Influence of dietary nitrate on the physiological determinants of exercise performance: a critical review
Bibliographic record
Abstract
Dietary nitrate supplementation, usually in the form of beetroot juice, has been heralded as a possible new ergogenic aid for sport and exercise performance. Early studies in recreationally active participants indicated that nitrate ingestion significantly reduces the O2 cost of submaximal exercise and improves performance during high-intensity endurance exercise. Subsequent studies have begun to address the physiological mechanisms underpinning these observations and to investigate the human populations in whom, and the exercise conditions (high- vs. low-intensity, long- vs. short-duration, continuous vs. intermittent, normoxic vs. hypoxic) under which, nitrate supplementation may be beneficial. Moreover, the optimal nitrate loading regimen in terms of nitrate dose and duration of supplementation has been explored. Depending on these factors, nitrate supplementation has been shown to exert physiological effects that could be conducive to exercise performance enhancement, at least in recreationally active or sub-élite athletes. This article provides a "state-of-the-art" review of the literature pertinent to the evaluation of the efficacy of nitrate supplementation in altering the physiological determinants of sport and exercise performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".