Effectiveness of Video Teletherapy in Treating Child and Adolescent Obsessive-Compulsive Disorder with Exposure and Response Prevention: a Retrospective Longitudinal Observational Study
Bibliographic record
Abstract
Abstract Background An effective primary treatment for obsessive-compulsive disorder (OCD) in children and adolescents as well as adults is exposure and response prevention, a form of cognitive-behavioral therapy. Despite strong evidence supporting the efficacy and effectiveness of exposure and response prevention from studies in research and real-world settings, its clinical use remains limited. This underuse is often attributed to access barriers such as the scarcity of properly trained therapists, geographical constraints, and costs. Some of these barriers may be addressed with virtual behavioral health, providing exposure and response prevention for OCD through video teletherapy and supplemented by app-based therapeutic tools and messaging support between sessions. While studies of teletherapy exposure and response prevention in adults with OCD have shown benefits in research and real-world settings in both small and large samples, studies in children and adolescents thus far have been in small samples and limited to research settings. Objective This study reports on the real-world effectiveness of teletherapy exposure and response prevention for OCD in the largest sample (N=2173) of child and adolescent patients to date. Methods Children and adolescents with OCD were treated with live, face-to-face video teletherapy sessions, with parent/caregiver involvement, using exposure and response prevention. Assessments were conducted at baseline, after 7-11 weeks, and after 13-17 weeks. Additionally, longitudinal assessments of OCD symptoms were performed at weeks 18-30, 31-42, and 43-54. Results Treatment resulted in a median 37.3% decrease in OCD symptoms at 13-17 weeks, and 53.4% of youth met full response criteria at this point. In addition, there were significant reductions in the severity of depression, anxiety, and stress symptoms. The median amount of therapist involvement was 13 appointments and 11.5 hours. Further, symptom improvements were maintained, or improved upon, in the longitudinal assessment period. Conclusions These results show that remote ERP treatment, assisted by technology, can effectively improve symptoms in children and adolescents with OCD in a real-world setting. Notable outcomes were achieved in a relatively small amount of therapist time, demonstrating its efficiency. These findings have implications for widespread dissemination of accessible, evidence-based care for children and adolescents with OCD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".