Helsel Brian C, Hibbing Paul R, Montgomery Robert N, Vidoni Eric D, Ptomey Lauren T, Clutton Jonathan, Washburn Richard A
KU Alzheimer's Disease Research Center, The University of Kansas Medical Center, Kansas City, KS, USA.
Department of Kinesiology and Nutrition, University of Illinois Chicago, Chicago, IL, USA.
J Meas Phys Behav. 2024 Jan;7(1). doi: 10.1123/jmpb.2023-0037. Epub 2024 Apr 26.
Portable accelerometers are used to capture physical activity in free-living individuals with the ActiGraph being one of the most widely used device brands in physical activity and health research. Recently, in February 2022, ActiGraph published their activity count algorithm and released a Python package for generating activity counts from raw acceleration data for five generations of ActiGraph devices. The nonproprietary derivation of the ActiGraph count improved the transparency and interpretation of accelerometer device-measured physical activity, but the Python release of the count algorithm does not integrate with packages developed by the physical activity research community using the R Statistical Programming Language. In this technical note, we describe our efforts to create an R-based translation of ActiGraph's Python package with additional extensions to make data processing easier and faster for end users. We call the resulting R package and provide an inside look at its key functionalities and extensions while discussing its prospective impacts on collaborative open-source software development in physical behavior research. We recommend that device manufacturers follow ActiGraph's lead by providing open-source access to their data processing algorithms and encourage physical activity researchers to contribute to the further development and refinement of and other open-source software.
便携式加速度计用于捕捉自由生活个体的身体活动,其中ActiGraph是身体活动与健康研究中使用最广泛的设备品牌之一。最近,在2022年2月,ActiGraph发布了其活动计数算法,并发布了一个Python包,用于从五代ActiGraph设备的原始加速度数据生成活动计数。ActiGraph计数的非专有推导提高了加速度计设备测量身体活动的透明度和可解释性,但计数算法的Python版本与使用R统计编程语言的身体活动研究社区开发的包不兼容。在本技术说明中,我们描述了我们为创建基于R的ActiGraph Python包翻译版本所做的努力,并进行了额外扩展,以使最终用户的数据处理更轻松、更快速。我们将由此产生的R包称为 ,并深入介绍其关键功能和扩展,同时讨论其对身体行为研究中协作式开源软件开发的潜在影响。我们建议设备制造商效仿ActiGraph的做法,提供对其数据处理算法的开源访问,并鼓励身体活动研究人员为 和其他开源软件的进一步开发与完善做出贡献。