The Bidirectional Relationship Between Subjective Well-Being and Depression: A Cross-Sectional and Cross-Lagged Network Analysis.

IF 2.8 3区 心理学 Q2 PSYCHOLOGY, CLINICAL
Psychology Research and Behavior Management Pub Date : 2025-03-21 eCollection Date: 2025-01-01 DOI:10.2147/PRBM.S508588
Chen Cao, Guilan Yu, Liwei Chen, Jun Qin, Zhongyong Lin
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Abstract

Purpose: Network modeling has been suggested as an effective approach to uncover intricate relationships among emotional states and their underlying symptoms. This study aimed to explore the dynamic interactions between subjective well-being (SWB) and depressive symptoms over time, using cross-sectional and cross-lagged network analysis.

Methods: Data were drawn from three waves (2016, 2018, and 2020) of the China Family Panel Studies (CFPS), including 13,409 participants aged 16 and above. SWB was measured through indicators like life satisfaction and future confidence, while depressive symptoms were assessed using the CES-D8 scale. Symptom-level interactions were analyzed via cross-sectional network analysis at each wave, and cross-lagged panel network analysis was employed to examine the temporal dynamics and bidirectional relationships between SWB and depressive symptoms.

Results: The cross-sectional symptom network analysis showed that the number of non-zero edges at T1, T2, and T3 were 50, 44, and 49, respectively, with network densities of 0.90, 0.80, and 0.89. The core symptom "feeling sad" (D7) consistently had a significantly higher strength than other symptoms. The negative correlation between "life satisfaction" (Z2) and depressive symptoms was particularly evident at T3. The cross-lagged symptom network analysis revealed the key roles of "feeling lonely" (D5) and "feeling sad" (D7), as well as "feeling unhappy" (D4) and "not enjoying life" (D6) across different time periods, which may form a negative feedback loop. "Life satisfaction" (Z2) and "confidence in the future" (Z3) exhibited significant protective effects, forming a positive feedback loop that suppresses negative emotions through mutual reinforcement. Stability analysis showed that the network structure was stable, with a centrality stability coefficient of 0.75.

Conclusion: The study reveals a dynamic, bidirectional relationship between SWB and depressive symptoms. These results offer valuable insights for targeted interventions and public health initiatives aimed at improving mental well-being.

主观幸福感与抑郁的双向关系:横断面与交叉滞后网络分析。
目的:网络建模被认为是揭示情绪状态及其潜在症状之间复杂关系的有效方法。本研究旨在探讨主观幸福感(SWB)与抑郁症状之间随时间的动态相互作用,采用横断面和交叉滞后网络分析。方法:数据来自中国家庭面板研究(CFPS)的三波(2016年、2018年和2020年),包括13409名16岁及以上的参与者。通过生活满意度和对未来的信心等指标来衡量幸福感,而使用CES-D8量表来评估抑郁症状。通过横截面网络分析每个波的症状水平相互作用,并采用交叉滞后面板网络分析来研究主观幸福感与抑郁症状之间的时间动态和双向关系。结果:断面症状网络分析显示,T1、T2、T3处非零边数分别为50、44、49条,网络密度分别为0.90、0.80、0.89。核心症状“感到悲伤”(D7)的强度始终显著高于其他症状。“生活满意度”(Z2)与抑郁症状之间的负相关在T3时尤为明显。交叉滞后症状网络分析揭示了“感到孤独”(D5)和“感到悲伤”(D7)以及“感到不快乐”(D4)和“不享受生活”(D6)在不同时间段的关键作用,可能形成负反馈循环。“生活满意度”(Z2)和“对未来的信心”(Z3)表现出显著的保护作用,形成一个正反馈循环,通过相互强化来抑制负面情绪。稳定性分析表明,网络结构稳定,中心性稳定系数为0.75。结论:本研究揭示了主观幸福感与抑郁症状之间的动态、双向关系。这些结果为旨在改善精神健康的有针对性的干预措施和公共卫生举措提供了宝贵的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.50
自引率
4.70%
发文量
341
审稿时长
16 weeks
期刊介绍: Psychology Research and Behavior Management is an international, peer-reviewed, open access journal focusing on the science of psychology and its application in behavior management to develop improved outcomes in the clinical, educational, sports and business arenas. Specific topics covered in the journal include: -Neuroscience, memory and decision making -Behavior modification and management -Clinical applications -Business and sports performance management -Social and developmental studies -Animal studies The journal welcomes submitted papers covering original research, clinical studies, surveys, reviews and evaluations, guidelines, expert opinion and commentary, case reports and extended reports.
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