Nutrition for Ultramarathon Running: Trail, Track, and Road
Bibliographic record
Abstract
Ultramarathon running events and participation numbers have increased progressively over the past three decades. Besides the exertion of prolonged running with or without a loaded pack, such events are often associated with challenging topography, environmental conditions, acute transient lifestyle discomforts, and/or event-related health complications. These factors create a scenario for greater nutritional needs, while predisposing ultramarathon runners to multiple nutritional intake barriers. The current review aims to explore the physiological and nutritional demands of ultramarathon running and provide general guidance on nutritional requirements for ultramarathon training and competition, including aspects of race nutrition logistics. Research outcomes suggest that daily dietary carbohydrates (up to 12 g·kg −1 ·day −1 ) and multiple-transportable carbohydrate intake (∼90 g·hr −1 for running distances ≥3 hr) during exercise support endurance training adaptations and enhance real-time endurance performance. Whether these intake rates are tolerable during ultramarathon competition is questionable from a practical and gastrointestinal perspective. Dietary protocols, such as glycogen manipulation or low-carbohydrate high-fat diets, are currently popular among ultramarathon runners. Despite the latter dietary manipulation showing increased total fat oxidation rates during submaximal exercise, the role in enhancing ultramarathon running performance is currently not supported. Ultramarathon runners may develop varying degrees of both hypohydration and hyperhydration (with accompanying exercise-associated hyponatremia), dependent on event duration, and environmental conditions. To avoid these two extremes, euhydration can generally be maintained through “drinking to thirst.” A well practiced and individualized nutrition strategy is required to optimize training and competition performance in ultramarathon running events, whether they are single stage or multistage.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".