Beetroot-based gel supplementation improves handgrip strength and forearm muscle O<sub>2</sub> saturation but not exercise tolerance and blood volume in jiu-jitsu athletes
Bibliographic record
Abstract
The ergogenic effect of beetroot on the exercise performance of trained cyclists, runners, kayakers, and swimmers has been demonstrated. However, whether or not beetroot supplementation presents a beneficial effect on the exercise performance of jiu-jitsu athletes remains inconclusive. Therefore, the present study assessed the effect of beetroot-based gel (BG) supplementation on maximal voluntary contraction (MVC), exercise time until fatigue (ETF), muscle O 2 saturation (SmO 2 ), blood volume (tHb), and plasma nitrate and lactate in response to handgrip isotonic exercise (HIE) in jiu-jitsu athletes. In a randomized, crossover, double-blind design, 12 jiu-jitsu athletes performed 3 sets of HIE at 40% of the MVC until fatigue after 8 days (the eighth dose was offered 120 min previous exercise) of BG supplementation or a nitrate-depleted gel (PLA), and forearm SmO 2 and tHb were continuously monitored by using near-infrared spectroscopy. Blood samples were taken before, immediately after exercise, and 20 min after exercise recovery in the PLA and BG conditions. MVC was evaluated at baseline and 20 min after HIE. There was a significant reduction in ΔMVC decline after HIE in the BG condition. Forearm SmO 2 during exercise recovery was significantly greater only after BG supplementation. No significant difference in ETF and tHb were observed between both BG and PLA in response to HIE. Plasma nitrate increased only after BG, whereas the exercise-induced increase in plasma lactate was significantly lower in BG when compared with PLA. In conclusion, BG supplementation may be a good nutritional strategy to improve forearm SmO 2 and prevent force decline in response to exercise in jiu-jitsu athletes.
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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 | 0.000 | 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".