Research Status and Trends in Virtual Reality Technology for Older Adults: Bibliometric and Visual Analysis
Bibliographic record
Abstract
Background: Virtual reality (VR) technology is increasingly applied in aging-related research. Although existing bibliometric studies have focused on specific applications, such as older adults' acceptance of VR and its use in cognitive rehabilitation, no comprehensive mapping of the global research landscape on VR for older populations has been conducted. This study fills this gap by providing a holistic bibliometric and thematic analysis of VR applications in older adults, mapping research trends, intellectual structures, and emerging frontiers. Objective: This study aims to explore the current applications, potential benefits, and future directions of virtual reality technology for older adults, based on literature published between January 1, 2015, and April 30, 2025. Methods: This study used bibliometric methods to systematically examine the current status and developmental trends in VR research for older adults. We searched the Web of Science Core Collection for research articles and reviews published in English. A total of 1609 publications were included in the final analysis. Using CiteSpace and VOSviewer, we conducted coauthorship network analysis, keyword clustering, and burst detection to map research hot spots, academic collaboration patterns, and emerging trends in the field. Results: Our analysis of 1609 publications revealed a steady growth in the application of VR technology for older adults. The predominant research areas included meta-analysis, rehabilitation, dementia, and gait. The United States and China were the two most productive countries, with Tel Aviv University emerging as the leading institution. Frontiers in Aging Neuroscience and Applied Sciences Basel were the most prolific journals, each publishing 40 papers. The most cited article evaluated the effects of VR-based physical and cognitive training on executive function and dual-task gait performance in older adults with mild cognitive impairment. Emerging research themes include artificial intelligence, association, and depression. Conclusions: VR research for older adults is rapidly expanding and globally collaborative. Although applications span multiple geriatric domains, future efforts should prioritize mental health, disease integration, and artificial intelligence-enhanced VR technologies.
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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 | 0.030 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.242 | 0.276 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".