Longitudinal digital phenotyping of activity rhythms and biological aging using commercial wearables

Abstract

Abstract Disrupted rest-activity rhythms have been associated with aging and chronic disease, yet longitudinal evidence from free-living populations has been lacking. Here we integrate multi-year Fitbit activity data from the All of Us Research Program with clinical biomarker-derived PhenoAge from 2,222 participants (8,447 person-years). Through high-dimensional digital phenotyping, we show that circadian rest-activity rhythm intensity, timing, and stability are associated with biological aging trajectories. Higher rhythm intensity was associated with 26–46% lower odds of accelerated aging. Associations of timing and regularity were stronger in females. In males, accelerated aging followed a biphasic instability pattern with early-morning surges and late-evening rebounds. These findings provide large-scale longitudinal evidence that consumer wearable-derived rest-activity rhythms may serve as digital biomarkers of aging trajectories. By linking population-scale digital phenotyping to biological aging, this work highlights the potential of scalable digital measures for aging-related risk assessment and future healthy-aging research.