Effects of Land versus Water Walking Interventions on Vascular Function in Older Adults
Bibliographic record
Abstract
PURPOSE: Endothelial dysfunction is an early and integral atherogenic event. Interventions that improve endothelial function also reduce cardiovascular risk. Due largely to the direct hemodynamic effects of repetitive exercise on the artery wall, exercise training has shown to enhance endothelial function. Land walking (LW) and water walking (WW) induce distinct hemodynamic responses, so the comparison of their effects provides an approach to study shear stress effects on endothelial function. We hypothesized that LW and WW training would have different effects on peripheral artery endothelial function. METHODS: Fifty-one sedentary, older (age = 61.9 ± 6.6 yr, 23.5% male) individuals were randomized into one of three groups: control (n = 16), or one of two exercise groups consisting of 3 × 50 min supervised and individually tailored walking sessions per week for 24 consecutive weeks, performed either on LW (n = 17) or on WW (n = 18). Brachial artery endothelial function (flow-mediated dilation) and smooth muscle cell function (glyceryl trinitrate administration) were tested in all participants before (week 0) and after (week 24) the intervention. RESULTS: Differences were apparent in flow-mediated dilation change between the LW group (week 0, 5.39% ± 0.71%, to week 24, 7.77% ± 0.78%; P = 0.009) and the control group (week 0, 5.87% ± 0.73%, to week 24, 5.78% ± 0.78%). No differences in artery dilation response were found after glyceryl trinitrate administration (all P > 0.05). CONCLUSION: This study suggests that 6-month center-based LW may be superior to WW in terms of improvement in arterial endothelial function in older sedentary individuals.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".