A multicomponent psychodynamic treatment for comorbid disorders: a baseline and post-treatment network comparison

ABSTRACT

This study aims to assess pre-post change of 439 patients undergoing a multicomponent treatment (psychodynamic psychotherapy complemented with other treatment components) using a novel network methodology targeting symptoms comorbidity. Patients were recruited from seven clinical sites in the Czech Republic. First, the effectiveness of the treatment was assessed traditionally as a pre-post change in wellbeing, depression, and anxiety using a Bayesian mixed model. The Bayesian factors of time effect (pre-post comparison) on the three outcomes indicate evidence in favor of hypotheses suggesting psychotherapy effectiveness. Second, a network analysis of individual items measuring all three outcomes (Gaussian Graphical Model) was conducted to compare baseline and post-treatment patients’ networks in global edge strength, the topography of the network, the centrality of nodes, and the clique percolation. The network density represented by global edge strength was not affected by the treatment. Nevertheless, the network structure changed in a more qualitative manner into a clearer and separated set of node communities, potentially showing a reduction in comorbidity. The central position of the depression node community in the patients’ self-reported outcome assessment network was replaced by anxiety and wellbeing node communities after treatment.

KEYWORDS:

• Network model
• psychotherapy
• wellbeing
• depression-anxiety
• comorbidity

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The anonymized open data are available within the Open Science Foundation platform (https://osf.io/dfrma).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/09515070.2023.2292212

Additional information

Funding

This study was funded by the Internal Grant Agency of Masaryk University, grant no. CZ.02.2.69/0.0/0.0/19_073/0016943. Data collection was funded by the Czech Science Foundation, grant no. GA18-08512S.

Notes on contributors

Adam Klocek

Adam Klocek, PhD, is a researcher in Psychology Research Institute at the Faculty of Social Studies, Masaryk University. He focuses on psychometrics, mechanisms of change, and the application of complexity science in psychotherapy research.

Tomáš Řiháček

Tomáš Řiháček, PhD, is an assistant professor at the Faculty of Social Studies, Masaryk University, Department of Psychology. He focuses on the application of machine learning and feedback in psychotherapy, medically unexplained physical symptoms, negative outcomes of psychotherapy, and the personal psychotherapeutic approach. He is a founder of the Center for Psychotherapy Research in the Czech Republic.