{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Perez-Pozuelo I"],"funding":["NIHR Cambridge Biomedical Research","GlaxoSmithKline","Engineering and Physical Sciences Research","National Institute for Health Research (NIHR)"],"pagination":["7956"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9106748"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["12(1)"],"pubmed_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."],"journal":["Scientific reports"],"pubmed_title":["Detecting sleep outside the clinic using wearable heart rate devices."],"pmcid":["PMC9106748"],"funding_grant_id":["17100053","IS-BRC-1215-20014","EP/N509620/1"],"pubmed_authors":["Perez-Pozuelo I","Mascolo C","Palotti J","Brage S","Westgate K","Spathis D","Posa M","Wareham N"],"additional_accession":[]},"is_claimable":false,"name":"Detecting sleep outside the clinic using wearable heart rate devices.","description":"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.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 May","modification":"2025-04-19T16:20:30.025Z","creation":"2025-04-19T16:20:30.025Z"},"accession":"S-EPMC9106748","cross_references":{"pubmed":["35562527"],"doi":["10.1038/s41598-022-11792-7"]}}