| Title: | Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality |
| Journal: | npj Digital Medicine |
| Published: | 30 Jun 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38769347/ |
| DOI: | https://doi.org/10.1038/s41746-024-01065-0 |
| URL: | https://www.nature.com/articles/s41746-024-01065-0.pdf |
| Citations: | 1 (1 in last 2 years) as of 8 Aug 2024 |
Publication 11758
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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, 1113 participant nights of data were used for training. The difference between polysomnography and the model classifications on the external validation was 48.2 min (95% limits of agreement (LoA): −50.3 to 146.8 min) for total sleep duration, −17.1 min for REM duration (95% LoA: −56.7 to 91.0 min) and 31.1 min (95% LoA: −67.3 to 129.5 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 66,262 UK Biobank participants, 1644 mortality events were observed. Short sleepers (<6 h) had a higher risk of mortality compared to participants with normal sleep duration 6-7.9 h, 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: 1.04-1.61). 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>
15 Authors
- Hang Yuan
- Tatiana Plekhanova
- Rosemary Walmsley
- Amy C. Reynolds
- Kathleen J. Maddison
- Maja Bucan
- Philip Gehrman
- Alex Rowlands
- David W. Ray
- Derrick Bennett
- Joanne McVeigh
- Leon Straker
- Peter Eastwood
- Simon D. Kyle
- Aiden Doherty
1 Application
| Application ID | Title |
|---|---|
| 59070 | Statistical machine learning of wearable sensor data to predict disease outcomes |
Enabling scientific discoveries that improve human health