Capsaicinoid and Capsinoids as an Ergogenic Aid: A Systematic Review and the Potential Mechanisms Involved
Bibliographic record
Abstract
CONTEXT: Capsaicinoids and capsinoids (CAP) are natural substances found primarily in chili peppers and other spicy foods that agonize the transient receptor potential vanilloid-1 in the mouth, stomach, and small intestine. Several studies have shown CAP to be a potential antiobesity agent and to exhibit an analgesic effect in both rodents and humans. However, there is no scientific consensus about the effects of CAP on physical exercise performance and its physiological mechanisms of action. PURPOSE: This systematic review aimed to better elucidate the effects of CAP compounds as ergogenic aids and to discuss underlying mechanisms of action by which this supplement may potentially enhance endurance performance and muscular strength. CONCLUSIONS: Among 22 studies included in the review, 14 examined the effects of capsaicinoid or capsinoid compounds on endurance and resistance exercise performance in animals, with 9 studies showing benefits on performance. In humans, 8 studies were included: 3 demonstrated significant acute endurance benefits and 2 showed acute resistance exercise performance benefits compared with a placebo condition. Therefore, while more mechanistic studies are necessary to confirm these outcomes in humans, the available scientific literature appears to suggest that these compounds could be considered an effective nutritional strategy to improve exercise performance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".