Network of positive affect and depression in older adults

Abstract

Background: Depression in older adults poses significant health challenges, yet the protective role of positive affect remains understudied. This research examined the complex network of positive affect and depression in older adults using advanced network analysis techniques to identify potential targets for intervention. Methods: Bayesian Gaussian Graphical Models and Directed Acyclic Graph modelling were used to analyse associations between ten positive affect variables and depression. Exploratory and confirmatory network analyses ensured stability and node predictability quantified variable influence. Stepwise linear regression confirmed whether specific positive affective variables identified in the networks predicted lower depression scores. Results: Enthusiasm emerged as a key ancestral node with the highest predictability (R2 = 0.65), initiating cascades of positive affect. A primary pathway to depression was identified through feeling active (strength = 1.00, direction = 0.79), with an indirect pathway from feeling enthusiastic via active (strength = 0.98, direction = 0.79) to depression (strength = 1.00, direction = 0.79). Confirmatory longitudinal analysis showed that feeling active and enthusiastic consistently predicted lower depression scores (p < 0.001). The network structure remained stable across analyses. Conclusions: Enthusiasm was identified as a central catalyst in the positive affect network, revealing clear pathways through which positive affect may protect against depression in older adults. Enhancing enthusiastic and active emotional experiences emerged as potential effective, nonpharmacological strategies for preventing and treating depression in older adults.