Comparative Analysis and Conversion Between Actiwatch and ActiGraph Open-Source Counts

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Paul H. Lee Southampton Clinical Trials Unit, University of Southampton, Southampton, United Kingdom

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https://orcid.org/0000-0002-5729-6450 *
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Ali Neishabouri ActiGraph LLC, Pensacola, FL, USA

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https://orcid.org/0000-0003-0259-5645
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Andy C.Y. Tse Department of Health and Physical Education, Education University of Hong Kong, Hong Kong, Hong Kong

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https://orcid.org/0000-0002-6187-9499
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Christine C. Guo ActiGraph LLC, Pensacola, FL, USA

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Body-worn sensors have contributed to a rich and growing body of literature in public health and clinical research in the last decades. A major challenge in sensor research is the lack of consistency and standardization of the collection and reporting of the sensor data. The algorithms used to derive these activity counts can be vastly different between manufactures and not always transparent to the researchers. With Philips, one of the major research-grade wearable device manufacturers, discontinuing this product line, many researchers are left in need of alternative solutions and at the risk of not being able to relate their historical data using the Philips Actiwatch 2 devices to future findings with other devices. We herein provide a comparison analysis and conversion method that can be used to convert activity counts from Philips to those from ActiGraph, another major manufacturer who provide both raw acceleration data and count data based on their open-source algorithm to the research community. This work provides an approach to maximize the scientific value of historical actigraphy data collected by the Actiwatch devices to support research continuity in this community. The conversion, however, is not perfect and only offers an approximation, due to the intrinsic difference in the count algorithms between the two accelerometers, and the permanent information loss during data reduction. We encourage future research using body-worn sensors to retain the raw sensor data to ensure data consistency, comparability, and the ability to leverage future algorithm improvement.

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