A multidimensional meta-analysis of psychotherapy and pharmacotherapy for obsessive-compulsive disorder

Background: Selective serotonin reuptake inhibitors (SSRIs) and exposure with response prevention for treatment of obsessivecompulsive disorder (OCD) have demonstrated empirical support; however, a substantial number of patients remain with clinically significant OCD symptoms after such treatments. Objectives: The aim of this study was to compare the effectiveness of acceptance and commitment therapy (ACT), selective serotonin reuptake inhibitors (SSRIs) and combination of ACT and SSRIs in the treatment of adults with obsessive-compulsive disorder (OCD). Patients and Methods: Thirty-two outpatients meeting Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, TextRevision criteria for OCD were randomly assigned to one of the three treatment conditions: ACT, SSRIs and combined treatment. The YaleBrown Obsessive-Compulsive Scale and Acceptance and Action Questionnaire were administered at pre-treatment and post-treatment. Twenty-seven patients completed the study. Data was analyzed using one-way analysis of variance (ANOVA) and one-way analysis of covariance (ANCOVA), clinically significant change (CSC) and complete remission status. Results: ANCOVA revealed that patients treated with ACT and combined treatment experienced a significantly greater improvement in obsessive-compulsive (OC) symptoms and experiential avoidance (EA) at post-treatment compared to those treated with SSRIs alone. However, there were no significant differences between ACT and combined treatment on OC symptoms and EA. CSC and complete remission status results showed that unlike SSRI, ACT and combined treatment led to more improvement in OC symptoms. Conclusions: ACT and combined treatment are more effective than SSRIs alone in treating OC symptoms and EA. However, it appears that adding SSRIs to ACT does not increase the effectiveness of ACT in the treatment of adults with OCD in the short-term.

RESULTS: The great majority of clinicians rated themselves and their teams as being better clinicians than their peers, though not to as extreme a level as in the previous study. They also reported exceptionally positive therapy outcomes. In general, these self-assessment biases were associated with higher levels of emotional stability, conscientiousness, and openness, but not with all clinic variables (e.g., there was a link between additional accreditation and reported recovery rates, but no relationship with supervision or professional background).CONCLUSION: Different possible explanations for these self-assessment biases are outlined, including conscious and unconscious processes. Methods for enhancing accurate skill perception are discussed, including self-monitoring and supervision.

Threat conditioning procedures have allowed the experimental investigation of the pathogenesis of Post-Traumatic Stress Disorder. The findings of these procedures have also provided stable foundations for the development of relevant intervention programs (e.g. exposure therapy). Statistical inference of threat conditioning procedures is commonly based on p-values and Null Hypothesis Significance Testing (NHST). Nowadays, however, there is a growing concern about this statistical approach, as many scientists point to the various limitations of p-values and NHST. As an alternative, the use of Bayes factors and Bayesian hypothesis testing has been suggested. In this article, we apply this statistical approach to threat conditioning data. In order to enable the easy computation of Bayes factors for threat conditioning data we present a new R package named condir, which can be used either via the R console or via a Shiny application. This article provides both a non-technical introduction to Bayesian analysis for researchers using the threat conditioning paradigm, and the necessary tools for computing Bayes factors easily.