Rest-Activity Rhythm Patterns and Their Associations With Depression and Obesity: A Study Using Actigraphy and...

Background: This study aimed to empirically derive subgroups based on both actigraphy- and app-measured rest-activity rhythm (RAR) patterns and investigate the relationship between these profiles and health outcomes, including depression and obesity. Methods: We developed a mobile app, Rhythm, to record human-smartphone interactions and calculate RAR patterns alongside standard actigraphy in 135 participants (mean age: 43.8 ± 12.3 years, 64% women) with and without major depressive disorder and/or obesity. Wrist actigraphy and Rhythm app recorded activity data for at least 4 weeks, totaling 3978 person-days. Person-centered clustering was conducted to identify subgroups based on RAR characteristics, and their associations with clinical outcomes were evaluated using multivariable regression models. Results: Three distinct groups with different RAR patterns were identified based on acrophase, interdaily stability (IS), and intradaily variability (IV), measured by actigraphy and human-smartphone interactions, respectively. The "earlier" group exhibited earlier acrophase both by actigraphy and the app and had lower depressive symptom severity than the other two groups. The "later" group showed a later acrophase and a lower body mass index (BMI) compared to the "earlier" group. The "irregular" group, characterized by higher IV, lower IS, and desynchronized actigraphy- and app-measured acrophase, was associated with higher levels of depressive symptom severity and BMI. Conclusions: Our study highlights the usefulness of human-smartphone interaction patterns in providing a comprehensive understanding of individuals' circadian rhythms beyond standard actigraphy measurements. Identifying distinct RAR profiles based on both actigraphy and app measurements contributes to a better understanding of the associations between circadian disruptions and mental and physical health outcomes.