The association of self-perceptions of aging, cognitive reserve, and depression among older adults: A network analysis approach

AIM: Evidence shows that self-perceptions of aging and cognitive reserve are respectively risk and protective factors for depression. However, prior research has largely relied on traditional regression analyses without adopting a systems perspective. Network analysis can reveal complex association patterns in depression-related systems. This study aimed to identify core and bridge nodes in a depression-related network model and examine the importance of self-perceptions of aging. METHODS: A survey was conducted with 1019 participants from March to October 2023. Network analysis was conducted in R 4.4.2 using a Gaussian Graphical Model (GGM) with graphical LASSO (GLASSO) regularization and the Extended Bayesian Information Criterion (EBIC) for model selection. RESULTS: (1) According to expected influence (EI) values (a node centrality measure), the core nodes (i.e., the most influential nodes within the mixed-level network) were: ph4 (Fatigue) (EI=1.481), ph2 (Depressed or sad mood) (EI=1.156), ph6 (Feeling of worthlessness) (EI=1.107), ph1 (Anhedonia) (EI=1.034), ER (Emotional representations) (EI=0.966). (2) Based on bridge expected influence (BEI) values (a measure of a node's role in connecting different communities), the bridge nodes (i.e., nodes that connect the cognitive reserve and depression communities) were: cr3 (Leisure activities) (BEI=-3.120), ER (Emotional representations) (BEI=1.546), and ph1 (Anhedonia) (BEI=0.945). (3) Network comparison results showed that the overall network strength was significantly higher in the negative self-perceptions of aging group than in the positive group (4.23 vs. 3.10; S = 1.43, p = 0.020). CONCLUSIONS: The identified core and bridge nodes are potential intervention targets. Self-perceptions of aging played a key role in older adults' mental health.