ArticleAnnals of medicine2024
Predicting sleep based on physical activity, light exposure, and Heart rate variability data using wearable devices.
Article in Annals of medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- From Reactive to Proactive Healthcare: Synergizing Wearable Biomarkers and Machine Learning in Digital Therapeutics.Bioengineering (Basel, Switzerland) · 2026Review
- Unobtrusive inference of diurnal rhythms from smartphone data.NPJ digital medicine · 2025Review
- Letter to the editor regarding: 'Electroacupuncture versus sham for insomnia in patients undergoing maintenance hemodialysis: study protocol for a pilot randomized controlled trial'.Annals of medicine · 2025Article
- A bibliometric analysis of wearable device research in sleep science: Trends and hotspots.Medicine · 2025Review
- Unobtrusive inference of circadian rhythms from smartphone data.medRxiv : the preprint server for health sciences · 2025Article
- Machine Learning Models to Identify Individuals With Imminent Suicide Risk Using a Wearable Device: A Pilot Study.Psychiatry investigation · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe aimed to improve the performance of sleep prediction algorithms by increasing the data amount, adding variables reflecting psychological state, and adjusting the data length. MATERIALS AND
methodsWe used ActiGraph GT3X+
resultsAmong 278 participants who denied having sleep disturbance, we used data including 2136 total days and nights from 230 participants. The performance of the sleep prediction algorithms improved with an increased amount of data and added HRV data. The model with the best performance was the extreme gradient boosting model; XGBoost, using both sources combined data with HRV, and 2-day data (accuracy=.85, area under the curve =.80).
conclusionsThe results show that the performance of the sleep prediction models improved by increasing the data amount and adding HRV data. Further studies targeting insomnia patients and applied researches on non-pharmacological insomnia treatment are needed.
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Registered trials
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