Impact of Water- and Land-Based Exercise Training on Risk Factors and Vascular Function in Middle-Aged and Older Men and Women
Bibliographic record
Abstract
INTRODUCTION: Exercise improves vascular function, but it is unclear whether benefits are mediated by traditional cardiovascular risk factors or whether sex differences in training effects exist in older adults. We hypothesized that exercise would improve cardiovascular risk factors, that males and females would benefit similarly, and that improvements in risk factors would correlate with changes in vascular function. METHODS: Seventy-two healthy middle-aged/older adults (age, 62 ± 7 yr; 26%♂) were randomized to a land-walking ( n = 23), water-walking ( n = 25), or a nonexercise control group (C; n = 23). The exercise groups undertook supervised and monitored training three times a week for 50 min per session, across 24 wk. Blood pressure, body composition (dual x-ray absorptiometry), blood lipids and glucose, and flow-mediated brachial artery dilation were assessed in all participants at weeks 0 and 24. To maximize power for sex differences and correlation analyses, we pooled the training groups (land-walking + water-walking). RESULTS: Training prevented increases in LDL and total cholesterol/HDL ratio observed in the nonexercise control group. No group by time interactions were observed for other risk factors. Sex differences in training effects existed for visceral fat (-187 ± 189 g♂ vs -15 ± 161 g♀; P = 0.006) and lean mass (-352 ± 1045 g♂ vs 601 ± 1178 g♀; P = 0.008). Improvement in flow-mediated brachial artery dilation was correlated with decreased waist girth ( r = -0.450, P = 0.036), but not with other risk factors. CONCLUSIONS: Exercise training prevented deterioration in lipid levels, whereas sex differences existed for body composition changes with training. Improvement in vascular function was not dependent on changes in risk factors in middle-aged/older adults, suggesting that artery health may be dependent on other exercise-related stimuli.
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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".