Cheng P, Walch O, Huang Y, Mayer C, Sagong C, Cuamatzi Castelan A, Burgess HJ, Roth T, Forger DB, and Drake CL. Predicting circadian misalignment with wearable technology: Validation of wrist-worn actigraphy and photometry in night shift workers. Sleep 2020.
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 a prototype assessment tool are available open source.
RESULTS: Model predictions of DLMO showed good concordance with in-lab DLMO, with a 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 hours, with 76% and 91% of the predictions falling with 2 and 4 hours, 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.
ePub ahead of print