Time_long2

This function is a whole dataset wrapper for <code>Time</code>

Mobile Motor Activity Research Consortium for Health (mMARCH) is a collaborative network of studies of clinical and community samples that employ common clinical, biological, and digital mobile measures across involved studies. One of the main scientific goals of mMARCH sites is developing a better understanding of the inter-relationships between accelerometry-measured physical activity (PA), sleep (SL), and circadian rhythmicity (CR) and mental and physical health in children, adolescents, and adults. Currently, there is no consensus on a standard procedure for a data processing pipeline of raw accelerometry data, and few open-source tools to facilitate their development. The R package 'GGIR' is the most prominent open-source software package that offers great functionality and tremendous user flexibility to process raw accelerometry data. However, even with 'GGIR', processing done in a harmonized and reproducible fashion requires a non-trivial amount of expertise combined with a careful implementation. In addition, novel accelerometry-derived features of PA/SL/CR capturing multiscale, time-series, functional, distributional and other complimentary aspects of accelerometry data being constantly proposed and become available via non-GGIR R implementations.  To address these issues, mMARCH developed a streamlined harmonized and reproducible pipeline for loading and cleaning raw accelerometry data, extracting features available through 'GGIR' as well as through non-GGIR R packages, implementing several data and feature quality checks, merging all features of PA/SL/CR together, and performing multiple analyses including Joint Individual Variation Explained (JIVE), an unsupervised machine learning dimension reduction technique that identifies latent factors capturing joint across and individual to each of three domains of PA/SL/CR.  In detail, the pipeline generates all necessary R/Rmd/shell files for data processing after running 'GGIR' for accelerometer data. In module 1, all csv files in the 'GGIR' output directory were read, transformed and then merged. In module 2, the 'GGIR' output files were checked and summarized in one excel sheet. In module 3, the merged data was cleaned according to the number of valid hours on each night and the number of valid days for each subject. In module 4, the cleaned activity data was imputed by the average Euclidean norm minus one (ENMO) over all the valid days for each subject. Finally, a comprehensive report of data processing was created using Rmarkdown, and the report includes few exploratory plots and multiple commonly used features extracted from minute level actigraphy data.  Reference: Guo W, Leroux A, Shou S, Cui L, Kang S, Strippoli MP, Preisig M, Zipunnikov V, Merikangas K (2022) Processing of accelerometry data with GGIR in Motor Activity Research Consortium for Health (mMARCH) Journal for the Measurement of Physical Behaviour, 6(1): 37-44.

Wei Guo

mMARCH.AC

Processing of Accelerometry Data with 'GGIR' in mMARCH

Andrew Leroux

Vadim Zipunnikov

Kathleen Merikangas

Time_long2 function

<dl><dt>count.data</dt>
<dd><code>data.frame</code> of dimension n*1442 containing the 1440 minute activity data for all n subject days.
The first two columns have to be ID and Day.</dd>
<dt>weartime</dt>
<dd><code>data.frame</code> with dimension of <code>count.data</code>.
The first two columns have to be ID and Day.</dd>
<dt>thresh</dt>
<dd>threshold to binarize the data.</dd>
<dt>smallerthan</dt>
<dd>Find a state that is smaller than a threshold, or greater than or equal to.</dd>
<dt>bout.length</dt>
<dd>minimum duration of defining an active bout; defaults to 1.</dd></dl>

Arguments

Timne Metrics for Whole Dataset — Time_long2

<dl>

<dt>count.data</dt>
<dd><code>data.frame</code> of dimension n*1442 containing the 1440 minute activity data for all n subject days.
The first two columns have to be ID and Day.</dd>


<dt>weartime</dt>
<dd><code>data.frame</code> with dimension of <code>count.data</code>.
The first two columns have to be ID and Day.</dd>


<dt>thresh</dt>
<dd>threshold to binarize the data.</dd>


<dt>smallerthan</dt>
<dd>Find a state that is smaller than a threshold, or greater than or equal to.</dd>


<dt>bout.length</dt>
<dd>minimum duration of defining an active bout; defaults to 1.</dd>

</dl>

Time_long2: Timne Metrics for Whole Dataset

Description

Usage

Value

Arguments