Pre-Exercise Hyperhydration-Induced Bodyweight Gain Does Not Alter Prolonged Treadmill Running Time-Trial Performance in Warm Ambient Conditions
Bibliographic record
Abstract
This study compared the effect of pre-exercise hyperhydration (PEH) and pre-exercise euhydration (PEE) upon treadmill running time-trial (TT) performance in the heat. Six highly trained runners or triathletes underwent two 18 km TT runs (~28 °C, 25%-30% RH) on a motorized treadmill, in a randomized, crossover fashion, while being euhydrated or after hyperhydration with 26 mL/kg bodyweight (BW) of a 130 mmol/L sodium solution. Subjects then ran four successive 4.5 km blocks alternating between 2.5 km at 1% and 2 km at 6% gradient, while drinking a total of 7 mL/kg BW of a 6% sports drink solution (Gatorade, USA). PEH increased BW by 1.00 ± 0.34 kg (P < 0.01) and, compared with PEE, reduced BW loss from 3.1% ± 0.3% (EUH) to 1.4% ± 0.4% (HYP) (P < 0.01) during exercise. Running TT time did not differ between groups (PEH: 85.6 ± 11.6 min; PEE: 85.3 ± 9.6 min, P = 0.82). Heart rate (5 ± 1 beats/min) and rectal (0.3 ± 0.1 °C) and body (0.2 ± 0.1 °C) temperatures of PEE were higher than those of PEH (P < 0.05). There was no significant difference in abdominal discomfort and perceived exertion or heat stress between groups. Our results suggest that pre-exercise sodium-induced hyperhydration of a magnitude of 1 L does not alter 80-90 min running TT performance under warm conditions in highly-trained runners drinking ~500 mL sports drink during exercise.
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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) | 0.001 | 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 | 0.001 |
| 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".