Systematic Review of AR/VR Applications for Health and Wellness: Trends and Future Opportunities
Bibliographic record
Abstract
Augmented and virtual reality (AR and VR) have gained a lot of attention from the research community over the years. They have been widely adopted in healthcare and have been used to deliver interventions tailored to general health, physical health, mental health and other areas of health. Although many interventions exist, few studies have systematically examined the trends and challenges affecting their overall effectiveness. This research addresses that gap by systematically reviewing 174 papers published between 2014 and 2024. Our review uncovers how AR and VR interventions have been designed to manage a range of health-related conditions, their success in achieving health outcomes, and the emerging trends in multimodal designs that may guide future research. Findings from the review suggest that adopting multimodal approaches holds promise for addressing multiple health conditions simultaneously. We provide insights and recommendations for designing more effective AR and VR health interventions, thereby contributing to advancement of HCI research and practical applications in healthcare.
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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 | 0.014 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".