Head-Mounted Virtual Reality and Mental Health: Critical Review of Current Research
Bibliographic record
Abstract
BACKGROUND: eHealth interventions are becoming increasingly used in public health, with virtual reality (VR) being one of the most exciting recent developments. VR consists of a three-dimensional, computer-generated environment viewed through a head-mounted display. This medium has provided new possibilities to adapt problematic behaviors that affect mental health. VR is no longer unaffordable for individuals, and with mobile phone technology being able to track movements and project images through mobile head-mounted devices, VR is now a mobile tool that can be used at work, home, or on the move. OBJECTIVE: In line with recent advances in technology, in this review, we aimed to critically assess the current state of research surrounding mental health. METHODS: We compiled a table of 82 studies that made use of head-mounted devices in their interventions. RESULTS: Our review demonstrated that VR is effective in provoking realistic reactions to feared stimuli, particularly for anxiety; moreover, it proved that the immersive nature of VR is an ideal fit for the management of pain. However, the lack of studies surrounding depression and stress highlight the literature gaps that still exist. CONCLUSIONS: Virtual environments that promote positive stimuli combined with health knowledge could prove to be a valuable tool for public health and mental health. The current state of research highlights the importance of the nature and content of VR interventions for improved mental health. While future research should look to incorporate more mobile forms of VR, a more rigorous reporting of VR and computer hardware and software may help us understand the relationship (if any) between increased specifications and the efficacy of treatment.
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.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".