Enhancing Therapist Training in the Delivery of Exposure Therapy for Individuals with Anxiety Disorders Using Virtual Reality Simulation: Randomized Feasibility Trial

JMIR Medical Education · 2026-01-17

Background: Exposure-based cognitive behavioral therapy is among the least used evidence-based practices for anxiety disorders in routine care. Providers' negative beliefs about exposure (eg, fears of harm or intolerability) are a major barrier. Experiential methods can reduce these beliefs but are limited by accessibility, standardization, and fidelity. Virtual reality (VR) offers a scalable way to deliver standardized experiential practice. Guided by an "exposure to exposure" (E2E) framework, we conceptualized VR training as an exposure intervention targeting therapists' own anxious beliefs about exposure. Objective: This feasibility study examined a VR-based exposure training program (SET-VR) (1) to evaluate usability and effects on therapist learning targets (knowledge, self-efficacy, attitudes) and (2) to test whether a high-immersion head-mounted display (HMD) format provides added benefit over a lower-immersion desktop format. Eligibility included holding an active caseload. Methods: Eligible clinicians (ie, aged >21 years with an active caseload; n=41) completed a 4-hour didactic workshop on exposure and were randomized (1:1, blinded) to the desktop or HMD condition. In the experiential phase, therapists delivered 3 rounds of exposure with a virtual patient. They titrated exposure intensity (increase, decrease, continue as is) at fixed decision points based on state-dependent visual (character animations) and auditory (prerecorded verbalizations) cues reflecting the patient's distress. Exposure knowledge, self-efficacy, and beliefs about exposure were measured at baseline, post-didactic, post-experiential, and follow-up. Participants also rated the acceptability, usability, and authenticity of the program. Results: Both groups (desktop and HMD) showed significant improvement in exposure knowledge (d=0.52, P=.006; d=0.58, P=.002), self-efficacy (d=0.88, P<.001; d=1.36, P<.001), and beliefs (d=0.61, P=.001; d=1.05, P<.001) from baseline to post-didactic training using binomial generalized estimating equations. There were no significant differences between the low- and high-immersion groups on any measure after didactics. Both groups demonstrated significant improvement in exposure self-efficacy (d=0.66, P<.001; d=0.93, P<.001) and beliefs (d=0.46, P<.01; d=0.66, P<.001) from post-didactic to post-experiential. Both groups gave positive ratings for acceptability, usability, and authenticity. No adverse events or side effects were reported. Conclusions: In this feasibility randomized controlled trial, an E2E-guided VR training program produced promising improvements in therapists' self-efficacy and negative beliefs about exposure beyond gains from didactic training alone. This work is innovative in testing immersion as a dose parameter while also applying an explicit framework (E2E) to target a key mechanism (ie, therapist beliefs) in the underuse of exposure therapy. Compared to prior VR training studies focused on skills and knowledge acquisition, our findings support the standardization of an emotionally engaging exposure practice context that shifts therapist-level mechanisms linked to actual delivery. The lack of clear advantages for HMD over desktop VR suggests that lower-immersion, more scalable implementations may provide a sufficient experiential "dose." Larger, more diverse trials are needed to confirm effectiveness and determine the real-world impact of VR-based exposure training on access to evidence-based care.

Who calls, who engages? Families seeking treatment for anxiety and OCD

The Brown University Child and Adolescent Behavior Letter · 2025-02-04

Pediatric anxiety is among the most common mental health diagnoses for American youth, yet few youths diagnosed with anxiety/obsessive‐compulsive disorder (OCD) receive treatment. The majority of parents nationwide report at least some difficulty accessing mental health care for their child. Within the state of Rhode Island, where 12.7% of youth experienced anxiety concerns during 2021, 59% of caregivers reported difficulty accessing mental health care of any kind (Child and Adolescent Health Measurement Initiative, 2021‐2022). Access to exposure‐based CBT (exposure therapy), despite strong evidence as a frontline treatment for anxiety/OCD, is especially limited.

What works for whom in pediatric OCD: description of causally interpretable meta-analysis methods and report on trial data harmonization

Psychological Medicine · 2025-01-01

BACKGROUND: Improving patient outcomes will be enhanced by understanding "what works, for whom?" enabling better matching of patients to available treatments. However, answering this "what works, for whom?" question requires sample sizes that exceed those of most individual trials. Conventional methods for combining data across trials, including aggregate-data meta-analysis, suffer from key limitations including difficulty accounting for differences across trials (e.g., comparing "apples to oranges"). Causally interpretable meta-analysis (CI-MA) addresses these limitations by pairing individual-participant-data (IPD) across trials using advancements in transportability methods to extend causal inferences to clinical "target" populations of interest. Combining IPD across trials also requires careful acquisition and harmonization of data, a challenging process for which practical guidance is not well-described in the literature. METHODS: We describe methods and work to date for a large harmonization project in pediatric obsessive-compulsive disorder (OCD) that employs CI-MA. RESULTS: We review the data acquisition, harmonization, meta-data coding, and IPD analysis processes for Project Harmony, a study that (1) harmonizes 28 randomized controlled trials, along with target data from a clinical sample of treatment-seeking youth ages 4-20 with OCD, and (2) applies CI-MA to examine "what works, for whom?" We also detail dissemination strategies and partner involvement planned throughout the project to enhance the future clinical utility of CI-MA findings. Data harmonization took approximately 125 hours per trial (3,000 hours total), which was considerably higher than preliminary projections. CONCLUSIONS: Applying CI-MA to harmonize data has the potential to answer "what works for whom?" in pediatric OCD.

Automated classification of exposure and encourage events in speech data from pediatric OCD treatment

JAMIA Open · 2025-10-06 · 1 citations

Objective: To develop and evaluate an automated classification system for labeling Exposure Process Coding System (EPCS) quality codes-specifically exposure and encourage events-during in-person exposure therapy sessions using automatic speech recognition (ASR) and natural language processing techniques. Materials and Methods: The system was trained and tested on 360 manually labeled pediatric Obsessive-Compulsive Disorder (OCD) therapy sessions from 3 clinical trials. Audio recordings were transcribed using ASR tools (OpenAI's Whisper and Google Speech-to-Text). Transcription accuracy was evaluated via word error rate (WER) on manual transcriptions of 2-minute audio segments compared against ASR-generated transcripts. The resulting text was analyzed with transformer-based models, including Bidirectional Encoder Representations from Transformers (BERT), Sentence-BERT, and Meta Llama 3. Models were trained to predict EPCS codes in 2 classification settings: sequence-level classification, where events are labeled in delimited text chunks, and token-level classification, where event boundaries are unknown. Classification was performed either with fine-tuned transformer-based models, or with logistic regression on embeddings produced by each model. Results: With respect to transcription accuracy, Whisper outperformed Google Speech-to-Text with a lower WER (0.31 vs 0.51). For sequence classification setting, Llama 3 models achieved high performance with area under the ROC curve (AUC) scores of 0.95 for exposures and 0.75 for encourage events, outperforming traditional methods and standard BERT models. In the token-level setting, fine-tuned BERT models performed best, achieving AUC scores of 0.85 for exposures and 0.75 for encourage events. Discussion and Conclusion: Current ASR and transformer-based models enable automated quality coding of in-person exposure therapy sessions. These findings demonstrate potential for real-time assessment in clinical practice and scalable research on effective therapy methods. Future work should focus on optimization, including improvements in ASR accuracy, expanding training datasets, and multimodal data integration.

Enhancing Exposure Therapy Training through Virtual Reality Simulation: A Randomized Pilot Trial (Preprint)

2025-07-03

<sec> <title>BACKGROUND</title> Despite robust empirical support, exposure-based CBT remains one of the least utilized evidence-based practices (EBPs) for anxiety disorders in typical practice settings. Research suggests providers’ negative beliefs about the risk of negative events during exposure delivery are a major predictor of its underutilization. Studies have demonstrated that incorporating experiential learning such as role-playing into conventional didactic training can reduce therapists’ negative beliefs. However, these methods face limitations in terms of accessibility, standardization, and fidelity to real-life experiences. Emerging evidence suggests virtual reality (VR) simulations may be an effective and scalable alternative for improving skills and attitudes pertinent to mental health treatment. </sec> <sec> <title>OBJECTIVE</title> This study examines the initial efficacy of a novel VR simulation-based exposure training program (SET-VRTM) based on (1) perceptions of usability, and (2) degree of change in therapist learning targets (i.e. knowledge, self-efficacy, attitudes). Clinician participants were randomly assigned to a low-immersion desktop version or a high-immersion head-mounted display (HMD) version of the SET-VRTM program to explore the influence of immersion on key outcomes. </sec> <sec> <title>METHODS</title> Clinician participants (N=41) were recruited from a variety of practice settings. Before randomization, both groups received conventional (4-hour) didactic training for exposure therapy. Next, groups were assigned to immersion modality (desktop or HMD) and began delivering exposures to a virtually simulated patient. Participants practiced titrating exposure intensity (increase, decrease, continue) based on real-time visual and auditory cues from the virtual patient. Participants completed three rounds of exposure delivery to a simulated patient and reviewed their decisions with feedback at the end of each round. Exposure knowledge, exposure self-efficacy, and beliefs about exposures were measured at baseline, post-didactic, and post-VR. Participants also rated the acceptability, usability, and real-world authenticity of VR exposure training. </sec> <sec> <title>RESULTS</title> Both groups (desktop, HMD) showed significant improvement in exposure knowledge (p&lt;.01; p&lt;.01), self-efficacy (p&lt;.01; p&lt;.01), and beliefs about exposure (p&lt;.01; p&lt;.01) between baseline and didactic training. There were no significant differences between the low and high immersion groups on any measure at baseline or after didactics. Both groups demonstrated significant improvement in exposure self-efficacy (p&lt;.01; p&lt;.01) and beliefs (P&lt;.001; p=.012) from post-didactic to post-exposure delivery. Neither showed improved knowledge from post-didactic to post-exposure delivery (p&gt;.05; p&gt;.05). Both groups gave highly positive ratings for the acceptability, usability, and authenticity of the simulated training experience. Taken together, results indicate that VR training significantly improved therapists’ self-efficacy and beliefs about exposures beyond gains from didactic training alone. </sec> <sec> <title>CONCLUSIONS</title> VR exposure therapy training is both well-received and effective in addressing clinician-level barriers to optimal exposure delivery. Supplementing conventional didactic training with experiential learning via VR sessions may be a promising next step in optimizing the standardization, scalability, and effectiveness of exposure training. </sec> <sec> <title>CLINICALTRIAL</title> Clinicaltrials.gov Identifier: NCT06706245 </sec>

Exploring the association between sleep and fear extinction learning in adolescents with anxiety and/or OCD: a study protocol

SLEEP Advances · 2025-01-01

Abstract Pediatric anxiety disorders (including Obsessive Compulsive Disorder) are prevalent and impairing. Youth with anxiety disorders frequently experience sleep disturbances. Exposure, the primary component of gold-standard Cognitive Behavioral Therapy (CBT) for treating anxiety disorders, works by harnessing fear extinction learning. Given that sleep plays a critical role in the consolidation and retrieval of emotional memories, we hypothesize that shorter sleep quantity and greater sleep disruption are associated with psychophysiological responses indicating reduced fear extinction learning and reduced fear extinction recall in adolescents with anxiety and OCD. In this protocol paper, we describe a pilot study testing this hypothesis in a clinical sample of adolescents participating in a CBT-based partial hospital program (PHP) dedicated to the treatment of anxiety disorders. Participants complete a multi-method sleep assessment over 10 days during the first portion of their admission in the program (within the first 4 weeks) and at the end of their stay (at least over 5-7 days before discharge). Standardized clinical interviews and sleep questionnaires are coupled with multi-modal at-home sleep monitoring using sleep diaries, patch-based actigraphy, and wearable sleep electroencephalography (EEG). Participants also complete a computerized task assessing initial fear learning (day 1), fear extinction learning (day 2), and extinction recall (day 3) as measured by skin conductance responses (SCR). This use of multi-method sleep assessments in a clinical sample of youths with more clinically severe anxiety disorders is innovative and, to our knowledge, has not yet been done. Statement of Significance Results of this study will provide data for understanding sleep problems in relation to one of the underlying mechanisms of exposure therapy among adolescents with anxiety disorders, and evidence for feasibility of multi-method sleep assessments in a relatively acute mental health setting. If this line of research is successful, it will allow for the development and evaluation of augmentation strategies that target specific aspects of sleep that contribute to suboptimal fear extinction learning. Results from this work may serve as proof-of-concept for identifying sleep targets to guide the augmentation of behavioral treatments.

Harnessing Natural Language Processing for Automated Exposure Therapy Coding in Youth with OCD

2025-07-01

Objective: To develop and evaluate an automated classification system for labeling Exposure Process Coding System quality codes -- specifically exposure and encourage events -- during in-person exposure therapy sessions using automatic speech recognition (ASR) and natural language processing techniques.Methods: The system was trained and tested on 360 manually labeled pediatric OCD therapy sessions from three clinical trials. Audio data were processed using ASR tools (OpenAI's Whisper and Google Speech-to-Text). Manual transcriptions of two-minute audio segments were compared against ASR-generated transcripts to assess transcription accuracy via word error rate (WER). The resulting text was analyzed with transformer-based models, including BERT, SBERT, and Meta Llama 3. Two classification settings were explored: sequence-level classification, where events are labeled in delimited text chunks, and token-level classification, where event boundaries are unknown. Classification was performed either with fine-tuned transformer-based models, or with logistic regression on embeddings produced by each model.Results: Whisper outperformed Google Speech-to-Text with a lower WER (0.31 vs. 0.51). In the sequence classification setting, Llama 3 models achieved high performance with AUC scores of 0.95 for exposures and 0.75 for encourage events, outperforming traditional methods and standard BERT models. In the token-level setting, fine-tuned BERT models performed best, achieving AUC scores of 0.85 for exposures and 0.75 for encourage events.Conclusion: Automated quality coding of in-person exposure therapy sessions is feasible using current ASR and transformer-based models. These findings suggest potential for real-time quality assessment in clinical practice and scalable research on effective therapy methods. Finally, future work is needed for optimization, including improvements in ASR accuracy, expanded training datasets, and multimodal data integration.

Harnessing Natural Language Processing for Automated Exposure Therapy Coding in Youth with OCD

2025-04-24

Objective: To develop and evaluate an automated classification system for labeling Exposure Process Coding System quality codes -- specifically exposure and encourage events -- during in-person exposure therapy sessions using automatic speech recognition (ASR) and natural language processing techniques.Methods: The system was trained and tested on 360 manually labeled pediatric OCD therapy sessions from three clinical trials. Audio data were processed using ASR tools (OpenAI's Whisper and Google Speech-to-Text). Manual transcriptions of two-minute audio segments were compared against ASR-generated transcripts to assess transcription accuracy via word error rate (WER). The resulting text was analyzed with transformer-based models, including BERT, SBERT, and Meta Llama 3. Two classification settings were explored: sequence-level classification, where events are labeled in delimited text chunks, and token-level classification, where event boundaries are unknown. Classification was performed either with fine-tuned transformer-based models, or with logistic regression on embeddings produced by each model.Results: Whisper outperformed Google Speech-to-Text with a lower WER (0.31 vs. 0.51). In the sequence classification setting, Llama 3 models achieved high performance with AUC scores of 0.95 for exposures and 0.75 for encourage events, outperforming traditional methods and standard BERT models. In the token-level setting, fine-tuned BERT models performed best, achieving AUC scores of 0.85 for exposures and 0.75 for encourage events.Conclusion: Automated quality coding of in-person exposure therapy sessions is feasible using current ASR and transformer-based models. These findings suggest potential for real-time quality assessment in clinical practice and scalable research on effective therapy methods. Finally, future work is needed for optimization, including improvements in ASR accuracy, expanded training datasets, and multimodal data integration.

2.37 Virtual Practice Makes Perfect: A Novel Virtual Reality Training Platform for Exposure Therapy

Journal of the American Academy of Child & Adolescent Psychiatry · 2025-10-01

Open Trial of a Telehealth Adaptation of Team-Based Delivery of Cognitive Behavioral Treatment for Pediatric Anxiety and Obsessive-Compulsive Disorder

Evidence-Based Practice in Child and Adolescent Mental Health · 2024-01-08 · 3 citations

This brief report presents the results of an open trial of a telehealth adaptation of a novel team-based approach to cognitive behavioral treatment (CBT) for pediatric anxiety and obsessive-compulsive disorder (OCD). This telehealth modification was necessary as a response to COVID-19 to allow clients to continue to receive treatment during a pause to in-person care in a larger trial of team-based treatment for anxious youth. Participants included 46 youth between the ages of 5 and 18 who received telehealth delivered CBT via a task-sharing model whereby patients and families met monthly with a supervising licensed clinician and with non-licensed staff all other weeks of the month. Participants received treatment for up to 6 months and completed symptom assessments every 6 weeks throughout treatment. Descriptive results demonstrated high patient and caregiver treatment engagement and satisfaction. Anxiety and OCD symptoms decreased significantly from baseline to post-treatment with 68% of participants classified as treatment responders. Patient- and caregiver-reported Top Problems and caregiver-reported quality of life improved significantly from baseline to post-treatment. In addition, the clinical capacity of the licensed provider increased more than two-fold by leveraging non-licensed staff. This novel telehealth delivery model using team-based care has potential to increase provider capacity and reduce barriers to mental health care access for youth.