{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T23:58:45Z","timestamp":1787961525928,"version":"build-2784847793"},"reference-count":49,"publisher":"American Medical Association (AMA)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Neurol"],"published-print":{"date-parts":[[2026,7,1]]},"abstract":"\n Importance<\/jats:title>\n Disruptions in the sleep-wake cycle have been reported in the preclinical period of dementia; whether they contribute to dementia prediction remains unclear.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To examine associations of accelerometer-derived sleep-wake cycle metrics with incident dementia and their contribution to dementia risk prediction in models containing age and known risk factors.<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n This study included 2 prospective UK population-based cohort studies: (1) UK Biobank (derivation study) and (2) Whitehall II (validation study). A UK Biobank accelerometer substudy was undertaken from 2013 to 2015, yielding accelerometer data on 103\u202f278 participants. A Whitehall II accelerometer substudy was undertaken from 2012 to 2013 that provided data on 4267 participants. Analyses were performed between August 2024 and November 2025. Included participants were 60 years and older, without dementia, and with valid accelerometer and covariate data.<\/jats:p>\n <\/jats:sec>\n \n Exposures<\/jats:title>\n Thirty-six accelerometer-derived sleep-wake cycle metrics were extracted. A machine learning approach identified and combined metrics predicting dementia risk.<\/jats:p>\n <\/jats:sec>\n \n Main Outcome and Measure<\/jats:title>\n Incident all-cause dementia, ascertained from electronic health records.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Analyses were based on 53\u202f448 UK Biobank participants (mean [SD] age,\u200967.5\u2009[4.2] years; 28\u202f448 female [54.2%]; mean [SD] follow-up,\u20097.8\u2009[1.1] years) and 3965 Whitehall II participants (mean [SD] age,\u200969.4\u2009[5.7] years; 1025 female [25.9%]; mean [SD] follow-up,\u200910.6\u2009[2.4] years). In UK Biobank, 9 sleep-wake cycle metrics were combined in 2 components. Higher values in component 1 represented shorter durations and less frequent bouts of moderate to vigorous physical activity, more time in low-intensity activity, lower diversity of activity intensities, and higher probabilities to transition from activity to rest during daytime. Higher component 2 corresponded to more extreme sleep durations, longer wake bouts during sleep, lower probabilities to transition from wake to sleep, and earlier waking time. Both components were associated with higher dementia risk (component 1: hazard ratio [HR],\u20091.43; 95% CI,\u20091.33-1.54; component 2: HR, 1.10; 95% CI,\u20091.04-1.17) and improved prediction of a model including sociodemographic, behavioral, and health-related factors (increase in C index\u2009=\u20090.018; 95% CI,\u20090.011-0.025). Results were confirmed in the Whitehall II cohort study. Compared with an age-only prediction model, adding the components led to an increase in C index equivalent to that for\n APOE<\/jats:italic>\n genotype.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n Results of this cohort study show that accelerometer-derived sleep-wake cycle measures were associated with dementia, and made a modest, statistically significant contribution to its prediction. Future studies should evaluate their clinical utility as scalable markers alongside established predictors for early identification of individuals at risk of dementia.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamaneurol.2026.1232","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T15:30:16Z","timestamp":1779118216000},"page":"694","source":"Crossref","is-referenced-by-count":2,"title":["Digital Sleep-Wake Cycle Metrics and Dementia Prediction in Older Adults"],"prefix":"10.1001","volume":"83","author":[{"given":"Cl\u00e9mence","family":"Cavaill\u00e8s","sequence":"first","affiliation":[{"name":"Universit\u00e9 Paris Cit\u00e9 and Universit\u00e9 Sorbonne Paris Nord, Inserm U1153, INRAE, Centre for Research in Epidemiology and Statistics, Epidemiology of Ageing and Neurodegenerative Diseases, Paris, France"},{"name":"Department of Psychiatry and Behavioral Sciences, University of California, San 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