Effect of Glycerol-Induced Hyperhydration on a 5-kilometer Running Time-Trial Performance in the Heat in Recreationally Active Individuals
Bibliographic record
Abstract
Maximal oxygen consumption (V˙O2max) is a major determinant of 5-km running time-trial (TT) performance. Glycerol-induced hyperhydration (GIH) could improve V˙O2max in recreationally active persons through an optimal increase in plasma volume. Moreover, ingestion of a large bolus of cold fluid before exercise could decrease thermal stress during exercise, potentially contributing to improved performance. We determined the effect of GIH on 5-km running TT performance in 10 recreationally active individuals (age: 24 ± 4 years; V˙O2max: 48 ± 3 mL/kg/min). Using a randomized and counterbalanced protocol, participants underwent two, 120-min hydration protocols where they ingested a 1) 30 mL/kg fat-free mass (FFM) of cold water (~4 °C) with an artificial sweetener + 1.4 g glycerol/kg FFM over the first 60 min (GIH) or 2) 7.5 mL/kg FFM of cold water with an artificial sweetener over the first 20 min (EUH). Following GIH and EUH, participants underwent a 5-km running TT at 30 °C and 50% relative humidity. After 120 min, GIH was associated with significantly greater fluid retention (846 ± 415 mL) and plasma volume changes (10.1 ± 8.4%) than EUH, but gastrointestinal (GI) temperature did not differ. During exercise, 5-km running TT performance (GIH: 22.95 ± 2.62; EUH: 22.52 ± 2.74 min), as well as heart rate, GI temperature and perceived exertion did not significantly differ between conditions. This study demonstrates that the additional body water and plasma volume gains provided by GIH do not improve 5-km running TT performance in the heat in recreationally active individuals.
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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) | 0.001 | 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".