Examining the Efficacy of Extended Reality–Enhanced Behavioral Activation for Adults With Major Depressive Disorder: Randomized Controlled Trial
Bibliographic record
Abstract
Background Major depressive disorder (MDD) is a global concern with increasing prevalence. While many evidence-based psychotherapies (EBPs) have been identified to treat MDD, there are numerous barriers to patients accessing them. Virtual reality (VR) has been used as a treatment enhancement for a variety of mental health disorders, but few studies have examined its clinical use in treating MDD. Behavioral activation (BA) is a simple yet effective and established first-line EBP for MDD that has the potential to be easily enhanced and adapted with VR technology. A previous report by our group explored the feasibility and acceptability of VR-enhanced BA in a small clinical proof-of-concept pilot. This study examines the clinical efficacy of a more immersive extended reality (XR)–enhanced BA (XR-BA) prototype. This is the first clinical efficacy test of an XR-BA protocol. Objective This study examined whether XR-BA was feasible and efficacious in treating MDD in an ambulatory telemedicine clinic. Methods A nonblinded between-subject randomized controlled trial compared XR-BA to traditional BA delivered via telehealth. The study used a previously established, brief 3-week, 4-session BA EBP intervention. The experimental XR-BA participants were directed to use a Meta Quest 2 (Reality Labs) VR headset to engage in simulated pleasant or mastery activities and were compared to a control arm, which used only real-life mastery or pleasant activities as between-session homework. The Patient Health Questionnaire (PHQ)–9 was the primary outcome measure. Independent-sample and paired-sample t tests (2-tailed) were used to determine statistical significance and confirmed using structural equation modeling. Results Overall, 26 participants with MDD were randomized to receive either XR-BA (n=13, 50%) or traditional BA (n=13, 50%). The mean age of the 26 participants (n=6, 23% male; n=19, 73% female; n=1, 4% nonbinary or third gender) was 50.3 (SD 17.3) years. No adverse events were reported in either group, and no substantial differences in dropout rates or homework completion were observed. XR-BA was found to be statistically noninferior to traditional BA (t18.6=−0.28; P=.78). Both the XR-BA (t9=2.5; P=.04) and traditional BA (t10=2.3; P=.04) arms showed a statistically significant decrease in PHQ-9 and clinical severity from the beginning of session 1 to the beginning of session 4. There was a significant decrease in PHQ-8 to PHQ-9 scores between the phone intake and the beginning of session 1 for the XR-BA group (t11=2.6; P=.03) but not the traditional BA group (t11=1.4; P=.20). Conclusions This study confirmed previous findings that XR-BA may be a feasible, non-inferior, and acceptable enhancement to traditional BA. Additionally, there was evidence that supports the potential of XR to enhance expectation or placebo effects. Further research is needed to examine the potential of XR to improve access, outcomes, and barriers to MDD care. Trial Registration ClinicalTrials.gov NCT05525390; https://clinicaltrials.gov/study/NCT05525390
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| 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 | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".