Exploratory and Confirmatory Bayesian Networks Identify the Central Role of Non-judging in Symptoms of Depression

Abstract

Objectives: Depression is a highly heterogeneous disorder, and meta-analyses of mindfulness-based interventions show moderate efficacy for reducing depressive symptoms. However, the mechanisms governing their efficacy remain unclear, highlighting the need for hypothesis-generating analyses to guide future research. Methods: We used Bayesian network analysis in three cross-sectional samples (N = 1135) of undergraduates and participants from the community to identify links between individual symptoms of depression and specific facets of mindfulness. In two exploratory studies, we assessed depression using the Patient Health Questionnaire (n = 384) or the Depression Anxiety and Stress Scale (n = 350) and mindfulness using the Five-Facet Mindfulness Scale. Results: Across these samples and measures, exploratory analyses indicated that non-judging was a central bridge between facets of mindfulness and symptoms of depression. We confirmed this finding in a pre-registered replication (n = 401) using a recently developed confirmatory testing framework for network analysis. Non-judging was consistently a central bridge in the networks and specifically linked to the symptoms of depression related to feelings of failure and worthlessness. Conclusions: These findings provide strong evidence that non-judging is an essential feature of mindfulness in the context of depression and provides direction for future research testing mindfulness-oriented treatment prescriptions for depression.