Author ORCID Identifier
Philip Cheng: 0000-0002-2874-658X
Yitong Huang: 0000-0002-5200-8077
Caleb Mayer: 0000-0002-8286-2186
Helen J. Burgess: 0000-0003-3816-8194
Christopher L. Drake: 0000-0002-5486-3587
Document Type
Article
Publication Date
2-1-2021
Publication Title
Sleep
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.
Keywords
actigraphy, circadian rhythm, mathematical model, night shift work
Volume
44
Issue
2
DOI
10.1093/sleep/zsaa180
ISSN
01618105
Recommended Citation
Cheng, Philip; Walch, Olivia; Huang, Yitong; Mayer, Caleb; Sagong, Chaewon; Cuamatzi Castelan, Andrea; Burgess, Helen J.; Roth, Thomas; Forger, Daniel B.; and Drake, Christopher L., "Predicting Circadian Misalignment With Wearable Technology: Validation of Wrist-Worn Actigraphy and Photometry in Night Shift Workers" (2021). Mathematics Sciences: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/mth_facpubs/192
Comments
Archived as published.