Adverse events in cognitive behavioral therapy and relaxation training for children and adolescents with obsessive-compulsive disorder: A mixed methods study and analysis plan for the TECTO trial.

Background: Knowledge on adverse events in psychotherapy for youth with OCD is sparse. No official guidelines exist for defining or monitoring adverse events in psychotherapy. Recent recommendations call for more qualitative and quantitative assessment of adverse events in psychotherapy trials. This mixed methods study aims to expand knowledge on adverse events in psychotherapy for youth with OCD. Methods: This is an analysis plan for a convergent mixed methods study within a randomized clinical trial (the TECTO trial). We include at least 128 youth aged 8-17 years with obsessive-compulsive disorder (OCD). Participants are randomized to either family-based cognitive behavioral therapy (FCBT) or family-based psychoeducation and relaxation training (FPRT). Adverse events are monitored quantitatively with the Negative Effects Questionnaire. Furthermore, we assess psychiatric symptoms, global functioning, quality of life, and family factors to investigate predictors for adverse events. We conduct semi-structured qualitative interviews with all youths and their parents on their experience of adverse events in FCBT or FPRT. For the mixed methods analysis, we will merge 1) a qualitative content analysis with descriptive statistics comparing the types, frequencies, and severity of adverse events; 2) a qualitative content analysis of the perceived causes for adverse events with prediction models for adverse events; and 3) a thematic analysis of the participants' treatment evaluation with a correlational analysis of adverse events and OCD severity. Discussion: The in-depth mixed methods analysis can inform 1) safer and more effective psychotherapy for OCD; 2) instruments and guidelines for monitoring adverse events; and 3) patient information on potential adverse events. The main limitation is risk of missing data. Trial registration: ClinicalTrials.gov identifier: NCT03595098. Registered on July 23, 2018.

Associations Between the Severity of Obsessive-Compulsive Disorder and Vocal Features in Children and Adolescents: Protocol for a Statistical and Machine Learning Analysis.

BACKGROUND: Artificial intelligence tools have the potential to objectively identify youth in need of mental health care. Speech signals have shown promise as a source for predicting various psychiatric conditions and transdiagnostic symptoms. OBJECTIVE: We designed a study testing the association between obsessive-compulsive disorder (OCD) diagnosis and symptom severity on vocal features in children and adolescents. Here, we present an analysis plan and statistical report for the study to document our a priori hypotheses and increase the robustness of the findings of our planned study. METHODS: Audio recordings of clinical interviews of 47 children and adolescents with OCD and 17 children and adolescents without a psychiatric diagnosis will be analyzed. Youths were between 8 and 17 years old. We will test the effect of OCD diagnosis on computationally derived scores of vocal activation using ANOVA. To test the effect of OCD severity classifications on the same computationally derived vocal scores, we will perform a logistic regression. Finally, we will attempt to create an improved indicator of OCD severity by refining the model with more relevant labels. Models will be adjusted for age and gender. Model validation strategies are outlined. RESULTS: Simulated results are presented. The actual results using real data will be presented in future publications. CONCLUSIONS: A major strength of this study is that we will include age and gender in our models to increase classification accuracy. A major challenge is the suboptimal quality of the audio recordings, which are representative of in-the-wild data and a large body of recordings collected during other clinical trials. This preregistered analysis plan and statistical report will increase the validity of the interpretations of the upcoming results. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/39613.