Effects of Plyometric Training and Beta-Alanine Supplementation on Maximal-Intensity Exercise and Endurance in Female Soccer Players
Bibliographic record
Abstract
Abstract Plyometric training and beta-alanine supplementation are common among soccer players, although its combined use had never been tested. Therefore, a randomized, double-blind, placebo-controlled trial was conducted to compare the effects of a plyometric training program, with or without beta-alanine supplementation, on maximal-intensity and endurance performance in female soccer players during an in-season training period. Athletes (23.7 ± 2.4 years) were assigned to either a plyometric training group receiving a placebo (PLACEBO, n = 8), a plyometric training group receiving beta-alanine supplementation (BA, n = 8), or a control group receiving placebo without following a plyometric training program (CONTROL, n = 9). Athletes were evaluated for single and repeated jumps and sprints, endurance, and change-of-direction speed performance before and after the intervention. Both plyometric training groups improved in explosive jumping (ES = 0.27 to 1.0), sprinting (ES = 0.31 to 0.78), repeated sprinting (ES = 0.39 to 0.91), 60 s repeated jumping (ES = 0.32 to 0.45), endurance (ES = 0.35 to 0.37), and change-of-direction speed performance (ES = 0.36 to 0.58), whereas no significant changes were observed for the CONTROL group. Nevertheless, compared to the CONTROL group, only the BA group showed greater improvements in endurance, repeated sprinting and repeated jumping performances. It was concluded that beta-alanine supplementation during plyometric training may add further adaptive changes related to endurance, repeated sprinting and jumping ability.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".