Identifying Central Negative Thoughts Using Experience Sampling and Network Analysis

Abstract

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