The Impact of Fitness Status on Vascular and Baroreceptor Function in Healthy Women and Men
Bibliographic record
Abstract
BACKGROUND: Chronic endurance exercise training elicits desirable physiological adaptations in the cardiovascular system. The volume of exercise training required to generate healthy adaptations is unclear. This study assessed the effects of differing exercise training levels on arterial stiffness, compliance, and autonomic function. METHODS: Eighty healthy adults (38.5 ± 9.7 years; 44% female) defined as endurance-trained (ET, n = 29), normally active (NA, n = 27), or inactive (IN, n = 24) participated. Cardiovascular markers, including hemodynamics, large arterial compliance and small arterial compliance (LAC and SAC), carotid-femoral pulse wave velocity (PWV), and spontaneous baroreceptor sensitivity (BRS) were assessed. RESULTS: ET showed significantly greater LAC values (21.4 ± 6.5) than NA (16.9 ± 2.5; p = 0.002) and IN (14.7 ± 3.2 mL × mm Hg × 10; p = 0.028). Values for SAC and BRS were significantly higher in ET than IN (p < 0.001 and p = 0.028, respectively), but not NA. Compared to IN, PWV values for ET and NA were significantly lower (p < 0.003). After adjusting for covariates (age, sex, and SBP), significant associations with cardiovascular fitness status were noted for all markers but BRS. CONCLUSION: Endurance exercise increases LAC likely due to high-volume training; however, lower volumes of physical activity may be sufficient to positively benefit vascular health overall.
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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 | 0.000 | 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) | 0.002 | 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".