The acute caffeine ingestion improved performance during traditional and cluster‐based resistance training models in resistance‐trained male athletes
Bibliographic record
Abstract
Caffeine (Caf) is a well‐established ergogenic aid among various training models, including strength and/or power. However, there is limited information on the ergogenic effects of Caf ingestion during many strength and power training models. Purpose: This study investigated the acute effects of Caf ingestion in bench press power and velocity when training sessions were planned using cluster (CL) and traditional (TR) strength training programs. Methods: Twelve trained men (22 ± 1.6 y, 80.4 ± 10.7 kg, 175.6 ± 4.6 cm) ingested 6 mg/kg of Caf one hour before exercise. Then they performed 4 sets of 6 reps on the bench press with 80% of 1 RM at six experimental trials (Cluster: 4 sets of 2 reps with 140‐sec rest between sets, TR: 4 sets of 6 reps with 180‐sec rest between sets) in a randomized, double‐blind placebo (PL)‐controlled, crossover design [CL+Caf, CL+PL, CL+control (CON), TR+Caf, TR+PL, and TR+CON]. The peak and mean power, velocity, and time under tension were evaluated at concentric and eccentric phases during the bench press exercise. Results: We observed that Caf ingestion improves peak and mean power and velocity at concentric and eccentric phases of both training models compared to those in the PL and CON conditions ( p < 0.05). There was no significant performance difference between the training models ( p > 0.05). The Caf ingestion reduced time under tension at both training models ( p < 0.05) compared to that in the PL and CON conditions, while no difference was seen between training models ( p > 0.05). Conclusion: Acute Caf ingestion positively affects strength and power training performance regardless of the training models.
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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 | 0.000 |
| 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".