Patterns of sedentary time accumulation according to age in the United States: A 2003-2006 NHANES analysis

This repository contains the materials relating to the abstract submission to 2024 ICAMPAM conference. The code implements an analytical pipeline using R programming language. The pipeline performs the followings:

• Download NHANES 2003-2006 demographic, anthropometric, and accelerometer data.
• Process accelerometer data.
• Join data.
• Generate a report of the analyses.

The analyses of accelerometer data are based on the {activAnalyzer} package. If you want to reproduce the analytical pipeline on your machine using an RStudio environment, please follow the steps below.

• Step 1: Install R (recommended versions: >=4.3.1), RStudio, and the latest version of Rtools (if your are a Windows user) on your machine. To retrieve previous releases of R, you can go here for Windows, and here for Mac.

• Step 2: Go to the GitHub repository of the project and click on the green button called Code. Then click on Download ZIP.

• Step 3: Unzip the zipped folder on your machine, open it, and double-click on the activAnalyzer.nhanes.Rproj file to open the project in RStudio.

• Step 4: Restore the package dependencies of the project with {renv} using in the Console the command line shown below and then following the instructions proposed in the Console.

renv::restore()

This may take several minutes so that all the required packages are downloaded from the web and then installed in the project directory.

• Step 5: Run the analytical pipeline with {targets} using the following command line in the Console:

targets::tar_make()

This last step will produce all the objects relating to the analytical process.

Running the whole pipeline will take several hours (likely between 5 and 7 hours depending on the features of your machine). This is due to the high volume of data to be downloaded from the web, as well as to an important number of accelerometer data files to be processed (more than 12,000 files). Processing accelerometer data here is pretty slow, even using parallel processing, because some functions of the {activAnalyzer} package are pretty time consumming and cannot easily be improved as they depend on other packages. There are some packages available on the web that allow very fast analyses of NHANES accelerometer data thanks to the implementation of C++ code and a smarter structure of the data (e.g., {accelerometry}). So why the {activAnalyzer} package ? This is mainly due to a lack of awareness of other packages available to compute particular metrics as activity accumulation metrics, which was required for the present work.

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