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Record W4406856716 · doi:10.2196/66715

Effectiveness of Video Teletherapy in Treating Obsessive-Compulsive Disorder in Children and Adolescents With Exposure and Response Prevention: Retrospective Longitudinal Observational Study

2025· article· en· W4406856716 on OpenAlexaff
Jamie D. Feusner, Nicholas R. Farrell, Mia Nuñez, Nicholas Lume, Catherine W. MacDonald, Patrick B. McGrath, Larry Trusky, Stephen M. Smith, Andreas Rhode

Bibliographic record

VenueJournal of Medical Internet Research · 2025
Typearticle
Languageen
FieldPsychology
TopicObsessive-Compulsive Spectrum Disorders
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsContext (archaeology)Observational studyAnxietyIntervention (counseling)Clinical psychologyLongitudinal studyMedicineExposure and response preventionPsychologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: An effective primary treatment for obsessive-compulsive disorder (OCD) in children and adolescents as well as adults is exposure and response prevention (ERP), a form of intervention in the context of cognitive-behavioral therapy. Despite strong evidence supporting the efficacy and effectiveness of ERP 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 ERP for OCD through video teletherapy and supplemented by app-based therapeutic tools and messaging support between sessions. Studies of teletherapy ERP in adults with OCD have shown benefits in research and real-world settings in both small and large samples. However, studies of teletherapy ERP in children and adolescents thus far have been in small samples and limited to research rather than real-world settings. OBJECTIVE: This study reports on the real-world effectiveness of teletherapy ERP 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 or caregiver involvement, using ERP. 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. We analyzed longitudinal outcomes of OCD symptoms, depression, anxiety, and stress using linear mixed models. RESULTS: Treatment resulted in a median 38.46% (IQR 12.50%-64.00%) decrease in OCD symptoms at 13-17 weeks, and 53.4% of youth met full response criteria at this point. Improvements were observed in all categories of starting symptom severity: mild (median 40.3%, IQR 8.5%-79.8%), moderate (median 38.4%, IQR 13.3%-63.6%), and severe (median 34.1%, IQR 6.6%-58.5%). In addition, there were significant reductions in the severity of depression, anxiety, and stress symptoms. The median amount of therapist involvement was 13 (IQR 10.0-16.0) appointments and 11.5 (IQR 9.0-15.0) hours. Further, symptom improvements were maintained or improved upon in the longitudinal assessment periods of weeks 18-30, 31-42, and 43-54. CONCLUSIONS: These results show that remote ERP treatment, assisted by technology, can effectively improve both core OCD and related depression, anxiety, and stress 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. Demonstrating the usefulness of a delivery format that overcomes several traditional barriers to treatment, these findings have implications for widespread dissemination of accessible, evidence-based care for children and adolescents with OCD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.413
Teacher spread0.382 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2025
Admission routes1
Has abstractyes

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