Validating Apple Watch in Predicting Circadian Phase for Night Shift Workers

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“These results have significant implications for the scalability of circadian medicine, particularly as consumer-based wearable technology is already commonplace,” investigators explain.

Validating Apple Watch in Predicting Circadian Phase for Night Shift Workers

Philip Cheng, PhD

Credit: Henry Ford Health

Novel data from SLEEP 2023 in Indianapolis, IN, indicated the Apple Watch estimates of circadian phase exhibited comparable validity with actigraphy used in research. The findings are the first to provide evidence of this degree of reliability regarding the common consumer-based wearable device, according to the investigation.

“These results have significant implications for the scalability of circadian medicine, particularly as consumer-based wearable technology is already commonplace,” Philip Cheng, PhD, Henry Ford Health, and investigators wrote.

The lack of feasible methods for measuring circadian phase, especially in clinical settings, is one of the primary obstacles in circadian medicine. Tools like the assessment of dim light melatonin onset (DLMO) are resource-intensive and less practical, particularly for populations with disrupted circadian rhythms such as night shift workers.

Cheng validated the use of the Apple Watch to predict DLMO in individuals who work night shifts.

A group of night shift workers (N = 21) with consistent schedules participated in the study. They wore an Apple Watch continuously for a duration of 2 weeks prior to undergoing DLMO assessment in a controlled laboratory environment.

To determine DLMO, researchers collected salivary melatonin samples at hourly intervals throughout a 24-hour period under dim light conditions (< 10 lux). The participants' activity data, recorded by the Apple Watch, served as input for the Hannay model, a mathematical representation of the circadian clock.

This model generated a predicted DLMO, which was subsequently compared to the DLMO measured during the in-lab assessment. The model's estimations of DLMO demonstrated a robust association with the DLMO measured in the laboratory, displaying a high Lin's concordance correlation coefficient (CCC) of 0.81.

The mean absolute error (MAE) between the predicted DLMO and the in-lab DLMO was 2.10. These findings were similar to those reported in a previous validation study that employed research-grade actigraphy (CCC = 0.70; MAE = 2.88).

The prevalence of Apple Watch use among consumers further supports the potential application in circadian medicine research. The study noted that further evaluations should expand on these findings and explore the use of other wearable devices, particularly those at a lower price point, to enhance accessibility.

“Apple Watches are also the most common consumer-based wearable device. Future research should extend findings to other wearable devices, especially devices at a lower price-point to increase accessibility," investigators wrote.

References:

  1. Cheng P, Walch O, Hannay K, Roth T, Drake C. 0004 Using Apple Watch to predict circadian phase in night shift workers. SLEEP. 2023;46(Supplement_1):A2.
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