Daily Activity-Temperature Misalignment Predicts Disease and Early Death
A study of 90,000 people found that wearable devices can detect misalignment between physical activity and body temperature rhythms—a simple marker of circadian disruption that predicts type 2 diabetes, heart disease, depression, and premature death years in advance. The discovery could transform how insurers, employers, and health systems use wearable data to identify at-risk populations before illness strikes.
Originaltitel: Wearable‐Derived Diurnal Alignment Between Physical Activity and Device Temperature Predicts Future Disease and Mortality Risk
Bärbara sensorer kan förutsäga allvarliga sjukdomar genom att mäta tidsöverensstämmelsen mellan rörelseaktivitet och kroppstemperatur. Ett team vid Sir Run Run Shaw Hospital analyserade veckalånga data från cirka 90 000 deltagare i UK Biobank (medelålder 63 år) och identifierade tre nyckelparametrar för denna överensstämmelse. Högre 24-timmarskoppling korrelerade med lägre risk för typ 2-diabetes, hjärt-kärlsjukdomar, depression, sömnökning och mortalitet. Högre fasavvikelse associerades med ökad kardiometabolisk risk, medan högt 12-timmarsmönster skyddade mot mag- och psykiatriska sjukdomar. Resultaten följde upp mellan 7–11 år. Metoden validerades på oberoende SHARE-kohort, vilket bekräftar portabiliteten mellan olika sensorer. För leverantörer av bärbara enheter och folkhälsoaktörer öppnas vägen till skalbar långtidsövervakning baserad på enfaldiga accelerometri- och temperaturdatasekvenser — utan invasiva invasiva mätningar.
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.