Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality.

Autor: Yuan H; Nuffield Department of Population Health, University of Oxford, Oxford, UK.; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK., Plekhanova T; Diabetes Research Centre, University of Leicester, Leicester, UK., Walmsley R; Nuffield Department of Population Health, University of Oxford, Oxford, UK.; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK., Reynolds AC; College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia., Maddison KJ; Centre of Sleep Science, School of Human Sciences, University of Western Australia, Perth, WA, Australia.; West Australian Sleep Disorders Research Institute, Department of Pulmonary Physiology, Sir Charles Gairdner Hospital, Nedlands, WA, Australia., Bucan M; Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA., Gehrman P; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA., Rowlands A; Diabetes Research Centre, University of Leicester, Leicester, UK.; NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester, UK., Ray DW; NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK.; Oxford Centre for Diabetes, Endocrinology and Metabolism, Oxford Kavli Centre for Nanoscience Discovery, University of Oxford, Oxford, UK., Bennett D; Nuffield Department of Population Health, University of Oxford, Oxford, UK.; Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK., McVeigh J; Curtin School of Allied Health, Curtin University, Perth, WA, Australia., Straker L; Curtin School of Allied Health, Curtin University, Perth, WA, Australia., Eastwood P; Health Futures Institute, Murdoch University, Murdoch, WA, Australia., Kyle SD; Sir Jules Thorn Sleep & Circadian Neuroscience Institute, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK., Doherty A; Nuffield Department of Population Health, University of Oxford, Oxford, UK. aiden.doherty@ndph.ox.ac.uk.; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK. aiden.doherty@ndph.ox.ac.uk.
Jazyk: angličtina
Zdroj: NPJ digital medicine [NPJ Digit Med] 2024 May 20; Vol. 7 (1), pp. 86. Date of Electronic Publication: 2024 May 20.
DOI: 10.1038/s41746-024-01065-0
Abstrakt: Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes. We developed a self-supervised deep neural network for sleep stage classification using concurrent laboratory-based polysomnography and accelerometry. After exclusion, 1448 participant nights of data were used for training. The difference between polysomnography and the model classifications on the external validation was 34.7 min (95% limits of agreement (LoA): -37.8-107.2 min) for total sleep duration, 2.6 min for REM duration (95% LoA: -68.4-73.4 min) and 32.1 min (95% LoA: -54.4-118.5 min) for NREM duration. The sleep classifier was deployed in the UK Biobank with 100,000 participants to study the association of sleep duration and sleep efficiency with all-cause mortality. Among 66,214 UK Biobank participants, 1642 mortality events were observed. Short sleepers (<6 h) had a higher risk of mortality compared to participants with normal sleep duration of 6-7.9 h, regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.58; 95% confidence intervals (CIs): 1.19-2.11) or high sleep efficiency (HRs: 1.45; 95% CIs: 1.16-1.81). Deep-learning-based sleep classification using accelerometers has a fair to moderate agreement with polysomnography. Our findings suggest that having short overnight sleep confers mortality risk irrespective of sleep continuity.
(© 2024. The Author(s).)
Databáze: MEDLINE
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