Immersive Virtual Reality in Health Care: Systematic Review of Technology and Disease States
Bibliographic record
Abstract
Background Immersive virtual reality (IVR) presents new possibilities for application in health care. Health care professionals can now immerse their patients in environments to achieve exposure to a specific scene or experience, evoke targeted emotional responses, inspire, or distract from an experience occurring in reality. Objective This review aimed to identify patient-focused applications for head-mounted IVR for acute treatment of health conditions and determine the technical specifications of the systems used. Methods A systematic review was conducted by searching medical and engineering peer-reviewed literature databases in 2018. The databases included PubMed, EMBASE, Cumulative Index to Nursing and Allied Health Literature, Association for Computing Machinery, Institute of Electrical and Electronics Engineers, Scopus, and Web of Science. Search terms relating to health and IVR were used. To be included, studies had to investigate the effectiveness of IVR for acute treatment of a specific health condition. IVR was defined as a head-mounted platform that provides virtual and auditory immersion for the participant and includes a minimum of 3 degrees of orientation tracking. Once identified, data were extracted from articles and aggregated in a narrative review format. Results A total of 58 studies were conducted in 19 countries. The studies reported IVR use for 5 main clinical areas: neurological and development (n=10), pain reduction through distraction (n=20), exposure therapy for phobias (n=9), psychological applications (n=14), and others (n=5). Studies were primarily feasibility studies exploring systems and general user acceptance (n=29) and efficacy studies testing clinical effect (n=28). Conclusions IVR has a promising future in health care, both in research and commercial realms. As many of the studies examined are still exploring the feasibility of IVR for acute treatment of health conditions, evidence for the effectiveness of IVR is still developing.
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".