Chronic beetroot juice supplementation attenuates neuromuscular fatigue etiology during simulated soccer match play
Bibliographic record
Abstract
The aim of the present study was to assess the effect of beetroot juice supplementation (BEET) on neuromuscular fatigue etiology during simulated soccer match play. In a randomized, double-blind, crossover design, 13 soccer players completed the Loughborough Intermittent Shuttle Test (LIST). Players received either BEET (2×150 mL; ∼8 mmol/L nitrate) or placebo (PLA) for 7 days (6 days prior to the experimental session and on the day of trial, 2 h before LIST). Neuromuscular assessments were performed at baseline, 45 min (half time: HT), and 90 min (full time: FT) following LIST. Maximal voluntary contraction (MVC) and twitch responses, delivered through electrical femoral nerve stimulation, were used to assess peripheral (quadriceps resting twitch force Qtw,pot) and central fatigue (voluntary activation, VA). Compared with baseline, MVC Qtw,pot and VA values decreased in PLA and BEET conditions at HT and FT ( P < 0.05). Compared with PLA, the decrease in MVC and Qtw,pot was significantly attenuated with BEET at HT and FT ( P < 0.001). Likewise, BEET attenuated the decrease in VA at HT ( P < 0.001, d = 1.3) and FT ( P < 0.001, d = 1.5) compared with the PLA condition. Chronic beetroot juice supplementation attenuates neuromuscular fatigue development during simulated soccer matches, and this is due to both central and peripheral factors. Consequently, chronic beetroot may optimize physical performance.
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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 | 0.001 |
| 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".