PREACT

Research on psychotherapy outcomes shows that cognitive-behavioral treatments (CBT) are effective for internalizing disorders. However, available evidence also suggests that a substantial proportion of patients does not (or not sufficiently) benefit. The identification of patients with a high risk for treatment non-response (NR) is crucial for ensuring the (cost)effectiveness of psychotherapy. The present project (SP5) is part of a Research Unit (RU) that aims to set up a prospective-longitudinal observational cohort of N = 500 patients with mental disorders from the internalizing spectrum who are treated with cognitive-behavioral therapy (CBT). Our RU aims at identifying predictors of NR with a multimodal approach, including assessing emotion regulation (ER) with ecologic momentary assessments (EMA), as well as with neural and psychophysiological signatures of ER. Within the RU, we aim to implement state-of-the-art predictive analytics (e.g., machine learning) to search for the best predictors of (non-) response. SP5 seeks to contribute to this aim by examining the relevance of specific psychological mechanisms and processes (so-called transdiagnostic factors) for NR in internalizing disorders. We will focus on emotion regulation (ER) strategies as a putative key mechanism of change underlying cognitive-behavioral therapy (CBT) and assess psychological ER processes that match and enrich – in terms of the targeted constructs – the prediction tools of the other subprojects. Moreover, we assess further transdiagnostic skills and factors such as illness perceptions and treatment expectancies as well as the therapeutic alliance. The relevance of these transdiagnostic factors will be examined above and beyond routinely available clinical diagnostic and sociodemographic data. Further, an initial mediation model accounting for the interdependence of transdiagnostic factors will be examined. Moreover, this subproject will focus on early changes in transdiagnostic factors in CBT, thereby acknowledging that early changes can predict treatment outcomes in internalizing disorders. Lastly, we adopt a cost-effectiveness perspective and deliver estimates on the relation of costs of the Research Unit’s (RU) assessments (i.e. resources spent on the input for the prediction) to their contribution to increase prediction accuracy. Further, we model implications of a better prediction of NR in “standard CBT” in terms of cost-effectiveness when patients with a poor prognosis would be assigned to alternative or modified treatment options (which might address the psychological processes and mechanisms that have been identified to be associated with risk of NR).