The Effects of Low- and High-Glycemic Index Sport Nutrition Bars on Metabolism and Performance in Recreational Soccer Players
Bibliographic record
Abstract
Consumption of low-glycemic index (GI) carbohydrates (CHO) may be superior to high-GI CHO before exercise by increasing fat oxidation and decreasing carbohydrate oxidation. We compared the effects of pre-exercise feeding of a low-GI lentil-based sports nutrition bar with a high-GI bar on metabolism and performance during a simulated soccer match. Using a randomized, double-blind, counterbalanced, crossover design, participants (n = 8) consumed 1.5 g/kg available CHO from a low-GI bar (GI = 45) or high-GI bar (GI = 101) two hours before a 90 min simulated soccer match, and 0.38 g/kg body mass during a 15 min half-time break. The test involved alternating 6 min intervals of paced jogging, running, walking, and sprinting, and 3 min intervals of soccer-specific skills (timed ball dribbling, agility running, heading, kicking accuracy). Carbohydrate oxidation rate was lower during the match after consuming the low-GI compared to high-GI bar (2.17 ± 0.6 vs. 2.72 ± 0.4 g/min; p < 0.05). Participants performed better during the low-GI versus high-GI bar condition on the agility test (5.7 ± 0.4 versus 6.1 ± 0.6 s; p < 0.01) and heading (i.e., jumping height 24.7 ± 4.3 versus 22.2 ± 4.5 cm; p < 0.01) late in the soccer match (72 min). A low-GI lentil-based sports nutrition bar provides a metabolic benefit (lower carbohydrate oxidation rate) and a modest improvement in agility running and jumping height (heading) late in the test.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".