Glycerol-Induced Hyperhydration: A Method for Estimating the Optimal Load of Fluid to Be Ingested Before Exercise to Maximize Endurance Performance
Bibliographic record
Abstract
Glycerol-induced hyperhydration (GIH) has been shown to increase endurance performance (EP). However, EP starts declining at a dehydration level >2% body weight (BW). It thus appears that the use of GIH is only required when athletes anticipate that their fluid intake during exercise would not be sufficient to prevent a loss of BW >2%. In such a scenario, the optimal GIH load to be ingested before exercise would correspond to the amount of fluid that cannot be drunk during exercise and that would be just sufficient to keep the dehydration level <2% BW. No method exists enabling the estimation of the most optimal GIH load to be drunk before exercise to optimize EP. Here, such a method comprising 3 easy steps is presented. Step 1 provides a formula allowing users to determine relative exercise-induced dehydration level based on individual BW, exercise time, and estimated hourly sweat rate and fluid consumption during exercise. Step 2 takes into account the result of step 1 and provides a formula allowing determination of the minimal GIH load required before exercise to prevent a loss of BW >2%. Step 3 consists of identifying, among those pre-selected, a GIH protocol that increases body water by at least the amount computed in step 2. This method will remove much of the guess work involved in the decision-making process of the optimal amount of GIH that should be ingested before exercise by athletes for maximizing EP and will serve as a practical reference tool for all athletes using, and coaches, practitioners, and exercise physiologists recommending the utilization of, GIH as an ergogenic aid.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".