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

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

    Research output: Contribution to journalArticlepeer-review

    1 Citation (Scopus)

    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.

    Original languageEnglish
    Article number10878
    Number of pages12
    JournalScientific Reports
    Volume11
    Issue number1
    DOIs
    Publication statusPublished - 25 May 2021

    Keywords

    • Circadian rhythms and sleep
    • Diagnostic markers

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