The effects of water temperature on cerebral blood flow during aquatic exercise
Bibliographic record
Abstract
PURPOSE: Recent studies suggest that episodic increases in cerebral blood flow (CBF) may contribute to the improvement in brain health associated with exercise training. Optimising CBF during exercise may enhance this benefit. Water immersion in ~ 30-32 °C augments CBF at rest and during exercise; however, the impact of water temperature on the CBF response has not been investigated. We hypothesised that cycle ergometry in water would increase CBF compared to land-based exercise, and that warm water would attenuate the CBF benefits. METHODS: Eleven young heathy participants (nine males; 23.8 ± 3.1 yrs) completed 30 min of resistance-matched cycle exercise in three separate conditions; non-immersion (Land), 32 °C and 38 °C water immersion up to the level of the waist. Middle cerebral artery velocity (MCAv), blood pressure, and respiratory measures were assessed throughout the exercise bouts. RESULTS: Core temperature was significantly higher in the 38 °C immersion than 32 °C (+ 0.84 ± 0.24 vs + 0.04 ± 0.16, P < 0.001), whilst mean arterial pressure was lower during 38 °C exercise compared to Land (84 ± 8 vs 100 ± 14 mmHg, P < 0.001) and 32 °C (92 ± 9, P = 0.03). MCAv was higher in 32 °C immersion compared to the Land and 38 °C conditions throughout the exercise bout (68 ± 10 vs 64 ± 11 vs 62 ± 12 cm/s, P = 0.03 and P = 0.02, respectively). CONCLUSION: Our findings suggest that cycle exercise in warm water attenuates the beneficial impact of water immersion on CBF velocity due to redistribution of blood flow to subserve thermoregulatory demand. Our findings suggest that, whilst water-based exercise can have beneficial effects on cerebrovascular function, water temperature is a key determinant of this benefit.
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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 | 0.000 |
| 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".