The Glycemic Index of Sport Nutrition Bars Affects Performance and Metabolism During Cycling and Next‐Day Recovery
Bibliographic record
Abstract
Low-glycemic index carbohydrates are potentially better for endurance performance as they result in greater fat oxidation and lower carbohydrate oxidation due to lower insulin release. We compared the effects of pre-exercise feeding with a low-glycemic index lentil-based sports nutrition bar, a commercially-available sports nutrition bar with moderate-glycemic index, and a non-caloric placebo on metabolism and performance during endurance cycling (Trial 1). Using a randomized, counterbalanced, crossover design, endurance-trained individuals (n = 11; eight males; 26 ± 6y; VO2peak 51.4 ± 1.6 mL/kg/min) consumed 1.5 g/kg available carbohydrate from a lentil bar and a moderate-glycemic index bar, as well as a placebo, 1h before endurance cycling (75 min at 65% VO2peak, followed by a 7 km time trial). We also compared post-exercise consumption of the low-glycemic index bar with another moderate-glycemic index bar on next-day exercise performance as an assessment of recovery (Trial 2). In Trial 1, fat or carbohydrate oxidation rates were not different between the bar conditions (p > 0.05). Blood lactate was lower during the low- versus the moderate-glycemic index condition after 75 minutes of cycling (2.6 versus 4.0 mmol/L, p < 0.05) and at the end of the time trial (7.4 versus 9.1 mmol/L, p < 0.05). Time trial performance improved (p < 0.05) after consumption of the low- (574 ± 55 s) and moderate-glycemic index (583 ± 59 s) bars compared to the placebo (619 ± 81 s). In Trial 2 (next-day recovery), performance improved (p < 0.05) with the low-glycemic index bar (547 ± 42 s) compared to the moderate-glycemic index bar (569 ± 42 s) and the placebo (566 ± 34 s). Low- and moderate-glycemic index sports nutrition bars improved cycling exercise performance; however, only the low-glycemic index bar improved next day performance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".