Whey Protein Supplementation Is Superior to Leucine-Matched Collagen Peptides to Increase Muscle Thickness During a 10-Week Resistance Training Program in Untrained Young Adults
Bibliographic record
Abstract
The purpose of this study was to investigate the effects of supplementation of whey protein (WP) versus leucine-matched collagen peptides (CP) on muscle thickness MT and performance after a resistance training (RT) program in young adults. Twenty-two healthy untrained participants were randomly assigned to either a WP (n = 11) or leucine-matched CP (n = 11) group and then submitted to a supervised 10-week RT program (3 days/week). The groups were supplemented with an equivalent amount of WP (35 g, containing 3.0 g of leucine) and CP (35 g, containing 1.0 g of leucine and 2.0 g of free leucine) during the intervention period (after each workout and in the evening on nontraining days). MT of the vastus lateralis and biceps brachii, isokinetic peak torque and mean power output of the elbow flexors, and peak power output of the lower body were assessed before and after the RT program. The WP group experienced a greater (interaction, p < .05) increase in the vastus lateralis (effect size, WP = 0.68 vs. CP = 0.38; % Δ, WP = 8.4 ± 2.5 vs. CP = 5.6 ± 2.6%) and biceps brachii muscle thickness (effect size, WP = 0.61 vs. CP = 0.35; % , WP = 10.1 ± 3.8 vs. CP = 6.0 ± 3.2%), with a similar increase in muscle performance (peak torque, mean power output, and peak power output) between groups (time p < .05). Supplementation with WP was superior to leucine content-matched CP supplementation in increasing muscle size, but not strength and power, after a 10-week RT program in young adults.
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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".