ArticleFrontiers in digital health2023
SciKit digital health package for accelerometry-measured physical activity: comparisons to existing solutions and investigations of age effects in healthy adults.
Article in Frontiers in digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 2 citations in OpenAlex.
- A Head-to-Head Comparison of Three Literature Algorithms for Physical Activity Endpoints from a Wrist Accelerometer in Free-Living Conditions.Sensors (Basel, Switzerland) · 2026Article
- Cleaning and pre-processing of actigraphy data for physical activity and sleep research: a scoping review.Physiological measurement · 2026Article
- Validation and optimisation of wearable accelerometer data pre-processing for digital measure implementation and development.PloS one · 2026Article
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Authors and funding
8 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Introduction: 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. Methods: 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: Results showed that activity metrics derived with SKDH had moderate to excellent ICC values ( Discussion: These results demonstrate the validity of the implemented SKDH physical activity algorithm, and the potential of the implemented PA metrics in assessing activity changes in free-living conditions.
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