Predicting circadian phase in community-dwelling later-life adults using actigraphy data

The accurate estimation of circadian phase in the real-world has a variety of applications, including chronotherapeutic drug delivery, reduction of fatigue, and optimal jet lag or shift work scheduling. Recent work has developed and adapted algorithms to predict time-consuming and costly laboratory circadian phase measurements using mathematical models with actigraphy or other wearable data. Here, we validate and extend these results in a home-based cohort of later-life adults, ranging in age from 58 to 86 years. Analysis of this population serves as a valuable extension to our understanding of phase prediction, since key features of circadian timekeeping (including circadian amplitude, response to light stimuli, and susceptibility to circadian misalignment) may become altered in older populations and when observed in real-life settings. We assessed the ability of four models to predict ground truth dim light melatonin onset, and found that all the models could generate predictions with mean absolute errors of approximately 1.4 h or below using actigraph activity data. Simulations of the model with activity performed as well or better than the light-based modelling predictions, validating previous findings in this novel cohort. Interestingly, the models performed comparably to actigraph-derived sleep metrics, with the higher-order and nonphotic activity-based models in particular demonstrating superior performance. This work provides evidence that circadian rhythms can be reasonably estimated in later-life adults living in home settings through mathematical modelling of data from wearable devices.