In this talk, we discuss the paper “Self-supervised learning of accelerometer data provides new insights for sleep and
\nits association with mortality” by H. Yuan et.al, npj digital medicine, 2024, at the Journal Club.<\/p>\n
Abstract\u00a0<\/strong><\/p>\n 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, 1113 participant nights of data were used for training. The difference between polysomnography and the model classifications on the external validation was 48.2\u2009min (95% limits of agreement (LoA): \u221250.3 to 146.8\u2009min) for total sleep duration, \u221217.1\u2009min for REM duration (95% LoA: \u221256.7 to 91.0\u2009min) and 31.1\u2009min (95% LoA: \u221267.3 to 129.5\u2009min) 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 66,262 UK Biobank participants, 1644 mortality events were observed. Short sleepers (<6\u2009h) had a higher risk of mortality compared to participants with normal sleep duration 6\u20137.9\u2009h, regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.36; 95% confidence intervals (CIs): 1.18 to 1.58) or high sleep efficiency (HRs: 1.29; 95% CIs: 1.04\u20131.61). 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.<\/p>\n","protected":false},"excerpt":{"rendered":" In this talk, we discuss the paper “Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality” by H. Yuan et.al, npj digital medicine, 2024, … <\/p>\n