Repository logo
 

Detecting sleep outside the clinic using wearable heart rate devices.

Simple item page
cam.depositDate2022-06-01
cam.issuedOnline2022-05-13
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, Søren
dc.contributor.authorPalotti, Joao
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.date.accessioned2022-06-01T23:30:30Z
dc.date.available2022-06-01T23:30:30Z
dc.date.issued2022-05-13
dc.date.updated2022-06-01T07:45:47Z
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.identifier.doi10.17863/CAM.85078
dc.identifier.eissn2045-2322
dc.identifier.issn2045-2322
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/337672
dc.language.isoeng
dc.publisherNature Publishing Group
dc.publisher.departmentoffice of The School of Clinical Medicine
dc.publisher.departmentMrc Epidemiology Unit
dc.publisher.urlhttp://dx.doi.org/10.1038/s41598-022-11792-7
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
dcterms.dateAccepted2022-04-04
prism.issueIdentifier1
prism.publicationDate2022
prism.publicationNameScientific Reports
prism.startingPage7956
prism.volume12
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/N509620/1)
pubs.funder-project-idNational Institute for Health and Care 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)
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
pubs.licence-identifierapollo-deposit-licence-2-1
rioxxterms.typeJournal Article/Review
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1038/s41598-022-11792-7

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Sci Rep 2022.pdf
Size:
7.39 MB
Format:
Adobe Portable Document Format
Description:
Published version
Licence
https://creativecommons.org/licenses/by/4.0/
Download

Collections

Cambridge University Research Outputs