Virtual Reality and Stress Management: A Systematic Review
Bibliographic record
Abstract
Amidst the growing prevalence of chronic stress and its potential negative impacts on mental health, this review explores the use of virtual reality (VR) as a stress management solution, aiming to assess its viability and effectiveness in this context. A comprehensive search was conducted on MEDLINE, PsycINFO, and Embase from inception until February 2024. Eligible studies were primary research papers that focused on the use of VR as an intervention to mitigate psychological stress and/or distress. We included studies where the assessment of stress levels primarily relied on self-report measures. A total of 50 studies involving 2885 participants were included in our systematic review. VR-based interventions varied across studies, implementing tools such as cognitive behavioural therapy, exposure therapy, mindfulness and relaxation, repetition tasks, and psychoeducation. The reviewed studies yielded mixed results; however, a strong indication was present in highlighting the promising potential of VR-based interventions. Many studies observed a decrease in psychiatric symptoms in participants and reported increased quality of life. Various studies also found VR to be a valuable tool in promoting stress reduction and relaxation. VR was proven useful in exposing participants to stressors in a safe, controlled way. These potential benefits appear to come with no risk of harm to the participants. Although the findings are heterogenous, there is sufficient evidence supporting the use of VR for stress management across a range of contexts and populations. Overall, VR appears to be a generally low-risk, feasible intervention for those struggling with stress.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".