Detecting sleep outside the clinic using wearable heart rate devices
<p dir="ltr">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...
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2022
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| الموضوعات: | |
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| _version_ | 1864513518598881280 |
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| author | Ignacio Perez-Pozuelo (14153007) |
| author2 | Marius Posa (13395057) Dimitris Spathis (18426975) Kate Westgate (792877) Nicholas Wareham (153226) Cecilia Mascolo (321076) Søren Brage (211618) Joao Palotti (8479842) |
| author2_role | author author author author author author author |
| author_facet | Ignacio Perez-Pozuelo (14153007) Marius Posa (13395057) Dimitris Spathis (18426975) Kate Westgate (792877) Nicholas Wareham (153226) Cecilia Mascolo (321076) Søren Brage (211618) Joao Palotti (8479842) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ignacio Perez-Pozuelo (14153007) Marius Posa (13395057) Dimitris Spathis (18426975) Kate Westgate (792877) Nicholas Wareham (153226) Cecilia Mascolo (321076) Søren Brage (211618) Joao Palotti (8479842) |
| dc.date.none.fl_str_mv | 2022-05-13T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1038/s41598-022-11792-7 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Detecting_sleep_outside_the_clinic_using_wearable_heart_rate_devices/25671915 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Oncology and carcinogenesis Multisensor wearables Longitudinal monitoring Sleep tracking Personalized algorithms Device-agnostic Heart rate-based algorithm Free-living conditions |
| dc.title.none.fl_str_mv | Detecting sleep outside the clinic using wearable heart rate devices |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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 - 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 - 29.07 and - 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.</p><p><br></p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-022-11792-7" target="_blank">https://dx.doi.org/10.1038/s41598-022-11792-7</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_dc5bcd92ebb3ccdc95915fbbc023b4da |
| identifier_str_mv | 10.1038/s41598-022-11792-7 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25671915 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Detecting sleep outside the clinic using wearable heart rate devicesIgnacio Perez-Pozuelo (14153007)Marius Posa (13395057)Dimitris Spathis (18426975)Kate Westgate (792877)Nicholas Wareham (153226)Cecilia Mascolo (321076)Søren Brage (211618)Joao Palotti (8479842)Biomedical and clinical sciencesOncology and carcinogenesisMultisensor wearablesLongitudinal monitoringSleep trackingPersonalized algorithmsDevice-agnosticHeart rate-based algorithmFree-living conditions<p dir="ltr">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 - 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 - 29.07 and - 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.</p><p><br></p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-022-11792-7" target="_blank">https://dx.doi.org/10.1038/s41598-022-11792-7</a></p>2022-05-13T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1038/s41598-022-11792-7https://figshare.com/articles/journal_contribution/Detecting_sleep_outside_the_clinic_using_wearable_heart_rate_devices/25671915CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/256719152022-05-13T03:00:00Z |
| spellingShingle | Detecting sleep outside the clinic using wearable heart rate devices Ignacio Perez-Pozuelo (14153007) Biomedical and clinical sciences Oncology and carcinogenesis Multisensor wearables Longitudinal monitoring Sleep tracking Personalized algorithms Device-agnostic Heart rate-based algorithm Free-living conditions |
| status_str | publishedVersion |
| title | Detecting sleep outside the clinic using wearable heart rate devices |
| title_full | Detecting sleep outside the clinic using wearable heart rate devices |
| title_fullStr | Detecting sleep outside the clinic using wearable heart rate devices |
| title_full_unstemmed | Detecting sleep outside the clinic using wearable heart rate devices |
| title_short | Detecting sleep outside the clinic using wearable heart rate devices |
| title_sort | Detecting sleep outside the clinic using wearable heart rate devices |
| topic | Biomedical and clinical sciences Oncology and carcinogenesis Multisensor wearables Longitudinal monitoring Sleep tracking Personalized algorithms Device-agnostic Heart rate-based algorithm Free-living conditions |