guarantee exact replication original studies.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter4_AccMetrics.html","id":"our-guess-on-the-missing-information","dir":"Articles","previous_headings":"Acceleration metrics available in GGIR > Notes on implementation of zero crossing counts","what":"Our guess on the missing information","title":"4. From Raw Data to Acceleration Metrics","text":"Following challenges, implementation zero-crossing count GGIR based educated guess used information find literature product documentation. relation missing information listed : allow specify axis want use parameter Sadeh_axis choose default second axis. use 0.25 - 3 Hertz band-pass filter order 2, can modify parameters zc.lb, zc.hb, zc.order. use 0.01 g stop band, can change parameter zc.sb. assume band-passed signal comparable absence evidence contrary. evaluation, zero-crossing count value range looks plausible compared value range original publications. note ActiGraph users: decide compare GGIR Cole-Kripke estimates ActiLife’s Cole Kripke estimates, aware ActiLife may adopted different Cole-Kripke algorithm original publication presented four algorithms. potential source variation. , ActiLife may used different educated guesses Motionlogger counts calculated.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter4_AccMetrics.html","id":"embedding-your-own-metrics","dir":"Articles","previous_headings":"","what":"Embedding your own metrics","title":"4. From Raw Data to Acceleration Metrics","text":"GGIR users may like use metrics covered GGIR. facilitate , allow external function embedding discussed vignette Embedding external functions GGIR. fact, allows include entire algorithms step detection new sleep classification algorithm like test inside GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter4_AccMetrics.html","id":"related-output","dir":"Articles","previous_headings":"","what":"Related output","title":"4. From Raw Data to Acceleration Metrics","text":"GGIR part 2, output derived acceleration metric derived GGIR part 1 except metrics anglex, angley, anglez. GGIR part 4, output derived metrics used sleep detection, typically angle count (Neishabouri counts zero-crossing count). GGIR part 5, output derived single metric specified parameter acc.metric. reason constraint part 5 produces many variables creating multiple metrics computationally expensive substantially increase complexity underlying code.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter4_AccMetrics.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"4. From Raw Data to Acceleration Metrics","text":"compiled list related articles may find useful: Van Hees et al. 2011 Estimation Daily Energy Expenditure Pregnant Non-Pregnant Women Using Wrist-Worn Tri-Axial Accelerometer. van Hees et al. 2013 Separating Movement Gravity Components Acceleration Signal Implications Assessment Human Daily Physical Activity. Migueles et al. 2019 Comparability accelerometer signal aggregation metrics across placements dominant wrist cut points assessment physical activity adults. Aittasalo et al. 2015 Mean amplitude deviation calculated raw acceleration data: novel method classifying intensity adolescents’ physical activity irrespective accelerometer brand. Neishabouri et al. 2022 Quantification acceleration activity counts ActiGraph. Karas et al. 2022 Comparison accelerometry-based measures physical activity: retrospective observational data analysis study. van Hees 2019 Ten Misunderstandings surrounding Information Extraction Wearable Accelerometer data.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter5_StudyProtocol.html","id":"selectingmasking-the-data","dir":"Articles","previous_headings":"","what":"Selecting/Masking the data","title":"5. Accounting for Study Protocol","text":"important GGIR masks data outside time window participant instructed wear accelerometer. Study protocols differ duration expected wear period, GGIR offers variety ways account study protocol. main parameter data_masking_strategy. requires numeric value indicating one following strategies: data_masking_strategy = 1 indicate specific number hours masked start /end recording, specified parameters hrs.del.start hrs.del.end, respectively. data_masking_strategy = 2 indicate data first last midnight recording considered. data_masking_strategy = 3 indicate active X 24-h blocks starting time day used, X specified parameter ndayswindow. Note can combined aforementioned parameters hrs.del.start hrs.del.end, trim window start end recording. data_masking_strategy = 4 indicate data first midnight considered. data_masking_strategy = 5 similar data_masking_strategy = 3, yet selects X complete calendar days, X specified parameter ndayswindow. Additionally, can set maximum duration accelerometer worn recording starts. Use parameter maxdur specify duration number 24 hour blocks parameter max_calendar_daysfor number calendar days.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter5_StudyProtocol.html","id":"key-parameters","dir":"Articles","previous_headings":"","what":"Key parameters","title":"5. Accounting for Study Protocol","text":"data_masking_strategy hrs.del.start hrs.del.end ndayswindow maxdur max_calendar_days","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/chapter6_DataImputation.html","id":"imputation-of-invalid-epoch-data","dir":"Articles","previous_headings":"","what":"Imputation of invalid epoch data","title":"6. How GGIR Deals with Invalid Data","text":"time segments classified non-wear clipping (see Chapter 3) masked study protocol (see Chapter 5) treated invalid data. GGIR part 2, epoch level metric values imputed, log kept epochs imputed. subsequent analysis done GGIR, imputed time series used. time series without invalid segments used analyses: Weighted average full recording Cosinor analysis (see Chapter 10) specific non-default configuration sleep analysis (see Chapter 8) imputation epoch data done based mean metric value corresponding valid values time day days recording. However, time interval marked invalid across recorded days, value imputed zero, except metric EN imputed 1. example, imagine 5-day recording following ENMO metric data two specific epochs day across five days: imputed shown average 3, 4, 3 3 3.25:","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter6_DataImputation.html","id":"controlling-the-imputation","dir":"Articles","previous_headings":"","what":"Controlling the imputation","title":"6. How GGIR Deals with Invalid Data","text":"worth noting option disable imputation setting parameter .imp = FALSE. means values kept imputed omitted. Disabling imputation recommended use-cases, can relevant studies controlled sleep exercise laboratories sensor known worn throughout experiment. alternative way control imputation specify time segments invalid epochs imputed zeros (ones metric EN) instead following standard GGIR imputation method. , use parameter TimeSegments2ZeroFile.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter6_DataImputation.html","id":"key-parameters","dir":"Articles","previous_headings":"","what":"Key parameters","title":"6. How GGIR Deals with Invalid Data","text":".imp, TimeSegments2ZeroFile","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter6_DataImputation.html","id":"related-output","dir":"Articles","previous_headings":"","what":"Related output","title":"6. How GGIR Deals with Invalid Data","text":"GGIR part 2, plots check data quality highlight segments file considered invalid imputed. plots can found folder “results/QC/”. GGIR part 5, time series produced optionally stored within folder “meta/ms5out.raw/” either csv RData format. time series contain indicator epochs considered invalid imputed.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter7_DescribingDataWithoutKnowingSleep.html","id":"data-quality-indicators","dir":"Articles","previous_headings":"","what":"Data quality indicators","title":"7. Describing the Data Without Knowing Sleep","text":"GGIR part 2 summarises data quality checks done previous four chapters, ranging report successfulness auto-calibration procedure number valid days. way, GGIR part 2 ideal place start data quality assurance.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter7_DescribingDataWithoutKnowingSleep.html","id":"basic-descriptives","dir":"Articles","previous_headings":"","what":"Basic descriptives","title":"7. Describing the Data Without Knowing Sleep","text":"Descriptive variables calculated reported valid days , criteria valid day defined parameter includedaycrit.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter7_DescribingDataWithoutKnowingSleep.html","id":"average-acceleration","dir":"Articles","previous_headings":"Basic descriptives","what":"Average acceleration","title":"7. Describing the Data Without Knowing Sleep","text":"Average acceleration known correlated total activity-related energy expenditure. GGIR part 2 provide two types average acceleration: Average per day, stored day considered valid. Note descriptive descriptives also stored GGIR averages across days, weekend days, weekdays, discuss detail later . Weighted average valid data points recording, weighted timing day valid epochs, regardless whether come days whole classified valid .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter7_DescribingDataWithoutKnowingSleep.html","id":"acceleration-distribution","dir":"Articles","previous_headings":"Basic descriptives","what":"Acceleration distribution","title":"7. Describing the Data Without Knowing Sleep","text":"distribution acceleration values can informative . GGIR facilitates two ways: specifying quantiles distribution parameter qlevels, fed base R function quantile, GGIR gives us acceleration metric values corresponding quantiles (quantile multiplied 100 percentile). describing time spent acceleration ranges, defined parameter ilevels . distribution acceleration values often referred intensity distribution physical activity literature.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/chapter7_DescribingDataWithoutKnowingSleep.html","id":"sets-of-quantiles-mx-metrics-by-rowlands-et-al-","dir":"Articles","previous_headings":"Derived descriptives","what":"Sets of quantiles (MX metrics by Rowlands et al.)","title":"7. Describing the Data Without Knowing Sleep","text":"quantiles, discussed , can used describe accelerations participants exceed active “X” accumulated minutes day. specific approach, proposed Rowlands et al., quantiles referred MX metrics. MX metrics confused active continuous X hours, e.g. M10, used circadian rhythm research also can derived GGIR (see parameter winhr). use MX metrics proposed Rowlands et al., specify durations 24h day want identify accelerations values. example, generate minimum acceleration value active accumulated 30 minutes, can call qlevels = (1410/1440). parameter also accepts vector generate multiple MX metrics. example, call M60, M30, M10, can specify following: qlevels = c(c(1380/1440), c(1410/1440), c(1430/1440)). Note: time segments shorter 24 hours specified parameter qwindow, 8-hour school day (described Fairclough et al 2020), denominator qlevels change 1440 (24h) specific segment length. example, use 480 (8h). Accordingly, argument call M60, M30, M10 : qlevels = c(c(420/480), c(450/480), c(470/480)). moment, works one segment length GGIR facilitate generation MX metrics multiple unequal time segments within GGIR function call. output part 2 summary report file refer percentile day. Thus, 24-h day, M30 appear “p97.91666_ENMO_mg_0.24hr”. create radar plots MX metrics first described Rowlands et al., GitHub repository provides R code detailed instructions make radar plots using data.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter7_DescribingDataWithoutKnowingSleep.html","id":"intensity-gradient","dir":"Articles","previous_headings":"Derived descriptives","what":"Intensity gradient","title":"7. Describing the Data Without Knowing Sleep","text":"plot time spent equally spaced acceleration ranges, end asymptotic-shaped curve, indicating little time spent high intensities (acceleration levels) much time spent low intensities. shape distribution may informative hard quantify single number standard form. Therefore, new concept called intensity gradient proposed Rowlands colleagues. intensity gradient defines slope log-transformed axes intensity distribution. specifically, calculate time accumulated incremental acceleration bins (bin size = 25 mg) also keep track mid-point intensity bin, e.g. 62.5 mg bin ranging 50 75 mg. mid-point acceleration bin expressed mg time spent bin expressed minutes log-transformed. log-transformation expected change asymptotic-shaped curve straight line. Subsequently, linear regression fitted data points. slope regression line represents intensity gradient. , calculate correlation coefficient data points help verify degree form straight line (R^2). intensity gradient calculated default. include metric part 2 output, set iglevels = TRUE. , want methodological research , can use parameter define alternative acceleration bins, e.g. using bins 20 instead 25 mg iglevels = c(seq(0, 4000, = 20), 8000).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter7_DescribingDataWithoutKnowingSleep.html","id":"key-arguments","dir":"Articles","previous_headings":"","what":"Key arguments","title":"7. Describing the Data Without Knowing Sleep","text":"includedaycrit ilevels qlevels iglevels qwindow .report","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter7_DescribingDataWithoutKnowingSleep.html","id":"related-output","dir":"Articles","previous_headings":"","what":"Related output","title":"7. Describing the Data Without Knowing Sleep","text":"GGIR part 2 generates three csv reports: part2_daysummary.csv, part2_summary.csv, data_quality_report.csv. data_quality_report.csv discussed chapter 3, focus first two reports chapter. variables part2_summary.csv recording level aggregates variables part2_daysummary.csv. , variable names starting “AD_” refer average across days, “WD” refers average across weekdays, “” refers average across weekend days, “WWE” refers weighted weekend days ensure weekend days contribute equally, “WWD” refers weighted weekdays ensure weekdays contribute equally. , GGIR part 2 generates report named part2_daysummary_longformat.csv, generated GGIR used day segment analysis, see documentation parameter qwindow. report contains exact information part2_daysummary.csv, long format instead wide format. part2_daysummary_longformat.csv, row represents one segment one day one recording, part2_daysummary.csv, row contains one day one recording segments day organised different columns.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter7_DescribingDataWithoutKnowingSleep.html","id":"descriptive-variables","dir":"Articles","previous_headings":"Related output","what":"Descriptive variables","title":"7. Describing the Data Without Knowing Sleep","text":"clarify b refers part2_summary.csv part2_daysummary.csv, r refers part2_summary.csv .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter8_SleepFundamentalsSibs.html","id":"sib-vanhees2015","dir":"Articles","previous_headings":"","what":"SIB: vanHees2015","title":"8. Sleep Fundamentals: Sustained Inactivity Bout Detection (SIB)","text":"algorithm looks periods time z-angle change 5 degrees least 5 minutes. algorithm proposed 2015 article. idea behind algorithm interpretable heuristic compared conventional approaches use magnitude acceleration distinguish sustained inactivity bouts. reason assume vanHees2015 better worse reflection sleep, advancement purely intended terms interpretability. vanHees2015 algorithm default. values 5 5 algorithm can modified parameters anglethreshold timethreshold, currently see basis recommend advise sticking default values.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter8_SleepFundamentalsSibs.html","id":"sib-notworn-experimental","dir":"Articles","previous_headings":"","what":"SIB: NotWorn (EXPERIMENTAL)","title":"8. Sleep Fundamentals: Sustained Inactivity Bout Detection (SIB)","text":"Disclaimer: status SIB algorithm experimental described evaluated peer-reviewed publication yet. means revisions algorithm can expected algorithm matures. algorithms named “NotWorn” sib guider (next chapter) designed studies instruction wear accelerometer night. obvious facilitate meaningful sleep analysis. Nonetheless need crude estimate night time versus day time order GGIR part 5 characterise day time behaviours. case dataset use guider setting HASPT.algo = \"NotWorn\" discussed next chapter. , recommend combining using “NotWorn” : .imp = FALSE, HASPT.ignore.invalid = NA, ignorenonwear = FALSE. detection sib periods based acceleration metric defined parameter acc.metric.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter8_SleepFundamentalsSibs.html","id":"combined-with-count-acceleration-metrics","dir":"Articles","previous_headings":"SIB: NotWorn (EXPERIMENTAL)","what":"Combined with count acceleration metrics","title":"8. Sleep Fundamentals: Sustained Inactivity Bout Detection (SIB)","text":"using count accelereration metric set HASIB.algo = \"NotWorn\". part 4 sib set equal detected guider window. , effectively guider sib algorithm identical case.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter8_SleepFundamentalsSibs.html","id":"combined-with-gravitational-unit-acceleration-metrics","dir":"Articles","previous_headings":"SIB: NotWorn (EXPERIMENTAL)","what":"Combined with gravitational unit acceleration metrics","title":"8. Sleep Fundamentals: Sustained Inactivity Bout Detection (SIB)","text":"using acceleration metrics expresses acceleration gravitational units set HASIB.algo accelerometer expected worn specify second guider parameter HASPT.algo discussed int next chapter, e.g. HASPT.algo = c(\"NotWorn\", \"HDCZA)\". way GGIR first search long non-wear periods indicator sleep use define sleep window found fall back sib-algortihm specified HASIB.algo, e.g. \"vanHees2015\".","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter8_SleepFundamentalsSibs.html","id":"sib-count-based-algorithms-experimental","dir":"Articles","previous_headings":"","what":"SIB: Count based algorithms (EXPERIMENTAL)","title":"8. Sleep Fundamentals: Sustained Inactivity Bout Detection (SIB)","text":"Disclaimer: status SIB algorithm experimental described evaluated peer-reviewed publication yet. means revisions algorithm can expected algorithm matures. Accelerometers used sleep research since 1990s. However, initial accelerometers store data gravitational units sub-second level use nowadays stored data 30 60 second epoch aggregates. Although aggregates referred counts many manufacturers calculation counts differs manufacturer. attempted facilitate several sleep detection algorithms literature period “Sadeh1994”, “ColeKripke1992”, “Galland2012”. problem algorithms preprocessing done generate counts insufficiently described literature. zero-crossing count used Sadeh1994 ColeKripke1992 attempt made collect much information found made educated guess missing information. zero-crossing counts discussed chapter 4 acceleration metrics. counts calculated can use following SIB algorithms. uncertain whether Y-axis direction modern accelerometers matches direction Y-axis literature old studies direction Y-axis knowledge never clarified.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter8_SleepFundamentalsSibs.html","id":"sadeh1994","dir":"Articles","previous_headings":"SIB: Count based algorithms (EXPERIMENTAL)","what":"Sadeh1994","title":"8. Sleep Fundamentals: Sustained Inactivity Bout Detection (SIB)","text":"algorithm proposed Sadeh et al. link. use set parameter HASIB.algo = \"Sadeh1994\" argument Sadeh_axis = \"Y\" indicate algorithm use Y-axis sensor.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter8_SleepFundamentalsSibs.html","id":"galland2012","dir":"Articles","previous_headings":"SIB: Count based algorithms (EXPERIMENTAL)","what":"Galland2012","title":"8. Sleep Fundamentals: Sustained Inactivity Bout Detection (SIB)","text":"algorithm proposed Galland et al. link. use implementation Galland2012 algorithm specify parameter HASIB.algo = \"Galland2012\". , set Sadeh_axis = \"Y\" specify algorithm use Y-axis.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter8_SleepFundamentalsSibs.html","id":"colekripke1992","dir":"Articles","previous_headings":"SIB: Count based algorithms (EXPERIMENTAL)","what":"ColeKripke1992","title":"8. Sleep Fundamentals: Sustained Inactivity Bout Detection (SIB)","text":"algorithm proposed Cole et al. link, specifically GGIR uses algortihm proposed paper 10-second non-overlapping epochs counts expressed average per minute. skip re-scoring steps paper showed marginal added value added complexity. use GGIR implementation algortihm, specify parameters HASIB.algo = \"ColeKripke1992\" Sadeh_axis = \"Y\" indicate algorithm use Y-axis sensor.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter8_SleepFundamentalsSibs.html","id":"dealing-with-expected-or-detected-nonwear-time-segments","dir":"Articles","previous_headings":"","what":"Dealing with expected or detected nonwear time segments","title":"8. Sleep Fundamentals: Sustained Inactivity Bout Detection (SIB)","text":"Depending study protocol may want interpret invalid data (typically non-wear) differently. set parameter ignorenonwear=TRUE (default) ignore non-wear period SIB detection. useful prevent nonwear episodes going bed waking contributing sleep.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter9_SleepFundamentalsGuiders.html","id":"time-window-used-for-sleep-analyses","dir":"Articles","previous_headings":"","what":"Time window used for sleep analyses","title":"9. Sleep Fundamentals: Guiding sleep detection","text":"default sleep analysis considers window noon-noon, ideal shift workers may go bed early day wake noon. address , GGIR changes window analysis seems case: sleep log indicates person woke noon, sleep analysis part 4 done window 6pm-6pm. Similarly, guider indicates person woke 11 , sleep analysis part 3 4 done window 6pm-6pm. way method sensitive individuals main sleep period starting noon ending noon, referred daysleepers output. example case shift workers. Note guider L5+/-12 (discussed ) able , consider noon-noon time window.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/chapter9_SleepFundamentalsGuiders.html","id":"guider-sleeplog","dir":"Articles","previous_headings":"Guiders","what":"Guider: Sleeplog","title":"9. Sleep Fundamentals: Guiding sleep detection","text":"sleep log (diary) already used studies. way GGIR uses sleeplog first described 2015 article. Two sleeplog file structures supported: -called basic advanced sleeplog. use guider set location sleeplog value parameter loglocation. General notes GGIR uses sleeplogs guider: GGIR expects start end sleep window specified. one missing sleeplog data assumed missing entire night. GGIR impute sleeplog data. feel imputation desirable running GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter9_SleepFundamentalsGuiders.html","id":"basic-sleep-log","dir":"Articles","previous_headings":"Guiders > Guider: Sleeplog","what":"Basic sleep log","title":"9. Sleep Fundamentals: Guiding sleep detection","text":"Example basic sleeplog: One column participant id, first column. Specify column argument colid. Alternatingly one column onset time one column waking time. Specify column column first night argument coln1, example coln1=2. Timestamps stored without date hh:mm:ss hour values ranging 0 23 (24). onset corresponds lights intention fall asleep, specify sleepwindowType = \"TimeInBed\". can multiple sleeplogs spreadsheet. row representing single recording. First row: first row spreadsheet needs filled column names. basic sleep log format matter column names . first night basic sleeplog assumed correspond first recorded night accelerometer recording. know sleep log start later day make sure add columns labels without timestamps. Note recorded night mean data regardless whether data valid. , participant wear accelerometer first night still first night recording.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter9_SleepFundamentalsGuiders.html","id":"advanced-sleep-log","dir":"Articles","previous_headings":"Guiders > Guider: Sleeplog","what":"Advanced sleep log","title":"9. Sleep Fundamentals: Guiding sleep detection","text":"Example advanced sleeplog two recordings: Relative basic sleeplog format advanced sleep log format comes following changes: Recording stored rows, information per days stored columns. Information per day preceded one columns holds calendar date. GGIR designed recognise handle date format assumes used date format consistently sleeplog. Per calendar date column wakeup time followed column onset -bed time. Note different basic sleep log, wakeup time follows column onset -bed time. , advanced sleep log calendar date oriented: asking participant woke fell asleep certain date. However, sleep onset time 2am, still fill 02:00:00, even though 02:00:00 next calendar date. timestamps known certain date, can skip date sleep log. Note different basic format sleep log columns left empty missing night(s). can add columns relating self-reported napping time nonwear time. used sleep analysis g.part3 g.part4, used g.part5 facilitate napping analysis, see argument .sibreport paragraph naps. Multiple naps multiple nonwear periods can entered per day. Leave cells missing values blank. Column names critical advanced sleeplog format: Date columns recognised GGIR column name word “date” . advanced sleep log format recognised GGIR looking occurrence least two column names word “date” name. Wakeup times recognised words “wakeup” column name. Sleeponset times recognised columns word “onset” column name. Time going bed recognised column names “lightsout”, “inbed”, “tobed”, “bedstart”. Time getting recognised column names “lightson”, “outbed”, “bedend”. Napping times recognised columns word “nap” name. Nonwear times recognised columns word “nonwear” name. GGIR guesses data format looping common date formats. date falls within 30 days start date accelerometer recording date format assumed found. starts attempting “Y-m-d” (2015-06-25). column name includes word “impute” column treated log diary imputation outside GGIR preceding night. value shown visualreport stored time series GGIR part 5. imputation codes used testing format C0000 indicate imputation inbed, sleeponset, wakeup outbed, respectively. one numbers 1 reflect imputation.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter9_SleepFundamentalsGuiders.html","id":"guider-hdcza","dir":"Articles","previous_headings":"Guiders","what":"Guider: HDCZA","title":"9. Sleep Fundamentals: Guiding sleep detection","text":"HDCZA algorithm designed studies wrist-worn accelerometer (raw) data sleep log available. algorithm first described 2018 article, modified slightly: Step 6 Figure 1 replaced single threshold (0.2 default). short, step 1-6 attempt classify time periods limited change posture. Next, step 7 extracts time blocks longer 30 minutes, step 8 includes intermittent time periods shorter 60 minutes, step 9 looks longest resulting block day, step 10 represents guider window. Note step 10 Figure 1 paper gives false impression step represents final classification SPT window. way guider used identify SPT window described chapter 10. time segment HDCZA derived default noon noon. However, ends 11am noon applied 6pm-6pm time segment. use guider set parameter HASPT.algo = \"HorAngle\".","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter9_SleepFundamentalsGuiders.html","id":"guider-l5-12-legacy-algorithm","dir":"Articles","previous_headings":"Guiders","what":"Guider: L5+/-12 (LEGACY ALGORITHM)","title":"9. Sleep Fundamentals: Guiding sleep detection","text":"Disclaimer: legacy algorithm used publications therefore kept inside GGIR. performance expected less available algorithm, recommend using . guider reflects twelve hour window centred around least active 5 hours day. crude approach likely inferior guiders, easy describe. first presented 2018 article. use guider set parameter def.noc.sleep = c().","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter9_SleepFundamentalsGuiders.html","id":"guider-setwindow-legacy-algorithm","dir":"Articles","previous_headings":"Guiders","what":"Guider: setwindow (LEGACY ALGORITHM)","title":"9. Sleep Fundamentals: Guiding sleep detection","text":"Disclaimer: legacy algorithm used publications therefore kept inside GGIR. performance expected less available algorithm, recommend using . guider uses set window day recording. Start end time specified argument def.noc.sleep. example, use guider window 10pm 8am set parameter def.noc.sleep = c(22, 8).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter9_SleepFundamentalsGuiders.html","id":"guider-horangle-experimental","dir":"Articles","previous_headings":"Guiders","what":"Guider: HorAngle (EXPERIMENTAL)","title":"9. Sleep Fundamentals: Guiding sleep detection","text":"Disclaimer: status guider experimental described evaluated peer-reviewed publication yet. means revisions algorithm can expected algorithm matures. guider designed hip-worn accelerometer (raw) data, looking longest period horizontal trunk. needs GGIR part 1 2 derived angle longitudinal axis. Setting parameter sensor.location=\"hip\" triggers identification longitudinal axis looking angle strongest 24-hour lagged correlation. can also force GGIR use specific axis longitudinal axis parameter longitudinal_axis. Next, algorithm identifies horizontal axis -45 45 degrees considers horizontal posture. Next, used identify largest time bed period, considering horizontal time segments least 30 minutes, looking longest horizontal period day gaps less 60 minutes ignored. Therefore, last 4 steps algorithm identical last four steps HDCZA algorithm. use guider set parameter HASPT.algo = \"HorAngle\"","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter9_SleepFundamentalsGuiders.html","id":"guider-notworn-experimental","dir":"Articles","previous_headings":"Guiders","what":"Guider: NotWorn (EXPERIMENTAL)","title":"9. Sleep Fundamentals: Guiding sleep detection","text":"Disclaimer: status guider experimental described evaluated peer-reviewed publication yet. means revisions algorithm can expected algorithm matures. already referenced previous chapter NotWorn guider designed studies instruction wear accelerometer night. obvious facilitate meaningful sleep analysis. Nonetheless need crude estimate night time versus day time order GGIR part 5 characterise day time behaviours. First NotWorn algorithm calculates 5 minute rolling average acceleration metric values (.e., acceleration metric defined parameter acc.metric) applies threshold 5% standard deviation resulting signal. However, threshold less minimum value signal threshold set equal 10th percentile distribution. Next, used identify largest non-movement period, considering segments least 30 minutes, looking longest segment day gaps less 60 minutes ignored. Therefore, last 4 steps algorithm identical last four steps HDCZA HorAngle algorithms. algorithm expected work acceleration metric, count-type metrics metrics gravitational units. use guider set parameter HASPT.algo = \"NotWorn\". , recommend combining using “NotWorn” : .imp = FALSE ignorenonwear = FALSE. Internally HASPT.ignore.invalid always set NA “NotWorn” used. used also define resulting window SIB period ignore identified SIB window ensure entire window treated sleep. , SIB periods detected ignored. However, know experience participants occasionally wear accelerometer night even told . GGIR offers solution working count data accelerometer metrics gravitational units. case, possible specify second guider use accelerometer worn less 25% time detection window (noon-noon 6pm-6pm). happens check whether parameter HASPT.algo two guiders specified. use second one. example, HASPT.algo = c(\"NotWorn\", \"HDCZA\") HASPT.algo = c(\"NotWorn\", \"HorAngle\").","code":""},{"path":"https://wadpac.github.io/GGIR/articles/chapter9_SleepFundamentalsGuiders.html","id":"dealing-with-expected-or-detect-nonwear-time-segments","dir":"Articles","previous_headings":"","what":"Dealing with expected or detect nonwear time segments","title":"9. Sleep Fundamentals: Guiding sleep detection","text":"Depending study protocol may want interpret invalid data (typically non-wear) differently: want rely available time series invalid time segments imputed leave parameter HASPT.ignore.invalid = FALSE default. want guider ignore invalid segment despite efforts impute , see HASPT.ignore.invalid = TRUE. approach may helpful studies accelerometer often worn waking hour day. want guider consider invalid segments movement period set parameter HASPT.ignore.invalid = NA. approach may helpful studies accelerometer often worn night. used, guider name output shown “+invalid” end, e.g. “HDCZA+invalid”, reflect guider enhanced.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/Cookbook.html","id":"handling-externally-derived-data","dir":"Articles","previous_headings":"","what":"Handling externally derived data","title":"Cookbook","text":"refers situation external software hardware used derive epoch level aggregates accelerometer data.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/Cookbook.html","id":"in--awd-format","dir":"Articles","previous_headings":"Handling externally derived data > Actiwatch data","what":"in .AWD format","title":"Cookbook","text":"","code":"GGIR(datadir = \"/media/actiwatch_awd\", # folder with epoch level .AWD file   outputdir = \"/media/myoutput\",   dataFormat = \"actiwatch_awd\",   extEpochData_timeformat = \"\\%m/\\%d/\\%Y \\%H:\\%M:\\%S\",   windowsizes = c(60, 900, 3600), # 60 is the expected epoch length   HASIB.algo = \"Sadeh1994\",   def.noc.sleep = c()) # <= because we cannot use HDCZA for ZCY"},{"path":"https://wadpac.github.io/GGIR/articles/Cookbook.html","id":"in--csv-format","dir":"Articles","previous_headings":"Handling externally derived data > Actiwatch data","what":"in .CSV format","title":"Cookbook","text":"","code":"GGIR(datadir = \"/media/actiwatch_csv\", # folder with epoch level .AWD file   outputdir = \"/media/myoutput\",   dataFormat = \"actiwatch_csv\",   extEpochData_timeformat = \"\\%m/\\%d/\\%Y \\%H:\\%M:\\%S\",   windowsizes = c(15, 900, 3600), # 15 is the expected epoch length   HASIB.algo = \"Sadeh1994\",   def.noc.sleep = c()) # <= because we cannot use HDCZA for ZCY"},{"path":"https://wadpac.github.io/GGIR/articles/Cookbook.html","id":"uk-biobank-data-in--csv-format","dir":"Articles","previous_headings":"Handling externally derived data","what":"UK Biobank data in .CSV format","title":"Cookbook","text":"applicable UK Biobank 5 second epoch csv exports raw accelerometer data. Please see https://wadpac.github.io/GGIR/ documentation processing raw accelerometer data (multiple values per second).","code":"GGIR(datadir = \"/media/ukbiobank\",    outputdir = \"/media/myoutput\",    dataFormat = \"ukbiobank_csv\",    extEpochData_timeformat = \"\\%m/\\%d/\\%Y \\%H:\\%M:\\%S\",    windowsizes = c(5, 900, 3600), # We know that data was stored in 5 second epoch    desiredtz = \"Europe/London\") # We know that data was collected in the UK"},{"path":"https://wadpac.github.io/GGIR/articles/Cookbook.html","id":"actigraph-count-data-in--csv-format","dir":"Articles","previous_headings":"Handling externally derived data","what":"ActiGraph count data in .CSV format","title":"Cookbook","text":"","code":"GGIR(datadir = \"/examplefiles\",    outputdir = \"\",    dataFormat = \"actigraph_csv\",    windowsizes = c(5, 900, 3600),    threshold.in = round(100 * (5/60), digits = 2),    threshold.mod = round(2500 * (5/60), digits = 2),    threshold.vig = round(10000 * (5/60), digits = 2),    extEpochData_timeformat = \"\\%m/\\%d/\\%Y \\%H:\\%M:\\%S\",    do.neishabouricounts = TRUE,    acc.metric = \"NeishabouriCount_x\")"},{"path":"https://wadpac.github.io/GGIR/articles/Cookbook.html","id":"senwear-data-in--xls-format","dir":"Articles","previous_headings":"Handling externally derived data","what":"Senwear data in .xls format","title":"Cookbook","text":"","code":"GGIR(datadir = \"C:/yoursenseweardatafolder\",     outputdir = \"D:/youroutputfolder\",     windowsizes = c(60, 900, 3600),     threshold.in = 1.5,     threshold.mod = 3,     threshold.vig = 6,     dataFormat = \"sensewear_xls\",     extEpochData_timeformat = \"\\%d-\\%b-\\%Y \\%H:\\%M:\\%S\",     HASPT.algo = \"NotWorn\")"},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/Cookbook.html","id":"not-worn-during-night","dir":"Articles","previous_headings":"Handling study protocol","what":"Not worn during night","title":"Cookbook","text":"Data type: Study protocol: Worn day, taken night Wear location: “NotWorn” specified second guider can supplied parameter shown . second guider used accelerometer worn 75 percent night. example shows HDCZA.","code":"GGIR(HASPT.algo = c(\"NotWorn\", \"HDCZA\"),     HASIB.algo = \"vanHees2015\",     do.imp = FALSE, # Do not impute nonwear because sensor was never worn 24/7     HASPT.ignore.invalid = NA, # Treat nonwear as potential part of guider window     ignorenonwear = FALSE, # Consider nonwear as potential sleep     includenightcrit = 8,     includedaycrit = 8)"},{"path":"https://wadpac.github.io/GGIR/articles/CutPoints.html","id":"considerations","dir":"Articles","previous_headings":"","what":"Considerations","title":"Published cut-points and how to use them in GGIR","text":"physical activity research field used called cut-points segment accelerometer time series based level intensity. vignette compiled list published cut-points instructions use GGIR. Please note GGIR refers cut-points thresholds, referring thing: value set values help split levels movement intensity. newer cut-points frequently published list may date. Please let us know aware published cut-points missed!","code":""},{"path":"https://wadpac.github.io/GGIR/articles/CutPoints.html","id":"cut-points-expressed-in-gravitational-units-this-vignette","dir":"Articles","previous_headings":"Considerations","what":"Cut-points expressed in gravitational units (this vignette)","title":"Published cut-points and how to use them in GGIR","text":"vignette focuses cut-points metrics attempt quantify average acceleration per epoch gravitational units. strength metrics values affected sampling rate epoch length improving comparability across studies.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/CutPoints.html","id":"cut-points-not-expressed-in-gravitational-units-not-in-this-vignette","dir":"Articles","previous_headings":"Considerations","what":"Cut-points NOT expressed in gravitational units (not in this vignette)","title":"Published cut-points and how to use them in GGIR","text":"However, GGIR also facilitates metrics whose values expressed gravitational units historically used. example, metric described Neishabouri (see GGIR argument .neishabouricounts) reflects indicator accumulated body movement time, referred counts, calculated ActiLife software ActiGraph accelerometer brand. Cut-points counts corresponding ActiGraph brand recurrently proposed literature, example, see systematic review stratification age group. Note cut-points ActiGraph counts proposed introduction multiday raw data collection likely hardware-based calculations may perfectly align ActiGraph software-based (Actilife) calculations counts Neishabouri described. result, older cut-points may need used caution. cut-points find literature ActiGraph counts applied Neishabouri counts directly epoch length specific. cut-points literature need corrected conversion factor. conversion factor calculated epoch length new study (e.g. 5 seconds) divided epoch length original study (e.g. 60 seconds). Note correction differences sampling rate needed Neishabouri counts already account via -sampling. want use cut-point “100 counts per minute” literature 5 second epoch data, GGIR function call look like :","code":"GGIR([...],      mode = 1:5,      windowsizes = c(5, 900, 3600),      do.neishabouricounts = TRUE,      acc.metric = \"NeishabouriCount_y\",      threshold.in = 100 * (5/60),      [...])"},{"path":"https://wadpac.github.io/GGIR/articles/CutPoints.html","id":"relevant-arguments-to-use-cut-points-in-ggir","dir":"Articles","previous_headings":"","what":"Relevant arguments to use cut-points in GGIR","title":"Published cut-points and how to use them in GGIR","text":"argument mvpathreshold used part 2 quantify time accumulated user-specified threshold moderate--vigorous intensity expected occur. mvpathreshold applied metrics extracted part 1 arguments .metric (e.g., .enmo, .mad, .neishabouricounts). part 5, threshold.lig, threshold.mod, threshold.vig used indicate thresholds separate inactivity light, light moderate, moderate vigorous, respectively.thresholds applied metric defined acc.metric (default = “ENMO”). summary table parameters definition calculate acceleration metrics previously used calibration cut-points define used physical activity intensity classification cut-points.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/CutPoints.html","id":"cut-points-for-preschoolers","dir":"Articles","previous_headings":"Summary of published cut-points","what":"Cut-points for preschoolers","title":"Published cut-points and how to use them in GGIR","text":"*publications used acceleration metrics sum values per epoch rather average per epoch like GGIR . , use cut-point GGIR, provide scaled version cut-points presented paper : (CutPointFromPaper_in_gsecs/85.7) * 1000. Note sample frequency 87.5 reported publication incorrect based correspondence authors replaced 85.7.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/CutPoints.html","id":"cut-points-for-childrenadolescents","dir":"Articles","previous_headings":"Summary of published cut-points","what":"Cut-points for children/adolescents","title":"Published cut-points and how to use them in GGIR","text":"*publications used acceleration metrics sum values per epoch rather average per epoch like GGIR . , use cut-point GGIR, provide scaled version cut-points presented paper : (CutPointFromPaper_in_gmins/(sampleRateFromPaper * EpochLengthInSecondsPaper)) * 1000 ** publication used acceleration metrics expressed cut-points g units. , use cut-point GGIR, provide cut-point multiplied 1000.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/CutPoints.html","id":"cut-points-for-adults","dir":"Articles","previous_headings":"Summary of published cut-points","what":"Cut-points for adults","title":"Published cut-points and how to use them in GGIR","text":"*publications used acceleration metrics sum values per epoch rather average per epoch like GGIR . , use cut-point GGIR, provide scaled version cut-points presented paper : (CutPointFromPaper_in_gmins/(sampleRateFromPaper * EpochLengthInSecondsPaper)) * 1000 † publication, cut-point based data sampled 30 Hz 100 Hz. scaling cut-points specified (*), resulting thresholds virtually (ones presented table).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/CutPoints.html","id":"cut-points-for-older-adults","dir":"Articles","previous_headings":"Summary of published cut-points","what":"Cut-points for older adults","title":"Published cut-points and how to use them in GGIR","text":"*Cut-points derived applying Youden index ROC curves. ** Cut-points derived increasing Sensitivity Specificity light vice versa moderate ROC curves (see paper details).† publications used acceleration metrics sum values per epoch rather average per epoch like GGIR . , use cut-point GGIR, provide scaled version cut-points presented paper : (CutPointFromPaper_in_gmins/(sampleRateFromPaper * EpochLengthInSecondsPaper)) * 1000 ‡ cut-points excluding data aided walking washing activities can found publication.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/CutPoints.html","id":"notes-on-cut-point-validity","dir":"Articles","previous_headings":"","what":"Notes on cut-point validity","title":"Published cut-points and how to use them in GGIR","text":"Sensor calibration studies , excluding Hildebrand et al. 2016, effort made calibrate acceleration sensors relative gravitational acceleration prior cut-point development. Theoretically can expected cause bias cut-point estimates proportional calibration error device, especially cut-points based acceleration metrics rely assumption accurate calibration metrics: ENMO, EN, ENMOa, also metric SVMgs used studies Esliger 2011, Phillips 2013, Dibben 2020. Idle sleep mode ActiGraph discussed main package vignette, studies using ActiGraph sensor often forget clarify whether idle sleep mode used , accounted data processing. criticism towards cut-point methods? elaborate reflection limitations cut-points motivation cut-points still value GGIR see: https://www.accelting.com/updates/--ggir-facilitate-cut-points/","code":""},{"path":"https://wadpac.github.io/GGIR/articles/CutPoints.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"Published cut-points and how to use them in GGIR","text":"Aittasalo 2015: https://doi.org/10.1186/s13102-015-0010-0 Bammann 2021: https://doi.org/10.1371/journal.pone.0252615 Dibben 2020: https://doi.org/10.1186/s13102-020-00196-7 Dillon 2016: https://doi.org/10.1371%2Fjournal.pone.0109913 Esliger 2011: https://doi.org/10.1249/mss.0b013e31820513be Fraysse 2020: https://doi.org/10.3389%2Ffspor.2020.579278 Hildebrand 2014: https://doi.org/10.1249/mss.0000000000000289 Hildebrand 2016: https://doi.org/10.1111/sms.12795 Migueles 2021: https://doi.org/10.3390%2Fs21103326 Phillips 2013: https://doi.org/10.1016/j.jsams.2012.05.013 Sanders 2018: https://doi.org/10.1080/02640414.2018.1555904 Schaefer 2014: https://doi.org/10.1249%2FMSS.0000000000000150 Roscoe 2017: https://doi.org/10.1007/s00431-017-2948-2 Vähä-Ypyä 2015: https://doi.org/10.1371/journal.pone.0134813","code":""},{"path":"https://wadpac.github.io/GGIR/articles/ExternalFunction.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Embedding external functions in GGIR","text":"like GGIR want use algorithms raw data included GGIR external function embedding feature can solution. example, may want pilot new machine learned classifiction algorithm want write data cleaning aggregation steps needed analysis real life ‘lab’ acceleormeter data. works: Internally GGIR loads raw accelerometer data memory blocks 24 hours. data memory, corrected calibration error, resampled sample rate required function, GGIR applies default algorithms well external function provided (Python R). external function expected take input: three-column matrix acceleration data corresponding three acceleration axes, optional parameters argument can R format (character, list, vector, data.frame, etc). output external function expected produce matrix data.frame one multiple columns corresponding output external function.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/ExternalFunction.html","id":"example-with-external-r-function","dir":"Articles","previous_headings":"","what":"Example with external R function","title":"Embedding external functions in GGIR","text":"example apply function counts() R package activityCounts raw data, produces estimate Actigraph counts per second.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/ExternalFunction.html","id":"write-external-function","dir":"Articles","previous_headings":"Example with external R function","what":"Write external function","title":"Embedding external functions in GGIR","text":"Create file calculateCounts.R insert following code:","code":"calculateCounts = function(data=c(), parameters=c()) {   # data: 3 column matrix with acc data   # parameters: the sample rate of data   library(\"activityCounts\")   if (ncol(data) == 4) data= data[,2:4]   mycounts = counts(data=data, hertz=parameters,                      x_axis=1, y_axis=2, z_axis=3,                     start_time = Sys.time())   mycounts = mycounts[,2:4] #Note: do not provide timestamps to GGIR   return(mycounts) }"},{"path":"https://wadpac.github.io/GGIR/articles/ExternalFunction.html","id":"provide-external-function-to-ggir","dir":"Articles","previous_headings":"Example with external R function","what":"Provide external function to GGIR","title":"Embedding external functions in GGIR","text":"Create new .R file running GGIR analysis, e.g. named myscript.R, insert following code. forget update filepath first line point calculateCounts.R file. code creates object myfun type list expected come following elements: FUN character string specifying location external function want apply. parameters parameters used function, can stored format (vector, matrix, list, data.frame). user make sure external function can handle object. expected_sample_rate Expected sample rate, inputdata difference sample rate, data resampled. expected_unit Expected unit acceleration external function: “mg”, “g” “ms2”. input data different converted. colnames Character vector names columns produced external function. outputres resolution (seconds) output produced external function. Note, needs equal multitude short epoch size g.part1 output (5 seconds) short epoch size multitude resolution. way GGIR can aggregate repeat external function output used inside GGIR. minlength minimum length (seconds) input data needed, typically window per output provided. outputtype Character indicate type external function output. Set “numeric” data stored numbers (numeric format), “character” character string. aggfunction data needs aggregated match short epoch size g.part1 output (5 seconds) element specifies function used aggregation, e.g. mean, sum, median. timestamp Boolean indicated whether timestamps (seconds since 1-1-1970) passed external function first columm data matrix.. reporttype Character indicate type reporting GGIR: “scalar” averaged per day, “event” summed per day, “type” categorical variable can aggregated per day tabulating . name Character indicate name entity detected cases event detection, name used output filename, e.g. part2_stepsummary.csv name “step”. moment name use event detection algorithms Next, add call GGIR function GGIR myfun provided one arguments: Please see  information function GGIR.","code":"source(\"~/calculateCounts.R\") myfun =  list(FUN=calculateCounts,               parameters= 30,               expected_sample_rate= 30,               expected_unit=\"g\",               colnames = c(\"countsX\",\"countsY\",\"countsZ\"),               outputres = 1,               minlength = 1,               outputtype=\"numeric\",               aggfunction = sum,               timestamp=F,               reporttype=\"scalar\") library(GGIR) GGIR(datadir=\"~/myaccelerometerdata\",              outputdir=\"~/myresults\",              mode=1:2,              epochvalues2csv = TRUE,              do.report=2,              myfun=myfun) #<= this is where object myfun is provided to GGIR"},{"path":"https://wadpac.github.io/GGIR/articles/ExternalFunction.html","id":"example-with-external-python-function","dir":"Articles","previous_headings":"","what":"Example with external Python function","title":"Embedding external functions in GGIR","text":"example use external Python function estimate dominant signal frequency per acceleration axis. Note can also done R, shows even Python functions can provided.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/ExternalFunction.html","id":"write-external-function-1","dir":"Articles","previous_headings":"Example with external Python function","what":"Write external function","title":"Embedding external functions in GGIR","text":"Create dominant_frequency.py insert code shown : Create dominant_frequency.R calls python function insert following code:","code":"import numpy  def dominant_frequency(x, sf):   # x: vector with data values   # sf: sample frequency   fourier = numpy.fft.fft(x)   frequencies = numpy.fft.fftfreq(len(x), 1/sf)   magnitudes = abs(fourier[numpy.where(frequencies > 0)])   peak_frequency = frequencies[numpy.argmax(magnitudes)]   return peak_frequency dominant_frequency = function(data=c(), parameters=c()) {   # data: 3 column matrix with acc data   # parameters: the sample rate of data   source_python(\"dominant_frequency.py\")   sf=parameters   N = nrow(data)   ws = 5 # windowsize   if (ncol(data) == 4) data= data[,2:4]   data = data.frame(t= floor(seq(0,(N-1)/sf,by=1/sf)/ws),                     x=data[,1], y=data[,2], z=data[,3])   df = aggregate(data, by = list(data$t),                   FUN=function(x) {return(dominant_frequency(x,sf))})   df = df[,-c(1:2)]   return(df) } }"},{"path":"https://wadpac.github.io/GGIR/articles/ExternalFunction.html","id":"provide-external-function-to-ggir-1","dir":"Articles","previous_headings":"Example with external Python function","what":"Provide external function to GGIR","title":"Embedding external functions in GGIR","text":"Create new .R file running GGIR analysis, e.g. named myscript.R, insert following blocks code. Specification Python environment use, can also conda environment docker container (see documentation R package  details). Make sure Python environment required dependencies external function, need numpy. Specify myfun object explained R example. forget update filepath \"~/dominant_frequency.R\" file. Add call function GGIR myfun provided argument. Note , .parallel set FALSE. Unfortunately Python embedding R package reticulate multi-threading R package foreach used GGIR combine well.","code":"library(\"reticulate\")   use_virtualenv(\"~/myvenv\", required = TRUE) # Local Python environment   py_install(\"numpy\", pip = TRUE) source(\"~/dominant_frequency.R\") myfun =  list(FUN=dominant_frequency,               parameters= 30,               expected_sample_rate= 30,               expected_unit=\"g\",               colnames = c(\"domfreqX\", \"domfreqY\", \"domfreqZ\"),               minlength = 5,               outputres = 5,               outputtype=\"numeric\",               aggfunction = median               timestamp=F,               reporttype=\"scalar\") library(GGIR) GGIR(datadir=\"~/myaccelerometerdata\",              outputdir=\"~/myresults\",              mode=1:2,              epochvalues2csv = TRUE,              do.report=2,              myfun=myfun,              do.parallel = FALSE)"},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/ExternalFunction.html","id":"part-1","dir":"Articles","previous_headings":"Integration in GGIR output","what":"Part 1","title":"Embedding external functions in GGIR","text":"external function output included time series produced function GGIR function g.part1 stored RData-file /output_nameofstudy/meta/basic. resolution output GGIR set GGIR argument windowsizes, c(5,900,3600) default. , first element 5 specifies short epoch size seconds. output external function less resolution aggregated function specificied aggfunction myfun object. count example used sum dominant frequency example used median.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/ExternalFunction.html","id":"part-2","dir":"Articles","previous_headings":"Integration in GGIR output","what":"Part 2","title":"Embedding external functions in GGIR","text":"Next, part2 GGIR aims detect non-wear periods imputes . impute time series can found part 2 milestone data folder: /output_nameofstudy/meta/ms2.. want directly stored csv file set argument epochvalues2csv = TRUE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/ExternalFunction.html","id":"external-functions-released-by-ggir-collaborators","dir":"Articles","previous_headings":"","what":"External functions released by GGIR collaborators:","title":"Embedding external functions in GGIR","text":"Wrist-based step detection algorithm: https://github.com/ShimmerEngineering/Verisense-Toolbox/tree/master/Verisense_step_algorithm Wrist-based sleep classification described Sundararajan et al. 2021 link paper, corresponding code : https://github.com/wadpac/Sundararajan-SleepClassification-2021/tree/master/ggir_ext","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"what-is-ggir","dir":"Articles","previous_headings":"Introduction","what":"What is GGIR?","title":"Accelerometer data processing with GGIR","text":"GGIR R-package process multi-day raw accelerometer data physical activity sleep research. term raw refers data expressed m/s2 gravitational acceleration opposed previous generation accelerometers stored data accelerometer brand specific units. signal processing includes automatic calibration, detection sustained abnormally high values, detection non-wear calculation average magnitude dynamic acceleration based variety metrics. Next, GGIR uses information describe data per recording, per day measurement, (optionally) per segment day measurement, including estimates physical activity, inactivity sleep. published overview paper GGIR 2019 link. vignette provides general introduction use GGIR interpret output, additionally can find introduction video mini-tutorial YouTube. want use algorithms raw data GGIR facilitates ’s external function embedding feature, documented separate vignette: Embedding external functions GGIR. GGIR increasingly used research groups across world. non-exhaustive overview academic publications related GGIR can found . R package GGIR possible without support contributors listed author list GGIR, specific code contributions time since April 2016 (GGIR development moved GitHub) shown . Cite GGIR: use GGIR publications forget cite properly makes research reproducible gives credit ’s developers. See paragraph Citing GGIR details.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"contributing-support-and-keeping-up-to-date","dir":"Articles","previous_headings":"Introduction","what":"Contributing, Support, and Keeping up to date","title":"Accelerometer data processing with GGIR","text":"contribute code? development version GGIR can found github, also find guidance contribute. can get service support? GGIR open source software come service support guarantees. However, user-community can help via GGIR google group GitHub issue tracker. Please use public platform rather private e-mails users can learn conversations. need dedicated support use GGIR need someone adapt GGIR needs Vincent van Hees available independent consultant. Training R essentials GGIR offer frequent online GGIR training courses. Check dedicated training website details option book training. questions training booking process? hesitate contact us via: training@accelting.com. Also interest may brief free R introduction tutorial. Change log log main changes GGIR time can found .","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"install-r-and-rstudio","dir":"Articles","previous_headings":"Setting up your work environment","what":"Install R and RStudio","title":"Accelerometer data processing with GGIR","text":"Download install R Download install RStudio Install GGIR dependencies CRAN. can one command console command line: Alternatively, install latest development version latest bug fixes use instead: Additionally, use-cases need install one multiple additional packages: working Axivity, GENEActiv, GENEA files, install GGIRread package install.packages(\"GGIRread\") working ActiGraph gt3x files, install read.gt3x package install.packages(\"read.gt3x\") want derive Neishabouricounts (.neishabouricounts = TRUE), install actilifecounts package install.packages(\"actilifecounts\") want derive circadian rhythm indicators using [Cosinor analysis Extended Cosinor analysis] (cosinor = TRUE part 2, part 6 always performed), install ActCR package install.packages(\"ActCR\")","code":"install.packages(\"GGIR\", dependencies = TRUE) install.packages(\"remotes\") remotes::install_github(\"wadpac/GGIR\")"},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"prepare-folder-structure","dir":"Articles","previous_headings":"Setting up your work environment","what":"Prepare folder structure","title":"Accelerometer data processing with GGIR","text":"GENEActiv .bin Axivity AX3 AX6 .cwa ActiGraph .csv .gt3x (.gt3x newer format generated firmware versions 2.5.0. Serial numbers start “NEO” “MRA” firmware version 2.5.0 earlier use older format .gt3x file). Note Actigraph users: want work .csv exports via commercial ActiLife software note option export data timestamps. Please causes memory issues GGIR. cope absence timestamps GGIR calculate timestamps sample frequency, start time start date presented file header. Movisens .bin files data stored folders. GGIR expects participant’s folder contains least file named acc.bin. accelerometer brand generates csv output, see documentation functions read.myacc.csv argument rmc.noise GGIR function documentation (pdf). Note functionality following file formats part GGIR deprecated required significant maintenance effort without clear use case community support: (1) .bin Genea monitor Unilever Discover, accelerometer used studies 2007 2012) .bin, (2) .wav files can exported Axivity Ltd OMGUI software. Please contact us think data formats facilitated GGIR interested supporting ongoing maintenance. accelerometer data needs analysed stored one folder, subfolders folder. Give folder appropriate name, preferable reference study project related rather just ‘data’, name folder used later identifier dataset.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"ggir-shell-function","dir":"Articles","previous_headings":"Setting up your work environment","what":"GGIR shell function","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"key-general-arguments","dir":"Articles","previous_headings":"Setting up your work environment > GGIR shell function","what":"Key general arguments","title":"Accelerometer data processing with GGIR","text":"probably never need think arguments listed , lot arguments included facilitate methodological studies researchers want control every little detail. See previous paragraph links documentation find default value parameter. bare minimum input needed GGIR : Argument datadir allows specify stored accelerometer data outputdir allows specify like output analyses stored. equal datadir. copy paste code new R script (file ending .R) Source R(Studio) dataset processed output stored specified output directory. highlighted key arguments may want aware . giving detailed explanation, please see package manual . mode - part GGIR run, GGIR constructed five parts sixth part development. overwrite - whether overwrite previously produced milestone output. GGIR part, GGIR stores milestone output ease re-running parts pipeline. idloc - tells GGIR find participant ID (default: inside file header) data_masking_strategy set value 1, check arguments hrs.del.start hrs.del.end. data_masking_strategy set value 3 5, check arguments ndayswindow, hrs.del.start hrs.del.end. maxdur - maximum number days expect data file based study protocol. desiredtz - time zone experiment. chunksize - way tell GGIR use less memory, can useful machines limited memory. includedaycrit - tell GGIR many hours valid data per day (midnight-midnight) acceptable. includenightcrit - tell GGIR many hours valid night (noon-noon) acceptable. qwindow - argument tell GGIR whether segment day day-segment specific analysis. mvpathreshold boutcriter - acceleration threshold bout criteria used calculating time spent MVPA (used GGIR part2). epochvalues2csv - export epoch level magnitude acceleration csv files (addition already stored RData file) dayborder - decide whether edge day midnight. iglevels - argument related intensity gradient method proposed . Rowlands. .report - specify reports need generated. viewingwindow visualreport - create visual report, works five parts GGIR successfully run. Note visual report initially developed provide something show study participants data quality checking purposes. time improved visual report also useful QC-ing data. however, scorings shown visual report created visual report may reflect scorings main GGIR analyses reported quantitative csv-reports. effort past 10 years gone making sure csv-report correct, visualreport mostly side project. unfortunate hope find funding future design new report specifically purpose QC-ing anlayses done GGIR. maxRecordingInterval - specified controls whether neighboring overlapping recordings participant ID brand appended epoch level. can useful intention monitor behaviour larger periods time accelerometers allow weeks data collection. GGIR never append alter raw input file, operation preformed derived data. study_dates_file - specified trims recorded data first last date study took place. relevant studies started recording several days accelerometers actually worn participants. used top data_masking_strategy, may combined strategies GGIR.","code":"library(GGIR) GGIR(datadir=\"C:/mystudy/mydata\",  outputdir=\"D:/myresults\")"},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"key-arguments-related-to-sleep-analysis","dir":"Articles","previous_headings":"Setting up your work environment > GGIR shell function","what":"Key arguments related to sleep analysis","title":"Accelerometer data processing with GGIR","text":"explanation sleep detected specific role various function arguments see section Sleep analysis. Arguments related configuring sleep detection algorithm: anglethreshold, timethreshold, HASPT.algo, HASIB.algo, Sadeh_axis, HASPT.ignore.invalid. ignorenonwear set TRUE ignore detected monitor non-wear periods detection sustained inactivity bouts avoid confusion monitor non-wear time. want create visualisation sleep period time sustained inactivity bouts match throughout day consider arguments .visual, outliers., criterror. want exclude first last night sleep analysis used excludefirstlast. def.noc.sleep specifies sleep period time window estimated sleeplog used. includenightcrit Minimum number valid hours per night (24 hour window noon noon 6pm-6pm). data_cleaning_file ginore specific nights specific individuals, see also section Data cleaning file. want sleep analysis guided sleeplog (diary) check arguments loglocation specifies location spreadsheet (csv) sleep log information. , use arguments colid coln1 specify details sleep log structure. GGIR facilitates two possible sleeplog file structures:","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"basic-sleep-log","dir":"Articles","previous_headings":"Setting up your work environment > GGIR shell function > Key arguments related to sleep analysis","what":"Basic sleep log","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"advanced-sleep-log","dir":"Articles","previous_headings":"Setting up your work environment > GGIR shell function > Key arguments related to sleep analysis","what":"Advanced sleep log","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"key-arguments-related-to-time-use-analysis","dir":"Articles","previous_headings":"Setting up your work environment > GGIR shell function","what":"Key arguments related to time use analysis","title":"Accelerometer data processing with GGIR","text":"explanation time use analysis performed see section Waking-waking 24 hour time-use analysis. excludefirstlast.part5 - whether ignore last first day. includedaycrit.part5 - tell GGIR fraction waking hours day (value 1) acceptable. minimum_MM_length.part5 - tell GGIR minimum length (hours) MM window part 5. Configure thresholds acceleration levels (may want interpret intensity levels): threshold.lig, threshold.mod, threshold.vig. single interruption can last < 1 min Repeated interruptions allowed provided total time exceed 20% bout duration time spent interruptions included duration MVPA bout. example: 25-minute bout can two 1 minute interruption, single 2-minute interruption. , full 25 minutes count towards duration MVPA bout. timewindow specify whether days defined midnight midnight \"MM\", waking-waking-\"WW\", sleep onset sleep onset \"OO\", combination . Configure durations bouts: boutdur.mvpa, boutdur., boutdur.lig. Note can vector multiple values indicating minimum maximum duration subsequent bout types, e.g. 1-5 minutes MVPA, 5-10 minutes MVPA, longer 10 minutes MVPA.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"published-cut-points-and-how-to-use-them","dir":"Articles","previous_headings":"Setting up your work environment > GGIR shell function","what":"Published cut-points and how to use them","title":"Accelerometer data processing with GGIR","text":"section rewritten moved. Please, visit vignette Published cut-points use GGIR details cut-points available, use , additional reflections use cut-points GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"example-call","dir":"Articles","previous_headings":"Setting up your work environment > GGIR shell function","what":"Example call","title":"Accelerometer data processing with GGIR","text":"consider arguments may end call GGIR look follows. used GGIR output directory (outputdir) filled milestone data results.","code":"library(GGIR) GGIR(mode=c(1,2,3,4,5),       datadir=\"C:/mystudy/mydata\",       outputdir=\"D:/myresults\",       do.report=c(2,4,5),       #=====================       # Part 2       #=====================       data_masking_strategy = 1,       hrs.del.start = 0,          hrs.del.end = 0,       maxdur = 9,                 includedaycrit = 16,       qwindow=c(0,24),       mvpathreshold =c(100),       excludefirstlast = FALSE,       includenightcrit = 16,       #=====================       # Part 3 + 4       #=====================       def.noc.sleep = 1,       outliers.only = TRUE,       criterror = 4,       do.visual = TRUE,       #=====================       # Part 5       #=====================       threshold.lig = c(30), threshold.mod = c(100),  threshold.vig = c(400),       boutcriter = 0.8,      boutcriter.in = 0.9,     boutcriter.lig = 0.8,       boutcriter.mvpa = 0.8, boutdur.in = c(1,10,30), boutdur.lig = c(1,10),       boutdur.mvpa = c(1),       includedaycrit.part5 = 2/3,       #=====================       # Visual report       #=====================       timewindow = c(\"WW\"),       visualreport=TRUE)"},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"configuration-file","dir":"Articles","previous_headings":"Setting up your work environment > GGIR shell function","what":"Configuration file","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"from-the-r-console-on-your-own-desktoplaptop","dir":"Articles","previous_headings":"Time for action: How to run your analysis?","what":"From the R console on your own desktop/laptop","title":"Accelerometer data processing with GGIR","text":"Create R-script put GGIR call . Next, can source R-script source function R: source(\"pathtoscript/myshellscript.R\") use Source button RStudio use RStudio.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"in-a-cluster","dir":"Articles","previous_headings":"Time for action: How to run your analysis?","what":"In a cluster","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"processing-time","dir":"Articles","previous_headings":"Time for action: How to run your analysis?","what":"Processing time","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"inspecting-the-results","dir":"Articles","previous_headings":"","what":"Inspecting the results","title":"Accelerometer data processing with GGIR","text":"GGIR generates following types output. - csv-spreadsheets variables need physical activity, sleep circadian rhythm research - Pdfs page low resolution plot data per file quality indicators - R objects milestone data - Pdfs visual summary physical activity sleep patterns identified (see example )","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"output-part-2","dir":"Articles","previous_headings":"Inspecting the results","what":"Output part 2","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"person-level-summary","dir":"Articles","previous_headings":"Inspecting the results > Output part 2","what":"Person level summary","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"day-level-summary","dir":"Articles","previous_headings":"Inspecting the results > Output part 2","what":"Day level summary","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"data_quality_report","dir":"Articles","previous_headings":"Inspecting the results > Output part 2","what":"Data_quality_report","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"output-part-4","dir":"Articles","previous_headings":"Inspecting the results","what":"Output part 4","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"night-level-summaries","dir":"Articles","previous_headings":"Inspecting the results > Output part 4","what":"Night level summaries","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"non-default-variables-in-part-4-csv-report","dir":"Articles","previous_headings":"Inspecting the results > Output part 4 > Night level summaries","what":"Non-default variables in part 4 csv report","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"visualisation_sleep-pdf","dir":"Articles","previous_headings":"Inspecting the results > Output part 4","what":"visualisation_sleep.pdf","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"output-part-5","dir":"Articles","previous_headings":"Inspecting the results","what":"Output part 5","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"output5","dir":"Articles","previous_headings":"Inspecting the results > Output part 5","what":"Day level summary","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"person-level-summary-1","dir":"Articles","previous_headings":"Inspecting the results > Output part 5","what":"Person level summary","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"output-part-6","dir":"Articles","previous_headings":"Inspecting the results","what":"Output part 6","title":"Accelerometer data processing with GGIR","text":"part6CR = TRUE vector specified paramter .report includes number 6, csv report stored following variables:","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"motivation-and-clarification","dir":"Articles","previous_headings":"","what":"Motivation and clarification","title":"Accelerometer data processing with GGIR","text":"chapter try collect motivations clarification behind GGIR may clear existing publications.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"reproducibilty-of-ggir-analyses","dir":"Articles","previous_headings":"Motivation and clarification","what":"Reproducibilty of GGIR analyses","title":"Accelerometer data processing with GGIR","text":"tips increase reproducibility findings: publish findings, please remember add GGIR package version number. GGIR archived CRAN available archive section package website. GGIR evolved years. get better understanding versions differ check NEWS sections package website Report configured accelerometer Report study protocol wear instructions given participants Report GGIR version Report GGIR used: Share config.csv file R script Report post-processed / cleaned GGIR output Report reported outcomes relate specific variable names GGIR","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"auto-calibration","dir":"Articles","previous_headings":"Motivation and clarification","what":"Auto-calibration","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"non-wear-detection","dir":"Articles","previous_headings":"Motivation and clarification","what":"Non-wear detection","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"clipping-score","dir":"Articles","previous_headings":"Motivation and clarification","what":"Clipping score","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"why-collapse-information-to-epoch-level","dir":"Articles","previous_headings":"Motivation and clarification","what":"Why collapse information to epoch level?","title":"Accelerometer data processing with GGIR","text":"Although many data points collected decide work aggregated values (e.g. 1 5 second epochs) following reasons: Accelerometers often used describe patterns metabolic energy expenditure. Metabolic energy expenditure typically defined per breath per minute (indirect calorimetry), per day (room calorimeter), per multiple days (doubly labelled water method). order validate methods reference standards need work similar time resolution. Collapsing data epoch summary measures helps standardise differences sample frequency studies. little evidence raw data accurate representation body acceleration. scientific evidence validity accelerometer data far based epoch averages. Collapsing data epoch summary measures may help average different noise levels make sensor brands comparable.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"why-does-the-first-epoch-not-allign-with-the-original-start-of-the-recording","dir":"Articles","previous_headings":"Motivation and clarification > Why collapse information to epoch level?","what":"Why does the first epoch not allign with the original start of the recording","title":"Accelerometer data processing with GGIR","text":"GGIR uses short (default 5 seconds) long epochs (default 15 minutes). epochs aligned hour day, . example, recording starts 9:52:00 GGIR work epochs derived 10:00:00 onward. recording starts 10:12 GGIR work epochs derived 10:15:00 onward. Motivation: allows us standardised time grid across recordings describe behaviour. allows us calculate behaviour exactly per day per specified time interval day. first 15 minute epochs start 9:52 next one start 10:07, makes impossible make statement behaviour 10:00 13:00.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"Sleep_analysis","dir":"Articles","previous_headings":"Motivation and clarification","what":"Sleep analysis","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"replication-of-the-movement-counts-needed","dir":"Articles","previous_headings":"Motivation and clarification > Sleep analysis > Notes on sleep classification algorithms designed for count data","what":"Replication of the movement counts needed","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"missing-information-for-replicating-movement-counts","dir":"Articles","previous_headings":"Motivation and clarification > Sleep analysis > Notes on sleep classification algorithms designed for count data","what":"Missing information for replicating movement counts","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"an-educated-guess-and-how-you-can-to-help-optimise-the-implementation","dir":"Articles","previous_headings":"Motivation and clarification > Sleep analysis > Notes on sleep classification algorithms designed for count data","what":"An educated guess and how you can to help optimise the implementation","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"guiders","dir":"Articles","previous_headings":"Motivation and clarification > Sleep analysis","what":"Guiders","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"daysleepers-nights-workers","dir":"Articles","previous_headings":"Motivation and clarification > Sleep analysis","what":"Daysleepers (nights workers)","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"Cleaningcode","dir":"Articles","previous_headings":"Motivation and clarification > Sleep analysis","what":"Cleaningcode","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"difference-between-cleaned-and-full-output","dir":"Articles","previous_headings":"Motivation and clarification > Sleep analysis","what":"Difference between cleaned and full output","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"Data_cleaning_file","dir":"Articles","previous_headings":"Motivation and clarification > Sleep analysis","what":"Data cleaning file","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"Waking-waking_or_24_hour","dir":"Articles","previous_headings":"Motivation and clarification","what":"Waking-waking or 24 hour time-use analysis","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"time-series-output-files","dir":"Articles","previous_headings":"Motivation and clarification > Waking-waking or 24 hour time-use analysis","what":"Time series output files","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"day-inclusion-criteria","dir":"Articles","previous_headings":"Motivation and clarification > Waking-waking or 24 hour time-use analysis","what":"Day inclusion criteria","title":"Accelerometer data processing with GGIR","text":"full part 5 output stored results/QC folder. default inclusion criteria days cleaned output part 5 (stored results folder) : MM WW defined days: valid (sensor worn) time fraction day needs fraction specified argument includedaycrit.part5 (default 2/3). MM defined days : length day needs least number hours specified minimum_MM_length.part5 (default 23). Note experiment started ended middle day default setting exclude incomplete first last days. think including days still meaningful work adjust argument minimum_MM_length.part5. Important notes: criteria set amount valid data SPT window, interested part 5 knowing borders night trust sufficiently estimated part 4. disagree please notice days included full report available results/QC folder. means argument includenightcrit used part 4 used part 5. data_cleaning_file argument discussed Data_cleaning_file also allows tell GGIR person(s) day(s) omitted part 5. day numbers excluded listed column day_part5 header.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"fragmentation-metrics","dir":"Articles","previous_headings":"Motivation and clarification > Waking-waking or 24 hour time-use analysis","what":"Fragmentation metrics","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR. Difference fragments blocks: Elsewhere part5 use term block. block sequence epochs belong behavioural class. may sound similar definition fragment, blocks distinguish every behavioural class, includes subcategories bouted unbouted behaviour. means variables Nblock_day_total_IN Nblock_day_total_LIG identical Nfrag_IN_day Nfrag_LIPA_day, respectively. contrast, fragments may group LIPA MVPA together refering fragmentation PA. Differences R package ActFrag: section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"why-use-data-metric-enmo-as-default","dir":"Articles","previous_headings":"Motivation and clarification","what":"Why use data metric ENMO as default?","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"what-does-ggir-stand-for","dir":"Articles","previous_headings":"Motivation and clarification","what":"What does GGIR stand for?","title":"Accelerometer data processing with GGIR","text":"wanted short name spend much time finding . abbreviation lost functional meaning, now use GGIR name.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"circadian-rhythm-analyses","dir":"Articles","previous_headings":"Motivation and clarification","what":"Circadian Rhythm analyses","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"actigraphs-idle-sleep-mode","dir":"Articles","previous_headings":"Motivation and clarification","what":"ActiGraph’s idle sleep mode","title":"Accelerometer data processing with GGIR","text":"idle sleep mode explained manufacturer’s website. short, idle sleep mode setting can turned user. turned device fall asleep periods movement, resulting time gaps data. functionality probably introduced safe battery life minimize data size. However, also means end time gaps need accounted .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"time-gap-imputation","dir":"Articles","previous_headings":"Motivation and clarification > ActiGraph’s idle sleep mode","what":"Time gap imputation","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"the-importance-of-reporting-idle-sleep-mode-usage","dir":"Articles","previous_headings":"Motivation and clarification > ActiGraph’s idle sleep mode","what":"The importance of reporting idle.sleep.mode usage","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"mx-metrics-minimum-intensity-of-most-active-x-minutes","dir":"Articles","previous_headings":"Motivation and clarification","what":"MX metrics (minimum intensity of most active X minutes)","title":"Accelerometer data processing with GGIR","text":"section migrated section GGIR github-pages, now main documentation resource GGIR.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"Minimum_recording_duration","dir":"Articles","previous_headings":"Motivation and clarification","what":"Minimum recording duration","title":"Accelerometer data processing with GGIR","text":"GGIR designed process multi-day recordings. minimum recording duration considered GGIR depends type analysis: Running part 1 2 File size; least 2MB, 2MB can adjusted argument minimumFileSizeMB. changed unless good reason believe smaller file size also acceptable. Recording duration: least two long epoch windows (default 60 minutes) g.readaccfile. size epoch can altered second third value vector argument windowsizes, third smaller second. example, short lasting lab-experiments may find easier set windowsizes = c(5, 600, 600) non-wear detection usually necessary lab studies. Running part 3 4 least one night data expected, night expected least timestamp midnight. midnight found sleep detection skipped. Running part 5 Ideally two valid consecutive nights waking hours .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"lux-sensor-data-processing","dir":"Articles","previous_headings":"Motivation and clarification","what":"LUX sensor data processing","title":"Accelerometer data processing with GGIR","text":"Although GGIR focuses accelerometer data brands come LUX data. part 1 GGIR calculates peak lux per long epoch default resolution 15 minutes, can modified argument windowsizes. Peak light offers reliable estimate light exposure per time window compared taking average. , LUX used auto-calibration. GGIR part 2 visualise LUX values qc plot. part 3 4 LUX used sleep classification relation light exposure sleep weak. part 5 calculate mean maximum peak LUX per epoch across waking hours day. , mean (peak per epoch) LUX indicate average light exposure per time segment, max peak indicate maximum light exposure per day. , calculate max mean peak LUX per active consecutive X hour day. intended offer alternative LUX exposure waking hours relies correct sleep classification. LUX exposure M10 may seen alternative unsure whether can trust sleep classification data set.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"other-resources","dir":"Articles","previous_headings":"","what":"Other Resources","title":"Accelerometer data processing with GGIR","text":"GGIR package manual provides documentation individual functions. general questions use GGIR join google group/mailing list. bug reports please post .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"CitingGGIR","dir":"Articles","previous_headings":"","what":"Citing GGIR","title":"Accelerometer data processing with GGIR","text":"correct citation research software important make research reproducible acknowledge effort goes development open-source software. , please report GGIR version used text. Additionally, please also cite: Migueles JH, Rowlands AV, et al. GGIR: Research Community–Driven Open Source R Package Generating Physical Activity Sleep Outcomes Multi-Day Raw Accelerometer Data. Journal Measurement Physical Behaviour. 2(3) 2019. doi: 10.1123/jmpb.2018-0063. work depends quantification physical activity also cite: van Hees VT, Gorzelniak L, et al. Separating Movement Gravity Components Acceleration Signal Implications Assessment Human Daily Physical Activity. PLoS ONE 8(4) 2013. link Sabia S, van Hees VT, Shipley MJ, Trenell MI, Hagger-Johnson G, Elbaz , Kivimaki M, Singh-Manoux . Association questionnaire- accelerometer-assessed physical activity: role sociodemographic factors. J Epidemiol. 2014 Mar 15;179(6):781-90. doi: 10.1093/aje/kwt330. Epub 2014 Feb 4. PMID: 24500862 link used auto-calibration functionality also cite: van Hees VT, Fang Z, et al. Auto-calibration accelerometer data free-living physical activity assessment using local gravity temperature: evaluation four continents. J Appl Physiol 2014. link used sleep detection also cite: van Hees VT, Sabia S, et al. novel, open access method assess sleep duration using wrist-worn accelerometer, PLoS ONE, 2015 link used sleep detection without relying sleep diary also cite: van Hees VT, Sabia S, et al. Estimating sleep parameters using accelerometer without sleep diary. Scientific Reports 2018. doi: 10.1038/s41598-018-31266-z. link used sleep regularity index also cite: Andrew J. K. Phillips, William M. Clerx, et al. Irregular sleep/wake patterns associated poorer academic performance delayed circadian sleep/wake timing. Scientific Reports. 2017 June 12 link.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIR.html","id":"copyright-for-ggir-logo","dir":"Articles","previous_headings":"Citing GGIR","what":"Copyright for GGIR logo","title":"Accelerometer data processing with GGIR","text":"copyright GGIR logo lies Accelting (Almere, Netherlands), please contact v.vanhees@acceleting.com ask permission use logo.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"ggir-part-1","dir":"Articles","previous_headings":"","what":"GGIR Part 1","title":"GGIR output","text":"GGIR part 1, outputs RData files used GGIR part 2. RData files intended direct use GGIR user.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"ggir-part-2","dir":"Articles","previous_headings":"","what":"GGIR Part 2","title":"GGIR output","text":"Part 2 generates following output: part2_summary.csv: Person level summary (see ) part2_daysummary.csv: Day level summary (see ) QC/data_quality_report.csv: Overview calibration results whether file corrupt short processed, QC/plots check data quality 1.pdf: pdf visualisation acceleration time series 15 minute resolution invalid data segments highlighted colours (yellow: non-wear based standard deviation threshold, brown: non-wear extra filtering step (introduced 2013), purple: clipping)","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"circadian-rhythm-cosinor","dir":"Articles","previous_headings":"GGIR Part 2 > Person level summary (csv)","what":"Circadian rhythm: Cosinor","title":"GGIR output","text":"following variables GGIR output derived ActCR package, documentation copied ActCR documentation. Additionally, GGIR derives:","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"day-level-summary-csv","dir":"Articles","previous_headings":"GGIR Part 2","what":"Day level summary (csv)","title":"GGIR output","text":"non-exhaustive list, concepts explained summary.csv","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"data_quality_report-csv","dir":"Articles","previous_headings":"GGIR Part 2","what":"Data_quality_report (csv)","title":"GGIR output","text":"data_quality_report.csv stored subfolder folder results/QC.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"ggir-part-3","dir":"Articles","previous_headings":"","what":"GGIR Part 3","title":"GGIR output","text":"GGIR part 3, outputs RData files used GGIR part 4 5. RData files intended direct use GGIR user.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"ggir-part-4","dir":"Articles","previous_headings":"","what":"GGIR Part 4","title":"GGIR output","text":"Part 4 generates following output:","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"night-level-summaries-csv","dir":"Articles","previous_headings":"GGIR Part 4","what":"Night level summaries (csv)","title":"GGIR output","text":"part4_nightsummary_sleep_cleaned.csv QC/part4_nightsummary_sleep_full.csv latter ‘_full’ name intended aid clarifying nights () excluded cleaned summary report. Although, nights accelerometer worn excluded . , 30 day recording accelerometer worn day 7 onward find last 22 nights either csv-report. csv. files contain variables shown . Non-default variables part 4 csv report additional stored used sleeplog captures time bed, using guider HorAngle hip-worn accelerometer data. either applies set argument sleepwindowType “TimeInBed”.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"person-level-summaries-csv","dir":"Articles","previous_headings":"GGIR Part 4","what":"Person level summaries (csv)","title":"GGIR output","text":"part4_summary_sleep_cleaned.csv QC/part4_summary_sleep_full.csv person level report variables derived variables night level summary. Minor extensions variable names explain variables aggregated across days. Please find extra clarification variable names meaning may obvious:","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"visualisation-pdf","dir":"Articles","previous_headings":"GGIR Part 4","what":"Visualisation (pdf)","title":"GGIR output","text":"Visualisation support data quality checks: - visualisation_sleep.pdf (optional) input argument .visual set TRUE GGIR can show following visual comparison time window asleep (bed) according sleeplog detected sustained inactivity bouts according accelerometer data. visualisation stored results folder visualisation_sleep.pdf. Explanation image: line represents one night. Colours used distinguish definitions sustained inactivity bouts (2 definitions case) indicate existence absence overlap sleeplog. argument outliers.set FALSE visualise available nights dataset. outliers.set TRUE visualise nights difference onset waking time sleeplog sustained inactivity bouts larger value argument criterror. visualisation outliers.set TRUE critererror set 4 powerful identify entry errors sleeplog data van Hees et al PLoSONE 2015. 25 thousand nights data, visualisation allowed us quickly zoom problematic nights investigate possible mistakes GGIR mistakes data entry.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"ggir-part-5","dir":"Articles","previous_headings":"","what":"GGIR Part 5","title":"GGIR output","text":"output part 5 dependent parameter configuration, generate many output files unique combination three thresholds provided. example, following files generated threshold configuration 30 light activity, 100 moderate 400 vigorous activity: part5_daysummary_MM_L30M100V400_T5A5.csv part5_daysummary_WW_L30M100V400_T5A5.csv part5_personsummary_MM_L30M100V400_T5A5.csv part5_personsummary_WW_L30M100V400_T5A5.csv file summary reports/Report_nameofdatafile.pdf","code":""},{"path":[]},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"old-napping-behaviour-variables-to-be-deprecated","dir":"Articles","previous_headings":"GGIR Part 5 > Day level summary (csv)","what":"Old napping behaviour variables (to be deprecated)","title":"GGIR output","text":"variables deprecated napping, detection turned , now behavioural category just like behaviours. Special note working compositional data analysis: duration dur_ variables _total_ name add total length waking hours day. Similarly, duration dur_ variables excluding variables _total_ name excluding variable dur_day_min, dur_spt_min, dur_day_spt_min also add length full day. Motivation default boutcriter.= 0.9: idea allow bouts 30 minutes make sense allow breaks 20 percent (6 minutes!) used stringent criteria highest category. Please note can change criteria via arguments boutcriter.mvpa, boutcriter., boutcriter.lig.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"person-level-summary-csv-1","dir":"Articles","previous_headings":"GGIR Part 5","what":"Person level summary (csv)","title":"GGIR output","text":"variables person level summary derived day level summary, extended _pla indicate variable calculated plain average across valid days. Variables extended _wei represent weighted average across days weekend days always weighted 2/5 relative contribution week days.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"ggir-part-6","dir":"Articles","previous_headings":"","what":"GGIR Part 6","title":"GGIR output","text":"Part 6 stores person level summary (csv). column names overlap part 5, now derived based full time series , whereas part 5 presents variables per day window aggregated person level.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRoutput.html","id":"cosinor-output-variables","dir":"Articles","previous_headings":"GGIR Part 6","what":"Cosinor output variables","title":"GGIR output","text":"variables names consistent variable names discussed GGIR part 2 output.","code":""},{"path":[]},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"default-argument-values","dir":"Articles","previous_headings":"","what":"Arguments/parameters description","title":"GGIR configuration parameters","text":"information shown auto-generated identical information provided GGIR package pdf manual.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"mode","dir":"Articles","previous_headings":"Arguments/parameters description > GGIR function input arguments","what":"mode","title":"GGIR configuration parameters","text":"Numeric (default = 1:5). Specify five parts need run, e.g., mode = 1 makes g.part1 run; mode = 1:5 makes whole GGIR pipeline run, g.part1 g.part5. Optionally mode can also include number 6 tell GGIR run g.part6 currently development.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"datadir","dir":"Articles","previous_headings":"Arguments/parameters description > GGIR function input arguments","what":"datadir","title":"GGIR configuration parameters","text":"Character (default = c()). Directory accelerometer files stored, e.g., “C:/mydata”, list accelerometer filenames directories, e.g. c(“C:/mydata/myfile1.bin”, “C:/mydata/myfile2.bin”).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"outputdir","dir":"Articles","previous_headings":"Arguments/parameters description > GGIR function input arguments","what":"outputdir","title":"GGIR configuration parameters","text":"Character (default = c()). Directory output needs stored. Note function attempt create folders directory uses folder keep output.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"studyname","dir":"Articles","previous_headings":"Arguments/parameters description > GGIR function input arguments","what":"studyname","title":"GGIR configuration parameters","text":"Character (default = c()). datadir folder, study given name data directory. datadir list filenames studyname specified input argument used name study.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"f0","dir":"Articles","previous_headings":"Arguments/parameters description > GGIR function input arguments","what":"f0","title":"GGIR configuration parameters","text":"Numeric (default = 1). File index start (default = 1). Index refers filenames sorted alphabetical order.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"f1","dir":"Articles","previous_headings":"Arguments/parameters description > GGIR function input arguments","what":"f1","title":"GGIR configuration parameters","text":"Numeric (default = 0). File index finish (defaults number files available).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-report","dir":"Articles","previous_headings":"Arguments/parameters description > GGIR function input arguments","what":"do.report","title":"GGIR configuration parameters","text":"Numeric (default = c(2, 4, 5, 6)). parts generate summary spreadsheet: 2, 4, 5, /6. Default c(2, 4, 5, 6). report generated based available milestone data. creating milestone data multiple machines advisable turn report generation generating milestone data, value = c(), merge milestone data turn report generation back setting overwrite FALSE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"configfile","dir":"Articles","previous_headings":"Arguments/parameters description > GGIR function input arguments","what":"configfile","title":"GGIR configuration parameters","text":"Character (default = c()). Configuration file previously generated function GGIR. See details.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"myfun","dir":"Articles","previous_headings":"Arguments/parameters description > GGIR function input arguments","what":"myfun","title":"GGIR configuration parameters","text":"List (default = c()). External function object applied raw data. See package vignette detailed tutorial examples use function embedding: https://cran.r-project.org/package=GGIR/vignettes/ExternalFunction.html","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"overwrite","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"overwrite","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). want overwrite analysis milestone data exists? overwrite = FALSE, milestone data previous analysis used available visual reports created .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"acc-metric","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"acc.metric","title":"GGIR configuration parameters","text":"Character (default = “ENMO”). one acceleration metrics want use acceleration magnitude analyses GGIR part 5 visual report? example: “ENMO”, “LFENMO”, “MAD”, “NeishabouriCount_y”, “NeishabouriCount_vm”. one acceleration metric can specified selected metric needs calculated part 1 (see g.part1) via arguments .enmo = TRUE .mad = TRUE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"maxncores","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"maxNcores","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Maximum number cores use argument .parallel set true. GGIR default uses either maximum number available cores number files process (whichever lower), argument allows set lower maximum.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"print-filename","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"print.filename","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). Whether print filename analysing (case .parallel = FALSE). Printing filename can useful investigate problems (e.g., verify file read).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-parallel","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"do.parallel","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). Whether use multi-core processing (works least 4 CPU cores available).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"windowsizes","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"windowsizes","title":"GGIR configuration parameters","text":"Numeric vector, three values (default = c(5, 900, 3600)). indicate lengths windows c(window1, window2, window3): window1 short epoch length seconds, default 5, time window acceleration angle metrics calculated; window2 long epoch length seconds non-wear signal clipping defined, default 900 (expected multitude 60 seconds); window3 window length data used non-wear detection default 3600 seconds. , window3 larger window2 use overlapping windows, window2 equals window3 non-wear periods assessed non-overlapping windows.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"desiredtz","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"desiredtz","title":"GGIR configuration parameters","text":"Character (default = ““, .e., system timezone). Timezone device configured experiments took place. experiments took place different timezone, use argument timezone experiments took place argument configtz specify device configured. Use ”TZ identifier” specified ://en.wikipedia.org/wiki/Zone.tabhttps://en.wikipedia.org/wiki/Zone.tab set desiredtz, e.g., “Europe/London”.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"configtz","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"configtz","title":"GGIR configuration parameters","text":"Character (default = ““, .e., system timezone). moment functional GENEActiv .bin, AX3 cwa, ActiGraph .gt3x, ad-hoc csv file format. Timezone accelerometer configured. use argument timezone configuration timezone recording took place different. Use ”TZ identifier” specified ://en.wikipedia.org/wiki/Zone.tabhttps://en.wikipedia.org/wiki/Zone.tab set configtz, e.g., “Europe/London”.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"idloc","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"idloc","title":"GGIR configuration parameters","text":"Numeric (default = 1). idloc = 1 code assumes ID number stored obvious header field. Note ActiGraph data ID never stored file header. value set 2, 5, 6, 7, GGIR looks filename extracts character string preceding first occurance “_” (idloc = 2), ” ” (space, idloc = 5), “.” (dot, idloc = 6), “-” (idloc = 7), respectively. may noticed idloc 3 4 skipped, used one study 2012, actively maintained anymore, legacy code omitted.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"dayborder","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"dayborder","title":"GGIR configuration parameters","text":"Numeric (default = 0). Hour days start end (dayborder = 4 mean 4 ).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"part5_agg2_60seconds","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"part5_agg2_60seconds","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). Whether use aggregate epochs 60 seconds part GGIR g.part5 analysis. Aggregation doen averaging. Note working count metrics Neishabouri counts means threshold can stay part 2, threshold expressed relative original epoch size, even averaged per minute. example want use cut-point 100 count per minute specify mvpathreshold = 100 * (5/60) well `threshold.mod = 100 * (5/60) regardless whether set part5_agg2_60seconds TRUE FALSE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"sensor-location","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"sensor.location","title":"GGIR configuration parameters","text":"Character (default = “wrist”). indicate sensor location, default wrist. hip, HDCZA algorithm sleep detection also requires longitudinal axis sensor -45 +45 degrees.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"expand_tail_max_hours","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"expand_tail_max_hours","title":"GGIR configuration parameters","text":"Numeric (default = NULL). parameter replaced recordingEndSleepHour.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"recordingendsleephour","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"recordingEndSleepHour","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Time (hours) recording end (later) expand g.part1 output synthetic data trigger sleep detection last night. Using argument recordingEndSleepHour implies assumption participant fell asleep end recording recording ended recordingEndSleepHour hour last day. assumption may always hold true used caution. synthetic data metashort entails: timestamps continuing regularly, zeros acceleration metrics EN, one EN. Angle columns created way triggers sleep detection using equation: round(sin((1:length_expansion) / (900/epochsize))) * 15. keep track tail expansion g.part1 stores length expansion RData files, passed via g.part2, g.part3, g.part4 g.part5. g.part5 tail expansion size included additional variable csv-reports. g.part4 csv-report last night omitted, know sleep estimates last night trustworthy. Similarly, g.part5 output columns related sleep assessment omitted last window avoid biasing averages. , synthetic data also ignored visualizations time series output avoid biased output.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"dataformat","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"dataFormat","title":"GGIR configuration parameters","text":"Character (default = “raw”). indicate format data datadir. Alternatives: ukbiobank_csv, actiwatch_csv, actiwatch_awd, actigraph_csv, sensewear_xls, correspond epoch level data files , respecitively, UK Biobank csv format, Actiwatch csv format, Actiwatch awd format, ActiGraph csv format, Sensewear xls format (also works xlsx). , assumed epoch size UK Biobank csvdata 5 seconds. epoch size non-raw data formats flexible, make sure set first value argument windowsizes accordingly. Also working non-raw data formats specify argument extEpochData_timeformat documented . ukbiobank_csv nonwear column data , actiwatch_csv, actiwatch_awd, actigraph_csv, sensewear_xls non-wear detected 60 minute rolling zeros. length window can modified third value argument windowsizes expressed seconds.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"maxrecordinginterval","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"maxRecordingInterval","title":"GGIR configuration parameters","text":"Numeric (default = NULL). indicate maximum gap hours repeated measurements ID recordings appended. , assumption ID can matched, make sure argument idloc set correctly. argument maxRecordingInterval set NULL (default) recordings appended. recordings overlap GGIR use data latest recording. recordings separated timegap recordings filled data points resemble monitor worn. maximum value maxFile gap 504 (21 days). recordings accelerometer brand appended. part 2 csv report show number appended recordings, sampling rate , time overlap gap names filenames respective recording.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"extepochdata_timeformat","dir":"Articles","previous_headings":"Arguments/parameters description > General Parameters","what":"extEpochData_timeformat","title":"GGIR configuration parameters","text":"Character (default = “%d-%m-%Y %H:%M:%S”). specify time format used external epoch level data argument dataFormat set “actiwatch_csv”, “actiwatch_awd”, “actigraph_csv” “sensewear_xls”. example “%Y-%m-%d %:%M:%S %p” “2023-07-11 01:24:01 PM” “%m/%d/%Y %H:%M:%S” “2023-07-11 13:24:01”","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"chunksize","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"chunksize","title":"GGIR configuration parameters","text":"Numeric (default = 1). Value specify size chunks loaded fraction approximately 12 hour period auto-calibration procedure fraction 24 hour period metric calculation, e.g., 0.5 equals 6 12 hour chunks, respectively. machines less 4Gb RAM memory < 2GB memory per process using .parallel = TRUE value 1 recommended. value constrained GGIR lower 0.05. Please note setting 0.05 produce output 3rd value parameter windowsizes 3600.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"spherecrit","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"spherecrit","title":"GGIR configuration parameters","text":"Numeric (default = 0.3). minimum required acceleration value (g) sides 0 g axis. Used judge whether sphere sufficiently populated","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"minloadcrit","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"minloadcrit","title":"GGIR configuration parameters","text":"Numeric (default = 168). minimum number hours code needs read autocalibration procedure effective (sensitive multitudes 12 hrs, values ceiled). loading hours extra data loaded calibration error reduced 0.01 g.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"printsummary","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"printsummary","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE print summary calibration procedure console done.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-cal","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"do.cal","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). Whether apply auto-calibration g.calibrate. Recommended setting TRUE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"backup-cal-coef","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"backup.cal.coef","title":"GGIR configuration parameters","text":"Character (default = “retrieve”). Option use backed-calibration coefficient instead deriving calibration coefficients analysing file twice. Argument backup.cal.coef two usecase. Use case 1: auto-calibration fails user option provide back-calibration coefficients via argument. value argument needs name directory csv-spreadsheet following column names subsequent values: “filename” names accelerometer files calibration coefficients need applied case auto-calibration fails; “scale.x”, “scale.y”, “scale.z” scaling coefficients; “offset.x”, “offset.y”, “offset.z” offset coefficients, ; “temperature.offset.x”, “temperature.offset.y”, “temperature.offset.z” temperature offset coefficients. can useful analysing short lasting laboratory experiments insufficient sphere data perform auto-calibration, calibration coefficients can derived alternative way. users responsibility compile csv-spreadsheet. Instead building file user can also Use case 2: user wants avoid performing auto-calibration repeatedly file. backup.cal.coef value set “retrieve” (default) GGIR look “data_quality_report.csv” file outputfolder QC, holds previously generated calibration coefficients. want happen, deleted data_quality_report.csv QC folder set value “redo”.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"dynrange","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"dynrange","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Provide dynamic range 8 gravity.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"minimumfilesizemb","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"minimumFileSizeMB","title":"GGIR configuration parameters","text":"Numeric (default = 2). Minimum File size MB required enter processing. argument can help avoid short uninformative files enter analyses. Given typical accelerometer collects several MBs per hour, default setting skip tiny files.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-dec","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.dec","title":"GGIR configuration parameters","text":"Character (default = “.”). Decimal used numbers, dec argument [utils]read.csv [data.table]fread.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-firstrow-acc","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.firstrow.acc","title":"GGIR configuration parameters","text":"Numeric (default = NULL). First row (number) acceleration data.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-firstrow-header","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.firstrow.header","title":"GGIR configuration parameters","text":"Numeric (default = NULL). First row (number) header. Leave blank file header.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-header-length","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.header.length","title":"GGIR configuration parameters","text":"Numeric (default = NULL). file header, specify header length (number rows).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-col-acc","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.col.acc","title":"GGIR configuration parameters","text":"Numeric, three values (default = c(1, 2, 3)). Vector three column (numbers) acceleration signals stored.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-col-temp","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.col.temp","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Scalar column (number) temperature stored. Leave default setting temperature available. temperature used g.calibrate.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-col-time","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.col.time","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Scalar column (number) timestamps stored. Leave default setting timestamps stored.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-unit-acc","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.unit.acc","title":"GGIR configuration parameters","text":"Character (default = “g”). Character unit acceleration values: “g”, “mg”, “bit”.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-unit-temp","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.unit.temp","title":"GGIR configuration parameters","text":"Character (default = “C”). Character unit temperature values: (K)elvin, (C)elsius, (F)ahrenheit.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-unit-time","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.unit.time","title":"GGIR configuration parameters","text":"Character (default = “POSIX”). Character unit timestamps: “POSIX”, “UNIXsec” (seconds since origin, see argument rmc.origin), “UNIXmsec” (UNIXsec milliseconds), “character”, “ActivPAL” (exotic timestamp format used ActivPAL activity monitor).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-format-time","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.format.time","title":"GGIR configuration parameters","text":"Character (default = “%Y-%m-%d %H:%M:%OS”). Character giving date-time format used [base]strptime. used rmc.unit.time: character POSIX.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-bitrate","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.bitrate","title":"GGIR configuration parameters","text":"Numeric (default = NULL). unit acceleration bit provide bit rate, e.g., 12 bit.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-dynamic_range","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.dynamic_range","title":"GGIR configuration parameters","text":"Numeric character (default = NULL). unit acceleration bit provide dynamic range deviation g zero, e.g., +/-6g mean argument needs 6. give argument character value code search file header elements name equal character value use corresponding numeric value next dynamic range.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-unsignedbit","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.unsignedbit","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). unsignedbit = TRUE means bits positive numbers. unsignedbit = FALSE bits positive negative.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-origin","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.origin","title":"GGIR configuration parameters","text":"Character (default = “1970-01-01”). Origin time unit time UNIXsec, e.g., 1970-1-1.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-desiredtz","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.desiredtz","title":"GGIR configuration parameters","text":"Character (default = NULL). Timezone experiments took place. argument scheduled deprecated now used overwrite desiredtz provided.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-configtz","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.configtz","title":"GGIR configuration parameters","text":"Character (default = NULL). Timezone device configured. argument scheduled deprecated now used overwrite configtz provided.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-sf","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.sf","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Sample rate Hertz, stored file header used instead (see argument rmc.headername.sf).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-headername-sf","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.headername.sf","title":"GGIR configuration parameters","text":"Character (default = NULL). file header: Row name sample frequency can found.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-headername-sn","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.headername.sn","title":"GGIR configuration parameters","text":"Character (default = NULL). file header: Row name serial number can found.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-headername-recordingid","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.headername.recordingid","title":"GGIR configuration parameters","text":"Character (default = NULL). file header: Row name recording ID can found.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-header-structure","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.header.structure","title":"GGIR configuration parameters","text":"Character (default = NULL). Used split header name header value, e.g., “:” ” “.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-check4timegaps","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.check4timegaps","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). indicate whether gaps time imputed zeros. sensing equipment provides accelerometer gaps time. rest GGIR designed , setting argument TRUE gaps time filled zeros.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-noise","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.noise","title":"GGIR configuration parameters","text":"Numeric (default = 13). Noise level acceleration signal m-units, used working ad-hoc .csv data formats using read.myacc.csv. read.myacc.csv take rmc.noise argument, interacting GGIR g.part1 rmc.noise used.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"nonwear_range_threshold","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"nonwear_range_threshold","title":"GGIR configuration parameters","text":"Numeric (default 150) used define maximum value range per axis non-wear detection, used combination brand specific standard deviation per axis.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-col-wear","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.col.wear","title":"GGIR configuration parameters","text":"Numeric (default = NULL). external wear detection outcome stored part data can used GGIR. argument specifies column wear detection (Boolean) stored.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-doresample","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.doresample","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). indicate whether resample data based available timestamps extracted sample rate file header.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"interpolationtype","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"interpolationType","title":"GGIR configuration parameters","text":"Integer (default = 1). indicate type interpolation used resampling time series (mainly relevant Axivity sensors), 1=linear, 2=nearest neighbour.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"imputetimegaps","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"imputeTimegaps","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). indicate whether timegaps larger 1 sample imputed. Currently used .gt3x data ActiGraph .csv format, timegaps can expected result Actigraph’s idle sleep.mode configuration.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"frequency_tol","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"frequency_tol","title":"GGIR configuration parameters","text":"Number (default = 0.1) passed readAxivity GGIRread package. Represents frequency tolerance fraction 0 1. relative bias per data block larger fraction data block imputed lack movement gravitational oriationed guessed recent valid data block. applicable Axivity .cwa data.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"rmc-scalefactor-acc","dir":"Articles","previous_headings":"Arguments/parameters description > Raw Data Parameters","what":"rmc.scalefactor.acc","title":"GGIR configuration parameters","text":"Numeric value (default 1) scale acceleration signals via multiplication. example, data provided m/s2 setting 1/9.81 derive gravitational units.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-anglex","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.anglex","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates angle X axis relative horizontal: = (^-1_rollmedian(x)(acc_rollmedian(y))^2 + (acc_rollmedian(z))^2) * 180/","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-angley","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.angley","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates angle Y axis relative horizontal: = (^-1_rollmedian(y)(acc_rollmedian(x))^2 + (acc_rollmedian(z))^2) * 180/","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-anglez","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.anglez","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). TRUE, calculates angle Z axis relative horizontal: = (^-1_rollmedian(z)(acc_rollmedian(x))^2 + (acc_rollmedian(y))^2) * 180/","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-zcx","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.zcx","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric zero-crossing count x-axis. computation specifics see source code function g.applymetrics","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-zcy","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.zcy","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric zero-crossing count y-axis. computation specifics see source code function g.applymetrics","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-zcz","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.zcz","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric zero-crossing count z-axis. computation specifics see source code function g.applymetrics","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-enmo","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.enmo","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). TRUE, calculates metric: = _x^2 + acc_y^2 + acc_z^2 - 1 (ENMO < 0, ENMO = 0).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-lfenmo","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.lfenmo","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric ENMO low-pass filtered accelerations (computation specifics see source code function g.applymetrics). filter bound defined parameter hb.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-en","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.en","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates Euclidean Norm raw accelerations: = _x^2 + acc_y^2 + acc_z^2","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-mad","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.mad","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates Mean Amplitude Deviation: = 1n|r_i - |","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-enmoa","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.enmoa","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric: = _x^2 + acc_y^2 + acc_z^2 - 1 (ENMOa < 0, ENMOa = |ENMOa|).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-roll_med_acc_x","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.roll_med_acc_x","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-roll_med_acc_y","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.roll_med_acc_y","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-roll_med_acc_z","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.roll_med_acc_z","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-dev_roll_med_acc_x","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.dev_roll_med_acc_x","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-dev_roll_med_acc_y","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.dev_roll_med_acc_y","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-dev_roll_med_acc_z","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.dev_roll_med_acc_z","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-bfen","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.bfen","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-hfen","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.hfen","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-hfenplus","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.hfenplus","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-lfen","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.lfen","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-lfx","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.lfx","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-lfy","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.lfy","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-lfz","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.lfz","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-hfx","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.hfx","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-hfy","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.hfy","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-hfz","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.hfz","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-bfx","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.bfx","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-bfy","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.bfy","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-bfz","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.bfz","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric. computation specifics see source code function g.applymetrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-brondcounts","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.brondcounts","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). option deprecated (October 2022) due issues activityCounts package used dependency. TRUE, calculated metric via R package activityCounts. called BrondCounts large number activity counts physical activity sleep research field. calling brondcounts clarify counts proposed Jan Brønd implemented R Ruben Brondeel. brondcounts intended imitation counts produced one closed source ActiLife software ActiGraph.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-neishabouricounts","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"do.neishabouricounts","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates metric via R package actilifecounts, implementation algorithm used closed-source software ActiLife ActiGraph (methods published doi: 10.1038/s41598-022-16003-x). use name first author (instead ActiLifeCounts) paper call NeishabouriCount uncertainty ActiLife implement algorithm time. use Neishabouri counts physical activity intensity classification part 5 (.e., metric threshold.lig, threshold.mod, threshold.vig applied), acc.metric argument needs set one following: “NeishabouriCount_x”, “NeishabouriCount_y”, “NeishabouriCount_z”, “NeishabouriCount_vm” use counts x-, y-, z-axis vector magnitude, respectively.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"hb","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"hb","title":"GGIR configuration parameters","text":"Numeric (default = 15). Higher boundary frequency filter (Hertz) used filter-based metrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"lb","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"lb","title":"GGIR configuration parameters","text":"Numeric (default = 0.2). Lower boundary frequency filter (Hertz) used filter-based metrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"n","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"n","title":"GGIR configuration parameters","text":"Numeric (default = n). Order frequency filter used filter-based metrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"zc-lb","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"zc.lb","title":"GGIR configuration parameters","text":"Numeric (default = 0.25). Used zero-crossing counts . Lower boundary cut-frequency filter.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"zc-hb","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"zc.hb","title":"GGIR configuration parameters","text":"Numeric (default = 3). Used zero-crossing counts . Higher boundary cut-frequencies filter.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"zc-sb","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"zc.sb","title":"GGIR configuration parameters","text":"Numeric (default = 0.01). Stop band used calculation zero crossing counts. Value acceleration threshold g units acceleration rounded zero.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"zc-order","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"zc.order","title":"GGIR configuration parameters","text":"Numeric (default = 2). Used zero-crossing counts . Order frequency filter.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"zc-scale","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"zc.scale","title":"GGIR configuration parameters","text":"Numeric (default = 1) Used zero-crossing counts . Scaling factor applied counts calculated (GGIR part 3).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"actilife_lfe","dir":"Articles","previous_headings":"Arguments/parameters description > Metrics Parameters","what":"actilife_LFE","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE, calculates NeishabouriCount metric low-frequency extension filter proposed closed source ActiLife software ActiGraph. applicable metric NeishabouriCount.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"includedaycrit","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"includedaycrit","title":"GGIR configuration parameters","text":"Numeric (default = 16). Minimum required number valid hours calendar day specific analysis part 2. specify two values c(16, 16) first value used part 2 second value used part 5 applied criterion full part 5 window. Note applied addition parameter includedaycrit.part5 looks valid data waking hours.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"ndayswindow","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"ndayswindow","title":"GGIR configuration parameters","text":"Numeric (default = 7). data_masking_strategy set 3 5, size window number days. data_masking_strategy 3 value can fractional, e.g. 7.5, data_masking_strategy 5 needs integer.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"strategy","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"strategy","title":"GGIR configuration parameters","text":"Deprecated replaced data_masking_strategy. strategy specified value passed used data_masking_strategy.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"data_masking_strategy","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"data_masking_strategy","title":"GGIR configuration parameters","text":"Numeric (default = 1). deal knowledge study protocol. data_masking_strategy = 1 means select data based hrs.del.start hrs.del.end. data_masking_strategy = 2 makes data first midnight last midnight used. data_masking_strategy = 3 selects active X days file X specified argument ndayswindow, days series 24-h blocks starting time day (X hours beginning end period can deleted arguments hrs.del.start hrs.del.end) data_masking_strategy = 4 use data first midnight. data_masking_strategy = 5 similar data_masking_strategy = 3, selects X complete calendar days X specified argument ndayswindow (X hours beginning end period can deleted arguments hrs.del.start hrs.del.end).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"maxdur","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"maxdur","title":"GGIR configuration parameters","text":"Numeric (default = 0). many DAYS start experiment experiment definitely stop? (set zero unknown).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"hrs-del-start","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"hrs.del.start","title":"GGIR configuration parameters","text":"Numeric (default = 0). many HOURS start experiment wearing monitor start? Used GGIR g.part2 data_masking_strategy = 1.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"hrs-del-end","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"hrs.del.end","title":"GGIR configuration parameters","text":"Numeric (default = 0). many HOURS end experiment wearing monitor definitely end? Used GGIR g.part2 data_masking_strategy = 1.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"includedaycrit-part5","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"includedaycrit.part5","title":"GGIR configuration parameters","text":"Numeric (default = 2/3). Inclusion criteria used part 5 number valid hours waking hours day, value smaller equal 1 used fraction waking hours, value 1 used absolute number valid hours required. confuse argument argument includedaycrit used GGIR part 2 applies entire day.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"excludefirstlast-part5","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"excludefirstlast.part5","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE first last window (waking-waking, midnight-midnight, sleep onset-onset) ignored g.part5.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"timesegments2zerofile","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"TimeSegments2ZeroFile","title":"GGIR configuration parameters","text":"Character (default = NULL). Takes path csv file columns “windowstart” “windowend” refer start end time time windows format “2024-10-12 20:00:00”, “filename” GGIR milestone data file without “meta_” segment name. GGIR part 2 uses set acceleration values zero non-wear classification zero (meaning sensor worn). Motivation: accelerometer worn night GGIR automatically labels invalid, user may like treat zero movement. Disclaimer: functionality developed 2019. hindsight generic enough need revision. Please contact GGIR maintainers like us invest time improving functionality.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-imp","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"do.imp","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). Whether impute missing values (e.g., suspected monitor non-wear clippling) g.impute GGIR g.part2. Recommended setting TRUE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"data_cleaning_file","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"data_cleaning_file","title":"GGIR configuration parameters","text":"Character (default = NULL). Optional path csv file create holds four columns: ID, day_part5, relyonguider_part4, night_part4. ID hold participant ID. Columns day_part5 night_part4 allow specify day(s) night(s) need excluded g.part5 g.part4, respectively. including multiple day(s)/night(s) create new line day/night. , done regardless whether rest GGIR thinks day(s)/night(s) valid. Column relyonguider_part4 allows specify nights g.part4 fully rely guider. See also package vignette.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"minimum_mm_length-part5","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"minimum_MM_length.part5","title":"GGIR configuration parameters","text":"Numeric (default = 23). Minimum length hours MM day included cleaned g.part5 results.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"excludefirstlast","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"excludefirstlast","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE first last night measurement ignored sleep assessment g.part4.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"includenightcrit","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"includenightcrit","title":"GGIR configuration parameters","text":"Numeric (default = 16). Minimum number valid hours per night (24 hour window noon noon), used sleep assessment g.part4.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"excludefirst-part4","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"excludefirst.part4","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE first night measurement ignored sleep assessment g.part4.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"excludelast-part4","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"excludelast.part4","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). TRUE last night measurement ignored sleep assessment g.part4.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"max_calendar_days","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"max_calendar_days","title":"GGIR configuration parameters","text":"Numeric (default = 0). maximum number calendar days include (set zero unknown).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"nonwearedgecorrection","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"nonWearEdgeCorrection","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). TRUE non-wear detection around edges recording (first last 3 hours) corrected following description vanHees2013 default since . functionality advisable working sleep clinic exercise lab data typically lasting less day.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"nonwear_approach","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"nonwear_approach","title":"GGIR configuration parameters","text":"Character (default = “2023”). Whether use traditional version non-wear detection algorithm (nonwear_approach = “2013”) new version (nonwear_approach = “2023”). 2013 version use longsize window (windowsizes[3], one hour default) check conditions nonwear identification flag nonwear mediumsize window (windowsizes[2], 15 min default) middle. 2023 version differs flag nonwear full longsize window. 2013 method longsize window centered centre mediumsize window, 2023 method longsizewindow aligned left edge left edge mediumsize window.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"segmentwearcrit-part5","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"segmentWEARcrit.part5","title":"GGIR configuration parameters","text":"Numeric (default = 0.5). Fraction qwindow segment expected valid part 5, 0.3 indicates least 30 percent time valid.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"segmentdaysptcrit-part5","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"segmentDAYSPTcrit.part5","title":"GGIR configuration parameters","text":"Numeric vector length 2 (default = c(0.9, 0)). Inclusion criteria proportion segment classified day (awake) spt (sleep period time) considered valid. interested comparing time spent behaviour better set one two numbers 0, defines proportion segment classified day spt, respectively. default setting focus waking hour segments includes segments overlap least 90 percent waking hours. order shift focus SPT use c(0, 0.9) ensures segments overlap least 90 percent SPT included. Setting zero problematic comparing time spent behaviours days individuals: complete segment averaged incomplete segments (someone going bed waking middle segment) longer clear whether person less active sleeps segment. Similarly clear whether person wakefulness SPT segment woke went bed segment.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"study_dates_file","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"study_dates_file","title":"GGIR configuration parameters","text":"Character (default = c()). Full path csv file containing first last date expected wear period every study participant (dates provided per individual). Expected format activity diary : First column headers followed one row per recording. three columns: first column recording ID, needs match ID GGIR extracts accelerometer file; second column contain first date study; third column last date study. Date columns default format “23-04-2017”, date format specified argument study_dates_dateformat (). specified (default), GGIR use first last day recording beginning end study. Note dates used top data_masking_strategy selected.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"study_dates_dateformat","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"study_dates_dateformat","title":"GGIR configuration parameters","text":"Character (default = “%d-%m-%Y”). specify date format used study_dates_file used [base]strptime.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"includecrit-part6","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"includecrit.part6","title":"GGIR configuration parameters","text":"Numeric (default = c(2/3, 2/3)) Vector two minimum fraction valid data required day spt time, respectively. criteria used circadian rhythm analysis.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"includenightcrit-part5","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"includenightcrit.part5","title":"GGIR configuration parameters","text":"Numeric (default = 0). Inclusion criteria used part 5 number valid hours sleep period hours day (night), value smaller equal 1 used fraction sleep period hours, value 1 used absolute number valid hours required. confuse argument argument includenightcrit used GGIR part 4 applies entire 24 hour window noon noon 6pm 6pm.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"nonwearfiltermaxhours","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"nonwearFiltermaxHours","title":"GGIR configuration parameters","text":"Numeric (default = NULL). NULL, ignore detected nonwear periods last shorter nonwearFiltermaxHours window defined parameter NonwearFilterWindow. NonwearFilterWindow provided (set NULL default) check whether qwindow defined activity diary file whether timestamps allow defining time bed, SPT lightsoff window. code looks column names diary terms inbed, sleeponset, lightsout define start window. , define end window code looks columns outbed, wakeup, lightsoff. yes, uses largest windows can defined timestamps. , use midnight-6am fall back setting. may realise columns advanced format sleelog accepted GGIR part 4. , already advanced format sleeplog part 4 can provide value qwindow parameter. want use qwindow guide nonwear filtering described want GGIR use day segment analysis part 2 5 make sure diary filename includes word “onlyfilter” “filteronly” tell GGIR use qwindow nonwear filtering. main purpose functionality offer option ignore short lasting nonwear episodes night suspicion falsely detect, e.g. individuals extended motionless sleep periods caused medication.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"nonwearfilterwindow","dir":"Articles","previous_headings":"Arguments/parameters description > Cleaning Parameters","what":"nonwearFilterWindow","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Vector length 2 specify start end hour night used functionality described NonwearFiltermaxHours.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"anglethreshold","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"anglethreshold","title":"GGIR configuration parameters","text":"Numeric (default = 5). Angle threshold (degrees) sustained inactivity periods detection. algorithm look periods time (timethreshold) angle variability lower anglethreshold. can specified multiple thresholds, implemented, e.g., anglethreshold = c(5,10).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"timethreshold","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"timethreshold","title":"GGIR configuration parameters","text":"Numeric (default = 5). Time threshold (minutes) sustained inactivity periods detection. algorithm look periods time (timethreshold) angle variability lower anglethreshold. can specified multiple thresholds, implemented, e.g., timethreshold = c(5,10).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"ignorenonwear","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"ignorenonwear","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). TRUE ignore detected monitor non-wear periods avoid confusion monitor non-wear time sustained inactivity.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"haspt-algo","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"HASPT.algo","title":"GGIR configuration parameters","text":"Character (default = “HDCZA”). indicate algorithm used sleep period time detection. Default “HDCZA” Heuristic algorithm looking Distribution Change Z-Angle described van Hees et al. 2018. options included: “HorAngle”, based HDCZA replaces non-movement detection HDCZA algorithm looking time segments angle longitudinal sensor axis angle relative horizontal plane -45 +45 degrees. “NotWorn” also HDCZA looks time segments rolling average acceleration magnitude 5 per cent standard deviation, see Cookbook vignette Annexes https://wadpac.github.io/GGIR/ detailed guidance use “NotWorn”.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"hasib-algo","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"HASIB.algo","title":"GGIR configuration parameters","text":"Character (default = “vanHees2015”). indicate algorithm used define sustained inactivity bouts (.e., likely sleep). Options: “vanHees2015”, “Sadeh1994”, “Galland2012”, “NotWorn”. See vignette ://wadpac.github.io/GGIR/articles/chapter8_SleepFundamentalsSibs.htmlchapter 8 details.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"sadeh_axis","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"Sadeh_axis","title":"GGIR configuration parameters","text":"Character (default = “Y”). indicate axis use Sadeh1994 algorithm, algortihms relied count-based Actigraphy Galland2012.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"longitudinal_axis","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"longitudinal_axis","title":"GGIR configuration parameters","text":"Integer (default = NULL). indicate axis longitudinal axis. provided, function estimate longitudinal axis axis highest 24 hour lagged autocorrelation. used sensor.location = “hip” HASPT.algo = “HorAngle”.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"haspt-ignore-invalid","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"HASPT.ignore.invalid","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). indicate whether invalid time segments ignored heuristic guiders. FALSE (default), imputed angle activity metric invalid time segments used. TRUE, invalid time segments ignored (.e., contribute guider). NA, invalid time segments considered movement segments can contribute guider. , guider name output shown “+invalid” end, e.g. “HDCZA+invalid”, reflect NA setting. HASPT.algo “NotWorn”, HASPT.ignore.invalid automatically set NA.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"loglocation","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"loglocation","title":"GGIR configuration parameters","text":"Character (default = NULL). Path csv file sleep log information. See package vignette format file.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"colid","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"colid","title":"GGIR configuration parameters","text":"Numeric (default = 1). Column number sleep log spreadsheet participant ID code stored.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"coln1","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"coln1","title":"GGIR configuration parameters","text":"Numeric (default = 2). Column number sleep log spreadsheet onset first night starts.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"nnights","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"nnights","title":"GGIR configuration parameters","text":"Numeric (default = NULL). argument deprecated.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"relyonguider","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"relyonguider","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). Sustained inactivity bouts (sib) overlap guider labelled sleep. relyonguider = FALSE sib overlaps partially guider sib defines edge SPT window guider. relyonguider = TRUE sib overlaps partially guider guider defines edge SPT window sib. participants instructed wear accelerometer waking hours ignorenonware=FALSE set relyonguider=TRUE, scenarios set FALSE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"def-noc-sleep","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"def.noc.sleep","title":"GGIR configuration parameters","text":"Numeric (default = 1). time window sustained inactivity assumed represent sleep, e.g., def.noc.sleep = c(21, 9). used sleep log entry available. left blank def.noc.sleep = c() 12 hour window centred least active 5 hours 24 hour period used instead. , L5 hardcoded change changing argument winhr function g.part2. def.noc.sleep filled single integer, e.g., def.noc.sleep=c(1) window detected based built algorithms. See argument HASPT.algo HASPT specifying algorithms use.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"sleeplogsep","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"sleeplogsep","title":"GGIR configuration parameters","text":"Character (default = NULL). argument deprecated.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"sleepwindowtype","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"sleepwindowType","title":"GGIR configuration parameters","text":"Character (default = “SPT”). indicate type information sleeplog, “SPT” sleep period time. Set “TimeInBed” sleep log recorded time bed enable calculation sleep latency sleep efficiency.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"possible_nap_window","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"possible_nap_window","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Numeric vector length two range clock hours naps assumed take place, e.g., possible_nap_window = c(9, 18). nap classification applied possible_nap_window possible_nap_dur specified. documentation follow 2025 development work completed.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"possible_nap_dur","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"possible_nap_dur","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Numeric vector length two range duration (minutes) nap, e.g., possible_nap_dur = c(15, 240). nap classification applied possible_nap_window possible_nap_dur specified. documentation follow 2025 development work completed.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"possible_nap_gap","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"possible_nap_gap","title":"GGIR configuration parameters","text":"Numeric (default = 0). Time gap expressed seconds allowed sustained inactivity bouts form naps.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"possible_nap_edge_acc","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"possible_nap_edge_acc","title":"GGIR configuration parameters","text":"Numeric (default = Inf). Maximum acceleration SIB nap considered. default allow possible naps.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"nap_model","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"nap_model","title":"GGIR configuration parameters","text":"Character (default = NULL). specify classification model. Currently option “hip3yr”, corresponds model trained hip data 3-3.5 olds trained parent diary data. functionality currently superseded nap detection triggered possible_nap_window possible_nap_dur. Leave nap_model NULL using new functionality. documentation follow 2025 development work completed.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"sleepefficiency-metric","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"sleepefficiency.metric","title":"GGIR configuration parameters","text":"Numeric (default = 1). 1 (default), sleep efficiency calculated detected sleep time SPT window divided log-derived time bed. 2, sleep efficiency calculated detected sleep time SPT window divided detected duration sleep period time plus sleep latency (sleep latency refers difference time bed sleep onset). sleepefficiency.metric considered parameter sleepwindowType = “TimeInBed”","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"hdcza_threshold","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"HDCZA_threshold","title":"GGIR configuration parameters","text":"Numeric (default = c()) HASPT.algo set “HDCZA” HDCZA_threshold NULL, (e.g., HDCZA_threshold = 0.2), value used threshold 6th step diagram Figure 1 van Hees et al. 2018 Scientific Report (doi: 10.1038/s41598-018-31266-z). However, supported research yet intended facilitate methodological research, advise sticking default line publication. , HDCZA_threshold set numeric vector length 2, e.g. c(10, 15), used percentile multiplier mentioned 6th step.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"sib_must_fully_overlap_with_timeinbed","dir":"Articles","previous_headings":"Arguments/parameters description > Sleep Parameters","what":"sib_must_fully_overlap_with_TimeInBed","title":"GGIR configuration parameters","text":"Boolean (default = c(TRUE, TRUE)). indicate whether sib must fully overlap TimeInBed considered sleep start end time bed, respectively. considered parameter sleepwindowType = “TimeInBed”. Note negative sleep latency, , reported night summary report (part4_nightsummary_sleep csv files) negative sleep latency corresponding sleep efficiency values calculating person summary aggregate stored person level report (part4_summary_sleep csv files).","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"mvpathreshold","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"mvpathreshold","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Legacy parameter, provided GGIR uses value threshold.mod . Acceleration threshold MVPA estimation GGIR g.part2. can single number vector numbers, e.g., mvpathreshold = c(100, 120). latter case code estimate MVPA separately threshold. variable left blank, e.g., mvpathreshold = c(), MVPA estimated.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"boutcriter","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"boutcriter","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Legacy parameter, provided GGIR uses value boutcriter.mvpa . number 0 1, defines fraction bout needs mvpathreshold, used GGIR g.part2.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"mvpadur","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"mvpadur","title":"GGIR configuration parameters","text":"Numeric (default = c(1, 5, 10)). bout duration(s) MVPA calculated. used GGIR g.part2.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"boutcriter-in","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"boutcriter.in","title":"GGIR configuration parameters","text":"Numeric (default = 0.9). number 0 1, defines fraction bout needs threshold.lig.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"boutcriter-lig","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"boutcriter.lig","title":"GGIR configuration parameters","text":"Numeric (default = 0.8). number 0 1, defines fraction bout needs threshold.lig threshold.mod.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"boutcriter-mvpa","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"boutcriter.mvpa","title":"GGIR configuration parameters","text":"Numeric (default = 0.8). number 0 1, defines fraction bout needs threshold.mod.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"threshold-lig","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"threshold.lig","title":"GGIR configuration parameters","text":"Numeric (default = 40). g.part5: Threshold light physical activity separate inactivity light. Value can one number vector multiple numbers, e.g., threshold.lig =c(30,40). multiple numbers entered analysis repeated combination threshold values. Threshold applied first metric milestone data, specified .enmo = TRUE applied ENMO.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"threshold-mod","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"threshold.mod","title":"GGIR configuration parameters","text":"Numeric (default = 100). g.part5: Threshold moderate physical activity separate light moderate. Value can one number vector multiple numbers, e.g., threshold.mod = c(100, 120). multiple numbers entered analysis repeated combination threshold values. Threshold applied first metric milestone data, specified .enmo = TRUE applied ENMO.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"threshold-vig","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"threshold.vig","title":"GGIR configuration parameters","text":"Numeric (default = 400). g.part5: Threshold vigorous physical activity separate moderate vigorous. Value can one number vector multiple numbers, e.g., threshold.vig =c(400,500). multiple numbers entered analysis repeated combination threshold values. Threshold applied first metric milestone data, specified .enmo = TRUE applied ENMO.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"boutdur-mvpa","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"boutdur.mvpa","title":"GGIR configuration parameters","text":"Numeric (default = c(1, 5, 10)). Duration(s) MVPA bouts minutes extracted. start identification longest shortest duration. default setting, start 10 minute bouts, followed 5 minute bouts rest data, followed 1 minute bouts rest data.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"boutdur-in","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"boutdur.in","title":"GGIR configuration parameters","text":"Numeric (default = c(10, 20, 30)). Duration(s) inactivity bouts minutes extracted. Inactivity bouts detected segments data labelled sleep MVPA bouts. start identification longest shortest duration. default setting, start identification 30 minute bouts, followed 20 minute bouts rest data, followed 10 minute bouts rest data. Note use term inactivity instead sedentary behaviour lowest intensity level behaviour. reason GGIR attempt classifying activity type sitting moment, feel using term sedentary behaviour fail communicate .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"boutdur-lig","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"boutdur.lig","title":"GGIR configuration parameters","text":"Numeric (default = c(1, 5, 10)). Duration(s) light activity bouts minutes extracted. Light activity bouts detected segments data labelled sleep, MVPA, inactivity bouts. start identification longest shortest duration. default setting, start identification 10 minute bouts, followed 5 minute bouts rest data, followed 1 minute bouts rest data.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"frag-metrics","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"frag.metrics","title":"GGIR configuration parameters","text":"Character (default = NULL). Fragmentation metric extract. Can “mean”, “TP”, “Gini”, “power”, “CoV”, “NFragPM”, metrics “”. See package vignette description fragmentation metrics.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"part6_threshold_combi","dir":"Articles","previous_headings":"Arguments/parameters description > Physical activity Parameters","what":"part6_threshold_combi","title":"GGIR configuration parameters","text":"Character (default = NULL) indicate threshold combination derived part 5 used part 6. example, “40_100_120”. left default value GGIR use first threshold value parameters threshold.lig, threshold.mod, threshold.vig.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"qwindow","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"qwindow","title":"GGIR configuration parameters","text":"Numeric character (default = c(0, 24)). specify windows variables calculated, e.g., acceleration distribution, number valid hours, LXMX analysis, MVPA. numeric, qwindow length two, e.g., qwindow = c(0, 24), variables calculated full 24 hours day. qwindow = c(8, 24) variables calculated window 0-8, 8-24 0-24. days recording segmented based values. want use day specific segmentation day can set qwindow full path activity diary file (character). Expected format activity diary : First column headers followed one row per recording, first column recording ID, needs match ID GGIR extracts accelerometer file. Followed date column format “23-04-2017”, date format specified parameter qwindow_dateformat (). Use character combination date, Date DATE column name. followed one multiple columns start times activity types day format hours:minutes:seconds. header column used label activity type. Insert new date column continuing activity types next day. Leave missing values empty. activity log used individuals appear activity log still processed value qwindow = c(0, 24). Dates activity log data can skipped, need column date followed column next date. times activity diary multiple short window size (epoch length), next epoch considered (e.g., epoch 5 seconds, 8:00:02 redefined 8:00:05 activity log). using qwindow functionality combination GGIR part 5 make sure check parameters segmentWEARcrit.part5 segmentDAYSPTcrit.part5 specified research needs. using activity diary aware column name including words “impute” “uncertain” ignored. means can, convenience, add columns log timestamps manually imputed considered uncertain.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"qlevels","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"qlevels","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Vector percentiles value needs extracted. need expressed fraction 1, e.g., c(0.1, 0.5, 0.75). limit number percentiles. left empty percentiles extracted. Distribution derived short epoch metric data. Parameter qlevels can example used MX-metrics (e.g. Rowlands et al) discussed ://cran.r-project.org/package=GGIR/vignettes/GGIR.htmlmain package vignette","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"qwindow_dateformat","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"qwindow_dateformat","title":"GGIR configuration parameters","text":"Character (default = “%d-%m-%Y”). specify date format used activity log used [base]strptime.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"ilevels","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"ilevels","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Levels acceleration value frequency distribution m, e.g., ilevels = c(0,100,200). limit number levels. left empty intensity levels extracted. Distribution derived short epoch metric data.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"ivis_windowsize_minutes","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"IVIS_windowsize_minutes","title":"GGIR configuration parameters","text":"argument deprecated.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"ivis_epochsize_seconds","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"IVIS_epochsize_seconds","title":"GGIR configuration parameters","text":"Numeric (default = NULL). parameter deprecated.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"ivis-activity-metric","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"IVIS.activity.metric","title":"GGIR configuration parameters","text":"argument deprecated.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"ivis_acc_threshold","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"IVIS_acc_threshold","title":"GGIR configuration parameters","text":"argument deprecated.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"qm5l5","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"qM5L5","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Percentiles (quantiles) calculated L5 M5 window.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"mx-ig-min-dur","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"MX.ig.min.dur","title":"GGIR configuration parameters","text":"Numeric (default = 10). Minimum MX duration needed order intensity gradient calculated.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"m5l5res","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"M5L5res","title":"GGIR configuration parameters","text":"Numeric (default = 10). Resolution L5 M5 analysis minutes.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"winhr","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"winhr","title":"GGIR configuration parameters","text":"Numeric (default = 5). Vector window size(s) (unit: hours) LX MX analysis, look least active consecutive number X hours.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"iglevels","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"iglevels","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Levels acceleration value frequency distribution mused intensity gradient calculation (according method Rowlands 2018). default parameter empty intensity gradient calculation done. user can either provide single value () make intensity gradient use bins iglevels = c(seq(0,4000,=25), 8000) user specify distribution. constriction number levels.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"luxthresholds","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"LUXthresholds","title":"GGIR configuration parameters","text":"Numeric (default = c(0, 100, 500, 1000, 3000, 5000, 10000)). Vector numeric sequence corresponding thresholds used calculate time spent LUX ranges.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"lux_cal_constant","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"LUX_cal_constant","title":"GGIR configuration parameters","text":"Numeric (default = NULL). LUX_cal_constant LUX_cal_exponent provided LUX values converted based formula y = constant * exp(x * exponent)","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"lux_cal_exponent","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"LUX_cal_exponent","title":"GGIR configuration parameters","text":"Numeric (default = NULL). LUX_cal_constant LUX_cal_exponent provided LUX LUX values converted based formula y = constant * exp(x * exponent)","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"lux_day_segments","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"LUX_day_segments","title":"GGIR configuration parameters","text":"Numeric (default = NULL). Vector hours day segmented LUX analysis.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"l5m5window","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"L5M5window","title":"GGIR configuration parameters","text":"deprecated version 1.5-24. parameter used define start end time, 24 hour clock hours, L5M5 needs calculated. Now done parameter qwindow.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"cosinor","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"cosinor","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). Whether apply cosinor analysis ActCR package part 2. part 6 cosinor analysis applied default turned .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"part6cr","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"part6CR","title":"GGIR configuration parameters","text":"Boolean (default = FALSE) indicate whether circadian rhythm analysis run part 6, includes: cosinor analysis, extended cosinor analysis, , IV, phi. Optionally can expanded detrended fluctutation analysis controlled parameter part6DFA.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"part6hca","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"part6HCA","title":"GGIR configuration parameters","text":"Boolean (default = FALSE) indicate whether Household Co Analysis run part 6.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"part6window","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"part6Window","title":"GGIR configuration parameters","text":"Character vector length two (default = c(“start”, “end”)) indicate start end time series used circadian rhythm analysis part 6. words, parameters used Household co-analysis. Alternative values : “Wx”, “Ox”, “Hx”, “x” number indicat xth wakeup, onset hour recording. Negative values “x” also possible count relative end recording. example, c(“W1”, “W-1”) goes first till last wakeup, c(“H5”, “H-5”) ignores first last 5 hours, c(“O2”, “W10”) goes second onset till 10th wakeup time.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"part6dfa","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"part6DFA","title":"GGIR configuration parameters","text":"Boolean (default = FALSE) indicate whether perform Detrended Fluctuation Analysis. Turned default can time consuming.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"clevels","dir":"Articles","previous_headings":"Arguments/parameters description > 24/7 Parameters","what":"clevels","title":"GGIR configuration parameters","text":"fully operational yet, actived 2025. Numeric vector (default = c(30, 150)) length 2 indicate cadence ranges used summarising step counts, available. set NULL, step counts summarised part 5 output.","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"epochvalues2csv","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"epochvalues2csv","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). g.part2: TRUE epoch values exported csv file. , non-wear time imputed possible.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"save_ms5rawlevels","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"save_ms5rawlevels","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). g.part5: Whether save time series classification (levels) csv RData files (defined save_ms5raw_format). Note time stamps stored column timenum UTC format (.e., seconds 1970-01-01). convert timenum time stamp format, need specify desired time zone, e.g., .POSIXct(mdat$timenum, tz = “Europe/London”). using GGIR part 6, interested visualreport generation, interested time series may want consider setting parameter FALSE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"save_ms5raw_format","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"save_ms5raw_format","title":"GGIR configuration parameters","text":"Character (default = “RData”). g.part5: specify data stored: “csv”, “RData”, via c(“csv”, “RData”). used save_ms5rawlevels = TRUE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"save_ms5raw_without_invalid","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"save_ms5raw_without_invalid","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). g.part5: indicate whether remove invalid days time series output files. used save_ms5rawlevels = TRUE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"storefolderstructure","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"storefolderstructure","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). Store folder structure accelerometer data.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"timewindow","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"timewindow","title":"GGIR configuration parameters","text":"Character (default = c(“MM”, “WW”)). g.part5: Timewindow summary statistics derived. Value can “MM” (midnight midnight), “WW” (waking time waking time), “OO” (sleep onset sleep onset), combination .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"viewingwindow","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"viewingwindow","title":"GGIR configuration parameters","text":"Numeric (default = 1). Centre day displayed around noon (viewingwindow = 1) around midnight (viewingwindow = 2) “Report_” visual report generated visualreport = TRUE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"dofirstpage","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"dofirstpage","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). indicate whether first page histograms summarizing whole measurement added file summary reports named “Report_” generated visualreport = TRUE.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"visualreport","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"visualreport","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). TRUE, store two visualreports outputfolder ‘results/file summary reports’ names starting “Report_…” “Time_report…”. report “Report_…” GGIR years based combined output GGIR part 2 4. initially quick development attempt something show study participants. report designed data quality checking purposes makes attempt exactly visualise classifications made part 5. Given number inaccuracies report eventually deprecated GGIR. turn old report use old_visualreport = FALSE. “Time_report”, new report GGIR 3.1-8 specifically aimed supporting data quality checks shows time series data generated used GGIR part 5. .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"week_weekend_aggregate-part5","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"week_weekend_aggregate.part5","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). g.part5: indicate whether week weekend-days aggregates stored. turned default generates large number extra columns output report.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-part3-pdf","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"do.part3.pdf","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). g.part3: Whether generate pdf g.part3.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"outliers-only","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"outliers.only","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). g.part4: used .visual = TRUE. FALSE, available nights included visual representation data sleeplog. TRUE, nights difference onset waking time larger variable parameter criterror included.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"criterror","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"criterror","title":"GGIR configuration parameters","text":"Numeric (default = 3). g.part4: used .visual = TRUE outliers.= TRUE. criterror specifies number minimum number hours difference sleep log accelerometer estimate night included visualisation.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-visual","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"do.visual","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). g.part4: TRUE, function generate pdf visual representation overlap sleeplog entries accelerometer detections. can used visually verify sleeplog entries come obvious mistakes.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-sibreport","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"do.sibreport","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). g.part4: indicate whether generate report sustained inactivity bouts (SIB). set TRUE advanced sleep diary available part 4 part 5 use generate summary statistics overlap self-reported nonwear napping SIB. , SIB can filter based parameter possible_nap_edge_acc first value possible_nap_dur","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"do-part2-pdf","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"do.part2.pdf","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). g.part2: Whether generate pdf g.part2.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"sep_reports","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"sep_reports","title":"GGIR configuration parameters","text":"Character (default = “,”). Value used sep parameter [data.table]fwrite writing csv reports.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"sep_config","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"sep_config","title":"GGIR configuration parameters","text":"Character (default = “,”). Value used sep parameter [data.table]fwrite writing csv config file.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"dec_reports","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"dec_reports","title":"GGIR configuration parameters","text":"Character (default = “.”). Value used dec parameter [data.table]fwrite writing csv reports.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"dec_config","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"dec_config","title":"GGIR configuration parameters","text":"Character (default = “.”). Value used dec parameter [data.table]fwrite writing csv config file.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"visualreport_without_invalid","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"visualreport_without_invalid","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). TRUE, reports generated visualreport = TRUE named “Report_” show windows sufficiently valid data according includedaycrit viewingwindow = 1 includenightcrit viewingwindow = 2","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"old_visualreport","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"old_visualreport","title":"GGIR configuration parameters","text":"Boolean (default = TRUE). TRUE, generate old visual report addition new visualreport. old visualreport eventually deprecated","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"visualreport_hrsperrow","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"visualreport_hrsPerRow","title":"GGIR configuration parameters","text":"Numeric (default = 36). Width plots new visualreport (named “Time_report…”) expressed hours. Expected range 24-48. 24 extra time overlaps beginning next plot.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"visualreport_focus","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"visualreport_focus","title":"GGIR configuration parameters","text":"Character (default = “day”) Whether new visual report (names start “Time_report…”) focuss day night. set ot “night” focus night.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"visualreport_validcrit","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"visualreport_validcrit","title":"GGIR configuration parameters","text":"Numeric (default = 0). Value 0 1 representing fraction data plot expected valid. criteria met row skipped. used new visual report (names start “Time_report…”).","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"require_complete_lastnight_part5","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"require_complete_lastnight_part5","title":"GGIR configuration parameters","text":"Boolean (default = FALSE). set TRUE: last WW window excluded recording ends midnight 3pm, starts date one day recording end date; last OO MM window excluded recording ends midnight 9am, starts date one day recording end date. avoid risk recording end biases sleep estimates last night.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/GGIRParameters.html","id":"method_research_vars","dir":"Articles","previous_headings":"Arguments/parameters description > Output Parameters","what":"method_research_vars","title":"GGIR configuration parameters","text":"Character (default = NULL). Vector names methodological variable categories store csv output files. Currently “nap” available affects part5 output. variables intended methodological research default turned .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/HouseHoldCoanalysis.html","id":"accelerometer-file-names","dir":"Articles","previous_headings":"","what":"Accelerometer file names","title":"Household Co-Analysis","text":"household co-analysis requires households family member can recognised. assume following logic file names: StudyNumber-HouseholdID-MemberID_anyotherinformation.bin example .bin file, applies .cwa .csv files. example files: 001-002-001_12345-2023.bin 001-002-002_23456-2023.bin 001-002-M_23456-2023.bin recognised household ID 002 member IDs 001, 002, M.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/HouseHoldCoanalysis.html","id":"configuring-ggir","dir":"Articles","previous_headings":"","what":"Configuring GGIR","title":"Household Co-Analysis","text":"following input arguments needed run Household co-analysis: Household co-analysis integrated GGIR part 6, need run GGIR part 1 6, mode = 1:6. windowsizes = c(5, 60, 3600). Setting second value 60 ensures light temperature, available, aggregated per minute. part5_agg2_60seconds = TRUE. ensures GGIR part 5 stores time series 1 minute resolution. part6HCA = TRUE tell GGIR perform Household Co-Analysis. part6_threshold_combi = \"30_100_400\" 30, 100 400 need correspond accelerometer threshold combination used part 5 want use part 6. GGIR part 5 facilitates multiple threshold combinations part 6 need select one. GGIR arguments can set according needs. example:","code":"datadir = \"C:/projects/studyZ/binfiles\" outputdir = \"C:/projects/studyZ\" library(GGIR) GGIR(mode = 1:5,       datadir = datadir,      idloc = 2,       outputdir = outputdir,      do.report = c(2, 4, 5),      do.parallel = TRUE,      overwrite = FALSE,      printsummary = TRUE,      desiredtz = \"America/Halifax\",      windowsizes = c(5, 60, 3600),      threshold.lig = 30,      threshold.mod = 100,      threshold.vig = 400,      part6_threshold_combi = \"30_100_400\",      boutcriter.in = 1, boutcriter.lig = 1,  boutcriter.mvpa = 0.9,      boutdur.in = 30,      boutdur.lig = 10,      boutdur.mvpa = 5,      part6HCA = TRUE,      save_ms5rawlevels = TRUE, # Not necessary because GGIR will set this to TRUE when part6HCA is TRUE.      save_ms5raw_without_invalid = FALSE, # <= Needed for household co-analysis      part5_agg2_60seconds = TRUE,      visualreport = FALSE)"},{"path":"https://wadpac.github.io/GGIR/articles/HouseHoldCoanalysis.html","id":"household-co-analysis","dir":"Articles","previous_headings":"","what":"Household co-analysis","title":"Household Co-Analysis","text":"GGIR part 1, 2, 3, 4, 5 recording processed individually without considering relations recordings. Next, part 6 subdivided alligning time series produced part 1 5 per household, pairwise analysis data.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/HouseHoldCoanalysis.html","id":"align-individuals","dir":"Articles","previous_headings":"Household co-analysis","what":"Align individuals","title":"Household Co-Analysis","text":"Household members one member ignored. Next, per household per household member code loads merges time series produced GGIR part 1 part 5. Days, defined waking-waking-, removed less 20% valid data waking hours, sleep period time window, day whole. Next, time series completed indicates valid household member pairs time points. Finally, store: aligned time series per household separate csv files GGIR output directory (.../results/part6HouseholdCoAnalysis/alignedTimeseries). columns file documented . pdf file names timeseriesPlot.pdf plots aligned time series facilitate visual inspection data completeness per household.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/HouseHoldCoanalysis.html","id":"pairwise-analysis","dir":"Articles","previous_headings":"Household co-analysis","what":"Pairwise analysis","title":"Household Co-Analysis","text":"Per household identify possible member pairs loop pairs. Per member pair code identify wake-time pairs. , wake-times occur within last 15 minutes time series ignored need least recording time quantify behaviour waking . Per wake-pair assess woke first second, time difference, corresponding calendar dates waking . Next, code quantifies: Activity person first woke minute second person woke Activity second person wake woke LUX person first woke tminute second person woke LUX second person wake woke . Describe matching waking hours pairs: Correlation continuous acceleration values (ENMO metric) Derive binary class inactivity/active (ENMO metric, threshold < 50) ICC based binary scores (irr package, model=twoway, type=agreement, unit=single) Cohen’s Kappa (psych package) Similarity binary scores (calculation line Sleep Regularity Index) Describe noon-noon window stronger focus sleep: Describe binary class sleep/wakefulness (note: attempt classify daytime naps) ICC based binary scores (irr package, model=twoway, type=agreement, unit=single) Cohen’s Kappa (psych package) Similarity binary scores (calculation line Sleep Regularity Index) Describe wakefulness dynamics SPT prior wakeup: Look indices spt prior wakeup individuals SPT. Assess fraction data valid Identify wake times night wake-time: Assess whether persons woke time, person wake within 5 minutes, person wake within 5 minutes. Store output csv one row per unique household pair, columns clarify household members pair household .","code":""},{"path":[]},{"path":"https://wadpac.github.io/GGIR/articles/HouseHoldCoanalysis.html","id":"alignedtimesieres","dir":"Articles","previous_headings":"Output variables","what":"alignedTimesieres","title":"Household Co-Analysis","text":"GGIR output folder .../results/part6HouseholdCoAnalysis/alignedTimeseries find csv files time series per household. data dictionary shows column names get household two members: X Y. columns copied time series output files, documented . Therefore, column documented .","code":""},{"path":"https://wadpac.github.io/GGIR/articles/HouseHoldCoanalysis.html","id":"pairwise-summary-report","dir":"Articles","previous_headings":"Output variables","what":"Pairwise summary report","title":"Household Co-Analysis","text":"stored inside pairwise_summary_all_housholds.csv","code":""},{"path":"https://wadpac.github.io/GGIR/articles/readmyacccsv.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Reading csv files with raw data in GGIR","text":"GGIR can automatically read data -frequently used accelerometer brands field: GENEActiv .bin Axivity AX3 AX6 .wav, .csv .cwa ActiGraph .csv .gt3x (.gt3x newer format generated firmware versions 2.5.0). Note Actigraph users: want work .csv exports via ActiLife note option export data timestamps. Please causes memory issues GGIR. cope absence timestamps GGIR re-caculate timestamps sample frequency start time date presented file header Movisens data stored folders Genea (accelerometer commercially available anymore, used studies 2007 2012) .bin .csv However, accelerometer manufacturers proliferating increasing number brands market. reason, GGIR includes read.myacc.csv function, able read accelerometer raw triaxial data stored csv files, independently brand. vignette provides general introduction use GGIR read accelerometer raw data stored csv files. works: Internally GGIR loads csv files accelerometer data standardises output format make data compatible GGIR functions. Data format standardisation includes unit measurement, timestamp, file header format, column locations.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/readmyacccsv.html","id":"the-read-myacc-csv-function","dir":"Articles","previous_headings":"","what":"The read.myacc.csv function","title":"Reading csv files with raw data in GGIR","text":"rest GGIR functions, read.myacc.csv intended used within function GGIR. arguments read.myacc.csv can easily recognized start “rmc”. GGIR checks whether argument rmc.firstrow.acc provided user; case, GGIR attempt read data function read.myacc.csv. words always need specify rmc.firstrow.acc use read.myacc.csv. , recommend always first test function argument settings first trying use function read.myacc.csv . works copy arguments GGIR call.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/readmyacccsv.html","id":"input-arguments","dir":"Articles","previous_headings":"The read.myacc.csv function","what":"Input arguments","title":"Reading csv files with raw data in GGIR","text":"read.myacc.csv function tries read csv files wide variety formats, key arguments specify depend characteristics csv file process. Overall, argument relevant, left default setting (e.g., csv file contain temperature data, arguments related temperature settings left default values). present summary available input arguments. Please see parameters vignette elaborate description input arguments. , arguments also covered function documentation read.myacc.csv function.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/readmyacccsv.html","id":"general-arguments","dir":"Articles","previous_headings":"The read.myacc.csv function > Input arguments","what":"General arguments","title":"Reading csv files with raw data in GGIR","text":"rmc.file - Filename file read working directory, full path file otherwise. rmc.nrow - Number rows read, nrow argument nrows . whole file read default (.e., rmc.nrow = Inf). rmc.skip - Number rows skip, skip argument . rmc.dec - Decimal separator used numbers, dec argument data.table::. “.” (default) usually “,”. rmc.firstrow.acc - First row (number) acceleration data. rmc.unit.acc - Character unit acceleration values: “g”, “mg”, “bit”. desiredtz - Timezone device worn. confgitz - Timezone device configured. rmc.sf - Sample rate Hertz, stored file header used instead.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/readmyacccsv.html","id":"header","dir":"Articles","previous_headings":"The read.myacc.csv function > Input arguments","what":"Arguments for files containing a header","title":"Reading csv files with raw data in GGIR","text":"rmc.firstrow.header - First row (number) file header. Leave blank (default) file file header. confused one row column header, file header typically takes several rows one two columns. header two columns, first column assumed header item names second column assumed header item values. header one column assumed value contains name value item. rmc.header.length - file header, specify header length (numeric). rmc.headername.sf - file header, row name (character) sample frequency can found, e.g. “sample_rate”. rmc.headername.sn - file header, row name (character) serial number can found, e.g. “serial_number”. rmc.headername.recordingid - file header, row name (character) recording ID can found, e.g. “ID”. rmc.header.structure - Character used split header name header value, e.g. “:” header value look like “ID: 123” ” ” ehader value like “ID 123”.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/readmyacccsv.html","id":"arguments-for-files-including-timestamps","dir":"Articles","previous_headings":"The read.myacc.csv function > Input arguments","what":"Arguments for files including timestamps","title":"Reading csv files with raw data in GGIR","text":"rmc.col.time - Scalar column (number) timestamps stored. Leave default setting timestamps stored. rmc.unit.time - Character unit timestamps: “POSIX”, “UNIXsec” (seconds since origin, see argument rmc.origin), “UNIXmsec” (milliseconds since origin, see argument rmc.origin), “character”, “ActivPAL” (exotic timestamp format used ActivPAL activity monitor). rmc.format.time - Character string giving date-time format used . used rmc.unit.time: character POSIX. rmc.origin - Origin time unit time UNIXsec, e.g. 1970-1-1.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/readmyacccsv.html","id":"arguments-for-files-with-acceleration-stored-in-bits","dir":"Articles","previous_headings":"The read.myacc.csv function > Input arguments","what":"Arguments for files with acceleration stored in bits","title":"Reading csv files with raw data in GGIR","text":"rmc.bitrate - Numeric: unit acceleration bit provide bit rate, e.g. 12 bit. rmc.dynamic_range - Numeric, unit acceleration bit provide dynamic range deviation g zero, e.g. +/-6g mean argument needs 6. give argument character value code search file header elements name equal character value use corresponding numeric value next dynamic range. rmc.unsignedbit - Boolean, unsignedbit = TRUE means bits positive numbers. unsignedbit = FALSE bits positive negative.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/readmyacccsv.html","id":"arguments-for-files-including-temperature","dir":"Articles","previous_headings":"The read.myacc.csv function > Input arguments","what":"Arguments for files including temperature","title":"Reading csv files with raw data in GGIR","text":"rmc.col.temp - Scalar column (number) temperature stored. Leave default setting temperature avaible. temperature used . rmc.unit.temp - Character unit temperature values: (K)elvin, (C)elsius, (F)ahrenheit.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/readmyacccsv.html","id":"arguments-for-files-including-wear-time-information","dir":"Articles","previous_headings":"The read.myacc.csv function > Input arguments","what":"Arguments for files including wear time information","title":"Reading csv files with raw data in GGIR","text":"rmc.col.wear - external wear detection outcome stored part data can used GGIR. argument specifies column wear detection (Boolean) stored.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/readmyacccsv.html","id":"arguments-to-find-time-gaps-and-resampling","dir":"Articles","previous_headings":"The read.myacc.csv function > Input arguments","what":"Arguments to find time gaps and resampling","title":"Reading csv files with raw data in GGIR","text":"rmc.check4timegaps - Boolean indicate whether gaps time imputed zeros. rmc.doresample - Boolean indicate whether resample data based available timestamps extracted sample rate file header interpolationType - Integer indicate type interpolation used resampling time series (mainly relevant Axivity sensors), 1=linear, 2=nearest neighbour.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/readmyacccsv.html","id":"usage-of-the-read-myacc-csv-function","dir":"Articles","previous_headings":"","what":"Usage of the read.myacc.csv function","title":"Reading csv files with raw data in GGIR","text":"section shows example real case read.myacc.csv function can used. csv file read following structure: file contains timestamps column 1 (formatted “%d/%m/%Y %H:%M:%OS”), acceleration signals (g’s) x, y, z axis columns 2, 3, 4, respectively, temperature information Celsius column 5. Also, file file header. can use GGIR, first test read file using read.myacc.csv function directly. object data list data.frame name data header. time column data.frame represents timestamps expressed seconds since 1-1-1970.","code":"library(GGIR) data = read.myacc.csv(rmc.file = \"C:/mystudy/mydata/datafile.csv\",                rmc.nrow = Inf,                rmc.skip = 0,                rmc.dec = \".\",                rmc.firstrow.acc = 2,                rmc.col.acc = 2:4,                rmc.col.temp = 5,                rmc.col.time=1,                rmc.unit.acc = \"g\",                rmc.unit.temp = \"C\",                rmc.unit.time = \"POSIX\",                rmc.format.time = \"%d/%m/%Y %H:%M:%OS\",                desiredtz = \"Europe/London\",                rmc.sf = 100)"},{"path":"https://wadpac.github.io/GGIR/articles/readmyacccsv.html","id":"example-using-the-shell-function","dir":"Articles","previous_headings":"Usage of the read.myacc.csv function","what":"Example using the shell function","title":"Reading csv files with raw data in GGIR","text":"rmc.firstrow.acc argument defined within GGIR function, data read read.myacc.csv. GGIR needs user specify row starts accelerometer data within csv, argument must always explicitly specified user. Thus, call GGIR including rmc arguments look follows (note rmc.file, rmc.nrow, rmc.skip arguments used GGIR arguments already defined datadir, strategy, header arguments, respectively).","code":"library(GGIR) GGIR(              mode=c(1,2,3,4,5),              datadir=\"C:/mystudy/mydata/datafile.csv\",              outputdir=\"D:/myresults\",              do.report=c(2,4,5),              #=====================              # read.myacc.csv arguments              #=====================              rmc.nrow = Inf,               rmc.dec = \".\",              rmc.firstrow.acc = 2,               rmc.col.acc = 2:4,               rmc.col.temp = 5,               rmc.col.time=1,              rmc.unit.acc = \"g\",               rmc.unit.temp = \"C\",               rmc.unit.time = \"POSIX\",              rmc.format.time = \"%d/%m/%Y %H:%M:%OS\",              desiredtz = \"Europe/London\",              rmc.sf = 100,              rmc.noise = 0.013 )"},{"path":"https://wadpac.github.io/GGIR/articles/TutorialDaySegmentAnalyses.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Day segment analyses with GGIR","text":"specific person active morning afternoon? children active work hours leisure time? much inactivity occurs work office workers? Questions like can answered GGIR first specify parameters. main input argument specified qwindow, can used following ways: specify clock hours day based segmented day analyses take place. specify activity log (diary) used guide segmentation per individual per day recording. following sections discuss scenarios.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/TutorialDaySegmentAnalyses.html","id":"clock-hour-based-segmentation","dir":"Articles","previous_headings":"","what":"Clock hour-based segmentation","title":"Day segment analyses with GGIR","text":"perform clock hour segmentation, need provide function GGIR argument qwindow assign numeric vector hours segmentation. start end day, explicitly provided vector GGIR add . Please find example values qwindow. number values used qwindow unlimited, aware analyses MX-metrics impossible small windows produce empty results. Day Saving Time (DST) taken account identifying start day, identifying day segments. words, 23 hour days processed 24 hours first midnight. ensure segment length identical across days week, needed ease comparison outcome variables across days.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/TutorialDaySegmentAnalyses.html","id":"segmentation-guided-by-activity-log","dir":"Articles","previous_headings":"","what":"Segmentation guided by activity log","title":"Day segment analyses with GGIR","text":"perform activity-log based segmentation, need provide function GGIR argument qwindow assign full path activity log .csv format, e.g. qwindow=\"C:/myactivitylog.csv\". activity log expected .csv-file following structure: Rows: First row represents column headers row represents one accelerometer recording. ID-column: first column expected hold recording ID, needs match ID GGIR extracts accelerometer file. unsure format ID values, apply GGIR sample accelerometer files using default argument settings. ID column generated part 2 .csv reports show participant ID extracted GGIR. ID extracted, see documentation argument idloc, helps specify location participant file name file header. ID extraction fails accelerometer files matched corresponding activity log entries. Date-column: ID column followed date column first log day. ensure GGIR recognises date correctly, specify argument qwindow_dateformat. default format \"\\%d-\\%m-\\%Y\" 23-2-2021 indicate 23rd February 2021. date formatted 2-23-21 specify\"\\%m-\\%d-\\%y\". column name date column needs include character combination “date” “Date” “DATE”. Use date format consistently throughout activity diary. Start-times: date column followed one multiple columns start times activity types day format hours:minutes:seconds. provide dates cells. header column used label activity type. Insert new date column continuing activity types next day. Leave missing values empty. Missing values: values missing preceding succeeding time point used edges segment. example means define segment -C ID 1234, ID 6789 defined segments -B B-C, segment -C derived . Notes: - activity log collected individuals processed qwindow value c(0,24). - Dates activity log data can skipped, need column date followed column next date. - end time one activity assumed start time next activity. currently facilitate overlapping time segments.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/TutorialDaySegmentAnalyses.html","id":"implementation-in-ggir","dir":"Articles","previous_headings":"","what":"Implementation in GGIR","title":"Day segment analyses with GGIR","text":"approaches implemented GGIR part 2 part 5. Therefore specific output variables calculated part 2 5 available per day, per person, per segment day based argument qwindow Note qwindow used part 5 timewindow includes \"MM\" (see specific documentation timewindow} parameters vignette) moment, specifying argument qwindow triggers calculation qwindow segments part 2 part 5, may result longer time finish analysis. interested segments either part 2 part 5, option might run GGIR parts 1:2 argument qwindow interest, set qwindow = NULL run GGIR parts 3:5 (vice versa: qwindow = NULL GGIR parts 1:2, desired qwindow segments running GGIR parts 3:5). information output variables calculated part pipeline, see main GGIR vignette.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/TutorialDaySegmentAnalyses.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"Day segment analyses with GGIR","text":"information use GGIR function call see explanation main GGIR vignette.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/TutorialDaySegmentAnalyses.html","id":"clock-hour-based-segmentation-1","dir":"Articles","previous_headings":"Examples","what":"Clock-hour based segmentation:","title":"Day segment analyses with GGIR","text":"","code":"library(\"GGIR\") GGIR(datadir = \"/your/data/directory\",              outputdir = \"/your/output/directory\",              mode = 1:5, # <= run GGIR parts 1 to 5              do.report = c(2, 5), # <= generate csv-report for GGIR part 2 and part 5              qwindow = c(0, 6, 12, 18, 24),              timewindow = \"MM\")"},{"path":"https://wadpac.github.io/GGIR/articles/TutorialDaySegmentAnalyses.html","id":"activity-log-based-segmentation","dir":"Articles","previous_headings":"Examples","what":"Activity log based segmentation:","title":"Day segment analyses with GGIR","text":"running code GGIR creates output folder output directory specified argument outputdir. subfolder results find csv files reports generated part 2 part 5 pipeline: Part 2 part2_summary.csv recording level summary, 1 row per recording recording level aggregates day segments columns. part2_daysummary.csv day level summary, 1 row per day day segment specific outcomes columns. part2_daysummary_longformat.csv day level summary long format, row represents one segment one day one recording. part2_summary.csv part2_daysummary.csv column names tell day segment correspond . example, column names ending _18-24hr refer time segment 18:00-24:00. part2_daysummary_longformat.csv time segment clarified via columns qwindow_timestamps qwindow_name. Part 5 part 5, information segments days exported different csv reports person-level day-level summaries. files include word “Segments” filename provided long format aggregated per day per person: part5_daysummary_Segments[...].csv day level summary long format, row represents one segment one day one recording. part5_personsummary_Segments[...].csv recording level summary long format, row represents average outcome one specific segments across days segment available per participant.","code":"library(\"GGIR\") GGIR(datadir = \"/your/data/directory\",              outputdir = \"/your/output/directory\",              mode = 1:5, # <= run GGIR parts 1 to 5              do.report = c(2, 5), # <= generate csv-report for GGIR part 2 and part 5              qwindow = \"/path/to/your/activity/log.csv\",              timewindow = \"MM\")"},{"path":"https://wadpac.github.io/GGIR/articles/TutorialDaySegmentAnalyses.html","id":"cleaning-parameters-for-day-segments-in-part-5","dir":"Articles","previous_headings":"","what":"Cleaning parameters for day segments (in part 5):","title":"Day segment analyses with GGIR","text":"part 5, analyses performed per segment day come possibility clean reports based information available segments. users can select include segments given amount wear time segment (segmentWEARcrit.part5), well given awake time sleep period time segment (segmentDAYSPTcrit.part5). arguments likely critical meaningful analysis data. presence sleep segment physical activity bias physical inactivity estimates presence physical activity segment sleep bias sleep estimates. become impossible quantify whether lack one presence behaviour drives association example health outcome.","code":""},{"path":"https://wadpac.github.io/GGIR/articles/TutorialDaySegmentAnalyses.html","id":"analyses-performed-per-day-segment","dir":"Articles","previous_headings":"","what":"Analyses performed per day segment","title":"Day segment analyses with GGIR","text":"analyses GGIR per segment day, include: Acceleration distribution (part 2): Derived argument ilevels specified. find variable names [0,36)_ENMO_mg means time spent 0 36 mg defined acceleration metric ENMO. Number valid hours data (part 2): recognise N_valid_hours_in_window tells number valid hours per time window, N_valid_hours number valid hours per day. Non-wear time percentage (part 5): nonwear_day_perc, nonwear_spt_perc, nonwear_day_spt_perc tell proportion segment classified non-wear awake time (day) sleep period time (spt). LXMX analysis (part 2 part5): LXMX analysis, stands least active X hours segment. recognise variable names like L5hr_ENMO_mg start time least active five hours defined metric ENMO, L5_ENMO_mg average acceleration hours. Intensity gradient analysis (part 2 part 5): find variables start ig_gradient_ See description GGIR part 2 output main GGIR vignette details. Time spent Moderate Vigorous Physical Activity (MVPA) (part 2 part 5): find variables MVPA_E5S_T201_ENMO MVPA_E5S_B1M80%_T201_ENMO. See description GGIR part 2 output main GGIR vignette details. Time spent sleeping, inactivity physical activity intensities (part 5): find variables part 5 reports, bouted, unbouted, total time version variables. See description GGIR part 5 output main GGIR vignette details.","code":""},{"path":"https://wadpac.github.io/GGIR/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Vincent T van Hees. Author, maintainer. Jairo H Migueles. Author. Severine Sabia. Contributor. Matthew R Patterson. Contributor. Zhou Fang. Contributor. Joe Heywood. Contributor. Joan Capdevila Pujol. Contributor. Lena Kushleyeva. Contributor. Mathilde Chen. Contributor. Manasa Yerramalla. Contributor. Patrick Bos. Contributor. Taren Sanders. Contributor. Chenxuan Zhao. Contributor. Ian Meneghel Danilevicz. Contributor. Victor Barreto Mesquita. Contributor. Gaia Segantin. Contributor. Medical Research Council UK. Copyright holder, funder. Accelting. Copyright holder, funder. French National Research Agency. Copyright holder, funder.","code":""},{"path":"https://wadpac.github.io/GGIR/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"van Hees V, Migueles J, Fang Z, Zhao J, Heywood J, Mirkes E, Sabia S (2025). GGIR: Raw Accelerometer Data Analysis. doi:10.5281/zenodo.1051064, R package version 3.1-11, https://CRAN.R-project.org/package=GGIR. van Hees V, Fang Z, Langford J, Assah F, MohammadMirkes , da Silva , Trenell M, White T, Wareham N, Brage S (2014). “Autocalibration accelerometer data free-living physical activity assessment using local gravity temperature: evaluation four continents.” Journal Applied Physiology, 117(7), 738–744. https://doi.org/10.1152/japplphysiol.00421.2014. van Hees V, Sabia S, Anderson K, Denton S, Oliver J, Catt M, Abell J, Kivimaki M, Trenell M, Singh-Manoux (2015). “Novel, Open Access Method Assess Sleep Duration Using Wrist-Worn Accelerometer.” PLoS One, 10(11). doi:10.1371/journal.pone.0142533, https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0142533. van Hees V, Sabia S, Jones S, Wood , Anderson K, Kivimaki M, Frayling T, Pack , Bucan M, Trenell M, Mazzotti D, Gehrman P, Singh-Manoux , Weedon M (2018). “Estimating sleep parameters using accelerometer without sleep diary.” Scientific Reports, 8(1). doi:10.1038/s41598-018-31266-z, https://www.nature.com/articles/s41598-018-31266-z. Migueles J, Rowlands , Huber F, Sabia S, van Hees V (2019). “GGIR: Research Community-Driven Open Source R Package Generating Physical Activity Sleep Outcomes Multi-Day Raw Accelerometer Data.” Journal Measurement Physical Behavior, 2(3). doi:10.1123/jmpb.2018-0063, https://doi.org/10.1123/jmpb.2018-0063.","code":"@Manual{,   title = {{GGIR}: Raw Accelerometer Data Analysis},   author = {Vincent T {van Hees} and Jairo H Migueles and Zhou Fang and Jing Hua Zhao and Joe Heywood and Evgeny Mirkes and Severine Sabia},   year = {2025},   note = {R package version 3.1-11},   doi = {10.5281/zenodo.1051064},   url = {https://CRAN.R-project.org/package=GGIR}, } @Article{,   title = {Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents},   author = {Vincent T {van Hees} and Zhou Fang and Joss Langford and Felix Assah and A MohammadMirkes and Inacio C {da Silva} and Michael I Trenell and Tom White and Nicholas J Wareham and Soren Brage},   journal = {Journal of Applied Physiology},   volume = {117},   number = {7},   pages = {738--744},   year = {2014},   url = {https://doi.org/10.1152/japplphysiol.00421.2014}, } @Article{,   title = {A Novel, Open Access Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer},   author = {Vincent T {van Hees} and Severine Sabia and Kirstie N Anderson and Sarah J Denton and James Oliver and Michael Catt and Jesica G Abell and Mika Kivimaki and Michael I Trenell and Archana Singh-Manoux},   doi = {10.1371/journal.pone.0142533},   journal = {PLoS One},   volume = {10},   number = {11},   year = {2015},   url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0142533}, } @Article{,   title = {Estimating sleep parameters using an accelerometer without sleep diary},   author = {Vincent T {van Hees} and Severine Sabia and Samuel E Jones and Andrew R Wood and Kirstie N Anderson and Mika Kivimaki and Tim M Frayling and Allan I Pack and Maja Bucan and Michael I Trenell and Diego R Mazzotti and Philip R Gehrman and Archana Singh-Manoux and Michael N Weedon},   doi = {10.1038/s41598-018-31266-z},   journal = {Scientific Reports},   volume = {8},   number = {1},   year = {2018},   url = {https://www.nature.com/articles/s41598-018-31266-z}, } @Article{,   title = {GGIR: A Research Community-Driven Open Source R Package for Generating Physical Activity and Sleep Outcomes From Multi-Day Raw Accelerometer Data},   author = {Jairo H Migueles and Alex V Rowlands and Florian Huber and Severine Sabia and Vincent T {van Hees}},   doi = {10.1123/jmpb.2018-0063},   journal = {Journal for the Measurement of Physical Behavior},   volume = {2},   number = {3},   year = {2019},   url = {https://doi.org/10.1123/jmpb.2018-0063}, }"},{"path":[]},{"path":"https://wadpac.github.io/GGIR/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"interest fostering open welcoming environment, contributors maintainers pledge making participation project community harassment-free experience everyone, regardless age, body size, disability, ethnicity, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, religion, sexual identity orientation.","code":""},{"path":"https://wadpac.github.io/GGIR/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes creating positive environment include: Using welcoming inclusive language respectful differing viewpoints experiences Gracefully accepting constructive criticism Focusing best community Showing empathy towards community members Examples unacceptable behavior participants include: use sexualized language imagery unwelcome sexual attention advances Trolling, insulting/derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical electronic address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://wadpac.github.io/GGIR/CODE_OF_CONDUCT.html","id":"our-responsibilities","dir":"","previous_headings":"","what":"Our Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Project maintainers responsible clarifying standards acceptable behavior expected take appropriate fair corrective action response instances unacceptable behavior. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, ban temporarily permanently contributor behaviors deem inappropriate, threatening, offensive, harmful.","code":""},{"path":"https://wadpac.github.io/GGIR/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within project spaces public spaces individual representing project community. Examples representing project community include using official project e-mail address, posting via official social media account, acting appointed representative online offline event. Representation project may defined clarified project maintainers.","code":""},{"path":"https://wadpac.github.io/GGIR/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported contacting main project maintainer v.vanhees@accelting.com. complaints reviewed investigated result response deemed necessary appropriate circumstances. project team obligated maintain confidentiality regard reporter incident. details specific enforcement policies may posted separately.","code":""},{"path":"https://wadpac.github.io/GGIR/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 1.4, available https://www.contributor-covenant.org/version/1/4/code--conduct.html","code":""},{"path":"https://wadpac.github.io/GGIR/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing guidelines","title":"Contributing guidelines","text":"welcome kind contribution software, simple comment question full fledged pull request. Please read follow Code Conduct. contribution can one following cases: question; think may found bug (including unexpected behavior); want make kind change code base (e.g. fix bug, add new feature, update documentation); want make new release code base. sections outline steps case.","code":""},{"path":"https://wadpac.github.io/GGIR/CONTRIBUTING.html","id":"questions","dir":"","previous_headings":"","what":"Questions","title":"Contributing guidelines","text":"use search functionality see someone already experienced issue; search yield relevant results, start new conversation.","code":""},{"path":"https://wadpac.github.io/GGIR/CONTRIBUTING.html","id":"bugs","dir":"","previous_headings":"","what":"Bugs","title":"Contributing guidelines","text":"use search functionality see someone already filed issue; issue search yield relevant results, make new issue, choose Bug report type. includes checklist make sure provide enough information rest community understand cause context problem.","code":""},{"path":"https://wadpac.github.io/GGIR/CONTRIBUTING.html","id":"changes-or-additions","dir":"","previous_headings":"","what":"Changes or additions","title":"Contributing guidelines","text":"(important) announce plan rest community start working. announcement form (new) issue. Choose Feature request type, includes checklist things consider get discussion going; (important) wait kind consensus reached idea good idea; needed, fork repository Github profile create feature branch latest master commit. working feature branch, make sure stay date master branch pulling changes, possibly ‘upstream’ repository (follow instructions ); make sure existing tests still work running test suite RStudio; add tests (necessary); update expand documentation, see package documentation guidelines; make sure release notes inst/NEWS.Rd updated; add name contributors lists DESCRIPTION file; push feature branch (fork ) GGIR repository GitHub; create pull request, e.g. following instructions . pull request template includes checklist items. case feel like ’ve made valuable contribution, don’t know write run tests , generate documentation: don’t let discourage making pull request; can help ! Just go ahead submit pull request, keep mind might asked append additional commits pull request.","code":""},{"path":"https://wadpac.github.io/GGIR/CONTRIBUTING.html","id":"coding-style","dir":"","previous_headings":"Changes or additions","what":"Coding style","title":"Contributing guidelines","text":"loosely follow tidyverse style guide, enforce every rule strictly. instance, prefer = instead <- default assignment operator. doubt style use, don’t hesitate get touch. general guidelines try adhere : Use standard R much possible, keep dependencies minimum. Keep loops minimum. Don’t make lines long. first time contributor, don’t worry coding style much. help get things shape.","code":""},{"path":"https://wadpac.github.io/GGIR/CONTRIBUTING.html","id":"package-documentation","dir":"","previous_headings":"Changes or additions","what":"Package documentation","title":"Contributing guidelines","text":"currently three sources documenting package: reference manual, including package basic information functions documentation files. package vignettes. github.io website (built pkgdown package).","code":""},{"path":"https://wadpac.github.io/GGIR/CONTRIBUTING.html","id":"reference-manual","dir":"","previous_headings":"Changes or additions > Package documentation","what":"Reference manual","title":"Contributing guidelines","text":"reference manual gets information .Rd documents within man folder package repository. Therefore, updating information files automatically update reference manual. Note GGIR functions intended direct interaction user, , documentation arguments centralized details section man/GGIR.Rd. example want add extra parameter params_247 documented . , forget include new argument functions  .","code":""},{"path":"https://wadpac.github.io/GGIR/CONTRIBUTING.html","id":"package-vignettes","dir":"","previous_headings":"Changes or additions > Package documentation","what":"Package vignettes","title":"Contributing guidelines","text":"folder vignettes GGIR repository contains .Rmd files. .Rmd files start word ‘chapter’ used traditional package vignettes hosted CRAN. Use files edit existing vignette, use structure vignettes build new one. .Rmd files name starts word ‘chapter’ ignored. chapter-vignettes used github.io website (see next section). create new vignette CRAN create new package vignette CRAN, please use usethis::use_vignette() make sure name vignette file start “chapter”. example, want create new vignette sleep CRAN, may following: create new “sleep.Rmd” file within vignettes folder GGIR repository. can edit file build vignette. remove vignette CRAN two ways remove vignette CRAN: Removing Rmd file corresponding vignette vignettes folder, note file information lost. Adding path vignette .Rbuildignore file available GGIR repository. example, remove GGIRParameters vignette CRAN, can add:","code":"usethis::use_vignette(name = \"sleep\",                       title = \"How to analyse your sleep data in GGIR\") ^vignettes/GGIRParameters.Rmd"},{"path":"https://wadpac.github.io/GGIR/CONTRIBUTING.html","id":"githubio-website","dir":"","previous_headings":"Changes or additions > Package documentation","what":"github.io website","title":"Contributing guidelines","text":"updating adding information github.io website, need use pkgdown configuration file can found repositories root directory, well chapter vignettes discussed . edit information existing chapter Open vignette corresponding chapter wish edit (see _pkgdown.yml) file chapter vignette path (href). Make changes vignette. Run pkgdown::build_site() function. add new chapter Create Rmd file vignette via usethis::use_vignette() make sure name vignette starts “chapter”, example: Open _pkgdown.yml file fill name reference new chapter menu. Make sure follow coding structure rest chapters. Run pkgdown::build_site() function. remove chapter Remove lines corresponding chapter _pkgdown.yml file line 42 onwards. Optionally may remove Rmd file corresponding chapter, step 1, chapter appear github.io website. Run pkgdown::build_site() function. edit name chapter Chapter names defined twice, _pkgdown.yml file vignette file . need make sure titles match first used drop-list github.io website specific page chapter.","code":"usethis::use_vignette(name = \"chapterSleep\",                       title = \"10. How to analyse your sleep data in GGIR\")"},{"path":"https://wadpac.github.io/GGIR/CONTRIBUTING.html","id":"adding-the-changes-to-the-master-branch","dir":"","previous_headings":"Changes or additions > Package documentation","what":"Adding the changes to the master branch","title":"Contributing guidelines","text":"last step committing pushing changes github making pull request contribution package. Note , running pkgdown::build_site() function edit files within docs folder, probably add new files. applies editing information github.io website. important changes files docs folder also part pull requests, otherwise website updated.","code":""},{"path":"https://wadpac.github.io/GGIR/CONTRIBUTING.html","id":"new-release","dir":"","previous_headings":"","what":"New release","title":"Contributing guidelines","text":"GGIR follows release cycle process described document.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/ABI.html","id":null,"dir":"Reference","previous_headings":"","what":"Activity balance index (ABI) — ABI","title":"Activity balance index (ABI) — ABI","text":"function estimates Activity balance index (ABI), transformation self-similarity parameter (SSP), also known scaling exponent alpha.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/ABI.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Activity balance index (ABI) — ABI","text":"","code":"ABI(x)"},{"path":"https://wadpac.github.io/GGIR/reference/ABI.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Activity balance index (ABI) — ABI","text":"x estimated self-similarity parameter (SSP)","code":""},{"path":"https://wadpac.github.io/GGIR/reference/ABI.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Activity balance index (ABI) — ABI","text":"estimated Activity balance index (ABI) real number zero one.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/ABI.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Activity balance index (ABI) — ABI","text":"ABI = exp(-abs(SSP-1)/exp(-2))","code":""},{"path":"https://wadpac.github.io/GGIR/reference/ABI.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Activity balance index (ABI) — ABI","text":"C.-K. Peng, S.V. Buldyrev, S. Havlin, M. Simons, H.E. Stanley, .L. Goldberger Phys. Rev. E, 49 (1994), p. 1685   Mesquita, Victor & Filho, Florencio & Rodrigues, Paulo. (2020). Detection crossover points detrended fluctuation analysis: application EEG signals patients epilepsy. Bioinformatics. 10.1093/bioinformatics/btaa955.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/ABI.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Activity balance index (ABI) — ABI","text":"Ian Meneghel Danilevicz <ian.meneghel-danilevicz@inserm.fr>","code":""},{"path":"https://wadpac.github.io/GGIR/reference/ABI.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Activity balance index (ABI) — ABI","text":"","code":"# Estimate Activity balance index of a very known time series   # available on R base: the sunspot.year.   if (FALSE) { # \\dontrun{       ssp = SSP(sunspot.year)     abi = ABI(ssp)   } # }"},{"path":"https://wadpac.github.io/GGIR/reference/aggregateEvent.html","id":null,"dir":"Reference","previous_headings":"","what":"Aggregates event data as produced by external function — aggregateEvent","title":"Aggregates event data as produced by external function — aggregateEvent","text":"Aggregates event data produced external function","code":""},{"path":"https://wadpac.github.io/GGIR/reference/aggregateEvent.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aggregates event data as produced by external function — aggregateEvent","text":"","code":"aggregateEvent(metric_name, epochsize, daysummary, ds_names,                           fi, di, vari, segmentInfo, myfun = NULL,                           params_247)"},{"path":"https://wadpac.github.io/GGIR/reference/aggregateEvent.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Aggregates event data as produced by external function — aggregateEvent","text":"metric_name Character name metric epochsize Numeric epochsize seconds timeseries daysummary Matrix prelimenary day summary ds_names Daysummary column names fi Column index daysummary di Row index daysummary representing recording day vari Time series derived metashort segmentInfo List passed g.analyse.perday time series indices     segment analyse segment name segment number. myfun See GGIR params_247 See GGIR","code":""},{"path":"https://wadpac.github.io/GGIR/reference/aggregateEvent.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Aggregates event data as produced by external function — aggregateEvent","text":"Vincent T van Hees <v.vanhees@accelting.com>","code":""},{"path":"https://wadpac.github.io/GGIR/reference/apply_cosinor_IS_IV_Analyses.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply Cosinor Analyses to time series — apply_cosinor_IS_IV_Analyses","title":"Apply Cosinor Analyses to time series — apply_cosinor_IS_IV_Analyses","text":"Wrapper function around cosinor_IS_IV_Analyses first prepares time series   applying cosinorAnlayses","code":""},{"path":"https://wadpac.github.io/GGIR/reference/apply_cosinor_IS_IV_Analyses.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply Cosinor Analyses to time series — apply_cosinor_IS_IV_Analyses","text":"","code":"apply_cosinor_IS_IV_Analyses(ts, qcheck, midnightsi, epochsizes, threshold = NULL)"},{"path":"https://wadpac.github.io/GGIR/reference/apply_cosinor_IS_IV_Analyses.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply Cosinor Analyses to time series — apply_cosinor_IS_IV_Analyses","text":"ts Data.frame timestamps acceleration metric. qcheck Vector equal length number rows ts value 1 invalid     timestamps, 0 otherwise. midnightsi Indices midnights time series epochsizes Epoch size ts qcheck respectively threshold See cosinor_IS_IV_Analyses","code":""},{"path":"https://wadpac.github.io/GGIR/reference/apply_cosinor_IS_IV_Analyses.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Apply Cosinor Analyses to time series — apply_cosinor_IS_IV_Analyses","text":"Vincent T van Hees <v.vanhees@accelting.com>","code":""},{"path":"https://wadpac.github.io/GGIR/reference/cosinor_IS_IV_Analyses.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply cosinor anlaysis and extended cosinor analysis — cosinor_IS_IV_Analyses","title":"Apply cosinor anlaysis and extended cosinor analysis — cosinor_IS_IV_Analyses","text":"Applies cosinor anlaysis ActCR package time series, well   IV, phi estimates.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/cosinor_IS_IV_Analyses.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply cosinor anlaysis and extended cosinor analysis — cosinor_IS_IV_Analyses","text":"","code":"cosinor_IS_IV_Analyses(Xi, epochsize = 60, timeOffsetHours = 0, threshold = NULL)"},{"path":"https://wadpac.github.io/GGIR/reference/cosinor_IS_IV_Analyses.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply cosinor anlaysis and extended cosinor analysis — cosinor_IS_IV_Analyses","text":"Xi Vector time series movement indicators maximum < 8 mean < 1     input assumed g-units multiplied 1000. epochsize Numeric epochsize seconds timeOffsetHours Numeric time hours relative next midnight threshold Numeric value use threshold distinguish inactivity active behaviour     IV analysis. GGIR uses parameter threshold.lig set threshold.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/cosinor_IS_IV_Analyses.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Apply cosinor anlaysis and extended cosinor analysis — cosinor_IS_IV_Analyses","text":"Vincent T van Hees <v.vanhees@accelting.com>","code":""},{"path":"https://wadpac.github.io/GGIR/reference/DFA.html","id":null,"dir":"Reference","previous_headings":"","what":"Detrended Fluctuation Analysis — DFA","title":"Detrended Fluctuation Analysis — DFA","text":"Detrended Fluctuation Analysis (DFA)","code":""},{"path":"https://wadpac.github.io/GGIR/reference/DFA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Detrended Fluctuation Analysis — DFA","text":"","code":"DFA(data, scale = 2^(1/8), box_size = 4, m = 1)"},{"path":"https://wadpac.github.io/GGIR/reference/DFA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Detrended Fluctuation Analysis — DFA","text":"data Univariate time series (must vector data frame) scale Specifies ratio successive box sizes (default scale = 2^(1/8)) box_size Vector box sizes (must used conjunction scale = \"F\") m integer polynomial order detrending (default m=1)","code":""},{"path":"https://wadpac.github.io/GGIR/reference/DFA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Detrended Fluctuation Analysis — DFA","text":"Estimated alpha real number zero two.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/DFA.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Detrended Fluctuation Analysis — DFA","text":"DFA fluctuation can computed geometric scale different choices boxes sizes.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/DFA.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Detrended Fluctuation Analysis — DFA","text":"possible estimating alpha multiple time series .","code":""},{"path":"https://wadpac.github.io/GGIR/reference/DFA.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Detrended Fluctuation Analysis — DFA","text":"C.-K. Peng, S.V. Buldyrev, S. Havlin, M. Simons, H.E. Stanley, .L. Goldberger Phys. Rev. E, 49 (1994), p. 1685   Mesquita, Victor & Filho, Florencio & Rodrigues, Paulo. (2020). Detection crossover points detrended fluctuation analysis: application EEG signals patients epilepsy. Bioinformatics. 10.1093/bioinformatics/btaa955.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/DFA.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Detrended Fluctuation Analysis — DFA","text":"Ian Meneghel Danilevicz <ian.meneghel-danilevicz@inserm.fr>   Victor Barreto Mesquita <victormesquita40@hotmail.com>","code":""},{"path":"https://wadpac.github.io/GGIR/reference/DFA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Detrended Fluctuation Analysis — DFA","text":"","code":"# Estimate self-similarity of a very known time series available   # on R base: the sunspot.year.   # Then the spend time with each method is compared.   if (FALSE) { # \\dontrun{     dfa = DFA(sunspot.year)   } # }"},{"path":"https://wadpac.github.io/GGIR/reference/filterNonwearNight.html","id":null,"dir":"Reference","previous_headings":"","what":"Filters short lasting nonwear during the night — filterNonwearNight","title":"Filters short lasting nonwear during the night — filterNonwearNight","text":"Filters short lasting nonwear night. intended   direct use GGIR user.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/filterNonwearNight.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filters short lasting nonwear during the night — filterNonwearNight","text":"","code":"filterNonwearNight(r1, metalong, qwindowImp, desiredtz, params_cleaning, ws2)"},{"path":"https://wadpac.github.io/GGIR/reference/filterNonwearNight.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filters short lasting nonwear during the night — filterNonwearNight","text":"r1 Object r1 created inside function g.weardec vector     zeros ones derived metalong 1 indicates nonwear 0 wear. metalong Object metalong created g.part1 qwindowImp See g.impute desiredtz See GGIR params_cleaning Parameters object cleaning paramete, see GGIR. ws2 Object ws2 equals second value vector parameter windowsizes","code":""},{"path":"https://wadpac.github.io/GGIR/reference/filterNonwearNight.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filters short lasting nonwear during the night — filterNonwearNight","text":"r1 object short lasting nonwear night removed","code":""},{"path":"https://wadpac.github.io/GGIR/reference/filterNonwearNight.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Filters short lasting nonwear during the night — filterNonwearNight","text":"Vincent T van Hees <v.vanhees@accelting.com>","code":""},{"path":"https://wadpac.github.io/GGIR/reference/MXLX.html","id":null,"dir":"Reference","previous_headings":"","what":"MX LX analysis — MXLX","title":"MX LX analysis — MXLX","text":"Identifies least active X hours","code":""},{"path":"https://wadpac.github.io/GGIR/reference/MXLX.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MX LX analysis — MXLX","text":"","code":"MXLX(Y = NULL, X = 5, epochSize = 1, tseg = c(0, 24), resolutionMin = 10)"},{"path":"https://wadpac.github.io/GGIR/reference/MXLX.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MX LX analysis — MXLX","text":"Y Vector numeric time series X Single numeric value X hours epochSize Numeric epoch size seconds Y tseg Numeric vector length two reflecting time windownof Y real clock hours.     example, Y represent noon-midnight     set tseg c(12, 24). resolutionMin Numeric value indicate resolution minutes MX LX search","code":""},{"path":"https://wadpac.github.io/GGIR/reference/MXLX.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MX LX analysis — MXLX","text":"data.frame : LX average value Y LX LXhr hour day LX starts start_LX index Y LX starts end_LX index Y LX ends MX average value Y MX MXhr hour day MX starts start_MX index Y MX starts end_MX index Y MX ends","code":""},{"path":"https://wadpac.github.io/GGIR/reference/MXLX.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"MX LX analysis — MXLX","text":"Vincent T van Hees <v.vanhees@accelting.com>","code":""},{"path":"https://wadpac.github.io/GGIR/reference/SSP.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimated self-similarity parameter — SSP","title":"Estimated self-similarity parameter — SSP","text":"function estimates self-similarity parameter (SSP), also known scaling exponent alpha.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/SSP.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimated self-similarity parameter — SSP","text":"","code":"SSP(data,scale = 2^(1/8),box_size = 4,m=1)"},{"path":"https://wadpac.github.io/GGIR/reference/SSP.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimated self-similarity parameter — SSP","text":"data Univariate time series (must vector data frame) scale Specifies ratio successive box sizes (default scale = 2^(1/8)) box_size Vector box sizes (must used conjunction scale = \"F\") m integer polynomial order detrending (default m=1)","code":""},{"path":"https://wadpac.github.io/GGIR/reference/SSP.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimated self-similarity parameter — SSP","text":"Estimated alpha real number zero two.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/SSP.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimated self-similarity parameter — SSP","text":"DFA fluctuation can computed geometric scale different choices boxes sizes.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/SSP.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Estimated self-similarity parameter — SSP","text":"possible estimating alpha multiple time series .","code":""},{"path":"https://wadpac.github.io/GGIR/reference/SSP.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Estimated self-similarity parameter — SSP","text":"C.-K. Peng, S.V. Buldyrev, S. Havlin, M. Simons, H.E. Stanley, .L. Goldberger Phys. Rev. E, 49 (1994), p. 1685   Mesquita, Victor & Filho, Florencio & Rodrigues, Paulo. (2020). Detection crossover points detrended fluctuation analysis: application EEG signals patients epilepsy. Bioinformatics. 10.1093/bioinformatics/btaa955.","code":""},{"path":"https://wadpac.github.io/GGIR/reference/SSP.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimated self-similarity parameter — SSP","text":"Ian Meneghel Danilevicz <ian.meneghel-danilevicz@inserm.fr>   Victor Barreto Mesquita <victormesquita40@hotmail.com>","code":""},{"path":"https://wadpac.github.io/GGIR/reference/SSP.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimated self-similarity parameter — SSP","text":"","code":"# Estimate self-similarity of a very known time series available on R base: the sunspot.year.   # Then the spend time with each method is compared.   if (FALSE) { # \\dontrun{     ssp = SSP(sunspot.year)   } # }"},{"path":"https://wadpac.github.io/GGIR/reference/visualReport.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate visualisation of time series produced by part 5. — visualReport","title":"Generate visualisation of time series produced by part 5. — visualReport","text":"Function called GGIR. intended   direct use user","code":""},{"path":"https://wadpac.github.io/GGIR/reference/visualReport.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate visualisation of time series produced by part 5. — visualReport","text":"","code":"visualReport(metadatadir = c(),                f0 = c(), f1 = c(), overwrite = FALSE,               desiredtz = \"\",               verbose = TRUE,               part6_threshold_combi = NULL, GGIRversion = NULL,               params_sleep = NULL,               params_output = NULL)"},{"path":"https://wadpac.github.io/GGIR/reference/visualReport.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate visualisation of time series produced by part 5. — visualReport","text":"metadatadir Directory holds folder 'meta' inside folder 'basic'     contains milestone data produced g.part1. folderstructure     normally created g.part1 GGIR recognise     value metadatadir . f0 File index start (default = 1). Index refers filenames sorted     alphabetical order f1 File index finish (defaults number files available, .e., f1 = 0) overwrite See GGIR desiredtz See GGIR verbose See GGIR part6_threshold_combi See GGIR GGIRversion Character GGIR version number params_sleep See GGIR params_output See GGIR","code":""},{"path":"https://wadpac.github.io/GGIR/reference/visualReport.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate visualisation of time series produced by part 5. — visualReport","text":"values, function generates plot","code":""},{"path":"https://wadpac.github.io/GGIR/reference/visualReport.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate visualisation of time series produced by part 5. — visualReport","text":"Vincent T van Hees <v.vanhees@accelting.com>","code":""},{"path":"https://wadpac.github.io/GGIR/RELEASE_CYCLE.html","id":null,"dir":"","previous_headings":"","what":"Version numbering","title":"Version numbering","text":"use version encoding .B-C: increases major changes affect backward compatibility previous releases like changes function names, function arguments file format. B increases every CRAN release. aim avoid four CRAN releases per year. C increases every GitHub release. aim avoid one GitHub release per month.","code":""},{"path":"https://wadpac.github.io/GGIR/RELEASE_CYCLE.html","id":"github-releases","dir":"","previous_headings":"","what":"GitHub releases","title":"Version numbering","text":"releasing, please make sure check following: Create GitHub issue least 1 weeks intended release announce release indicate release. Make sure change log inst/NEWS.Rd date says “GitHub--release date” rather “release date” Make sure third (last) digit version number incremented one relative master branch date present date. applies files DESCRIPTION, GGIR-package.Rd NEWS.Rd file. Use function prepareNewRelease.R root GGIR double check version number date consistent files. Update package contributor list new people contributed. Run R CMD check ---cran make sure tests checks pass. Note GitHub releases require release name. typically choose random name city town South America. Whatever choose easy read remember word.","code":""},{"path":"https://wadpac.github.io/GGIR/RELEASE_CYCLE.html","id":"cran-releases","dir":"","previous_headings":"","what":"CRAN releases","title":"Version numbering","text":"CRAN release, follow following steps: Create GitHub issue least 4 weeks intended CRAN release announcing release indicating release list. CRAN release come major changes covered GitHub-releases. change log now say “release date” rather “GitHub--release date”. Second digit version number incremented 1 relative current CRAN version. Check whether new R version released coming make sure GGIR also tested version. Run RStudio devtools::check( manual = TRUE, remote = TRUE, incoming = TRUE) help check urls Ask Vincent (GitHub tag: vincentvanhees) submit release CRAN needs come e-mail address.","code":""}]