Wearable-based Physical Activity as a Digital Biomarker of Inflammation, Biological Age, and Mortality: A Hierarchical Clustering Analysis of NHANES 2011-2014

Abstract

Abstract Current measures of health and longevity are based on factors such as inflammation, biological age, and mortality. The potential of using continuously collected data from digital devices to explain these measures remains largely unstudied. In this work, we investigate a data-driven segmentation of the 24-hour physical activity profiles from wearables as a novel digital biomarker for longevity in 7,297 U.S. adults from the 2011–2014 National Health and Nutrition Examination Survey. Using hierarchical clustering, we identified five clusters and described them as follows: “High physical activity (PA)”, “Low PA”, “Mild circadian rhythm (CR) disruption”, “Extreme CR disruption”, and “Very low PA”. Young adults with extreme CR disturbance are seemingly healthy with few comorbid conditions, but in fact associated with higher white blood cell, neutrophils, and lymphocyte counts (0.05–0.07 log-unit, all p < 0.05) and accelerated biological aging (1.45 years, p < 0.001). Older adults with CR disruption are significantly associated with increased systemic inflammation indexes (0.09–0.13 log-unit, all p < 0.001), biological aging advance (1.31 years, p = 0.008), and all-cause mortality risk (HR = 1.67, p = 0.019). Our findings highlight the importance of circadian alignment on longevity across all ages and suggest that digitally measured physical activity data can help in identifying at-risk populations and personalize treatments for healthier aging.