Virtual reality to promote road safety in older adults: Evaluation of a training-based preventive approach / Virtuelle Realität zur Förderung der Verkehrssicherheit bei älteren Erwachsenen: Evaluation eines trai-ningsbasierten präventiven Ansatzes
Bibliographic record
Abstract
Abstract Background Demographic change is increasing the proportion of older individuals in society, which also heightens the risk of accidents, particularly in road traffic. Virtual reality (VR) applications offer innovative opportunities to promote mobility, safety, and accident prevention among older adults through engaging and safe training environments. Objective This study examined the effects of a VR-based training program (Wegfest) on functional mobility, subjective safety, and accident prevention in older adults. It also explored connections to the digital transformation in health professions education and practice. Methods In a VR intervention study, older adults completed eight training sessions in a simulated traffic environment. Assessments included the Timed Up and Go Test (mobility), the Falls Efficacy Scale – International (FES-I), the Montreal Cognitive Assessment (MoCA), subjective sense of safety, and the number of collisions in the VR setting. Pre-post comparisons were analyzed using Wilcoxon signed-rank tests. Results The training led to significant improvements in mobility (p = .002; d = 0.784) and a reduction in fear of falling (p = .005). Subjective safety increased significantly (p < .001), while collision frequency decreased (p < .001). Cognitive performance remained stable (p = .56). These results indicate that VR training can enhance both objective and subjective aspects of accident prevention. Conclusion VR-based training represents a promising tool to support mobility and safety in older adults. In the context of digital transformation, programs like Wegfest offer valuable potential for patient-centered care and professional training, while also fostering digital literacy among healthcare professionals and older users.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".