A Local Enrichment Computational Framework to Identify Disease-Enriched Physical Activity Patterns
- 1University of Michigan
Abstract
Wearable accelerometers enable continuous monitoring of physical activity (PA), yet conventional summary measures and population-wide clustering approaches may overlook disease-associated behavioral heterogeneity. Here we introduce LocPA, a computational framework that identifies disease-enriched PA patterns through confounder-adjusted activity profiling, similarity network construction, local disease-enrichment testing and community detection. Applying LocPA to 24-hour accelerometry profiles from the UK Biobank, we identified two recurring PA phenotypes across six diseases spanning cardiometabolic, neurological, psychiatric, renal and cardiovascular conditions: a low-activity phenotype characterized by reduced daytime activity and a sleep-disrupted phenotype characterized by reduced daytime and elevated nocturnal activity. In hypertension and diabetes, the sleep-disrupted phenotype was associated with greater disease burden and less favorable clinical and metabolic profiles, differences not consistently captured by conventional PA summary measures. LocPA also identified disease-specific temporal patterns, particularly in Parkinsons disease. Analyses in an independent National Health and Nutrition Examination Survey cohort supported the recurrence of hypertension- and diabetes-enriched patterns across populations and accelerometry measurements. LocPA provides a flexible, disease-driven approach for uncovering clinically relevant temporal PA patterns, with potential applications in disease risk characterization and personalized behavioral interventions.
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BibTeX
@misc{qian2026local,
title = {{A Local Enrichment Computational Framework to Identify Disease-Enriched Physical Activity Patterns}},
author = {h. qian and c. wu and B. Li and A. Rosenzweig and M. Wang},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.22.26366260},
url = {https://www.medrxiv.org/content/10.64898/2026.10.22.26366260v1},
note = {CC BY-ND},
}
RIS
TY - UNPB
TI - A Local Enrichment Computational Framework to Identify Disease-Enriched Physical Activity Patterns
AU - h. qian
AU - c. wu
AU - B. Li
AU - A. Rosenzweig
AU - M. Wang
PY - 2026
DA - 2026/10/22
PB - medRxiv
DO - 10.64898/2026.10.22.26366260
UR - https://www.medrxiv.org/content/10.64898/2026.10.22.26366260v1
AB - Wearable accelerometers enable continuous monitoring of physical activity (PA), yet conventional summary measures and population-wide clustering approaches may overlook disease-associated behavioral heterogeneity. Here we introduce LocPA, a computational framework that identifies disease-enriched PA patterns through confounder-adjusted activity profiling, similarity network construction, local disease-enrichment testing and community detection. Applying LocPA to 24-hour accelerometry profiles from the UK Biobank, we identified two recurring PA phenotypes across six diseases spanning cardiometabolic, neurological, psychiatric, renal and cardiovascular conditions: a low-activity phenotype characterized by reduced daytime activity and a sleep-disrupted phenotype characterized by reduced daytime and elevated nocturnal activity. In hypertension and diabetes, the sleep-disrupted phenotype was associated with greater disease burden and less favorable clinical and metabolic profiles, differences not consistently captured by conventional PA summary measures. LocPA also identified disease-specific temporal patterns, particularly in Parkinsons disease. Analyses in an independent National Health and Nutrition Examination Survey cohort supported the recurrence of hypertension- and diabetes-enriched patterns across populations and accelerometry measurements. LocPA provides a flexible, disease-driven approach for uncovering clinically relevant temporal PA patterns, with potential applications in disease risk characterization and personalized behavioral interventions.
ER -