diff --git a/docs/404.html b/docs/404.html index 316e58822..98d8138bd 100644 --- a/docs/404.html +++ b/docs/404.html @@ -24,7 +24,7 @@ GGIR - 3.1-4 + 3.1-6 @@ -124,7 +125,7 @@

Attribution -

Site built with pkgdown 2.1.0.

+

Site built with pkgdown 2.1.1.

diff --git a/docs/CONTRIBUTING.html b/docs/CONTRIBUTING.html index e62c169e7..416c176d1 100644 --- a/docs/CONTRIBUTING.html +++ b/docs/CONTRIBUTING.html @@ -7,7 +7,7 @@ GGIR - 3.1-4 + 3.1-6 @@ -186,7 +187,7 @@

New release -

Site built with pkgdown 2.1.0.

+

Site built with pkgdown 2.1.1.

diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index edb2f430f..b1e71c725 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -7,7 +7,7 @@ GGIR - 3.1-4 + 3.1-6 @@ -286,7 +287,7 @@

License

diff --git a/docs/RELEASE_CYCLE.html b/docs/RELEASE_CYCLE.html index 3dc5753bc..cd9657511 100644 --- a/docs/RELEASE_CYCLE.html +++ b/docs/RELEASE_CYCLE.html @@ -7,7 +7,7 @@ GGIR - 3.1-4 + 3.1-6 @@ -116,7 +117,7 @@

CRAN releases -

Site built with pkgdown 2.1.0.

+

Site built with pkgdown 2.1.1.

diff --git a/docs/articles/Cookbook.html b/docs/articles/Cookbook.html index 1c9933ddf..e261e2956 100644 --- a/docs/articles/Cookbook.html +++ b/docs/articles/Cookbook.html @@ -21,7 +21,7 @@ GGIR - 3.1-4 + 3.1-6 + + + + + +
+ + + + +
+
+ + + +

In chapters 8, 9, and 10 we discussed the classification of sleep and +in chapter 11 we discussed the classification of daytime physical +behavioural classes. These are typically reported as time spent per +behavioural class. An complementary way of describing the data is by +looking at the fragmentation of these behaviours of time.

+
+

Defining fragments +

+

In GGIR, a fragment for daytime is a defined as a sequence of epochs +that belong to one of the four categories:

+
    +
  1. Inactivity
  2. +
  3. Light Physical Activity (LIPA)
  4. +
  5. Moderate or Vigorous Physical Acitivty (MVPA)
  6. +
  7. Physical activity (can be either LIPA or MVPA)
  8. +
+

Each of these categories represents the combination of bouted and +unbouted time in the respective categories. Inactivity and physical +activity add up to a full day (outside SPT), as well as inactivity, LIPA +and MVPA.

+

A fragment of SPT is defined as a sequence of epochs that belong to +one of the four categories:

+
    +
  1. Estimated sleep
  2. +
  3. Estimated wakefulness
  4. +
  5. Inactivity
  6. +
  7. Physical activity (can be either LIPA or MVPA)
  8. +
+

With parameter frag.metrics = "all" we can instruct GGIR +part 5 to derive behavioural fragmentation metrics. You may want to +consider combining this with parameter +part5_agg2_60seconds=TRUE as that will aggregate the time +series to 1 minute resolution as is common in behavioural fragmentation +literature. GGIR part 6 performs fragmentation analysis when +part6CR is set to TRUE. For this it uses the +time series output generated in part 5 as discussed in the previous +chapter.

+

GGIR derives fragmentation metrics in two ways:

+
    +
  • In part 5 fragmentation is quantified per waking hours of the day +and reported per day and as recording average of the daily +estimates.
  • +
  • In part 6 fragmentation is quantified based on all data in the +recording within the window as specifed by parameter +part6Window.
  • +
+

Calculation per day allows us to explore and possibly account for +behavioural differences between days of the week. However, a day level +estimate could be considered less robust than the recording level +estimates as generated in part 6.

+

The in internal function g.fragmentation for +fragmentation metric calculation is used in both part 5 and 6 ensuring +that the calculation are otherwise consistent.

+
+
+

Fragmentation metrics +

+

Note that from the fragmentation metrics discussed below only +fragmentation metrics TP and NFrag are +calculated for the SPT fragments.

+
    +
  • Coefficient of Variance (CoV) is calculated +according to Blikman et +al. 2014, which entails dividing the standard deviation by the mean +lognormal transformed fragment length (minutes).

  • +
  • Transition probability (TP) from Inactivity (IN) to +Physical activity (IN2PA), from Physical activity to inactivity (PA2IN), +and from IN to LIPA or MVPA are all calculated according to Danilevicz et +al. 2024.

  • +
  • Gini index is calculated with function Gini from the +ineq R package, and with ineq argument +corr set to TRUE.

  • +
  • Power law exponent metrics: Alpha, x0.5, and W0.5 are calculated +according to Chastin et +al. 2010. Note that compared with R package ActFrag as described in +Junrui Di et +al. 2017 we we use the theoretical minimum fragment duration instead +of the observed minimum fragment duration.

  • +
  • Number of fragment per minutes (NFragPM) is +calculated identical to metric fragmentation index in Chastin et +al. 2012, but it is renamed here to be a more specific reflection of +the calculation. The term fragmentation index appears too +generic given that all fragmentation metrics inform us about +fragmentation. Please note that this is close to the metrics for +transition probability, because total number divided by total sum in +duration equals 1 divided by average duration. Although the exact math +is slightly different.

  • +
+
+
+

Conditions for calculation +

+
    +
  • Metrics Gini and CoV are only +calculated if there are at least 10 fragments (e.g. 5 inactive and 5 +active). If this condition is not met the metric value will be set to +missing.

  • +
  • Metrics related to power law exponent alpha are also only +calculated when there are at least 10 fragments, but with the additional +condition that the standard deviation in fragment duration is not zero. +If these conditions are not met the metric value will be set to +missing.

  • +
  • Other metrics related to binary fragmentation +(mean_dur_PA and mean_dur_IN), are calculated +when there are at least 2 fragments (1 inactive, 1 active). If this +condition is not met the value will is set to zero.

  • +
  • Metrics related to TP are calculated if: There is at +least 1 inactivity fragment AND (1 LIPA OR 1 MVPA fragment). If this +condition is not met the TP metric value is set to +zero.

  • +
+

To keep an overview of which recording days met the criteria for +non-zero standard deviation and at least ten fragments, GGIR part 5 +stores variable Nvaliddays_AL10F at person level (i.e., +number of valid days with at least 10 fragments), and +SD_dur (i.e., standard deviation of fragment durations) at +day level as well as aggregated per person.

+
+
+

Key parameters +

+

The parameters related to cut-points and bout detection are mainly +the parameters listed under “Physical +activity parameters”.

+
+
+ +

In GGIR part 5 csv reports you will find:

+
    +
  • Fragmentation metrics at day level per waking hours of the day
  • +
+

In GGIR part 6 csv report you will find:

+
    +
  • Fragmentation metrics
  • +
+

For an overview of output variables see the GGIR +output annex.

+
+
+
+ + + +
+ + + +
+
+ + + + + + + diff --git a/docs/articles/chapter1_WhatIsGGIR.html b/docs/articles/chapter1_WhatIsGGIR.html index fb1f442ff..e71e431f3 100644 --- a/docs/articles/chapter1_WhatIsGGIR.html +++ b/docs/articles/chapter1_WhatIsGGIR.html @@ -21,7 +21,7 @@ GGIR - 3.1-4 + 3.1-6 @@ -96,6 +97,8 @@

All vignettes

13. Circadian Rhythm Analysis
+
14. Behavioural fragmentation
+
2. The GGIR pipeline
3. Data Quality Assurance
@@ -141,7 +144,7 @@

All vignettes

diff --git a/docs/articles/readmyacccsv.html b/docs/articles/readmyacccsv.html index f46f2b293..a07248c88 100644 --- a/docs/articles/readmyacccsv.html +++ b/docs/articles/readmyacccsv.html @@ -21,7 +21,7 @@ GGIR - 3.1-4 + 3.1-6 @@ -130,7 +131,15 @@

Authors

  • -

    Segantin Gaia. Contributor. +

    Ian Meneghel Danilevicz. Contributor. +

    +
  • +
  • +

    Victor Barreto Mesquita. Contributor. +

    +
  • +
  • +

    Gaia Segantin. Contributor.

  • @@ -153,13 +162,13 @@

    Citation

    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 3.1-4, https://CRAN.R-project.org/package=GGIR. +doi:10.5281/zenodo.1051064, R package version 3.1-6, https://CRAN.R-project.org/package=GGIR.

    @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 3.1-4},
    +  note = {R package version 3.1-6},
       doi = {10.5281/zenodo.1051064},
       url = {https://CRAN.R-project.org/package=GGIR},
     }
    @@ -234,7 +243,7 @@

    Citation

    diff --git a/docs/index.html b/docs/index.html index 6548b3fca..37f170660 100644 --- a/docs/index.html +++ b/docs/index.html @@ -26,7 +26,7 @@ GGIR - 3.1-4 + 3.1-6