Zhan Yuting, Ding Xu
Department of Psychology, School of Education and Teach, Ningxia University, Yinchuan, 750021, Ningxia Province, China.
Shandong Academy of Medical Sciences, Shandong First Medical University, Jinan, 271016, Shandong Province, China.
Sci Rep. 2025 May 12;15(1):16406. doi: 10.1038/s41598-025-01102-2.
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.
本研究采用网络分析方法，以探讨情绪调节策略与抑郁症之间的复杂关系，尤其关注数字倦怠这一当代压力源以及各种心理保护因素的调节作用。基于9400名中国参与者的大样本，我们使用EBIC-GLASSO正则化技术构建了一个包含抑郁症、数字倦怠、心理韧性、自我同情、情绪抑制、正念和睡眠质量的心理网络模型。结果显示，情绪抑制是网络中最核心的节点，其具有最高的中介中心性（2.268）、接近中心性（1.302）和强度中心性（1.157）。该网络在情绪抑制与抑郁症之间（0.890）以及情绪抑制与数字倦怠之间（0.848）呈现出显著的正相关。在睡眠质量与抑郁症之间（-0.780）以及韧性与数字倦怠之间（-0. (665)观察到显著的负相关。网络稳定性分析得出，所有中心性指标的CS系数均超过0.75，远高于推荐阈值0.5，证实了我们研究结果的可靠性。社区检测分析识别出两个不同的集群：一个风险因素集群（抑郁症、数字倦怠、情绪抑制）和一个保护因素集群（韧性、自我同情、正念）。节点的平均可预测性为39.5%，从认知重评的23.8%到抑郁症的74.4%不等。本研究的创新之处在于首次将数字倦怠纳入抑郁症网络，揭示了其作为连接变量的重要作用。我们的研究结果表明，针对情绪调节的干预措施可能特别有效；数字健康倡议可能会为心理健康带来连锁益处；同时解决韧性、自我同情和正念的综合干预措施可能比专注于单一保护因素的干预措施更有效。这些发现为理解数字时代的抑郁症提供了新的见解，并对临床实践和公共卫生政策都具有重要意义。
