Carbohydrate–protein ingestion improves subsequent running capacity towards the end of a football-specific intermittent exercise
Bibliographic record
Abstract
The majority of football players succumb to fatigue towards the end of the game. This study was designed to examine the influence of protein coingestion with carbohydrate (CHO) vs. an isocaloric CHO supplement on subsequent running capacity towards the end of a simulated football match. Six male amateur football players participated in 3 trials applied in a randomized cross-over experimental design. A laboratory-based, football-specific intermittent exercise was allocated for 75 min interspersed with a 15-min recovery, immediately followed by run time to fatigue (RTF) at 80% peak oxygen consumption. In each trial, prior to exercise and during half-time, participants randomly ingested a placebo (PLC), 6.9% CHO, or 4.8% CHO plus 2.1% protein (CHO-P) supplements matched for color and taste. CHO-P resulted in longer RTF (23.02 ± 5.27 min) than did CHO (16.49 ± 3.25 min) and PLC (11.00 ± 2.80 min) (p < 0.05). Blood glucose was higher in CHO-P at the point of fatigue (4.68 ± 0.64) compared with CHO and PLC (3.92 ± 0.29 and 3.66 ± 0.36, respectively; p < 0.05). Ratings of perceived exertion were lower in the CHO-P subjects at the onset of exercise and towards the end of intermittent exercise when compared with the PLC and CHO subjects (p < 0.05). When protein was added to a CHO supplement, subsequent running capacity following limited recovery from intermittent exercise was enhanced. This improvement suggests that protein coingestion may exert an ergogenic benefit upon endurance capacity during intermittent activity.
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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) | 0.001 | 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) | 0.001 | 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".