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dc.contributor.authorPerez-Pozuelo, Ignacio
dc.contributor.authorPosa, Marius
dc.contributor.authorSpathis, Dimitris
dc.contributor.authorWestgate, Kate
dc.contributor.authorWareham, Nicholas
dc.contributor.authorMascolo, Cecilia
dc.contributor.authorBrage, Soren
dc.contributor.authorPalotti, Joao
dc.date.accessioned2022-06-01T23:30:30Z
dc.date.available2022-06-01T23:30:30Z
dc.date.issued2022-05-13
dc.identifier.issn2045-2322
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/337672
dc.description.abstractThe 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.
dc.format.mediumElectronic
dc.publisherNature Publishing Group
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectHeart Rate
dc.subjectHumans
dc.subjectPolysomnography
dc.subjectReproducibility of Results
dc.subjectSleep
dc.subjectWearable Electronic Devices
dc.titleDetecting sleep outside the clinic using wearable heart rate devices.
dc.typeArticle
dc.publisher.departmentoffice of The School of Clinical Medicine
dc.publisher.departmentMrc Epidemiology Unit
dc.date.updated2022-06-01T07:45:47Z
prism.issueIdentifier1
prism.publicationDate2022
prism.publicationNameScientific Reports
prism.startingPage7956
prism.volume12
dc.identifier.doi10.17863/CAM.85078
dcterms.dateAccepted2022-04-04
rioxxterms.versionofrecord10.1038/s41598-022-11792-7
rioxxterms.versionVoR
dc.contributor.orcidWestgate, Kate [0000-0002-0283-3562]
dc.contributor.orcidWareham, Nicholas [0000-0003-1422-2993]
dc.contributor.orcidBrage, Soren [0000-0002-1265-7355]
dc.identifier.eissn2045-2322
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/N509620/1)
pubs.funder-project-idNational Institute for Health Research (IS-BRC-1215-20014)
pubs.funder-project-idMRC (MC_UU_00006/1)
pubs.funder-project-idCambridge University Hospitals NHS Foundation Trust (CUH) (146281)
pubs.funder-project-idMRC (MC_UU_00006/4)
cam.issuedOnline2022-05-13
cam.depositDate2022-06-01
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International