Longitudinal associations of daily affective dynamics with depression, generalized anxiety, and social anxiety symptoms

JOURNAL OF AFFECTIVE DISORDERS(2024)

引用 0|浏览3
暂无评分
摘要
Background: Low average affect, measured using ecological momentary assessment (EMA), has been consistently linked with depression, generalized anxiety, and social anxiety, supporting trait-like negative affect as a shared underlying feature. However, while theoretical models of emotion regulation would also implicate greater variability in daily affect in these conditions, empirical evidence linking EMA of mood variability with affective disorders is mixed. We used multilevel modeling to test relationships of daily mood and mood variability with depression, generalized anxiety, and social anxiety symptoms. Methods: Participants (N = 1004; 72.31 % female; Mage = 40.85) responded to EMA of mood 2-3x/day and completed measures of depression (PHQ-8), generalized anxiety (GAD -7), and social anxiety (SPIN) every three weeks. Results: Lower mean affect predicted all symptoms at both the between-person (PHQ-8: fl = -0.486, p < 0.001; GAD -7: fl = -0.429, p < 0.001; SPIN: fl = -0.284, p < 0.001) and within-person (PHQ-8: fl = -0.219, p < 0.001; GAD -7: fl = -0.196, p < 0.001; SPIN: fl = -0.049, p < 0.001) levels. Similarly, at the between-person level, greater affective variability was linked with all three clinical symptoms (PHQ-8: fl = 0.617, p < 0.001; GAD -7: fl = 0.703, p < 0.001; SPIN: fl = 0.449, p < 0.001). However, within-person, affective variability related to depression (fl = 0.144, p < 0.001) and generalized anxiety (fl = 0.150, p < 0.001), but not social anxiety (fl = 0.006, p = 0.712). Limitations: The COVID-19 pandemic lockdown period occurred midway through the study. Conclusion: Findings point to common and specific emotion dynamics that characterize affective symptoms severity, with implications for affective monitoring in a clinical context.
更多
查看译文
关键词
Depression,Anxiety,Social anxiety,Ecological momentary assessment,Digital health,Personal sensing
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要