SciKit digital health package for accelerometry-measured physical activity: comparisons to existing solutions and investigations of age effects in healthy adults

• Published in: Frontiers in digital health 2023-11, Vol.5, p.1321086-1321086
• Main Authors: Lin, Wenyi, Karahanoglu, F Isik, Demanuele, Charmaine, Khan, Sheraz, Cai, Xuemei, Santamaria, Mar, Di, Junrui, Adamowicz, Lukas
• Format: Article
• Language: English
• Subjects: Digital Health
• ISSN: 2673-253X; 2673-253X
• DOI: 10.3389/fdgth.2023.1321086

Accelerometry has become increasingly prevalent to monitor physical activity due to its low participant burden, quantitative metrics, and ease of deployment. Physical activity metrics are ideal for extracting intuitive, continuous measures of participants' health from multiple days or weeks of high frequency data due to their fairly straightforward computation. Previously, we released an open-source digital health python processing package, SciKit Digital Health (SKDH), with the goal of providing a unifying device-agnostic framework for multiple digital health algorithms, such as activity, gait, and sleep. In this paper, we present the open-source SKDH implementation for the derivation of activity endpoints from accelerometer data. In this implementation, we include some non-typical features that have shown promise in providing additional context to activity patterns, and provide comparisons to existing algorithms, namely GGIR and the GENEActiv macros. Following this reference comparison, we investigate the association between age and derived physical activity metrics in a healthy adult cohort collected in the Pfizer Innovation Research Lab, comprising 7-14 days of at-home data collected from younger (18-40 years) and older (65-85 years) healthy volunteers. Results showed that activity metrics derived with SKDH had moderate to excellent ICC values ( to compared to GGIR, to compared to the GENEActiv macros), with high correlations for almost all compared metrics (>0.733 except vs GGIR sedentary time, ). Several features show age-group differences, with Cohen's effect sizes >1.0 and