The effect of tart cherry juice compared to a sports drink on cycling exercise performance, substrate metabolism, and recovery
Bibliographic record
Abstract
Tart cherries have low glycemic index, antioxidant and anti-inflammatory properties, and therefore may benefit performance and recovery from exercise. We determined the effects of consuming tart cherry juice versus a high-glycemic index sports drink on cycling performance, substrate oxidation, and recovery of low-frequency fatigue. Using a randomized, counter-balanced cross-over design, with one-month washout, 12 recreational cyclists (8 males and 4 females; 35±16y; VO2peak 38.2±7.4 ml/kg/min) consumed cherry juice or sports drink twice a day (300mL/d) for 4d before and 2d after exercise. On the exercise day, beverages (providing 1g/kg carbohydrate) were consumed 45min before 90min of cycling at 65%VO2peak, followed by a 10km time trial. Blood glucose, lactate, carbohydrate and fat oxidation, respiratory exchange ratio (RER), O2 cost of cycling, and rating of perceived exertion (RPE) were measured during the initial 90min of cycling. Muscle soreness, maximal voluntary contraction (MVC) and low-frequency fatigue were determined at baseline and after the time trial on the exercise day, and 30min after beverage consumption 24 and 48h later. There were no differences for time trial performance (17±3min cherry juice vs. 17±2min sports drink, p = 0.27) or any other measures between drink conditions. There were time main effects (p<0.05) for isometric MVC (decreasing) and low-frequency fatigue (increasing; i.e. decreased force at low relative to high stimulation frequencies), changing significantly from baseline to post-exercise and then returning to baseline at 24h post-exercise. Tart cherry juice was not effective for improving performance, substrate oxidation during exercise, and recovery from exercise, compared to a high-glycemic index sports drink.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".