Wearable‐Derived Diurnal Alignment Between Physical Activity and Device Temperature Predicts Future Disease and Mortality Risk

ABSTRACT

Circadian rhythms coordinate physiology with the 24 h light‐dark cycle, and their disruption contributes to diseases spanning metabolic, cardiovascular, and neuropsychiatric domains. Whether the temporal coherence between wearable‐derived activity and temperature rhythms predicts long‐term health outcomes in free‐living humans remains unknown. Here, analyzing week‐long concurrent wrist‐worn acceleration and device temperature recordings from approximately 90,000 UK Biobank participants (median age 63 years), we decompose the circular cross‐correlation between behavioral and device temperature signals into three alignment features, including 24 h coupling strength (M ₂₄ ), phase deviation from expected antiphase (D ₂₄ ), and 12 h harmonic magnitude (M ₁₂ ). Over 7–11 years of prospective follow‐up, higher M ₂₄ is associated with lower risk of type 2 diabetes, cardiovascular disease, depression, sleep apnea, and all‐cause mortality, whereas higher D ₂₄ is associated with increased cardiometabolic risk. Higher M ₁₂ was associated with a lower risk of gastrointestinal and psychiatric conditions. Technical replication in the SHARE cohort supported the portability of the feature‐extraction framework across device protocols. These findings highlight wearable‐derived cross‐domain diurnal alignment as a scalable, prospective predictor of disease risk, with potential implications for population health surveillance.