The Influence of Virtual Reality Glasses Use on the Quality of Life of Older Adults: Protocol for a Prospective, Longitudinal Quasi-Experimental Study
Bibliographic record
Abstract
Background: Older adults are a rapidly growing demographic and often face social isolation and limited access to outdoor activities due to mobility issues, health conditions, and environmental barriers. These limitations can negatively impact their well-being, leading to reduced physical activity, cognitive decline, and emotional distress. Virtual reality (VR) technology offers a promising solution to bridge this gap by enabling access to immersive virtual environments, which may enhance the quality of life for residents in nursing homes. Objective: The aim of this study is to assess whether the use of VR glasses in nursing homes improves the quality of life of older adults by reducing the challenges they face in participating in recreational activities outside their care facilities. Methods: This study will adopt a prospective, longitudinal quasi-experimental design conducted in nursing homes within a basic health area of Catalonia, Spain. The intervention period will span 1 year. Participants will use VR glasses to interact with virtual environments, and their quality of life will be measured using the Rivas-Borda Quality of Life Scale, based on other validated scales. Results: Outcomes will focus on variations in quality-of-life scores before and after the intervention, as determined by the Rivas-Borda Scale. Statistical analysis will include detailed metrics such as sample size, confidence intervals, and P values to evaluate the intervention's effectiveness. Conclusions: This research seeks to confirm that VR technology can be a valuable tool for enhancing the quality of life in older adults residing in nursing homes, addressing issues like social isolation and limited access to outdoor activities in an innovative and engaging way.
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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 | 0.027 | 0.016 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".