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

Title: Light-based methods for predicting circadian phase in delayed sleep–wake phase disorder
Authors: Murray, JM; Magee, M; Sletten, TL; Gordon, C; Lovato, N; Ambani, K; Bartlett, DJ; Kennaway, DJ; Lack, LC; Grunstein, RR; Lockley, SW; Rajaratnam, SMW; Phillips, AJK
Publisher Information: NATURE PORTFOLIO
Publication Year: 2021
Collection: The University of Melbourne: Digital Repository
Description: 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.
Document Type: article in journal/newspaper
Language: English
ISSN: 2045-2322
Relation: https://hdl.handle.net/11343/274948
Availability: https://hdl.handle.net/11343/274948
Rights: https://creativecommons.org/licenses/by/4.0 ; CC BY
Accession Number: edsbas.36443AB7
Database: BASE