Betalain-rich concentrate supplementation improves exercise performance and recovery in competitive triathletes
Bibliographic record
Abstract
We aimed to determine the effects of a betalain-rich concentrate (BRC) of beetroots, containing no sugars or nitrates, on exercise performance and recovery. Twenty-two (9 men and 13 women) triathletes (age, 38 ± 11 years) completed 2 double-blind, crossover, randomized trials (BRC and placebo) starting 7 days apart. Each trial was preceded by 6 days of supplementation with 100 mg·day −1 of BRC or placebo. On the 7th day of supplementation, exercise trials commenced 120 min after ingestion of 50 mg BRC or placebo and consisted of 40 min of cycling (75 ± 5% maximal oxygen consumption) followed by a 10-km running time trial (TT). Subjects returned 24 h later to complete a 5-km running TT to assess recovery. Ten-kilometer TT duration (49.5 ± 8.9 vs. 50.8 ± 10.3 min, p = 0.03) was faster with the BRC treatment. Despite running faster, average heart rate and ratings of perceived exertion were not different between treatments. Five-kilometer TT duration (23.2 ± 4.4 vs 23.9 ± 4.7 min, p = 0.003), 24 h after the 10-km TT, was faster in 17 of the 22 subjects with the BRC treatment. Creatine kinase, a muscle damage marker, increased less (40.5 ± 22.5 vs. 49.7 ± 21.5 U·L −1 , p = 0.02) from baseline to after the 10-km TT and subjective fatigue increased less (–0.05 ± 6.1 vs. 3.23 ± 6.1, p = 0.05) from baseline to 24 h after the 10-km TT with BRC. In conclusion, BRC supplementation improved 10-km TT performance in competitive male and female triathletes. Improved 5-km TT performances 24 h after the 10-km TT and the attenuated increase of creatine kinase and fatigue suggest an increase in recovery while taking BRC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".