Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep-wake patterns from activity recordings

Bibliographic Details
Main Author: Loock, Ann-Sophie
Publication Date: 2021
Other Authors: Khan Sullivan, Ameena, Reis, Cátia, Paiva, Teresa, Ghotbi, Neda, Pilz, Luisa K., Biller, Anna M., Molenda, Carmen, Vuori-Brodowski, Maria T., Roenneberg, Till, Winnebeck, Eva C.
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10451/47788
Summary: © 2021 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
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spelling Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep-wake patterns from activity recordingsPSGAccuracyActigraphyAutomated sleep analysisSleep diarySleep-wake rhythms© 2021 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.Periods of sleep and wakefulness can be estimated from wrist-locomotor activity recordings via algorithms that identify periods of relative activity and inactivity. Here, we evaluated the performance of our Munich Actimetry Sleep Detection Algorithm. The Munich Actimetry Sleep Detection Algorithm uses a moving 24-h threshold and correlation procedure estimating relatively consolidated periods of sleep and wake. The Munich Actimetry Sleep Detection Algorithm was validated against sleep logs and polysomnography. Sleep-log validation was performed on two field samples collected over 54 and 34 days (median) in 34 adolescents and 28 young adults. Polysomnographic validation was performed on a clinical sample of 23 individuals undergoing one night of polysomnography. Epoch-by-epoch analyses were conducted and comparisons of sleep measures carried out via Bland-Altman plots and correlations. Compared with sleep logs, the Munich Actimetry Sleep Detection Algorithm classified sleep with a median sensitivity of 80% (interquartile range [IQR] = 75%-86%) and specificity of 91% (87%-92%). Mean onset and offset times were highly correlated (r = .86-.91). Compared with polysomnography, the Munich Actimetry Sleep Detection Algorithm reached a median sensitivity of 92% (85%-100%) but low specificity of 33% (10%-98%), owing to the low frequency of wake episodes in the night-time polysomnographic recordings. The Munich Actimetry Sleep Detection Algorithm overestimated sleep onset (~21 min) and underestimated wake after sleep onset (~26 min), while not performing systematically differently from polysomnography in other sleep parameters. These results demonstrate the validity of the Munich Actimetry Sleep Detection Algorithm in faithfully estimating sleep-wake patterns in field studies. With its good performance across daytime and night-time, it enables analyses of sleep-wake patterns in long recordings performed to assess circadian and sleep regularity and is therefore an excellent objective alternative to sleep logs in field settings.ASL received a stipend from the Max‐Weber‐Programm (Studienstiftung), AMB received funding from the Graduate School of Systemic Neurosciences Munich, CR received funding from the Fundação para a Ciência e Tecnologia (FCT) PhD research grants (PDE/BDE/114584/2016), LKP received a fellowship from the Coordenação de Aperfeiçoamento Pessoal de Nível Superior (CAPES, Finance Code 001), and NG received research funding from the FoeFoLe program at LMU (registration No. 37/2013).John Wiley & Sons, Inc.Repositório da Universidade de LisboaLoock, Ann-SophieKhan Sullivan, AmeenaReis, CátiaPaiva, TeresaGhotbi, NedaPilz, Luisa K.Biller, Anna M.Molenda, CarmenVuori-Brodowski, Maria T.Roenneberg, TillWinnebeck, Eva C.2021-05-12T13:23:44Z2021-05-072021-05-07T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10451/47788engJ Sleep Res. 2021 May 7:e133710962-110510.1111/jsr.133711365-2869info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-03-17T14:33:17Zoai:repositorio.ulisboa.pt:10451/47788Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T03:15:17.831969Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep-wake patterns from activity recordings
title Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep-wake patterns from activity recordings
spellingShingle Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep-wake patterns from activity recordings
Loock, Ann-Sophie
PSG
Accuracy
Actigraphy
Automated sleep analysis
Sleep diary
Sleep-wake rhythms
title_short Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep-wake patterns from activity recordings
title_full Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep-wake patterns from activity recordings
title_fullStr Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep-wake patterns from activity recordings
title_full_unstemmed Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep-wake patterns from activity recordings
title_sort Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep-wake patterns from activity recordings
author Loock, Ann-Sophie
author_facet Loock, Ann-Sophie
Khan Sullivan, Ameena
Reis, Cátia
Paiva, Teresa
Ghotbi, Neda
Pilz, Luisa K.
Biller, Anna M.
Molenda, Carmen
Vuori-Brodowski, Maria T.
Roenneberg, Till
Winnebeck, Eva C.
author_role author
author2 Khan Sullivan, Ameena
Reis, Cátia
Paiva, Teresa
Ghotbi, Neda
Pilz, Luisa K.
Biller, Anna M.
Molenda, Carmen
Vuori-Brodowski, Maria T.
Roenneberg, Till
Winnebeck, Eva C.
author2_role author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Loock, Ann-Sophie
Khan Sullivan, Ameena
Reis, Cátia
Paiva, Teresa
Ghotbi, Neda
Pilz, Luisa K.
Biller, Anna M.
Molenda, Carmen
Vuori-Brodowski, Maria T.
Roenneberg, Till
Winnebeck, Eva C.
dc.subject.por.fl_str_mv PSG
Accuracy
Actigraphy
Automated sleep analysis
Sleep diary
Sleep-wake rhythms
topic PSG
Accuracy
Actigraphy
Automated sleep analysis
Sleep diary
Sleep-wake rhythms
description © 2021 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
publishDate 2021
dc.date.none.fl_str_mv 2021-05-12T13:23:44Z
2021-05-07
2021-05-07T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10451/47788
url http://hdl.handle.net/10451/47788
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv J Sleep Res. 2021 May 7:e13371
0962-1105
10.1111/jsr.13371
1365-2869
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv John Wiley & Sons, Inc.
publisher.none.fl_str_mv John Wiley & Sons, Inc.
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833601641386868736

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