0637 Using Mathematical Modeling to Predict Circadian Phase in Night Shift Workers. (27th April 2018)

• Record Type: Journal Article
• Authors: Cheng, P; Walch, O; Tran, K; Cuamatzi, A; Drake, C
• Is Part Of: Sleep. Volume 41(2018)Supplement 1
• Page Start: A236
• Page End: A237
• Publication Date: 2018-04-27
• Subjects: Sleep -- Physiological aspects -- Periodicals; Sleep disorders -- Periodicals; Sommeil -- Aspect physiologique -- Périodiques; Sommeil, Troubles du -- Périodiques; Sleep disorders; Sleep -- Physiological aspects; Sleep -- physiological aspects; Sleep Wake Disorders; Psychophysiology; Electronic journals; Periodicals; 616.8498
• DOI: 10.1093/sleep/zsy061.636
• Languages: English
• ISSNs: 0161-8105

Abstract

Abstract: Introduction: A major barrier in addressing circadian misalignment in shift work disorder is the lack of feasibility in measuring circadian phase in the clinic, particularly because obtaining dim light melatonin onset (DLMO) is resource intensive. One promising solution is to use mathematical models to predict DLMO based on actigraphy (light and movement); however, these models have only been tested in adults with relatively small variations in daily light-dark schedules, especially compared to night shift workers. This study tested the feasibility of actigraphy in predicting DLMO in a sample of fixed-night shift workers. Methods: A sample of 10 fixed-night shift workers wore wrist actigraphy for 7 to 14 days (mean = 9, SD = 3.4) before completing DLMO in the lab. DLMO was assessed via hourly salivary melatonin samples collected in dim light (< 10 lux) for a period of 24 hours. Light information (i.e., timing and duration) augmented with actigraphy recordings was used in a Kronauer model of the circadian clock to produce a predicted DLMO, which was then compared to in-lab DLMO. Results: Model predictions of DLMO showed high correlation with in-lab DLMO, with an R 2 of 0.83. The 95% CI of the model predictions was ± 1.71 hours, which is comparable to studies using non-shift workers in the general population. Follow-up analyses extended the model by including PERIOD3 genotype (variable number tandem repeat) as a proxy for circadian period (tau), which raised the R 2 to 0.86. Conclusion: This study is the first to provide evidence suggesting that actigraphy may be a feasible alternative to in-lab measurement of circadian phase in night shift workers. Future research should explore how inclusion of addition predictors (e.g., biological measurement of tau) may increase accuracy, and further refine the necessary parameters for accurate prediction of circadian phase, such as duration of actigraphy collection. Support (If Any):