The Impact of 6-Month Land versus Water Walking on Cerebrovascular Function in the Aging Brain
Bibliographic record
Abstract
INTRODUCTION: To examine the hypothesis that exercise training induces adaptation in cerebrovascular function, we recruited 63 older adults (62 ± 7 yr, 46 females) to undertake 24 wk of either land walking or water walking, or participate in a nonexercise control group. This is the first multi-interventional study to perform a comprehensive assessment of cerebrovascular function in response to longer term (6-month) training interventions, including water-based exercise, in older healthy individuals. METHODS: Intracranial blood flow velocities (middle cerebral artery (MCAv) and posterior cerebral artery) were assessed at rest and in response to neurovascular coupling, hypercapnic reactivity, and cerebral autoregulation. RESULTS: We observed no change in resting MCAv in response to either training intervention (pre vs post, mean (95% confidence interval), land walking: 65 (59-70) to 63 (57-68) cm·s-1, P = 0.33; water walking: 63 (58-69) to 61 (55-67) cm·s-1, P = 0.92) compared with controls and no change in neurovascular coupling (land walking: P = 0.18, water walking: P = 0.17). There was a significant but modest improvement in autoregulatory normalized gain after the intervention in the water-walking compared with the land-walking group (P = 0.03). Hypercapnic MCAv reactivity was not different based on exercise group (land: P = 087, water: P = 0.83); however, when data were pooled from the exercise groups, increases in fitness were correlated with decreases in hypercapnic reactivity (r2 = 0.25, P = 0.003). CONCLUSIONS: Although exercise was not associated with systematic changes across multiple domains of cerebrovascular function, our data indicate that exercise may induce modest changes in autoregulation and CO2 reactivity. These findings should encourage further studies of the longer-term implications of exercise training on cerebrovascular health.
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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) | 0.001 | 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".