Virtual Reality in Clinical Nursing Practice Over the Past 10 Years: Umbrella Review of Meta-Analyses
Bibliographic record
Abstract
Background: Virtual reality (VR) has shown promising levels of effectiveness in nursing education, pain management, and rehabilitation. However, meta-analyses have discussed the effects of VR usage in nursing unilaterally and inconsistently, and the evidence base is diffuse and varied. Objective: We aimed to synthesize the combined evidence from meta-analyses that assessed the effects of nurses using VR technology on nursing education or patient health outcomes. Methods: We conducted an umbrella review by searching for meta-analyses about VR intervention in clinical nursing practice on Web of Science, Embase, Cochrane, and PubMed, and in reference lists. Eligible studies were published in English between December 1, 2012, and September 20, 2023. Meta-analyses of ≤2 intervention studies and meta-analyses without 95% CI or heterogeneity data were excluded. Characteristic indicators, population information, VR intervention information, and 95% CIs were extracted. A descriptive analysis of research results was conducted to discern relationships between VR interventions and outcomes. I2 and P values were used to evaluate publication bias. AMSTAR (A Measurement Tool to Assess Systematic Reviews) 2 and the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) checklist were used to appraise literature quality. Results: In total, 768 records were identified; 74 meta-analyses were included for review. The most reported VR study conditions were neuronursing (25/74, 34%), pediatric nursing (13/74, 18%), surgical and wound care (11/74, 15%), oncological nursing (11/74, 15%), and older adult nursing (10/74, 14%). Further, 30% (22/74) of meta-analyses reported publication bias, and 15% (11/74) and 8% (6/74) were rated as "high" based on AMSTAR 2 and the GRADE checklist, respectively. The main outcome indicators among all included meta-analyses were pain (37/214, 17.3%), anxiety (36/214, 16.8%), cognitive function (17/214, 7.9%), balance (16/214, 7.5%), depression (16/214, 7.5%), motor function (12/214, 5.6%), and participation in life (12/214, 5.6%). VR treatment for cognition, pain, anxiety, and depression was effective (all P values were <.05), while the utility of VR for improving motor function, balance, memory, and attention was controversial. Adverse effects included nausea, vomiting, and dizziness (incidence: range 4.76%-50%). The most common VR platforms were Pico VR glasses, head-mounted displays, the Nintendo Wii, and the Xbox Kinect. VR intervention duration ranged from 2 weeks to 12 months (typically ≥4 wk). VR session length and frequency ranged from 5 to 100 minutes and from 1 to 10 times per week, respectively. Conclusions: VR in nursing has positive effects-relieving patients' pain, anxiety, and depression and improving cognitive function-despite the included studies' limited quality. However, applying VR in nursing to improve patients' motor function, balance, memory, and attention remains controversial. Nursing researchers need to further explore the effects and standard operation protocols of VR in clinical practice, and more high-quality research on VR in nursing is needed.
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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 | 0.044 | 0.132 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.038 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".