Light-based methods for predicting circadian phase in delayed sleep–wake phase disorder

Abstract Methods for predicting circadian phase have been developed for healthy individuals. It is unknown whether these methods generalize to clinical populations, such as delayed sleep–wake phase disorder (DSWPD), where circadian timing is associated with functional outcomes. This study evaluated...

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Autores principales:	Jade M. Murray, Michelle Magee, Tracey L. Sletten, Christopher Gordon, Nicole Lovato, Krutika Ambani, Delwyn J. Bartlett, David J. Kennaway, Leon C. Lack, Ronald R. Grunstein, Steven W. Lockley, Shantha M. W. Rajaratnam, Andrew J. K. Phillips
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/bfa293dc3da141029fc23ac589637c43
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spelling	oai:doaj.org-article:bfa293dc3da141029fc23ac589637c432021-12-02T15:49:50ZLight-based methods for predicting circadian phase in delayed sleep–wake phase disorder10.1038/s41598-021-89924-82045-2322https://doaj.org/article/bfa293dc3da141029fc23ac589637c432021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89924-8https://doaj.org/toc/2045-2322Abstract Methods for predicting circadian phase have been developed for healthy individuals. It is unknown whether these methods generalize to clinical populations, such as delayed sleep–wake phase disorder (DSWPD), where circadian timing is associated with functional outcomes. This study evaluated two methods for predicting dim light melatonin onset (DLMO) in 154 DSWPD patients using ~ 7 days of sleep–wake and light data: a dynamic model and a statistical model. The dynamic model has been validated in healthy individuals under both laboratory and field conditions. The statistical model was developed for this dataset and used a multiple linear regression of light exposure during phase delay/advance portions of the phase response curve, as well as sleep timing and demographic variables. Both models performed comparably well in predicting DLMO. The dynamic model predicted DLMO with root mean square error of 68 min, with predictions accurate to within ± 1 h in 58% of participants and ± 2 h in 95%. The statistical model predicted DLMO with root mean square error of 57 min, with predictions accurate to within ± 1 h in 75% of participants and ± 2 h in 96%. We conclude that circadian phase prediction from light data is a viable technique for improving screening, diagnosis, and treatment of DSWPD.Jade M. MurrayMichelle MageeTracey L. SlettenChristopher GordonNicole LovatoKrutika AmbaniDelwyn J. BartlettDavid J. KennawayLeon C. LackRonald R. GrunsteinSteven W. LockleyShantha M. W. RajaratnamAndrew J. K. PhillipsNature 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 Jade M. Murray Michelle Magee Tracey L. Sletten Christopher Gordon Nicole Lovato Krutika Ambani Delwyn J. Bartlett David J. Kennaway Leon C. Lack Ronald R. Grunstein Steven W. Lockley Shantha M. W. Rajaratnam Andrew J. K. Phillips Light-based methods for predicting circadian phase in delayed sleep–wake phase disorder
description	Abstract Methods for predicting circadian phase have been developed for healthy individuals. It is unknown whether these methods generalize to clinical populations, such as delayed sleep–wake phase disorder (DSWPD), where circadian timing is associated with functional outcomes. This study evaluated two methods for predicting dim light melatonin onset (DLMO) in 154 DSWPD patients using ~ 7 days of sleep–wake and light data: a dynamic model and a statistical model. The dynamic model has been validated in healthy individuals under both laboratory and field conditions. The statistical model was developed for this dataset and used a multiple linear regression of light exposure during phase delay/advance portions of the phase response curve, as well as sleep timing and demographic variables. Both models performed comparably well in predicting DLMO. The dynamic model predicted DLMO with root mean square error of 68 min, with predictions accurate to within ± 1 h in 58% of participants and ± 2 h in 95%. The statistical model predicted DLMO with root mean square error of 57 min, with predictions accurate to within ± 1 h in 75% of participants and ± 2 h in 96%. We conclude that circadian phase prediction from light data is a viable technique for improving screening, diagnosis, and treatment of DSWPD.
format	article
author	Jade M. Murray Michelle Magee Tracey L. Sletten Christopher Gordon Nicole Lovato Krutika Ambani Delwyn J. Bartlett David J. Kennaway Leon C. Lack Ronald R. Grunstein Steven W. Lockley Shantha M. W. Rajaratnam Andrew J. K. Phillips
author_facet	Jade M. Murray Michelle Magee Tracey L. Sletten Christopher Gordon Nicole Lovato Krutika Ambani Delwyn J. Bartlett David J. Kennaway Leon C. Lack Ronald R. Grunstein Steven W. Lockley Shantha M. W. Rajaratnam Andrew J. K. Phillips
author_sort	Jade M. Murray
title	Light-based methods for predicting circadian phase in delayed sleep–wake phase disorder
title_short	Light-based methods for predicting circadian phase in delayed sleep–wake phase disorder
title_full	Light-based methods for predicting circadian phase in delayed sleep–wake phase disorder
title_fullStr	Light-based methods for predicting circadian phase in delayed sleep–wake phase disorder
title_full_unstemmed	Light-based methods for predicting circadian phase in delayed sleep–wake phase disorder
title_sort	light-based methods for predicting circadian phase in delayed sleep–wake phase disorder
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/bfa293dc3da141029fc23ac589637c43
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Light-based methods for predicting circadian phase in delayed sleep–wake phase disorder

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