Network Dynamics of Depressive Symptoms Across Age Groups: A Panel Network Analysis

Abstract

Most studies on depressive-symptom networks have relied on cross-sectional data or two-wave cross-lagged panel models, both of which conflate between-person and within-person effects, limiting the causal interpretability of symptom relations. Using four-wave longitudinal data from the China Family Panel Studies (CFPS; 2016–2022; N = 12,013), we applied a panel network approach that separates within-person and between-person processes, conceptually similar to the Random Intercept Cross-Lagged Panel Model (RI-CLPM). We estimated three complementary networks within each group (adolescents 10–16, young adults 17–39, middle-aged adults 40–59, and older adults 60–80): (a) a between-subjects network capturing trait-like differences across individuals, (b) a contemporaneous network modeling within-person symptom covariation at each wave, and (c) a temporal network estimating directed lagged associations among symptoms, interpretable as potential causal influences given standard longitudinal assumptions. Temporal networks revealed age-related heterogeneity in the directional dynamics of depressive symptoms. In adolescents, the network was relatively sparse, with “I felt lonely” showing the highest out-strength, indicating the strongest predictive influence on subsequent symptoms. Young adults and middle-aged adults displayed denser temporal connectivity, with “I felt depressed” (young adults) and “I felt life could not go on” (middle-aged adults) emerging as the primary out-strength symptoms. Among older adults, the temporal network displayed reduced overall connectivity yet retained “I felt depressed” as the central symptom. By separating between-person traits from within-person dynamic processes, this panel network analysis of a large-scale Chinese longitudinal dataset reveals substantial developmental variation in depressive-symptom dynamics. These findings provide empirical guidance for age-specific intervention strategies.

Keywords

depressive symptoms longitudinal network analysis temporal dynamics age heterogeneity China

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