Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/133778
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Type: | Journal article |
Title: | Light-based methods for predicting circadian phase in delayed sleep–wake phase disorder |
Author: | Murray, J.M. Magee, M. Sletten, T.L. Gordon, C. Lovato, N. Ambani, K. Bartlett, D.J. Kennaway, D.J. Lack, L.C. Grunstein, R.R. Lockley, S.W. Rajaratnam, S.M.W. Phillips, A.J.K. |
Citation: | Scientific Reports, 2021; 11(1):10878-1-10878-12 |
Publisher: | Springer Science and Business Media |
Issue Date: | 2021 |
ISSN: | 2045-2322 2045-2322 |
Statement of Responsibility: | Jade M. Murray, Michelle Magee, Tracey L. Sletten, Christopher Gordon, Nicole Lovato, Krutika Ambani ... et al. |
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. |
Keywords: | Humans Sleep Disorders, Circadian Rhythm Prognosis Trauma Severity Indices Sensitivity and Specificity Sleep Circadian Rhythm Light Adolescent Adult Middle Aged Female Male Young Adult Biomarkers |
Rights: | © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. |
DOI: | 10.1038/s41598-021-89924-8 |
Grant ID: | http://purl.org/au-research/grants/nhmrc/1031513 |
Published version: | http://dx.doi.org/10.1038/s41598-021-89924-8 |
Appears in Collections: | Mathematical Sciences publications |
Files in This Item:
File | Description | Size | Format | |
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hdl_133778.pdf | Published version | 1.51 MB | Adobe PDF | View/Open |
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