Comparative Analysis and Conversion between ActiWatch and ActiGraph Open-Source Counts

Abstract Body-worn sensors have contributed to a rich and growing body of literature in public health and clinical research in the last decades. The translation of these research findings to clinical application, however, is lagging. A major challenge is the lack of consistency and standardization of the collection and reporting of the sensor data. In the case of accelerometer, the most used body-worn sensor, device manufacturers provided “activity counts” as a summary of total acceleration for each 5s to 60s epoch of data collected. The algorithms used to derive these activity counts can be vastly different between manufactures and not always transparent to the researchers. This discrepancy in “activity counts” can lead to confusion and misuse of actigraphy data in research and make it challenging to compare data collected from different devices. 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 Philips devices to future findings with other devices. Recognizing this gap and urgent needs, we here 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 open-source count algorithm to the research community. This work provides an approach to maximize the scientific value of actigraphy data collected by Philips devices to support research continuity in this community. The conversion, however, is not perfect and only offers a proximation, due to the intrinsic difference in the count algorithms between ActiGraph and Philips, and the permanent information loss during data reduction. We encourage future research using body-worn sensors retain raw sensor data as the source data to ensure data consistency, comparability, and the ability to leverage future algorithm improvement.