Predicting circadian misalignment with wearable technology: validation of wrist-worn actigraphy and photometry in night shift workers. Issue 2 (11th September 2020)

• Record Type: Journal Article
• Authors: Cheng, Philip; Walch, Olivia; Huang, Yitong; Mayer, Caleb; Sagong, Chaewon; Cuamatzi Castelan, Andrea; Burgess, Helen J; Roth, Thomas; Forger, Daniel B; Drake, Christopher L
• Is Part Of: Sleep. Volume 44:Issue 2(2021)
• Publication Date: 2020-09-11
• Subjects: circadian rhythm -- night shift work -- actigraphy -- mathematical model; 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/zsaa180
• Languages: English
• ISSNs: 0161-8105

Abstract

Abstract: Study Objectives: A critical barrier to successful treatment of circadian misalignment in shift workers is determining circadian phase in a clinical or field setting. Light and movement data collected passively from wrist actigraphy can generate predictions of circadian phase via mathematical models; however, these models have largely been tested in non-shift working adults. This study tested the feasibility and accuracy of actigraphy in predicting dim light melatonin onset (DLMO) in fixed night shift workers. Methods: A sample of 45 night shift workers wore wrist actigraphs before completing DLMO in the laboratory (17.0 days ± 10.3 SD ). DLMO was assessed via 24 hourly saliva samples in dim light (<10 lux). Data from actigraphy were provided as input to a mathematical model to generate predictions of circadian phase. Agreement was assessed and compared to average sleep timing on non-workdays as a proxy of DLMO. Model code and an open-source prototype assessment tool are available (www.predictDLMO.com ). Results: Model predictions of DLMO showed good concordance with in-lab DLMO, with Lin's concordance coefficient of 0.70, which was twice as high as agreement using average sleep timing as a proxy of DLMO. The absolute mean error of the predictions was 2.88 h, with 76% and 91% of the predictions falling with 2 and 4 h, respectively. Conclusion: This study is the first to demonstrate the use of wrist actigraphy-based estimates of circadian phase as a clinically useful and valid alternative to in-lab measurement of DLMO in fixed night shift workers. Future research should explore how additional predictors may impact accuracy.