Identifying Central Negative Thoughts Using Experience Sampling and Network Analysis

Abstract Purpose Network analysis has promised to inform clinical practice about what should be prioritised in treating different psychological disorders. However, the pure phenomenological approach adopted by network did not help advance this goal considerably. We propose a theoretical based on cognitive model of psychopathology. Method used multivariate vector-autoregression analyse networks anxious symptoms, depressive and negative automatic thoughts. In preliminary study, we identified most common thoughts then monitored them alongside symptoms anxiety depression sample undergraduate students three times per day for weeks. Results revealed that have high bridge outdegree temporal (predict occurrence symptoms), while (are predicted thoughts). Thoughts related self-criticism, like “There is something wrong with me”, were central both could considered priority targets interventions. Conclusions Adopting proven helpful providing concrete therapy instead just identifying as it typically done studies. Future studies also consider adopting an psychotherapeutic theory aetiology