Wearable-Derived Digital Biomarkers in Ageing Research: A Systematic Review (Preprint)

BACKGROUND Wearable devices provide continuous, objective measurement of health-related behaviors. Their derived digital biomarkers have potential for monitoring ageing trajectories, yet their predictive validity and role in ageing research remain underexplored. OBJECTIVE This systematic review aims to evaluate the effectiveness of wearable-derived digital biomarkers in capturing ageing processes by synthesizing evidence from prospective cohort studies linking behavioral metrics to mortality and ageing-related outcomes. METHODS A systematic search was conducted in PubMed, Scopus, Web of Science, Embase, IEEE Xplore, and ACM Digital Library for studies published between 2020 and 2025. Eligible studies included adults aged ≥40 years and reported empirical findings on wearable-derived digital biomarkers in ageing. A total of 711 records were screened using Covidence, with 43 studies meeting inclusion criteria, encompassing over 740,000 participants across six countries (United Kingdom, United States, Brazil, Sweden, Spain, Australia, and Japan). Outcomes were synthesized across four domains: physical activity, sedentary behavior, sleep, and circadian rhythm. Hazard ratios (HRs) were standardized and pooled using random-effects meta-analysis with Restricted Maximum Likelihood. Study quality was assessed using a modified Newcastle–Ottawa Scale. RESULTS Higher levels of physical activity were consistently associated with reduced mortality. Pooled hazard ratios indicated strong protective effects, with step count (HR 0.69), moderate-to-vigorous physical activity (HR 0.53), light physical activity (HR 0.55), and overall physical activity (HR 0.49) all linked to lower all-cause mortality. Sedentary behavior was associated with increased risk, with total sedentary time showing an HR of 1.48 for all-cause mortality and 1.51 for cardiovascular mortality. Sedentary patterns, particularly prolonged bouts, showed the most pronounced effects, with hazard ratios reaching up to 14.0 for the highest-risk groups. Sleep duration followed a U-shaped pattern, with both short (<6 hours) and long (>8 hours) sleep associated with higher mortality risk. Greater sleep regularity, as measured by the Sleep Regularity Index, demonstrated protective associations (HR 0.7). Circadian rhythm alterations were generally associated with increased mortality risk, particularly when rhythmicity was diminished, while more robust rhythms (e.g., higher amplitude and earlier rest-activity timing) demonstrated protective associations, though evidence remains limited and heterogeneous. CONCLUSIONS Wearable-derived digital biomarkers, particularly those related to physical activity and sedentary behavior, are strong predictors of longevity. Sleep and circadian rhythm measures provide complementary prognostic information, with consistent sleep timing and robust 24-h activity rhythms conferring protective effects. Standardization of device protocols, multimodal sensor integration, and inclusion of diverse populations are needed to translate these findings into scalable interventions for healthy ageing.