Chen I-Ming, Lin Chen, She Guan-Jie, Chang Hsiang-Chih, Chuang Hai-Hua, Chen Tien-Yu, Lin Yu-Hsuan
Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan.
Department of Psychiatry, College of Medicine, National Taiwan University, Taipei, Taiwan.
Depress Anxiety. 2025 Mar 31;2025:2617282. doi: 10.1155/da/2617282. eCollection 2025.
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. 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. 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. 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.
本研究旨在基于活动记录仪和应用程序测量的静息-活动节律（RAR）模式，通过实证得出亚组，并研究这些特征与包括抑郁和肥胖在内的健康结果之间的关系。我们开发了一款名为Rhythm的移动应用程序，用于记录人与智能手机的交互，并与标准活动记录仪一起计算135名参与者（平均年龄：43.8±12.3岁，64%为女性）的RAR模式，这些参与者患有或未患有重度抑郁症和/或肥胖症。手腕活动记录仪和Rhythm应用程序记录了至少4周的活动数据，总计3978人日。进行以人为主的聚类分析，以根据RAR特征识别亚组，并使用多变量回归模型评估它们与临床结果的关联。根据分别通过活动记录仪和人与智能手机交互测量的峰相位、日际稳定性（IS）和日内变异性（IV），确定了具有不同RAR模式的三个不同组。“较早”组通过活动记录仪和应用程序测量均表现出较早的峰相位，且抑郁症状严重程度低于其他两组。与“较早”组相比，“较晚”组表现出较晚的峰相位和较低的体重指数（BMI）。“不规则”组的特征是IV较高、IS较低，且活动记录仪和应用程序测量的峰相位不同步，与较高水平的抑郁症状严重程度和BMI相关。我们的研究强调了人与智能手机交互模式在提供超越标准活动记录仪测量的对个体昼夜节律的全面理解方面的有用性。基于活动记录仪和应用程序测量识别不同的RAR特征有助于更好地理解昼夜节律紊乱与身心健康结果之间的关联。
