Potential ergogenic activity of grape juice in runners
Bibliographic record
Abstract
Recent studies have indicated that certain food products have ergogenic potential similar to that of sports supplements. The present study aimed to investigate the potential ergogenic effect of integral purple grape juice on the performance of recreational runners. Twenty-eight volunteers of both sexes (age, 39.8 ± 8.5 years; peak oxygen consumption, 43.2 ± 8.5 mL/(kg·min)) were randomized into either a group that received grape juice (grape juice group (GJG), n = 15; 10 mL/(kg·min) for 28 days) or a group that received an isocaloric, isoglycemic, and isovolumetric control beverage (control group (CG), n = 13). A time-to-exhaustion exercise test, anaerobic threshold test, and aerobic capacity test were performed, together with assessments of markers of oxidative stress, inflammation, immune response, and muscle injury, performed at baseline and 48 h after the supplementation protocol. The GJG showed a significant increase (15.3%) in running time-to-exhaustion (p = 0.002) without significant improvements in either anaerobic threshold (3.6%; p = 0.511) or aerobic capacity (2.2%; p = 0.605). In addition, GJG exhibited significant increases in total antioxidant capacity (38.7%; p = 0.009), vitamin A (11.8%; p = 0.016), and uric acid (28.2%; p = 0.005), whereas α-1-acid glycoprotein significantly decreased (20.2%; p = 0.006) and high-sensitivity C-reactive protein levels remained unchanged. In contrast, no significant changes occurred in any of these variables in the CG. In conclusion, supplementation with purple grape juice shows an ergogenic effect in recreational runners by promoting increased time-to-exhaustion, accompanied by increased antioxidant activity and a possible reduction in inflammatory markers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".