Ingestion of carbohydrate or carbohydrate plus protein does not enhance performance during endurance exercise: a randomized crossover placebo-controlled clinical trial
Bibliographic record
Abstract
A beverage containing protein (PRO) and carbohydrate (CHO) may have an ergogenic effect on endurance performance. However, evidence regarding its efficacy on similar conditions to athletes’ race day is still lacking. The objective of this study was to compare the effects of 3 different nutritional supplementation strategies on performance and muscle recovery in a duathlon protocol. Thirteen male athletes (29.7 ± 7.7 years) participated in 3 simulated Olympic-distance duathlon trials (SDTs) under 3 different, randomly assigned supplementation regimens: CHO drink (75 g CHO), isocaloric CHO plus PRO drink (60.5 g CHO and 14.5 g PRO), and placebo drink (PLA). Supplements were offered during the cycling bout. Blood samples were collected before, immediately after, and 24 h after each SDT for creatine kinase (CK) analysis. Isometric peak torque (PT) was measured before and 24 h after each SDT. The primary outcome measure was the time to complete the 5-km running section (t5km) at a self-selected pace. There was no difference in t5km between CHO (1270.3 ± 130.5 s), CHO+PRO (1267.2 ± 138.9 s), and PLA (1275.4 ± 120 s); p = 0.87, effect size (ES) ≤ 0.1. Pre–post changes for PT and CK were not significant for any of the 3 conditions (PT: p = 0.24, ES ≤ 0.4; CK: p = 0.32, ES = 0.3–1.04). For endurance sports lasting up to 2 h, with a pre-exercise meal containing CHO at 1.5 g·kg−1, supplementation with CHO or CHO+PRO does not offer additional benefits for performance and muscle recovery when compared with PLA.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".