1 - (SYM 29) Dynamic Interplay Between Depression and Negative Automatic Thoughts During Digital CBT: A Cross-lagged Network Analysis

Cognitive Behavioral Therapy (CBT) is an evidence-based intervention for depression and is known to reduce negative automatic thoughts (NATs). However, empirical evidence on how depressive symptoms and NATs dynamically influence each other over time during CBT remains limited. Network approaches offer a framework for examining such temporal, symptom-level processes beyond mean-level change.This study combined cross-sectional and longitudinal network analyses to characterize the dynamic interplay between depression and NATs during a digital CBT intervention.

 In Study 1, 548 university students with mild-to-moderate depressive symptoms completed the Negative Automatic Thoughts Questionnaire and the Beck Depression Inventory–II (BDI-II). Cross-sectional network analysis revealed robust associations between depressive symptoms and NATs. Using network modeling and confirmatory factor analysis, NATs were organized into five subdomains: self-positioning, challenge–withdrawal, social support, future hope, and self-doubt.

In Study 2, 411 university students with mild-to-moderate depressive symptoms received a digital CBT intervention and were assessed across four time points. Cross-lagged network analyses showed that both depressive symptom clusters and NAT clusters exhibited strong within-system temporal continuity across networks. Cross-system analyses indicated that the prospective influence of NATs on subsequent depressive symptoms declined over time, with the average predictive effect per NAT cluster decreasing from approximately 0.016 in the initial network to below 0.005 in later networks. In contrast, depressive symptom clusters maintained prospective influences on NATs, with heterogeneous patterns across clusters. Specifically, BDI_cluster1 showed a consistent overall negative predictive effect on NATs (approximately –0.08 to –0.22 before diminishing), whereas BDI_cluster2 and BDI_cluster3 exhibited positive predictive effects that gradually attenuated over time (approximately 0.065 to 0.040 and 0.13 to 0.05, respectively).

Overall, the results suggest that while depressive symptoms and NATs maintain stable internal temporal dynamics during digital CBT, cross-system predictive pathways—particularly those linking cognition and affect—gradually attenuate over time, with distinct directional patterns across depressive symptom clusters. A cross-lagged network perspective provides a fine-grained account of the evolving depression–NAT system during CBT.