Virtual reality for older people: effectiveness of a training program for accident prevention
Bibliographic record
Abstract
Background As people age, physical and cognitive limitations increasingly affect the daily mobility of older adults. Virtual reality (VR) applications offer novel opportunities for senior citizens to enhance their functional abilities. Routine activities, like crossing a street, can be simulated and practiced within a virtual environment. Objective This intervention study investigated the impact of a VR training application (‘Wegfest’) on physical function, fall-related confidence, and cognitive status in senior citizens. It was hypothesized that participation in the VR-based training program would lead to improvements in functional mobility, fall-related self-confidence, and cognitive performance. Method For this study, the VR application ‘Wegfest’ was developed to simulate various road scenarios. Over a 4-week training period, senior citizens practiced navigating diverse road-crossing situations. The effectiveness of the application was evaluated through measures of physical and cognitive performance, including the Timed Up and Go (TUG) Test, the Falls Efficacy Scale-International (FES-I), and the Montreal Cognitive Assessment (MoCA). In total, 29 senior citizens ( M = 74.95 years) were recruited, with 20 participants included in the final analysis. Data collection and statistical analyses were performed using a database specifically created for Wegfest. Results The VR application “Wegfest” received positive feedback from participants. Significant improvements were found between pre- and post-intervention measurements for TUG, t (19) = 3.50, p = 0.002, and for FES-I, z = −2.82, p = 0.005. No significant differences were observed in MoCA scores, z = 0.58, p = 0.564. Conclusion Virtual reality (VR) shows promise as an effective tool for supporting older adults in their daily activities. As a pilot (exploratory) study with a small sample size and a relatively high dropout rate, the results should be interpreted as preliminary and indicative rather than conclusive. The lack of behavioral outcome measures further underscores the exploratory nature of this investigation. Further research with larger samples and more comprehensive outcome measures is needed to evaluate the effectiveness and generalizability of ‘Wegfest’ and similar VR applications for enhancing everyday mobility.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".