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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".