ArticleScientific reports2022
Detecting sleep outside the clinic using wearable heart rate devices.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Beyond the Sleep Lab: A Narrative Review of Wearable Sleep Monitoring.Bioengineering (Basel, Switzerland) · 2025Review
- Long term monitoring shows phase lagged behaviorally driven locomotor autonomic coupling.Scientific reports · 2025Article
- Article
- Every 1,000 steps matter: incremental reductions in metabolic syndrome risk in Japanese office workers.Diabetology & metabolic syndrome · 2025Article
- The economic costs and consequences of (insufficient) sleep: a case study from Latin America.The European journal of health economics : HEPAC : health economics in prevention and care · 2025Article
- Transforming Sleep Monitoring: Review of Wearable and Remote Devices Advancing Home Polysomnography and Their Role in Predicting Neurological Disorders.Biosensors · 2025Review
- Association of Blood Glucose Data With Physiological and Nutritional Data From Dietary Surveys and Wearable Devices: Database Analysis.JMIR diabetes · 2024Article
- Associations Among Cardiometabolic Risk Factors, Sleep Duration, and Obstructive Sleep Apnea in a Southeastern US Rural Community: Cross-Sectional Analysis From the SLUMBRx-PONS Study.JMIR formative research · 2024Observational
- Objectively measured peri-vaccination sleep does not predict COVID-19 breakthrough infection.Scientific reports · 2024Article
- Sleep stage prediction using multimodal body network and circadian rhythm.PeerJ. Computer science · 2024Article
- Observational
- Validity of Two Consumer Multisport Activity Tracker and One Accelerometer against Polysomnography for Measuring Sleep Parameters and Vital Data in a Laboratory Setting in Sleep Patients.Sensors (Basel, Switzerland) · 2022Article
- An Automated Algorithm for Determining Sleep Using Single-Channel Electroencephalography to Detect Delirium: A Prospective Observational Study in Intensive Care Units.Healthcare (Basel, Switzerland) · 2022Article
- Integration of Sensor-Based and Self-Reported Metrics in a Sleep Diary: A Pilot Exploration.Behavioral sleep medicineArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
The adoption of multisensor wearables presents the opportunity of longitudinal monitoring of sleep in large populations. Personalized yet device-agnostic algorithms can sidestep laborious human annotations and objectify cross-cohort comparisons. We developed and tested a heart rate-based algorithm that captures inter- and intra-individual sleep differences in free-living conditions and does not require human input. We evaluated it on four study cohorts using different research- and consumer-grade devices for over 2000 nights. Recording periods included both 24 h free-living and conventional lab-based night-only data. We compared our optimized method against polysomnography, sleep diaries and sleep periods produced through a state-of-the-art acceleration based method. Against sleep diaries, the algorithm yielded a mean squared error of 0.04-0.06 and a total sleep time (TST) deviation of [Formula: see text]2.70 (± 5.74) and 12.80 (± 3.89) minutes, respectively. When evaluated with PSG lab studies, the MSE ranged between 0.06 and 0.11 yielding a time deviation between [Formula: see text]29.07 and [Formula: see text]55.04 minutes. These results showcase the value of this open-source, device-agnostic algorithm for the reliable inference of sleep in free-living conditions and in the absence of annotations.
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