Network analysis of depression emotion suppression digital burnout and protective psychological factors
Abstract This study employed network analysis to investigate the complex relationship between emotion regulation strategies and depression, with particular focus on digital burnout as a contemporary stressor and the moderating role of various psychological protective factors. Based on a large sample...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-01102-2 |
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| Summary: | Abstract This study employed network analysis to investigate the complex relationship between emotion regulation strategies and depression, with particular focus on digital burnout as a contemporary stressor and the moderating role of various psychological protective factors. Based on a large sample of 9400 Chinese participants, we constructed a psychological network model incorporating depression, digital burnout, psychological resilience, self-compassion, emotion suppression, mindfulness, and sleep quality using EBIC-GLASSO regularization technique. Results revealed emotion suppression as the most central node in the network, demonstrating the highest betweenness (2.268), closeness (1.302), and strength (1.157) centrality. The network exhibited significant positive connections between emotion suppression and depression (0.890), as well as between emotion suppression and digital burnout (0.848). Notable negative associations were observed between sleep quality and depression (− 0.780), and between resilience and digital burnout (− 0.665). Network stability analysis yielded CS-coefficients exceeding 0.75 for all centrality measures, substantially above the recommended threshold of 0.5, confirming the reliability of our findings. Community detection analysis identified two distinct clusters: a Risk Factor Community (depression, digital burnout, emotion suppression) and a Protective Factor Community (resilience, self-compassion, mindfulness). The average predictability of nodes was 39.5%, ranging from 23.8% for cognitive reappraisal to 74.4% for depression. The innovation of this research lies in being the first to integrate digital burnout into a depression network, revealing its significant role as a connecting variable. Our findings suggest that interventions targeting emotion regulation may be particularly effective; digital wellness initiatives might produce cascading benefits for mental health; and comprehensive interventions simultaneously addressing resilience, self-compassion, and mindfulness may be more effective than those focusing on single protective factors. These findings provide novel insights into understanding depression in the digital age and offer important implications for both clinical practice and public health policy. |
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| ISSN: | 2045-2322 |