arXiv 2404.112122024-04-17stat.AP

解读真实环境下季节性抑郁变化及天气变化、身体活动与抑郁严重程度之间的相互作用：来自RADAR-MDD纵向移动健康研究的启示

Deciphering seasonal depression variations and interplays between weather changes, physical activity, and depression severity in real-world settings: Learnings from RADAR-MDD longitudinal mobile health study

Yuezhou Zhang, Amos A. Folarin, Yatharth Ranjan, Nicholas Cummins, Zulqarnain Rashid, Pauline Conde, Callum Stewart, Shaoxiong Sun, Srinivasan Vairavan, Faith M… 展开作者

Yuezhou Zhang, Amos A. Folarin, Yatharth Ranjan, Nicholas Cummins, Zulqarnain Rashid, Pauline Conde, Callum Stewart, Shaoxiong Sun, Srinivasan Vairavan, Faith Matcham, Carolin Oetzmann, Sara Siddi, Femke Lamers, Sara Simblett, Til Wykes, David C. Mohr, Josep Maria Haro, Brenda W. J. H. Penninx, Vaibhav A. Narayan, Matthew Hotopf, Richard J. B. Dobson, Abhishek Pratap, RADAR-CNS consortium

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AI总结：

本研究基于RADAR-MDD队列数据，揭示了四种抑郁季节性变化模式，证实天气-身体活动-抑郁的相互作用存在个体异质性，为个性化理解环境因素对行为治疗效果的影响提供了依据。

摘要：

既往研究表明，季节和天气变化会对抑郁严重程度产生显著影响。然而，不同人群的研究结果并不一致，且天气、行为与抑郁之间的相互作用尚未得到充分量化。本研究分析了RADAR-MDD（复发性抑郁障碍远程监测）纵向移动健康研究中428名参与者（为队列的一个子集，占68.7%）的真实世界数据，旨在探究抑郁的季节性变化（通过经过验证的远程评估工具PHQ-8测量），并考察动态天气变化、身体活动（通过可穿戴设备监测）与抑郁严重程度之间的潜在相互作用。对PHQ-8得分的聚类分析识别出四种不同的抑郁严重程度季节性变化模式：一种稳定趋势和三种在不同季节出现抑郁峰值的变化模式。在这些模式中，稳定趋势组的参与者平均年龄最大（p=0.002），基线PHQ-8得分最低（p=0.003）。评估天气对身体活动和抑郁间接影响的中介分析显示，对天气有不同情感反应的参与者之间存在显著差异。这些发现阐明了个体季节性抑郁变化和对天气反应的异质性，凸显了采用个性化方法来帮助理解环境因素对行为治疗真实世界有效性影响的必要性。

英文摘要：

Prior research has shown that changes in seasons and weather can have a significant impact on depression severity. However, findings are inconsistent across populations, and the interplay between weather, behavior, and depression has not been fully quantified. This study analyzed real-world data from 428 participants (a subset; 68.7% of the cohort) in the RADAR-MDD longitudinal mobile health study to investigate seasonal variations in depression (measured through a remote validated assessment - PHQ-8) and examine the potential interplay between dynamic weather changes, physical activity (monitored via wearables), and depression severity. The clustering of PHQ-8 scores identified four distinct seasonal variations in depression severity: one stable trend and three varying patterns where depression peaks in different seasons. Among these patterns, participants within the stable trend had the oldest average age (p=0.002) and the lowest baseline PHQ-8 score (p=0.003). Mediation analysis assessing the indirect effect of weather on physical activity and depression showed significant differences among participants with different affective responses to weather. These findings illustrate the heterogeneity in individuals' seasonal depression variations and responses to weather, underscoring the necessity for personalized approaches to help understand the impact of environmental factors on the real-world effectiveness of behavioral treatments.

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