@misc{10481/76529,
year = {2022},
month = {7},
url = {http://hdl.handle.net/10481/76529},
abstract = {Digital clinical measures based on data collected by wearable devices have seen rapid growth in both
clinical trials and healthcare. The widely-used measures based on wearables are epoch-based physical
activity counts using accelerometer data. Even though activity counts have been the backbone of
thousands of clinical and epidemiological studies, there are large variations of the algorithms that
compute counts and their associated parameters—many of which have often been kept proprietary
by device providers. This lack of transparency has hindered comparability between studies using
different devices and limited their broader clinical applicability. ActiGraph devices have been the
most-used wearable accelerometer devices for over two decades. Recognizing the importance of
data transparency, interpretability and interoperability to both research and clinical use, we here
describe the detailed counts algorithms of five generations of ActiGraph devices going back to the
first AM7164 model, and publish the current counts algorithm in ActiGraph’s ActiLife and CentrePoint
software as a standalone Python package for research use. We believe that this material will provide
a useful resource for the research community, accelerate digital health science and facilitate clinical
applications of wearable accelerometry.},
organization = {Swedish Research Council for Health, Working Life and Welfare (FORTE)	2021-00036},
organization = {United States Department of Health & Human Services
National Institutes of Health (NIH) - USA
NIH National Institute of Mental Health (NIMH)	5U01MH116928},
publisher = {Nature},
title = {Quantification of acceleration as activity counts in ActiGraph wearable},
doi = {10.1038/s41598-022-16003-x},
author = {Neishabouri, Ali and Hidalgo Migueles, Jairo},
}