Effects of polyphenol-rich, berry supplementation on exercise performance: A systematic review and meta-analysis
Bibliographic record
Abstract
Background & AimsPolyphenols are plant secondary compounds that possess antioxidant properties associated with preventing inflammation-mediated ailments such as cardiovascular disease and cancer. Recent studies hint at their capacity to enhance exercise performance. Berries, in addition to containing high amounts of essential vitamins and minerals, are extremely rich in polyphenols. Therefore, the aim of this systematic review and meta-analysis was to compile relevant human randomized controlled trials exploring the potential of polyphenol-rich berries to enhance exercise performance and associated biomarkers.MethodsThe PubMed, Web of Science, and SPORTDiscus databases were searched using keywords related to berry supplementation, exercise performance, and biomarkers of performance. In total, 2374 articles were screened and 14 were included in the analysis.ResultsThe results indicate no statistically significant effect of berry supplementation on exercise performance and its associated biomarkers. However, there is a trend towards a positive pooled effect size of berry supplementation on time to exhaustion (SMD: 0.57, Z: 1.51, P-value: 0.13). Furthermore, all pooled effect sizes favor berry supplementation.ConclusionsDue to variations in testing protocols and biomarkers of interest among the studies included, no more than 7 articles were included for any given outcome measure. This underscores the necessity for additional high-quality randomized controlled trials (RCTs) to strengthen the evidence and allow for recommendations to be made regarding the performance enhancing effects of berry consumption. This systematic review and meta-analysis was registered on Open Science Framework (DOI 10.17605/OSF.IO/NCAVJ).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".