Virtual Reality Behavioral Activation for Adults With Major Depressive Disorder: Feasibility Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Major depressive disorder (MDD) is a global crisis with increasing incidence and prevalence. There are many established evidence-based psychotherapies (EBPs) for depression, but numerous barriers still exist; most notably, access and dissemination. Virtual reality (VR) may offer some solutions to existing constraints of EBPs for MDD. OBJECTIVE: We aimed to examine the feasibility, acceptability, and tolerability of using VR as a method of delivering behavioral activation (BA) for adults diagnosed with MDD during a global pandemic and to explore for signs of clinical efficacy by comparing VR-enhanced BA (VR BA) to a standard BA treatment and a treatment as usual control group for individuals diagnosed with MDD. METHODS: A feasibility trial using a 3-armed, unblinded, randomized controlled pilot design was conducted. The study took place remotely via Zoom telehealth visits between April 8, 2020, and January 15, 2021. This study used a 3-week, 4-session protocol in which VR BA participants used a VR headset to complete their BA homework. Feasibility was measured using dropout rates, serious adverse events, completion of homework, an adapted telepresence scale, the Simulator Sickness Questionnaire, the Brief Agitation Measure, and an adapted Technology Acceptance Model. Efficacy was assessed using the Patient Health Questionnaire-9. RESULTS: Of the 35 participants assessed for eligibility, 13 (37%) were randomized into VR BA (n=5, 38%), traditional BA (n=4, 31%), or a treatment as usual control (n=4, 31%). The mean age of the 13 participants (5/13, 38% male; 7/13, 54% female; and 1/13, 8% nonbinary or third gender) was 35.4 (SD 12.3) years. This study demonstrated VR BA feasibility in participants with MDD through documented high levels of acceptability and tolerability while engaging in VR-induced pleasurable activities in conjunction with a brief BA protocol. No adverse events were reported. This study also illustrated that VR BA may have potential clinical utility for treating MDD, as the average VR BA participant's clinical severity decreased by 5.67 points, signifying a clinically meaningful change in severity from a moderate to a mild level of depression as per the Patient Health Questionnaire-9 score. CONCLUSIONS: The findings of this study demonstrate that VR BA is safe and feasible to explore for the treatment of MDD. This study documented evidence that VR BA may be efficacious and justifies further examination in an adequately powered randomized controlled trial. This pilot study highlights the potential utility that VR technology may offer patients with MDD, especially those who have difficulty accessing real-world pleasant activities. In addition, for those having difficulty accessing care, VR BA could be adapted as a first step to help people improve their mood and increase their motivation while waiting to connect with a health care professional for other EBPs. TRIAL REGISTRATION: ClinicalTrials.gov NCT04268316; https://clinicaltrials.gov/ct2/show/NCT04268316. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/24331.
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 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".