Carbohydrate and Protein Co-Ingestion Postexercise Does Not Improve Next-Day Performance in Trained Cyclists
Bibliographic record
Abstract
Supplementing postexercise carbohydrate (CHO) intake with protein has been suggested to enhance recovery from endurance exercise. The aim of this study was to investigate whether adding protein to the recovery drink can improve 24-hr recovery when CHO intake is suboptimal. In a double-blind crossover design, 12 trained men performed three 2-day trials consisting of constant-load exercise to reduce glycogen on Day 1, followed by ingestion of a CHO drink (1.2 g·kg-1·2 hr-1) either without or with added whey protein concentrate (CHO + PRO) or whey protein hydrolysate (CHO + PROH) (0.3 g·kg-1·2 hr-1). Arterialized blood glucose and insulin responses were analyzed for 2 hr postingestion. Time-trial performance was measured the next day after another bout of glycogen-reducing exercise. The 30-min time-trial performance did not differ between the three trials (M ± SD, 401 ± 75, 411 ± 80, 404 ± 58 kJ in CHO, CHO + PRO, and CHO + PROH, respectively, p = .83). No significant differences were found in glucose disposal (area under the curve [AUC]) between the postexercise conditions (364 ± 107, 341 ± 76, and 330 ± 147, mmol·L-1·2 hr-1, respectively). Insulin AUC was lower in CHO (18.1 ± 7.7 nmol·L-1·2 hr-1) compared with CHO + PRO and CHO + PROH (24.6 ± 12.4 vs. 24.5 ± 10.6, p = .036 and .015). No difference in insulin AUC was found between CHO + PRO and CHO + PROH. Despite a higher acute insulin response, adding protein to a CHO-based recovery drink after a prolonged, high-intensity exercise bout did not change next-day exercise capacity when overall 24-hr macronutrient and caloric intake was controlled.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".