Cranberry supplementation improves physiological markers of performance in trained runners
Bibliographic record
Abstract
PURPOSE: Cranberries have the highest polyphenol and antioxidant capacity among fruits and vegetables and may protect against exercise-induced free radical production, consequently improving performance. This study aimed to investigate the effect of polyphenol-rich cranberry extract (CE) on time-trial performance and lactate response following exercise. METHODS: A total of 14 trained runners were tested at i) baseline, ii) 2 h following an acute CE dose (0.7 g/kg of body mass), and iii) 4 weeks after daily supplement consumption (0.3 g/kg of body mass). At each time point, runners performed a 1500-m race followed by a 400-m race where the live vastus lateralis oxygenation changes were determined by near-infrared spectroscopy and blood lactate was measured at rest and 1 and 3 min after each trial. The Shapiro-Wilk test and repeated-measures analysis of variance were used to establish significance (P <0.05). RESULTS: Cranberry supplementation over 28 d improved aerobic performance during the 1500-m time trial, whereas the acute dose had no effect. More specifically, muscle reoxygenation rates were significantly faster after 28 d compared to baseline (P = 0.04; η² = 0.29), and a trend towards slower deoxygenation rate was observed (P = 0.13; η² = 0.20). Chronic CE consumption also buffered the post-exercise lactate response for the 400-m race (P = 0.01; η² = 0.27), while no effects were seen for the longer race. CONCLUSION: Our results suggest that cranberry supplementation may have ergogenic effects, as it improves physiological markers of performance during short- and long-distance running.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".