Precise Segmentation of U.S. Adults from 24-Hour Wearable-based Physical Activity Profiles Using Machine Learning Clustering
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Date
2023-12-11Type
- Conference Paper
ETH Bibliography
yes
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Abstract
Growing evidence shows that wearable-based physical activity data (e.g., accelerometer) could be used as digital biomarkers to identify individuals at higher risk for chronic diseases and to monitor the effectiveness of interventions. Physical activity classification based on pre-established metrics misclassifies 20-40% of the time, and thus, underscores the need for a more precise and accurate classification method. To this end, we investigated different machine learning algorithms to improve the clustering of 24-hour wearable-based physical activity profiles of the U.S. population in 2011-2014 NHANES. Hierarchical clustering identified 46% of U.S. adults with "High Activity/Robust Pattern" (Cluster 1) and 54% with "Low Activity/Weak Pattern" (Cluster 2). Cluster 1 shows elevated amplitude, high stability, and a good circadian rhythm alignment. In contrast, Cluster 2 exhibits dampened amplitude, fragmented rhythm, and substantially high nocturnal activity. Cluster 2 was also associated with older age, males, and longer sedentary hours (all p<0.05). The data-driven clustering can improve population segmentation and digital phenotyping of the general population. Show more
Publication status
publishedExternal links
Book title
IEEE 11th International Conference on Healthcare Informatics (ICHI 2023)Pages / Article No.
Publisher
IEEEEvent
Subject
Digital biomarkers; Disease prevention; Machine learning; Risk factors; Wearables; Physical activityOrganisational unit
02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.03681 - Fleisch, Elgar / Fleisch, Elgar
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ETH Bibliography
yes
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