Nutritional Strategies to Support Adaptation to High-Intensity Interval Training in Team Sports
Bibliographic record
Abstract
Team sports are characterized by intermittent high-intensity activity patterns. Typically, play consists of short periods of very intense or all-out efforts interspersed with longer periods of low-intensity activity. Fatigue is a complex, multi-factorial process, but intense intermittent exercise performance can potentially be limited by reduced availability of substrates stored in skeletal muscle and/or metabolic by-products associated with fuel breakdown. High-intensity interval training (HIT) has been shown to induce adaptations in skeletal muscle that enhance the capacity for both oxidative and non-oxidative metabolism. Nutrient availability is a potent modulator of many acute physiological responses to exercise, including various molecular signaling pathways that are believed to regulate cellular adaptation to training. Several nutritional strategies have also been reported to acutely alter metabolism and enhance intermittent high-intensity exercise performance. However, relatively little is known regarding the effect of chronic interventions, and whether supplementation over a period of weeks or months augments HIT-induced physiological remodeling and promotes greater performance adaptations. Theoretically, a nutritional intervention could augment HIT adaptation by improving energy metabolism during exercise, which could facilitate greater total work and an enhanced chronic training stimulus, or promoting some aspect of the adaptive response during recovery, which could lead to enhanced physiological adaptations over time.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".