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

Area of Science:

• Psychology
• Mental Health Research
• Network Science

Background:

• Understanding the interplay between subjective well-being (SWB) and depressive symptoms is crucial for mental health.
• Network modeling offers a novel approach to explore complex relationships between emotional states and symptoms over time.

Purpose of the Study:

• To investigate the dynamic, bidirectional interactions between SWB and depressive symptoms using longitudinal data.
• To identify key symptoms and protective factors influencing mental well-being through network analysis.

Main Methods:

• Utilized data from three waves (2016, 2018, 2020) of the China Family Panel Studies (CFPS) with 13,409 participants.
• Employed cross-sectional and cross-lagged network analyses to examine symptom interactions and temporal dynamics.
• Measured SWB using life satisfaction and future confidence, and depressive symptoms via the CES-D8 scale.

Main Results:

• Cross-sectional analysis identified "feeling sad" as a core symptom. Cross-lagged analysis revealed feedback loops between specific depressive and SWB symptoms.
• "Life satisfaction" and "confidence in the future" demonstrated protective effects, forming a positive feedback loop.
• The network structure exhibited stability over time (centrality stability coefficient = 0.75).

Conclusions:

• A dynamic, bidirectional relationship exists between SWB and depressive symptoms.
• Findings provide valuable insights for developing targeted interventions to enhance mental well-being.
• Network analysis is a powerful tool for understanding complex mental health dynamics.