ArticleNPJ digital medicine2024
Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 26 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Associations between meeting 24-hour movement guidelines and health-related quality of life in older adults: a systematic review and meta-analysis.BMC public health · 2025Pooled it
- A systematic review of the performance of actigraphy in measuring sleep stages.Journal of sleep research · 2024Pooled it
- Accelerometer-derived real-world sleep stages and risk of incident diseases: A UK Biobank cohort study and phenome-wide association analysis.PLoS medicine · 2026Article
- Associations Between Accelerometer-Assessed Sleep Patterns, Proteomic Signatures, and Hallmarks of Aging in Adulthood.Aging cell · 2026Article
- Transition dynamics modeling of pre-trained accelerometry representations for time to diagnosis of Parkinson's disease.Lifetime data analysis · 2026Article
- IMU50: a high-frequency multi-modal wrist-worn IMU dataset of 50 subjects over 5 continuous days for supervised and self-supervised learning in free-living conditions.Data in brief · 2026Article
- Sleep and circadian health in the UK Biobank: report on the 2023 sleep questionnaire enhancement.Sleep · 2026Article
- ActiTect: a generalizable machine learning pipeline for REM sleep behavior disorder screening through standardized actigraphy.NPJ digital medicine · 2026Article
- Genetic architecture of sleep in a genome wide association study of device measured sleep traits.Nature communications · 2026Article
- Joint associations of device-measured step count and sleep duration with incident major adverse cardiovascular events: prospective analysis of the UK Biobank.EClinicalMedicine · 2026Article
- More time spent in moderate-to-vigorous physical activity and sleep is associated with a lower incidence of hip or knee osteoarthritis.BMJ open sport & exercise medicine · 2026Article
- Temporal changes in the discrepancy between subjective and objective sleep: an in-home electroencephalography study.Frontiers in public health · 2026Article
- Polygenic score for sleep duration in relation to the risk of Alzheimer's disease: results from the UK biobank.Frontiers in dementia · 2026Article
- Generation of a Free-Living Ground-Truth Validation Dataset for Wearable Measures of Physical Activity, Sedentary Behavior, Sleep, and Heart Rate in Adults (OxWEARS): Protocol for a Cross-Sectional Study.JMIR research protocols · 2025Article
- ViT-BiLSTM Multimodal Learning for Paediatric ADHD Recognition: Integrating Wearable Sensor Data with Clinical Profiles.Sensors (Basel, Switzerland) · 2025Article
- Transparent artificial intelligence-enabled interpretable and interactive sleep apnea assessment across flexible monitoring scenarios.Nature communications · 2025Article
- Enhancing Multi-User Activity Recognition in an Indoor Environment with Augmented Wi-Fi Channel State Information and Transformer Architectures.Sensors (Basel, Switzerland) · 2025Article
- A novel ViT-BILSTM model for physical activity intensity classification in adults using gravity-based acceleration.BMC biomedical engineering · 2025Article
- Association of healthy sleep patterns with incident sepsis: a large population-based prospective cohort study.Critical care (London, England) · 2025Article
- Social disadvantage, insufficient sleep, and cardiovascular disease.Frontiers in sleep · 2025Review
Corrections and comments
- Erratum issued
- Update of
Authors and funding
15 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.