The Effectiveness of Virtual Reality–Based Mindfulness Interventions for Managing Stress, Anxiety, and Depression: Protocol for a Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background While traditional mindfulness-based interventions demonstrate effectiveness in improving mental health outcomes, their delivery methods face significant challenges related to accessibility and engagement. Geographic barriers to trained facilitators, time constraints for in-person sessions, and participant dropout rates of 15% to 30% due to perceived monotony limit intervention reach and effectiveness. Virtual reality (VR) technology offers innovative solutions through multisensory immersion that creates presence, the subjective feeling of “being there,” enhancing attention regulation and reducing external distractions. Meta-analyses demonstrate that VR interventions achieve higher engagement rates and lower dropout compared to traditional delivery methods; however, systematic evaluation of VR-based mindfulness interventions remains limited. Objective Following the population, intervention, comparison, and outcome framework, this systematic review protocol aims to evaluate whether VR-based mindfulness interventions (intervention), compared to traditional face-to-face mindfulness interventions, digital mindfulness apps, active nonmindfulness controls, and waitlist or no-treatment groups (comparisons), effectively reduce stress, anxiety, and depression while improving mindfulness and well-being (outcomes) in adults aged 18 to 65 years from both general and clinical populations with diagnosed mental health conditions (population). Methods We will conduct comprehensive searches across 8 databases (PubMed, Web of Science, Embase, CINAHL, MEDLINE, the Cochrane Library, PsycINFO, and Scopus) from inception to June 2025, including gray literature and unpublished trials. Eligible studies include randomized controlled trials evaluating VR-based mindfulness interventions using immersive technology (head-mounted displays and cave environments) with explicit mindfulness content in adult populations. Primary outcomes include stress, anxiety, and depression; secondary outcomes encompass mindfulness levels, well-being, and user experience. Two independent reviewers will screen studies, extract data, and assess risk of bias using the Cochrane Risk of Bias 2 tool with standardized criteria. Meta-analysis will use random effects models with inverse variance weighting, calculating standardized mean differences with 95% CIs. Preplanned subgroup analyses will examine intervention duration (<2 wk, 2-8 wk, and >8 wk), VR technology type (head-mounted displays vs cave environments), population characteristics (clinical vs nonclinical samples), and mindfulness technique type, with heterogeneity quantification using prespecified I2 thresholds. Results Database searches will commence in June 2025, with data extraction planned for August 2025 to September 2025 and systematic review completion planned by December 2025. Expected results include pooled effect sizes for primary outcomes, forest plots displaying individual and combined study effects with comprehensive subgroup analyses, and heterogeneity statistics with Grading of Recommendations Assessment, Development, and Evaluation evidence quality assessments. Conclusions The review will provide definitive evidence regarding VR-based mindfulness interventions’ effectiveness for mental health outcomes. The findings will inform clinical practice guidelines for integrating VR-based mindfulness, guide technology development specifications, and establish evidence-based recommendations for health care policy regarding therapeutic VR reimbursement and regulatory frameworks. Trial Registration PROSPERO CRD42024585899; https://tinyurl.com/288evyku International Registered Report Identifier (IRRID) PRR1-10.2196/68231
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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 | 0.048 | 0.072 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.027 | 0.034 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.046 | 0.004 |
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".