Driving Automation for Older Adults with Preclinical Alzheimer’s Disease
Bibliographic record
Abstract
INTRODUCTION: Older adults with preclinical Alzheimer's disease (AD) show changes in on-road driving performance. The impact of preclinical AD on using automated vehicle (AV) technology is unknown. The aim was to evaluate safety and cognitive workload while operating AV technology in drivers with preclinical AD. INTRODUCTION: This cross-sectional study included 40 participants: 19 older adults (age 74.16 ± 4.78; MOCA scores 26.42 ± 2.52) with preclinical AD, evidenced by elevated cortical beta-amyloid; and 21 controls (age 73.81 ± 5.62; MOCA scores 28.24 ± 1.67). All participants completed two scenarios in a driving simulator. Scenario 1 included conditional automation with an emergency event that required a manual take-over maneuver. Scenario 2 was identical but with a cognitive distractor task. Emergency response time was the main safety outcome measure. Cognitive workload was calculated using moment-to-moment changes in pupillary size and converted into an Index of Cognitive Activity (ICA). Mann-Whitney U and independent t tests were used to compare group differences. RESULTS: Emergency response times were similar between drivers with preclinical AD and controls in scenario 1 (20.85 s ± 1.08 vs. 20.52 s ± 3.18; p = 0.83) and scenario 2 (14.83 s ± 7.37 vs. 13.45 s ± 10.43; p = 0.92). Likewise, no differences were found in ICA between drivers with preclinical AD and controls in scenario 1 (0.34 ± 0.08 vs. 0.33 ± 0.17; p = 0.74) or scenario 2 (0.30 ± 0.07 vs. 0.29 ± 0.17; p = 0.93). CONCLUSIONS: Older drivers with preclinical AD may safely operate AV technology, without increased response times or cognitive workload. Future on-road studies with AV technology should confirm these preliminary results in drivers with preclinical AD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".