Contemporary Nutrition Approaches to Optimize Elite Marathon Performance
Bibliographic record
Abstract
The professionalization of any sport must include an appreciation for how and where nutrition can positively affect training adaptation and/or competition performance. Furthermore, there is an ever-increasing importance of nutrition in sports that feature very high training volumes and are of a long enough duration that both glycogen and fluid balance can limit performance. Indeed, modern marathon training programs and racing satisfy these criteria and are uniquely suited to benefit from nutritional interventions. Given that muscle glycogen is limiting during a 2-h marathon, optimizing carbohydrate (CHO) intake and delivery is of maximal importance. Furthermore, the last 60 y of marathon performance have seen lighter and smaller marathoners, which enhances running economy and heat dissipation and increases CHO delivery per kg body mass. Finally, periodically training under conditions of low CHO availability (eg, low muscle glycogen) or periods of mild fluid restriction may actually further enhance the adaptive responses to training. Accordingly, this commentary highlights these key nutrition and hydration interventions that have emerged over the last several years and explores how they may assist in world-class marathon performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".