Validation of a computerized driving simulator test of cognitive abilities for fitness-to-drive assessments
DOI: 10.3389/fpsyg.2023.1294965
archive: archived pipeline: cataloged verified
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Summary
This study addresses the need for valid, cost-effective, and safe methods to assess fitness-to-drive, particularly for older adults whose cognitive abilities may decline with age or medical conditions. While on-road testing is considered the gold standard, it is often impractical, costly, and poses safety risks. The authors aimed to validate a computerized driving simulator test as a complementary tool for fitness-to-drive assessments by examining its reliability, concurrent validity against established cognitive tests, and sensitivity to age-related differences. The study involved 67 healthy participants (aged 19–87) divided into two groups: younger (<65 years) and older (≥65 years). Participants completed a driving simulator test consisting of three subtests of increasing difficulty, which measured reaction time, lane positioning, and response inhibition while driving on a virtual rural road. Concurrently, participants completed standardized off-road cognitive tests: the Trail Making Test (TMT) A and B, and the Useful Field of View (UFOV) test. The researchers analyzed internal consistency of the simulator subtests, conducted correlation analyses between simulator performance and cognitive test results, and performed multiple regression analyses. They also compared performance metrics between the two age groups to assess the simulator’s sensitivity to age-related cognitive decline. Results indicated good internal consistency for the simulator subtests. Significant and moderate correlations were found between simulator reaction times and UFOV 3 scores, as well as between most simulator metrics and TMT A scores. Lane positioning in the simulator showed significant, though lower, correlations with UFOV 3. Specifically, reaction time and double reaction time in subtest 3 correlated significantly with UFOV 2 and UFOV 3, and TMT A, respectively. Furthermore, significant mean differences with large effect sizes were observed between age groups for all reaction time and lane positioning tests, with the older group performing more poorly. These findings demonstrate that the simulator effectively captures cognitive deficits associated with aging. The study concludes that the driving simulator possesses concurrent validity, particularly with TMT A and UFOV 3, and is sensitive to age-related differences in cognitive abilities relevant to driving. This suggests the simulator has potential as a valuable complement to existing fitness-to-drive assessments, offering higher face validity than traditional off-road tests and greater safety and cost-efficiency than on-road testing. However, the authors note that further clinical studies are necessary to evaluate the simulator’s utility for patients with specific cognitive deficits, such as those resulting from stroke or dementia, before it can be fully integrated into clinical practice.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-07 |
| archive | success | canonical_url | — | — | 1 | 2026-06-09 |
| extract | success | pdftotext | — | — | 2 | 2026-06-09 |
| clean | success | clean | — | — | 1 | 2026-06-09 |
| chunk | success | chunk | — | — | 1 | 2026-06-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-09 |
| promote | success | — | — | — | 1 | 2026-06-07 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-09 |
| tag | success | vector_similarity | — | — | 8 | 2026-06-11 |
| verify | success | — | — | — | 1 | 2026-06-09 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-09; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- cognitive capacity variation
- fitness to drive assessment
- simulator validity fidelity
- useful field of view
- cognitive impairment
- mci dementia driving
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Methodological Resource: validation psychometrics, tool software
- Theoretical Contribution: computational model