Systematic Review of AR/VR Applications for Health and Wellness: Trends and Future Opportunities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it