The Impact of Cognitive Impairment on Pedal Control and Crash Risk Following Stroke: A Pilot Study
Bibliographic record
Abstract
Driving after stroke requires complex coordination of cognitive and motor systems, yet the influence of post-stroke cognitive impairment on lower limb motor control during driving remains poorly understood. This pilot study examined the association between cognitive function and lower limb motor control of gas/brake pedal control in stroke survivors. We hypothesized that compromised cognitive function would be associated with worse gas and brake pedal control. Twenty stroke survivors (65.89 ± 9.67 years; 6 females) participated. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) and Useful Field of View (UFOV) test scores for divided and selective attention. Participants performed a car-following task in a driving simulator requiring precise gas and brake control. Pedal control was quantified by gas pedal error, brake force error, and brake response time. Participants were categorized into cognitively normal and cognitively impaired groups (n=10 each). Driving behavior was assessed using the Driving Habits Questionnaire (DHQ), and crash risk was determined via UFOV classification. Increased gas pedal error was associated with poorer MoCA scores and selective attention deficits. Delayed brake response times correlated with lower MoCA scores and poorer divided and selective attention. Although self-reported driving behavior was comparable between groups, 60% of cognitively impaired participants demonstrated moderate to high crash risk compared to cognitively normal participants, who exhibited low crash risk. Cognitive impairment after stroke is significantly linked to impaired lower limb control during driving and elevated crash risk. These findings highlight an urgent need to integrate cognitive assessment along with motor assessments in post-stroke rehabilitation. Future advances in neuroengineering technologies, and personalized motor-cognitive interventions could play a critical role in restoring safe driving capabilities and mobility independence after stroke.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".