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Objective: Panic disorder is one of the most prevalent anxiety disorders and with the greatest impact on the functionality of patients. Knowing variables that influence the therapies outcome can improve the results of the interventions and reduce the socio-health cost. The current study examined possible predictors and moderators of outcome in cognitive-behavioral therapy (CBT) and acceptance and commitment therapy (ACT). Method: Eighty patients with diagnostic of panic disorder received 12 group sessions of CBT or ACT and were assessed with several measures at baseline, 12 weeks post-treatment and 24 weeks post-treatment.
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Panic disorder is characterized by the presence of recurrent and unexpected panic attacks, that is, the person suddenly suffers attacks of intense fear. Panic disorder has a significant prevalence and presents frequent comorbidity with other disorders (anxiety, depression or substance abuse, among others). In addition, its course is usually chronic, leading to a significant functional deterioration for the person, with a deterioration in their quality of life. In addition, it generates high direct and indirect costs (loss of working days, visits to emergency services, medical visits, health expenses, etc.).
The psychological therapy of choice for the treatment of panic disorder is cognitive behavioral therapy (CBT). However, around 25% of patients do not respond successfully to CBT treatment, relapse at the end of therapy or need other additional treatments (Brown & Barlow, 1995; Hofmann et al., 2012). In this context, the study of predictors and moderators of efficacy in psychotherapy is especially relevant, with acceptance and commitment therapy (ACT) being an alternative with growing empirical support. In addition, the group therapy format provides unique qualities and advantages, both at a psychotherapeutic level and in terms of saving resources.
The objective of this study was to analyze the effect of different potentially predictive and moderating variables on the efficacy of cognitive-behavioral therapy and acceptance and commitment therapy in a group format for patients with panic disorder. To this end, a randomized, single-centre, prospective, controlled clinical trial with a 24-week follow-up was carried out.
The sample consisted of 80 patients with panic disorder, randomized into two treatment groups: the experimental group, which received acceptance and commitment therapy, and the active comparator control group, which received cognitive-behavioral therapy. Panic disorder severity was assessed using the Panic Disorder Severity Scale-Self Report (PDSS-SR), fear of physical symptoms using the Body Sensations Questionnaire, BSQ) and the frequency of catastrophic thoughts using the Agoraphobic Cognitions Questionnaire (ACQ). These three measurements were taken before and at the end of treatment, as well as at three months of follow-up.
The potential predictors and moderators analyzed were age, gender, duration of the disorder, motivation to change using the University of Rhode Island Change Assessment Scale (URICA), responsibility through the Five Factor Personality Inventory (Neo Five Factor Inventory, NEO-FFI) and experiential avoidance with the Acceptance and Action Questionnaire-II (AAQ-II).
Participants were randomized to the two treatment groups (CBT or ACT). In both groups, the patients received 12 therapy sessions of 1 hour and 30 minutes duration, with a weekly frequency.
Data analysis was performed using the statistical computing software R version 3.5 and its random forest and Ime4 packages. Statistical analysis was performed by intention to treat (ITT). For the comparison of the sociodemographic and clinical characteristics between the two groups, the Student's t-test was used for the quantitative variables and the Chi-square test for the qualitative variables. To test the hypotheses, a model was designed to perform the analysis of covariance (ANCOVA) type III, which incorporates interactions, considering the time factor of the treatments as a covariate. Post-hoc analyzes were performed with the multcomp package built into the R statistical computing software, which allows performing Tukey-type simultaneous comparison tests for generalized models and generalized mixed models.
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80 participants in 2 patient groups
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Data sourced from clinicaltrials.gov
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