The Effects of Low– and High–Glycemic Index Foods on High-Intensity Intermittent Exercise
Bibliographic record
Abstract
UNLABELLED: The glycemic index (GI) of a preexercise meal may affect substrate utilization and performance during continuous exercise. PURPOSE: To examine the effects of low- and high-GI foods on metabolism and performance during high-intensity, intermittent exercise. METHODS: Seven male athletes participated in three experimental trials (low-GI, high-GI, and fasted control) separated by approximately 7 days. Foods were consumed 3 h before (approximately 1.3 g x kg(-1) carbohydrate) and halfway through (approximately 0.2 g x kg(-1) carbohydrate) 90 min of intermittent treadmill running designed to simulate the activity pattern of soccer. Expired gas was collected during exercise to estimate substrate oxidation. Performance was assessed by the distance covered on five 1-min sprints during the last 15 min of exercise. RESULTS: Respiratory exchange ratio was higher and fat oxidation lower during exercise in the high-GI condition compared with fasting (P < .05). The mean difference in total distance covered on the repeated sprint test between low GI and fasting (247 m; 90% confidence limits +/-352 m) represented an 81% (likely, probable) chance that the low-GI condition improved performance over fasting. The mean difference between high GI and fasted control (223 m; +/- 385 m) represented a 76% (likely, probable) chance of improved performance. There were no differences between low and high GI. CONCLUSIONS: When compared with fasting, both low- and high-GI foods consumed 3 h before and halfway through prolonged, high-intensity intermittent exercise improved repeated sprint performance. High-GI foods impaired fat oxidation during exercise but the GI did not appear to influence high-intensity, intermittent exercise 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 | 0.001 | 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".