Effect of Low- and High-Glycemic-Index Meals on Metabolism and Performance During High-Intensity, Intermittent Exercise
Bibliographic record
Abstract
Consuming carbohydrate-rich meals before continuous endurance exercise improves performance, yet few studies have evaluated the ideal preexercise meal for high-intensity intermittent exercise, which is characteristic of many team sports. The authors' purpose was to investigate the effects of low- and high-glycemic-index (GI) meals on metabolism and performance during high-intensity, intermittent exercise. Sixteen male participants completed three 90-min high-intensity intermittent running trials in a single-blinded random order, separated by ~7 d, while fasted (control) and 2 hr after ingesting an isoenergetic low-GI (lentil), or high-GI (potato and egg white) preexercise meal. Serum free fatty acids were higher and insulin lower throughout exercise in the fasted condition (p < .05), but there were no differences in blood glucose during exercise between conditions. Distance covered on a repeated-sprint test at the end of exercise was significantly greater in the low-GI and high-GI conditions than in the control (p < .05). Rating of perceived exertion was lower in the low-GI condition than in the control (p = .01). In a subsample of 5 participants, muscle glycogen availability was greater in the low-and high-GI conditions versus fasted control before the repeated-sprint test (p < .05), with no differences between low and high GI. When exogenous carbohydrates are not provided during exercise both low- and high-GI preexercise meals improve high-intensity, intermittent exercise performance, probably by increasing the availability of muscle glycogen. However, the GI does not influence markers of substrate oxidation during high-intensity, intermittent exercise.
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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) | 0.000 | 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".