The performance effect of early versus late carbohydrate feedings during prolonged exercise
Bibliographic record
Abstract
The purpose of this study was to determine how the timing of isoenergetic carbohydrate feedings during prolonged cycling affects performance in a subsequent 10-km cycling time trial. Recreationally trained male cyclists (n = 8; age, 34.5 ± 8.3 years; mass, 80.0 ± 6.3 kg; body fat, 16.0% ± 3.8%, peak oxygen uptake, 4.54 ± 0.42 L·min(-1)) completed 4 experimental trials consisting of cycling continuously for 2 h at 62.4% ± 1.9% of peak oxygen uptake, followed immediately by a self-paced 10-km time trial. The 4 conditions included no carbohydrate ingestion (PP), early carbohydrate ingestion (CP), late carbohydrate ingestion (PC), or carbohydrate ingestion throughout (CC). Blood samples were obtained at 0, 60, and 120 min of cycling as well as at the conclusion of the time trial. The 10-km time trial time to completion was faster in trials CC (17.70 ± 0.52 min) and PC (17.60 ± 0.62 min) as compared with trial PP (18.13 ± 0.52 min, p = 0.028 and p = 0.007, respectively) while trial CP (17.85 ± 0.58 min, p = 0.178) was not. Serum glucose increased with carbohydrate feedings (p < 0.05), while serum free fatty acid concentrations were lower in trials PC and CC than trials CP and PP (p < 0.05). There was no difference in oxygen uptake, heart rate, rating of perceived exertion, or substrate use between trials (p > 0.05). These data indicate that carbohydrate ingestion throughout or late during a 2-h cycling bout can improve subsequent 10-km time trial 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 | 0.001 |
| 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".