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Objective: This study aimed to explore the associations between depressionseverity and wearable-measured circadian rhythms, accounting for seasonalimpacts and quantifying seasonal changes in circadian rhythms.Materials andMethods: Data used in this study came from a large longitudinal mobile healthstudy. Depression severity (measured biweekly using the 8-item Patient HealthQuestionnaire [PHQ-8]) and behaviors (monitored by Fitbit) were tracked for upto two years. Twelve features were extracted from Fitbit recordings toapproximate circadian rhythms. Three nested linear mixed-effects models wereemployed for each feature: (1) incorporating the PHQ-8 score as an independentvariable; (2) adding the season variable; and (3) adding an interaction termbetween season and the PHQ-8 score. Results: This study analyzed 10,018 PHQ-8records with Fitbit data from 543 participants. Upon adjusting for seasonaleffects, higher PHQ-8 scores were associated with reduced activity, irregularbehaviors, and delayed rhythms. Notably, the negative association with dailystep counts was stronger in summer and spring than in winter, and the positiveassociation with the onset of the most active continuous 10-hour period wassignificant only during summer. Furthermore, participants had shorter and latersleep, more activity, and delayed circadian rhythms in summer compared towinter. Discussion and Conclusions: Our findings underscore the significantseasonal impacts on human circadian rhythms and their associations withdepression and indicate that wearable-measured circadian rhythms have thepotential to be the digital biomarkers of depression.

Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing