Effects of a Single Dose of a Creatine-Based Multi-Ingredient Pre-workout Supplement Compared to Creatine Alone on Performance Fatigability After Resistance Exercise: A Double-Blind Crossover Design Study
Bibliographic record
Abstract
Background: This study aims to investigate the acute effects of a single oral administration of a creatine-based multi-ingredient pre-workout supplement (MIPS) on performance fatigability and maximal force production after a resistance exercise protocol (REP). Methods: Eighteen adult males (age: 23 ± 1 years; body mass: 76.4 ± 1.5 kg; height: 1.77 ± 0.01 m) were enrolled in a randomized, double-blind, crossover design study. Subjects received a single dose of a MIPS (3 g of creatine, 2 g of arginine, 1 g of glutamine, 1 g of taurine, and 800 mg of β-alanine) or creatine citrate (CC) (3 g of creatine) or a placebo (PLA) in three successive trials 1 week apart. In a randomized order, participants consumed either MIPS, CC, or PLA and performed a REP 2 h later. Before ingestion and immediately after REP, subjects performed isometric contractions of the dominant biceps brachii: two maximal voluntary contractions (MVCs), followed by a 20% MVC for 90 s and a 60% MVC until exhaustion. Surface electromyographic indices of performance fatigability, conduction velocity (CV), and fractal dimension (FD) were obtained from the surface electromyographic signal (sEMG). Time to perform the task (TtT), basal blood lactate (BL), and BL after REP were also measured. Results: < 0.05) pre-post mean for ΔTtT between MIPS (-7.06 s) and PLA (+0.222 s), ΔCV slopes (20% MVC) between MIPS (0.0082%) and PLA (-0.0519%) and for ΔCV slopes (60% MVC) between MIPS (0.199%) and PLA (-0.154%) were found. A pairwise comparison analysis showed no statistically significant differences in other variables between groups and condition vs. condition. Conclusion: After REP, a creatine-enriched MIPS resulted in greater improvement of sEMG descriptors of performance fatigability and TtT compared with PLA. Conversely, no statistically significant differences in outcomes measured were observed between CC and PLA or MIPS and CC.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".