Metacognitive training for depression (D-MCT) is a new treatment concept for depression. It is conceptualized as a variant of cognitive behavioral therapy (CBT) that adopts a metacognitive perspective to focus on the modification of cognitive biases and dysfunctional beliefs. The training seeks to enable group members to recognize and correct their often automatic and unconscious thought patterns. To this end, D-MCT teaches participants about depressive thought patterns using creative and engaging strategies along with practical examples from daily life. It also targets dysfunctional assumptions about one’s thought processes as well as dysfunctional coping strategies (e.g., social withdrawal, thought suppression, rumination as problem-solving). While the training was inspired by Metacognitive Training (MCT) for Psychosis, it focuses on different (i.e., depression-specific) cognitive biases.

The feasibility and effectiveness of a beta version of the training were confirmed in an open-label pilot study (Jelinek, Otte, Arlt, & Hauschildt, 2013). The training proved to be effective in reducing depressive symptoms at a medium effect size (Cohen’s d between 0.56 and 0.73). Moreover, cognitive biases and rumination were significantly reduced and self-esteem was increased (effect sizes between d = 0.26 and 0.64). The experience from the pilot study helped to improve the training material. The updated version was compared to health-training in a randomized control trial (funded by the vffr).

All patients also received a standard psychosomatic outpatient treatment program at the RehaCentrum Hamburg. At the end of the treatment, as well as 6 months later at follow-up, symptom improvement was greater in the D-MCT than the health-training group (Jelinek et al., 2016), and change in depression was mediated by a decrease in dysfunctional metacognitive beliefs, particularly ‘need for control’ (Jelinek, Van Quaquebeke, Moritz, 2017). Moreover, analyses of subjective appraisal rating suggest that patient acceptance of D-MCT is high (Jelinek, Moritz, Hauschildt, 2017).