Despite the retrospective and generalized nature of the concept of emotion regulation as a cross-diagnostic mechanism for comorbid depression and anxiety, there is limited research exploring the intra-day emotion variation in individuals with comorbid depression and anxiety using ecological emotion data. Depending on the research orientation, both emotion dynamic networks that examine the relationships between emotions based on emotion categories and emotion dynamics that investigate patterns of emotional fluctuations based on emotion dimensions can extract individual emotional patterns throughout the day from ecological data, thereby reflecting real emotion regulation processes. Currently, there is still a lack of research on the comorbid depression and anxiety from the perspective of intra-day emotional variations. To enhance our understanding of the nature of comorbid depression and anxiety, it is necessary to establish a model of intra-day emotion variation from the perspective of emotion dynamic networks and emotion dynamics.
Study 1 utilized experience sampling data from college students and fitted emotion dynamic networks at both the group and individual levels. Regardless of whether it was based on edge count, absolute edge weights, or the mean of absolute edge weights, the dynamic emotion networks of the comorbid depression and anxiety group demonstrated stronger emotional inertia at both the group and individual levels. Additionally, the comorbid depression and anxiety group exhibited significantly higher levels of negative emotions compared to the healthy group, but there was no significant difference in positive emotions between the two groups. The accuracy rates of the two classifiers exceeded 0.8, indicating that the features extracted in Study 1 effectively characterized the emotional characteristics of individuals with comorbid depression and anxiety.
Study 2 utilized affective computing data from college students and older adults and employed innovative methods to extract features of intra-day emotion dynamics. Compared to the healthy participants, the comorbid depression and anxiety group showed higher mean levels of negative emotions and stronger inertia in negative emotions, but weaker inertia in the peak time intervals of positive and negative emotions. The effect of negative emotions in the older adult population was much smaller than that in the younger group. The accuracy rates of the two classifiers reached 0.8, demonstrating that the features extracted in Study 2 effectively characterized the emotional characteristics of individuals with comorbid depression and anxiety.
The results of this study support a three-level model of intra-day emotion variation in comorbid depression and anxiety. A healthy emotion regulation state may be reflected in low levels of negative emotions, emotional flexibility, and stability of emotional periods. Integrating research on emotion dynamics and emotion dynamic networks, as well as introducing affective computing techniques in emotion and psychiatric research, are both feasible and necessary.
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