Evaluating a Virtual Reality Game to Enhance Teen Distracted Driving Education: Mixed Methods Pilot Study
Bibliographic record
Abstract
Background Inexperienced adolescent drivers are particularly susceptible to engaging in distracted driving behaviors (DDBs) such as texting while driving (TWD). Traditional driver education approaches have shown limited success in reducing motor vehicle crashes among young drivers. Objective We tested an innovative approach to help address the critical issue of DDB among teenagers. We investigated the effectiveness of using a novel virtual reality (VR) game “Distracted Navigator” to educate novice teenage drivers about DDB. Methods The game consisted of maneuvering a spaceship around asteroids while engaging in simulated DDB (eg, inputting numbers into a keypad). A physician-facilitated discussion, based on the theory of planned behavior, linked gameplay to real-life driving. Teenagers were recruited for the in-person study and randomly assigned at the block level to intervention (VR gameplay or discussion) and control groups (discussion only), approximating a 2:1 ratio. Unblinded, bivariate statistical analyses (all 2-tailed t tests or chi-square tests) and regression analyses measured programming impact on TWD-related beliefs and intentions. Content analysis of focus group interviews identified thematic feedback on the programming. Results Of the 24 participants, 15 (63%) were male; their ages ranged from 14 to 17 (mean 15.8, SD 0.92) years, and all owned cell phones. Compared to the control group (n=7, 29%), the intervention group (n=17, 71%) was more likely to report that the programming had positively changed how they felt about texting and driving (?218=–8.3; P=.02). However, specific TWD attitudes and intentions were not different by treatment status. Irrespective of treatment, pre- and postintervention scores indicated reduced confidence in safely TWD (ie, perceived behavioral control; β=–.78; t46=–2.66; P=.01). Thematic analysis revealed the following: (1) the VR gameplay adeptly portrayed real-world consequences of texting and driving, (2) participants highly valued the interactive nature of the VR game and discussion, (3) both the VR game and facilitated discussion were deemed as integral and complementary components, and (4) feedback for improving the VR game and discussion. Conclusions Our findings show that the novel use of immersive VR experiences with interactive discussions can raise awareness of DDB consequences and is a promising method to enhance driving safety education. The widespread accessibility of VR technology allows for scalable integration into driver training programs, warranting a larger, prospective, randomized study.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".