Cognitive Behavioral Immersion for Depression: Protocol for a Three-Arm Randomized Controlled Trial of Peer-Based Coaching in the Metaverse
Bibliographic record
Abstract
BACKGROUND: Depression and anxiety are among the most common mental health concerns globally. Efficacious treatments such as cognitive behavioral therapy exist but remain difficult to access and scale. Cognitive behavioral immersion (CBI)-a cognitive behavioral skills training intervention delivered by peer coaches in the metaverse-has been developed to address these barriers. CBI can be used through a virtual reality headset or via flat-screen devices such as phones, tablets, or computers. Pilot data have established its usability among participants with clinical levels of depression and anxiety. However, more research is needed to determine whether CBI causes decreases in these symptoms and how delivery via virtual reality compares to flat-screen devices. OBJECTIVE: This protocol aims to conduct a randomized controlled trial evaluating the efficacy of immersive CBI accessed via a virtual reality headset (CBI-VR) as compared to a less immersive (but more accessible) CBI condition accessed via a flat-screen device (CBI-FS) and each to a delayed access control (DAC). METHODS: A total of 306 adults experiencing clinical levels of depressive symptoms are being recruited nationally to participate in this web-based trial. Participants will be randomized according to a 1:1:1 ratio to one of three conditions: (1) CBI-VR, (2) CBI-FS, and (3) DAC. The CBI program consists of eight weekly 60-minute group sessions led by trained peer coaches who teach cognitive behavioral skills. The acute period of each condition will last 8 weeks with a follow-up period of 6 months. The primary outcome is depressive symptoms; secondary outcomes are anxiety symptoms and quality of life. Outcomes will be assessed once at baseline, weekly during the course of the intervention, and monthly during follow-ups. We will use hierarchical linear models to assess differences in the rate of symptom change among conditions. We will also explore potential prognostic (demographics and immersion) and prescriptive (cognitive behavioral skills, group alliance, and program engagement) predictors, as well as potential mechanisms (cognitive change and social support) of response. RESULTS: We hypothesize that participants randomized to either CBI group will experience greater symptom improvement than those in DAC and that CBI-VR participants will improve more than CBI-FS participants. This study was funded in September 2023. Data collection began in February 2024. As of January 2025, all 306 participants have been enrolled. Data collection should conclude by September 2025. Data have not yet been analyzed. Expected results to be submitted for publication in the winter of 2025. CONCLUSIONS: This trial will determine if CBI via either device is efficacious as compared to DAC and whether virtual reality enhances outcomes. Findings will contribute to the literature on using the metaverse and virtual reality to facilitate effective accessible mental health interventions, particularly for depression. TRIAL REGISTRATION: ClinicalTrials.gov NCT06418997; https://clinicaltrials.gov/study/NCT06418997. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/65970.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.072 | 0.009 |
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 source (direct Gemma or distilled Codex), 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".