Nutritional needs of elite endurance athletes. Part I: Carbohydrate and fluid requirements
Bibliographic record
Abstract
Abstract Both carbohydrate depletion and dehydration have been shown to decrease performance whilst severe dehydration can also cause adverse health effects. Therefore carbohydrate and fluid requirements are increased with exercise. Ingestion of 200–300 g of CHO 3–4 h prior to exercise is an effective strategy in order to meet daily CHO demands and increase CHO availability during the subsequent exercise period. There is little evidence that CHO during the hour immediately prior to exercise has adverse effects such as rebound hypoglycaemia. CHO ingestion during exercise has been shown to improve performance as measured by enhanced work output or decreased exercise time to complete a fixed amount of work. Recent studies have demonstrated that exogenous CHO oxidation rates can be increased by ingesting combinations of CHO that use different intestinal CHO transporters. After exercise maximal muscle glycogen re‐synthesis rates can be achieved by ingesting CHO at a rate of ∼1.2 g/kg/h, in relatively frequent (e.g., 15–30 min) intervals for up to 5 h following exercise. Protein amino acid mixtures may increase glycogen synthesis further but only if relatively small amounts of CHO are ingested. Hypohydration and hyperthermia alone have negative effects on performance but their combination is particularly serious, both in terms of performance and health. Dehydration can be prevented by fluid ingestion pre exercise and during exercise. Because of large individual differences it is difficult to individualise the advice. Perhaps the best guidance for athletes is to weigh themselves to assess fluid losses during training and racing and limit weight losses to 1% during exercise lasting longer than 1.5 h. Excessive fluid intake has been associated with hyponatremia. Post exercise the volume of fluid ingested and sodium intake are important determinants of rehydration.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".