Data-driven modelling approach to circadian temperature rhythm profiles in free-living conditions

Abstract The individual variation in the circadian rhythms at the physiological level is not well understood. Albeit self-reported circadian preference profiles have been consolidated, their premises are grounded on human experience, not on physiology. We used data-driven, unsupervised time series m...

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Autores principales:	Jari Lipsanen, Liisa Kuula, Marko Elovainio, Timo Partonen, Anu-Katriina Pesonen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/676060cb329a47308ca25eb7a3c6618d
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spelling	oai:doaj.org-article:676060cb329a47308ca25eb7a3c6618d2021-12-02T17:03:50ZData-driven modelling approach to circadian temperature rhythm profiles in free-living conditions10.1038/s41598-021-94522-92045-2322https://doaj.org/article/676060cb329a47308ca25eb7a3c6618d2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94522-9https://doaj.org/toc/2045-2322Abstract The individual variation in the circadian rhythms at the physiological level is not well understood. Albeit self-reported circadian preference profiles have been consolidated, their premises are grounded on human experience, not on physiology. We used data-driven, unsupervised time series modelling to characterize distinct profiles of the circadian rhythm measured from skin surface temperature in free-living conditions. We demonstrate the existence of three distinct clusters of individuals which differed in their circadian temperature profiles. The cluster with the highest temperature amplitude and the lowest midline estimating statistic of rhythm, or rhythm-adjusted mean, had the most regular and early-timed sleep–wake rhythm, and was the least probable for those with a concurrent delayed sleep phase, or eveningness chronotype. While the clusters associated with the observed sleep and circadian preference patterns, the entirely unsupervised modelling of physiological data provides a novel basis for modelling and understanding the human circadian functions in free-living conditions.Jari LipsanenLiisa KuulaMarko ElovainioTimo PartonenAnu-Katriina PesonenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jari Lipsanen Liisa Kuula Marko Elovainio Timo Partonen Anu-Katriina Pesonen Data-driven modelling approach to circadian temperature rhythm profiles in free-living conditions
description	Abstract The individual variation in the circadian rhythms at the physiological level is not well understood. Albeit self-reported circadian preference profiles have been consolidated, their premises are grounded on human experience, not on physiology. We used data-driven, unsupervised time series modelling to characterize distinct profiles of the circadian rhythm measured from skin surface temperature in free-living conditions. We demonstrate the existence of three distinct clusters of individuals which differed in their circadian temperature profiles. The cluster with the highest temperature amplitude and the lowest midline estimating statistic of rhythm, or rhythm-adjusted mean, had the most regular and early-timed sleep–wake rhythm, and was the least probable for those with a concurrent delayed sleep phase, or eveningness chronotype. While the clusters associated with the observed sleep and circadian preference patterns, the entirely unsupervised modelling of physiological data provides a novel basis for modelling and understanding the human circadian functions in free-living conditions.
format	article
author	Jari Lipsanen Liisa Kuula Marko Elovainio Timo Partonen Anu-Katriina Pesonen
author_facet	Jari Lipsanen Liisa Kuula Marko Elovainio Timo Partonen Anu-Katriina Pesonen
author_sort	Jari Lipsanen
title	Data-driven modelling approach to circadian temperature rhythm profiles in free-living conditions
title_short	Data-driven modelling approach to circadian temperature rhythm profiles in free-living conditions
title_full	Data-driven modelling approach to circadian temperature rhythm profiles in free-living conditions
title_fullStr	Data-driven modelling approach to circadian temperature rhythm profiles in free-living conditions
title_full_unstemmed	Data-driven modelling approach to circadian temperature rhythm profiles in free-living conditions
title_sort	data-driven modelling approach to circadian temperature rhythm profiles in free-living conditions
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/676060cb329a47308ca25eb7a3c6618d
work_keys_str_mv	AT jarilipsanen datadrivenmodellingapproachtocircadiantemperaturerhythmprofilesinfreelivingconditions AT liisakuula datadrivenmodellingapproachtocircadiantemperaturerhythmprofilesinfreelivingconditions AT markoelovainio datadrivenmodellingapproachtocircadiantemperaturerhythmprofilesinfreelivingconditions AT timopartonen datadrivenmodellingapproachtocircadiantemperaturerhythmprofilesinfreelivingconditions AT anukatriinapesonen datadrivenmodellingapproachtocircadiantemperaturerhythmprofilesinfreelivingconditions
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Data-driven modelling approach to circadian temperature rhythm profiles in free-living conditions

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