Temporal Dynamics Between Depression and Anxiety Symptoms During Internet-Based Therapy and in the General Population

Abstract

Symptoms of depression and anxiety frequently co-occur, but traditional discrete-time models fail to capture their causal interactions. To explore the dynamic relationship between these symptoms, we applied two advanced methodologies—non-Gaussian direction of dependence analyses and continuous-time structural equation modeling—across two therapist-guided internet-based cognitive-behavioral therapy (iCBT) samples and two general-population cohorts ( N = 22,530). Our findings revealed that in iCBT, neither depression nor anxiety exhibited causal dominance; instead, changes were driven by shared transdiagnostic processes. In the general population, depression showed unidirectional causal dominance over anxiety; stable symptom levels were sustained by shared time-invariant factors over multiple years. Overall, this large-scale study suggests that the interplay between depression and anxiety is primarily driven by shared transdiagnostic processes alongside the causal primacy of depression. These insights underscore the importance of non-Gaussian and continuous-time modeling in understanding mental-health comorbidities and advocate for transdiagnostic practices in treating both depression and anxiety.