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Predictors and Moderators of Post-Treatment Depression Severity After Hypnotherapy and Cognitive Behavioral Therapy
Anna-Lena Brockmann1, Lucia Pfeiffer1, Anil Batra1
1Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health (TüCMH), University of Tübingen, Calwer Str. 14, 72076 Tübingen, Germany.
Abstract:
Depression is one of the most prevalent mental health conditions, and cognitive behavioral therapy (CBT) is one of the most efficacious treatments for depressive disorders. Several studies have demonstrated the efficacy of hypnotherapy (HT) in treating depression. However, it remains unclear which treatment works the best for each patient. This study investigated global and treatment-specific predictors of CBT and HT in a secondary analysis of an RCT for patients with mild-to-moderate major depression. A total of 152 patients were randomly assigned to receive either HT (n = 74) or CBT (n = 78) as an individual outpatient treatment for six months. Both treatments were comparably effective. Baseline characteristics including sociodemographic, depression-related, and psychological variables were assessed. The primary outcome was the clinician-rated Montgomery-Åsberg Depression Rating Scale (MADRS) scores after the treatment. Predictors were examined using correlation, regression, and moderation analyses for hypothesis-generating purposes. The results did not support the hypothesized cognitive and behavioral variables predicting post-treatment depression severity for CBT nor hypnotic susceptibility or expectations predicting outcome for HT. We found that lower baseline depression severity and higher self-efficacy predicted a better overall treatment outcome for all patients. As exploratory results, antidepressant medication and environmental quality of life predicted treatment outcome for patients receiving HT. In patients with CBT, educational level and psychological quality of life were found as exploratory predictors of treatment outcome. These results should be considered preliminary before strong conclusions can be drawn. Future research should further investigate HT and refine predictor models more strictly.