This repository provides a worked example of using wearables data available in NHANES for an epidemiological analysis associating daily step count with the risk of all-cause mortality. Analyses are based on this paper conducted in the UK Biobank.
The included R markdown files describe:
(1) Exploring the NHANES accelerometer data generated using stepcount.
(2) Conducting an analysis associating daily step count with the risk of all-cause mortality.
Visual inspection of the data is invaluable for understanding what the code is doing. Add statements to get a feel for the data as you work through the tutorials (e.g. head()
, str()
statements in R).
Analytic decisions should be made in the context of each research project. Choices in this repository reflect choices of the authors in the linked papers and the code authors, and should not be interpreted as definitive or widely generalisable.
Instructions for participants of the Reproducible Machine Learning of Wearables in Health Data Science short course
I. Launch JupyterLab and Clone the repository by opening the relevant link to your group which has already been provided to you:
OR
II. Open a Terminal instance from JupyterLab (File > New > Terminal) and then run:
$ git clone https://github.com/OxWearables/epidemiology_nhanes.git
IV. To work with the repository, change the directory into the repository:
$ cd epidemiology_nhanes
V. Open an RStudio session from JupyterLab (File > New Launcher > RStudio)
VI. You can now open and run the R markdown files from the epidemiology_nhanes
directory located on the bottom right panel (Files).
If you have a question, feel free to add an issue on GitHub.
There are probably bugs. If you find them, please let us know! Again, add an issue on GitHub.
This repository was generated for use in the Reproducible Machine Learning of Wearables in Health Data Science course by Ben Maylor and Charilaos Zisou, using material by Rosemary Walmsley and Junayed Naushad, with contributions and advice from Ondrej Klempir, Aiden Doherty, and Ben Busby.