Toward a Common Understanding of Diet–Exercise Strategies to Manipulate Fuel Availability for Training and Competition Preparation in Endurance Sport
Bibliographic record
Abstract
From the breakthrough studies of dietary carbohydrate and exercise capacity in the 1960s through to the more recent studies of cellular signaling and the adaptive response to exercise in muscle, it has become apparent that manipulations of dietary fat and carbohydrate within training phases, or in the immediate preparation for competition, can profoundly alter the availability and utilization of these major fuels and, subsequently, the performance of endurance sport (events >30 min up to ∼24 hr). A variety of terms have emerged to describe new or nuanced versions of such exercise-diet strategies (e.g., train low, train high, low-carbohydrate high-fat diet, periodized carbohydrate diet). However, the nonuniform meanings of these terms have caused confusion and miscommunication, both in the popular press and among the scientific community. Sports scientists will continue to hold different views on optimal protocols of fuel support for training and competition in different endurance events. However, to promote collaboration and shared discussions, a commonly accepted and consistent terminology will help to strengthen hypotheses and experimental/experiential data around various strategies. We propose a series of definitions and explanations as a starting point for a more unified dialogue around acute and chronic manipulations of fat and carbohydrate in the athlete's diet, noting philosophies of approaches rather than a single/definitive macronutrient prescription. We also summarize some of the key questions that need to be tackled to help produce greater insight into this exciting area of sports nutrition research and practice.
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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 | 0.018 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.050 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 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".