Effects of an aerobic exercise program on driving performance in adults with cardiovascular disease
Bibliographic record
Abstract
Cardiovascular disease (CVD) has been linked to decreases in driving performance and an increased crash risk. Regular exercise has been linked to improved driving performance among healthy adults. The aim of the current study was to investigate the relationship between a 12-week cardiac rehabilitation (CR) program and driving performance among individuals with CVD. Twenty-five individuals, including 12 cardiac adults and 13 healthy adults, took part in this study. Simulated driving performance was assessed using a standardized demerit-based scoring system at 0 and 12 weeks. Cardiac participants completed a 12-week CR program between evaluations. At baseline, cardiac participants had a higher number of demerit points than healthy adults (120.9±38.1 vs. 94.7±28.3, P=0.04). At follow-up, there was an improvement in both groups' driving evaluations, but the improvement was greater among the cardiac group such that there was no longer a difference in driving performance between both groups (94.6±30 vs. 86.9±34.8, P=0.51). Participation in an aerobic exercise-based CR program appears to lead to improvements in simulated driving performances of individuals with CVD.
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 | 0.000 | 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 | 0.000 | 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".