Light-based methods for predicting circadian phase in delayed sleep-wake phase disorder
| Autor: | Tracey L. Sletten, Shantha M W Rajaratnam, Krutika Ambani, Nicole Lovato, Ronald R. Grunstein, Delwyn J. Bartlett, David J. Kennaway, Michelle Magee, Christopher J. Gordon, Leon Lack, Steven W. Lockley, Jade M. Murray, Andrew J. K. Phillips |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Adult Male Mean squared error Adolescent Light Science Phase (waves) Sensitivity and Specificity Article Melatonin 03 medical and health sciences Young Adult 0302 clinical medicine Sleep Disorders Circadian Rhythm Linear regression Statistics medicine Humans Circadian rhythm Group delay and phase delay Phase response curve Mathematics Multidisciplinary Trauma Severity Indices Statistical model Diagnostic markers Middle Aged Prognosis Circadian Rhythm 030104 developmental biology Medicine Female Circadian rhythms and sleep Sleep 030217 neurology & neurosurgery Biomarkers medicine.drug |
| Zdroj: | Scientific Reports Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021) |
| ISSN: | 2045-2322 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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