Supplements and Nutritional Interventions to Augment High-Intensity Interval Training Physiological and Performance Adaptations—A Narrative Review
Bibliographic record
Abstract
High-intensity interval training (HIIT) involves short bursts of intense activity interspersed by periods of low-intensity exercise or rest. HIIT is a viable alternative to traditional continuous moderate-intensity endurance training to enhance maximal oxygen uptake and endurance performance. Combining nutritional strategies with HIIT may result in more favorable outcomes. The purpose of this narrative review is to highlight key dietary interventions that may augment adaptations to HIIT, including creatine monohydrate, caffeine, nitrate, sodium bicarbonate, beta-alanine, protein, and essential amino acids, as well as manipulating carbohydrate availability. Nutrient timing and potential sex differences are also discussed. Overall, sodium bicarbonate and nitrates show promise for enhancing HIIT adaptations and performance. Beta-alanine has the potential to increase training volume and intensity and improve HIIT adaptations. Caffeine and creatine have potential benefits, however, longer-term studies are lacking. Presently, there is a lack of evidence supporting high protein diets to augment HIIT. Low carbohydrate training enhances the upregulation of mitochondrial enzymes, however, there does not seem to be a performance advantage, and a periodized approach may be warranted. Lastly, potential sex differences suggest the need for future research to examine sex-specific nutritional strategies in response to HIIT.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".