Longitudinal Analysis of Driving Behavior and Cognitive Performance in Older Adults
Bibliographic record
Abstract
Abstract The early detection of cognitive impairment is vital to effective intervention and treatment. Real-world driving behavior may serve as an indicator of cognitive function. This study examines longitudinal changes in driving behaviors and cognitive performance. Twenty-two older adults (mean age = 74.15 years; 10 females) were assessed at baseline and at follow-up (range: 4.75–7.24 years; mean = 5.41, SD = 0.67). Cognitive function was evaluated using the Montreal Cognitive Assessment (MoCA). Driving behavior was monitored via vehicle GPS sensors over a three-month period at both time points. Maximum distance from home was defined as the vehicle’s displacement at the end of each drive, regardless of where the drive began. Approximately 97,539 miles were recorded. At baseline, the maximum distance from home averaged 466.27 miles (SD = 231.21), which declined to 160.69 miles (SD = 270.27) at follow-up. MoCA scores decreased from a mean of 26.73 (SD = 2.23) at baseline to 25.59 (SD = 2.38) at follow-up. Paired t-tests revealed a significant reduction in maximum driving distance (t = 3.854, p = 0.001) and a significant decline in MoCA scores (t = 2.435, p = 0.024). Pearson correlations showed no significant association between MoCA scores and maximum distance from home at baseline (r = -0.16, p = 0.49) or follow-up (r = -0.12, p = 0.6). The study found that both driving distance and cognitive performance declined over time. Overall, real-world driving behavior offers a longitudinal index to complement clinical assessments in patients at risk for cognitive decline and neurodegeneration.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".