Thermal and Cardiovascular Strain Mitigate the Potential Benefit of Carbohydrate Mouth Rinse During Self-Paced Exercise in the Heat
Bibliographic record
Abstract
PURPOSE: To determine whether a carbohydrate mouth rinse can alter self-paced exercise performance independently of a high degree of thermal and cardiovascular strain. METHODS: Eight endurance-trained males performed two 40-km cycling time trials in 35°C, 60% RH while swilling a 20-ml bolus of 6.5% maltodextrin (CHO) or a color- and taste-matched placebo (PLA) every 5 km. Heart rate, power output, rectal temperature (Tre), and mean skin temperature (Tsk) were recorded continuously; cardiac output, oxygen uptake (VO2), mean arterial pressure (MAP), and perceived exertion (RPE) were measured every 10 min. RESULTS: Performance time and mean power output were similar between treatments, averaging 63.9 ± 3.2 and 64.3 ± 2.8 min, and 251 ± 23 and 242 ± 18 W in CHO and PLA, respectively. Power output, stroke volume, cardiac output, MAP, and VO2 decreased during both trials, increasing slightly or remaining stable during a final 2-km end-spurt. Tre, Tsk, heart rate, and RPE increased throughout exercise similarly with both treatments. Changes in RPE correlated with those in Tre (P < 0.005) and heart rate (P < 0.001). CONCLUSIONS: These findings suggest that carbohydrate mouth rinsing does not improve ~1-h time trial performance in hot-humid conditions, possibly due to a failure in down-regulating RPE, which may be influenced more by severe thermal and cardiovascular strain.
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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".