Research Status and Trends in Virtual Reality Technology for the Elderly: A Bibliometric and Visual Analysis (Preprint)
Bibliographic record
Abstract
BACKGROUND With the world experiencing significant population growth and aging, the quality of life for the elderly is diminishing, presenting unprecedented challenges globally. Virtual Reality (VR), as a novel interactive medium, demonstrates substantial potential in healthcare and elder care. Research related to VR technology is rapidly expanding and becoming a prominent topic of interest. However, a comprehensive bibliometric overview of VR technology in elder care remains largely unexplored, highlighting the need for systematic analysis of current findings and trends to aid researchers in positioning their work effectively. OBJECTIVE This study aims to explore the current research landscape, identify hot topics, and uncover emerging trends in the application of virtual reality technology within elder. METHODS We conducted a search of the Web of Science database for relevant articles published from 2010 to August 31, 2024. The data collected included publication volume, authorship, journal impact, country of origin, institutional contributions, key citations, and keywords. We utilized VOSviewer, CiteSpace, and Scimago Graphica for bibliometric research and visual analysis. RESULTS A total of 1,429 publications were analyzed, revealing a growing trend in the application of VR technology among the elderly. Key research areas identified include rehabilitation, cognitive function, mental health, and social interaction. The United States leads with 317 publications, followed by Italy with 218. Tel Aviv University has the highest publication count among institutions, while Frontiers in Aging Neuroscience has published the most articles (n=39). The most cited reference is ''Canadian clinical practice guidelines for the management of anxiety, post-traumatic stress and obsessive-compulsive disorder''. Keyword co-occurrence analysis suggests that "cognitive training," "depression," and "validity" are emerging research hotspots. CONCLUSIONS This study outlines the evolution of VR applications in elder care over the past two decades. Given the context of global aging, VR technology holds significant promise for enhancing elderly rehabilitation. The findings serve as a valuable reference for future research directions in this field, although challenges such as the safety of VR technology remain to be addressed. CLINICALTRIAL
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 | 0.013 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.194 | 0.265 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".