The Effects of Beta-Alanine Supplementation on Performance: A Systematic Review of the Literature
Bibliographic record
Abstract
PURPOSE: To critically review the methodological quality and synthesize information from systematic reviews and high quality studies on the effects of beta alanine (BA) on exercise and athletic performance. METHODS: A search strategy was developed in accordance with the standards for the reporting of scientific literature via systematic reviews. Five databases were thoroughly searched from inception to November 2012. Inclusion criteria were English language, human studies, used BA to increase exercise or athletic performance, systematic reviews or randomized controlled trials and were published in a peer-reviewed journal. Included studies were systematically graded for their methodological quality by rotating pairs of reviewers and the results were qualitatively synthesized. RESULTS: One systematic review and 19 randomized trials were included in this review. There is one systematic review with several methodological weaknesses that limit the confidence in its results. There are moderate to high quality studies that appear to support that BA may increase power output and working capacity, decrease the feeling of fatigue and exhaustion, and have of positive effect on body composition and carnosine content. The reporting of side effects from BA supplementation in the athletic population was generally under-reported. CONCLUSIONS: There appears to be some evidence from this review that supplementation with BA may increase athletic performance. However, there is insufficient evidence examining the safety of BA supplementation and its side effects. It is therefore recommended to err on the side of caution in using BA as an ergogenic aid until there is sufficient evidence confirming its safety.
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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 | 0.029 | 0.110 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.010 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".