Development of the “DriveSafe60+” Application to Enhance Driving Competence and Learning among Older Adults
Bibliographic record
Abstract
The rapid growth of the global aging population presents a significant challenge to road safety, as older drivers face unique risk factors that lead to disproportionately higher rates of traffic-related injuries and fatalities when adjusted for distance traveled. One major contributor to this elevated risk is age-related physical decline. This study aimed to develop and evaluate the effectiveness of DriveSafe60+, a mobile application designed to enhance driving competence and learning among older adults. The application comprises five core components: Driving Ability Test, Health & Alert System, Driving Tips & Training, Safe Driving Report, and Emergency Support (SOS), which were developed based on the competency assessment frameworks of AAMVA, WHO road safety guidelines, Connected Health principles, and Adult Learning Theory. A one-group time-series design was employed with 100 licensed older drivers who used the application for 4 weeks. Driving competence, specifically confidence and decision-making, was assessed before, during, and after application use. Results from a One-way Repeated Measures ANOVA indicated statistically significant improvements in driving competence across all measurement points (F = 1599.932, p < .001). These findings demonstrate that DriveSafe60+ effectively enhanced older adults’ self-awareness and confidence in driving-related decision-making by supporting self-regulated learning and adaptive driving behaviors. The study suggests that mobile technology is a feasible and practical approach to promoting safer, longer driving among older adults in an aging society.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".