Nitrate Supplementation Combined with a Running Training Program Improved Time-Trial Performance in Recreationally Trained Runners
Bibliographic record
Abstract
Our purpose was to verify the effects of inorganic nitrate combined to a short training program on 10-km running time-trial (TT) performance, maximum and average power on a Wingate test, and lactate concentration ([La−]) in recreational runners. Sixteen healthy participants were divided randomly into two groups: Nitrate (n = 8) and placebo (n = 8). The experimental group ingested 750 mg/day (~12 mmol) of nitrate plus 5 g of resistant starch, and the control group ingested 6 g of resistant starch, for 30 days. All variables were assessed at baseline and weekly over 30 days. Training took place 3x/week. The time on a 10-km TT decreased significantly (p < 0.001) in all timepoints compared to baseline in both groups, but only the nitrate group was faster in week 2 compared to 1. There was a significant group × time interaction (p < 0.001) with lower [La] in the nitrate group at week 2 (p = 0.032), week 3 (p = 0.002), and week 4 (p = 0.003). There was a significant group time interaction (p = 0.028) for Wingate average power and a main effect of time for maximum power (p < 0.001) and [La−] for the 60-s Wingate test. In conclusion, nitrate ingestion during a four-week running program improved 10-km TT performance and kept blood [La−] steady when compared to placebo in recreational runners.
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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) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".