Common driving behaviors in older adults with dementia: Insights from a systematic literature review
Bibliographic record
Abstract
Dementia impairs driving skills, but the specific driving behaviors affected are not fully understood. This project reviewed the literature on driving behaviors more common among people with dementia compared to age-matched healthy controls. A search of Scopus, Medline All, and Embase databases (1994 to September 2024) identified relevant studies. Articles were included if they addressed driving behaviors among drivers with dementia during on-road tests, simulator experiments, or naturalistic driving, and included comparisons with non-dementia controls. Of 2359 citations, 26 studies were included: 3 used naturalistic driving, 14 driving simulators, and 9 used on-road tests. Drivers with dementia showed higher standard deviations of mean speeds, more traffic light tickets, greater out-of-lane drifting, and increased variability in mean headway distance compared to controls. Findings highlight distinct driving behavior patterns among drivers with dementia. However, these results should be interpreted cautiously due to methodological limitations, including small samples, lack of confounding factors, and non-validated settings. HIGHLIGHTS: Drivers with dementia exhibit distinct driving patterns that consistently set them apart from cognitively intact drivers. Compared to age-matched controls, drivers with dementia are more likely to demonstrate greater variability in mean speeds, accumulate more traffic light violations, exhibit higher instances of lane drifting, and show increased variability in mean headway distance. Driving simulators, on-road tests, and naturalistic driving methods have been used to study driving in individuals with dementia, although most evidence comes from simulator studies, which may not fully reflect real-world driving conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.025 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".