Sleep-Wake Cycle Patterns May Help Predict Dementia Risk: JAMA
France: Researchers have found, in a cohort study, that accelerometer-derived sleepp-wake cycle measures were associated with dementia and contributed modestly but statistically significantly to its prediction. These findings suggest that objective sleep and activity pattern monitoring may serve as a scalable tool alongside established risk factors for the early identification of individuals at increased risk of dementia.
Published in JAMA Neurology, the study was led by Clémence Cavaillès and colleagues from Université Paris Cité and related French research institutions. It assessed whether accelerometer-derived sleep–wake cycle (SWC) patterns are linked to future dementia risk and whether they enhance prediction beyond established clinical and demographic factors.
The analysis used two large cohorts: the UK Biobank for derivation and the Whitehall II study for external validation, including adults aged ≥60 years without dementia at baseline. In total, over 53,000 participants from the UK Biobank and nearly 4,000 from Whitehall II were included. Wrist-worn accelerometers generated 36 sleep, activity, and chronotype metrics, which were processed using machine learning to identify predictive patterns.
Nine key metrics were grouped into two components. The first reflected reduced and less varied daytime activity with lower moderate-to-vigorous physical activity, higher sedentary time, and more frequent transitions from activity to rest. The second captured disrupted sleep, including irregular duration, fragmented sleep with longer awakenings, unstable sleep onset, and earlier wake times.
The study led to the following findings:
- Over a median follow-up of around eight years in the UK Biobank, both sleep–wake cycle components were linked with a higher risk of developing dementia.
- Higher scores on the first component were associated with a 43% increased risk of incident dementia.
- The second component also showed a statistically significant, though smaller, association with dementia risk.
- These associations were replicated in the Whitehall II cohort, supporting the robustness of the findings.
- Adding sleep–wake cycle measures to existing prediction models improved dementia risk prediction beyond age, lifestyle, and clinical factors.
- The improvement in predictive accuracy was modest but statistically significant.
- The predictive contribution of these measures was comparable to that of genetic risk markers such as APOE status.
The researchers acknowledged several limitations. The follow-up period may be too short, given the long preclinical phase of dementia. Accelerometer data captured only one week of activity, which may not reflect habitual long-term patterns. Participants were also generally healthier than the wider population, limiting generalizability. In addition, the study could not assess dementia subtypes and lacked information on sleep disorders.
Overall, the findings suggest that disrupted sleep–wake rhythms and altered activity patterns may serve as early indicators of dementia risk. The authors highlight the need for further research to evaluate how such scalable, noninvasive measures could complement existing biomarkers for earlier identification of high-risk individuals.
Reference:
Cavaillès C, Danilevicz IM, Vidil S, et al. Digital Sleep-Wake Cycle Metrics and Dementia Prediction in Older Adults. JAMA Neurol. Published online May 18, 2026. doi:10.1001/jamaneurol.2026.1232
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