The effectiveness of psychotherapy under the magnifying glass: what did the network analysis reveal?

Mental illness often manifests itself in specific symptoms, but recognition is usually not straightforward. Symptoms can occur in various combinations. At the same time, they may be common to several mental illnesses (so-called comorbidity). We can imagine a network where we can see individual symptoms and their interconnectedness. The structure of this network can be grasped methodologically, and the analysis can be used to discern, for example, how the different symptoms of anxiety and depression are interrelated. That is what Adam Klocek and Tomáš Řiháček have focused on in their new study.

The aim of the research team from the Center for Psychotherapy Research/INPSY was to assess the effectiveness of a comprehensive psychodynamic treatment using a network analysis that focused on the comorbidity of mental illness. Specifically, they examined how participants' mental well-being and symptoms of anxiety and depression changed after they received multicomponent treatment. This comprehensive treatment consisted of various forms of psychotherapy (mostly group, but also music and other forms).

The authors assessed the efficacy of the treatment not only in the traditional way, i.e. by comparing the value of the presenting symptoms before and after treatment. They also examined changes in the interconnected network of symptoms using network analysis. The sample included 448 patients treated at seven clinics in the Czech Republic, mostly with anxiety disorders.

Was the complex treatment effective?

The results suggest that most patients experienced positive changes in observed depression, anxiety and psychological well-being after treatment, with minimal deterioration.

What did the network analysis reveal?

The research allowed for better differentiation of aspects of mental well-being, and a key finding was that the comorbidity of symptoms of both mental illnesses decreased after treatment. The multicomponent treatment helped to weaken the interdependence of depressive symptoms with anxiety, suggesting a reduction in comorbidity (i.e. the presence of multiple illnesses). Patients seemed to understand their symptoms better after treatment, which were less intertwined with each other.

The results further suggest that the effectiveness of psychotherapy is not so simple and depends on one's perspective. Four symptom clusters were identified prior to treatment: depression, anxiety, positive attitude and activity. After treatment, these groups changed significantly. Symptoms of depression subsided, and symptoms associated with mental well-being and anxiety increased. On the one hand, we can say that depressive symptoms are generally considered more problematic than anxiety symptoms. The replacement of a strongly linked depressive structure by a strong anxiety symptom structure may then indicate the effectiveness of this form of treatment. On the other hand, it is the presence of stronger interconnectedness of anxiety symptoms after treatment that may also indicate potential ineffectiveness of treatment. Anxiety symptoms showed some resistance to change (in contrast to depressive symptoms).

Comparison of the patient's condition before and after treatment suggests support for the efficacy of comprehensive psychotherapy. However, a detailed view of specific changes in symptom interactions provides a deeper understanding and may reveal new, often contradictory, insights. The authors encourage further investigation of the long-term effects of multicomponent treatment and symptom development, and in particular the use of network analysis in this type of research as well. Subsequent findings may lead to a deeper understanding of how this treatment modality works at the symptom level and serve as feedback for eventual improvement of services.

Recommended citation:

Klocek, A., & Řiháček, T. (2023). A multicomponent psychodynamic treatment for comorbid disorders: a baseline and post-treatment network comparison. Counselling Psychology Quarterly, 1–26. https://doi.org/10.1080/09515070.2023.2292212

Translated using DeepL.