A Walkthrough of ActiGraph Counts

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Ali Neishabouri ActiGraph, Pensacola, FL, USA

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https://orcid.org/0000-0003-0259-5645 *
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Joe Nguyen ActiGraph, Pensacola, FL, USA

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Matthew R. Patterson ActiGraph, Pensacola, FL, USA

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https://orcid.org/0000-0002-9774-4094
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Rakesh Pilkar ActiGraph, Pensacola, FL, USA

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https://orcid.org/0000-0002-3433-9913
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Christine C. Guo ActiGraph, Pensacola, FL, USA

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https://orcid.org/0000-0003-1530-0172
DOI:
https://doi.org/10.1123/jmpb.2023-0048
Keywords:
physical activity; accelerometer; wearable; open-source
First Published Online:
03 Sep 2024
In Print:
Volume 7: Issue 1
Article Number:
jmpb.2023-0048
Restricted access

Activity counts have been used for over two decades with over 22,000 published scientific papers in public health and clinical research. ActiGraph recently released the algorithm for computing counts from raw accelerometer data as an open-source Python library, which is now ported by researchers to other languages, notably R. The current commentary presents historical overview of ActiGraph counts, and its development and evolution as a measure of physical activity. Further, we provide general recommendations on extracting counts from raw accelerometer data and discuss specific considerations with respect to device types, resampling, nonwear, axes orientations, and epoch length that may influence counts. Last, we provide a tutorial on how to use ActiGraph’s open-source Python library, agcounts, for consistent, accurate, and reproducible count. We expect this commentary will provide familiarity and transparency needed to adopt and produce activity counts in a consistent manner, allowing researchers to conduct statistical comparisons across multiple data sets and studies.
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