Investigating uncertainty decomposition as a mechanism in interventions for anxiety

Abstract

Uncertainty has long been established as a key transdiagnostic mechanism behind anxiety and anxiety psychopathology. At present, not only has research into this field been afflicted by linguistic conflation of similar terms, it has also been plagued with a lack of consensus and conceptual clarity. Furthermore, research on interventions for anxiety psychopathology targeting uncertainty has focused on the intolerance of uncertainty (IU), that is, maladaptive responses to uncertainty, rather than uncertainty and its processes. This study utilized a statistically-based Bayesian model of uncertainty that breaks down uncertainty into quantifiable components in designing and delivering a novel treatment intervention for anxiety, targeting the actual processes of uncertainty. A randomized controlled trial design was employed, comparing this novel uncertainty decomposition (UD) approach with the well-established intervention of emotion psychoeducation. Results showed that the novel UD approach was comparable to emotion psychoeducation in its effectiveness to reduce symptoms of anxiety. Whilst no significant difference was found in the effect of either intervention approach in increasing uncertainty decomposition skills, the novel UD approach significantly increased such skill in certain domains. Furthermore, further evidence was found to support the relationship between uncertainty decomposition and reduction of anxiety symptoms, providing direction for future research into a promising new treatment model. Future research should include further randomized controlled trials, both expanding the scope of research, as well as further elucidating the exact mechanisms underlying the effect of uncertainty decomposition on anxiety.