ActiGraph LLC, 49 East Chase St., Pensacola, FL, 32502, US.
Athinoula A. Martinos Center for Biomedical Imaging, MGH/MIT/Harvard, 149, 13th St., Boston, MA, 02129, USA.
Sci Rep. 2022 Jul 13;12(1):11958. doi: 10.1038/s41598-022-16003-x.
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.
基于可穿戴设备收集的数据的数字临床测量在临床试验和医疗保健中都得到了快速发展。基于可穿戴设备的广泛使用的测量方法是基于加速度计数据的基于时间的体力活动计数。尽管活动计数一直是数千项临床和流行病学研究的基础，但计算计数及其相关参数的算法存在很大差异——其中许多算法通常由设备提供商保密。这种不透明性阻碍了使用不同设备的研究之间的可比性，并限制了它们更广泛的临床适用性。二十多年来，ActiGraph 设备一直是最常用的可穿戴加速度计设备。认识到数据透明度、可解释性和互操作性对研究和临床应用的重要性，我们在这里描述了回溯到第一代 AM7164 模型的五代 ActiGraph 设备的详细计数算法，并在 ActiGraph 的 ActiLife 和 CentrePoint 软件中发布了当前的计数算法，作为一个独立的 Python 研究用包。我们相信，这些材料将为研究界提供有用的资源，加速数字健康科学，并促进可穿戴加速度计的临床应用。
