Caffeine-containing energy drink improves physical performance of elite rugby players during a simulated match
Bibliographic record
Abstract
The purpose of this study was to investigate the effectiveness of a caffeine-containing energy drink in enhancing rugby players' physical performance during a simulated match. A second purpose was to determine the urinary caffeine excretion derived from the energy drink intake. In a randomized and counterbalanced order, 26 elite rugby players (mean ± SD for age and body mass, 25 ± 2 y and 93 ± 15 kg) played 2 simulated rugby games (2 × 30 min) 60 min after ingesting (i) 3 mg of caffeine per kilogram of body mass in the form of an energy drink (Fure, ProEnergetics) or (ii) the same drink without caffeine (placebo). During the matches, the individual running distance and the instantaneous speed were measured, and the number of running actions above 20 km·h(-1) (i.e., sprints) were determined, using global positioning system devices. The number of impacts above 5 g during the matches was determined by accelerometry. The ingestion of the energy drink, compared with the placebo, increased the total distance covered during the match (4749 ± 589 vs 5139 ± 475 m, p < 0.05), the running distance covered at more than 20 km·h(-1) (184 ± 38 vs 208 ± 38 m, p < 0.05), and the number of sprints (10 ± 7 vs 12 ± 7, p < 0.05). The ingestion of the energy drink also resulted in a greater overall number of impacts (481 ± 352 vs 641 ± 366, p < 0.05) and a higher postexercise urine caffeine concentration (0.1 ± 0.1 vs 2.4 ± 0.9 μg·mL(-1), p < 0.05). The use of an energy drink with a caffeine dose equivalent to 3 mg·kg(-1) considerably enhanced the movement patterns of rugby players during a simulated match.
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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 | 0.001 |
| 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 | 0.000 |
| 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".