Effects of agonist–antagonist complex resistance training on upper body strength and power development
Bibliographic record
Abstract
The objective of this study was to examine the chronic effects on strength and power of performing complex versus traditional set training over eight weeks. Fifteen trained males were assessed for throw height, peak velocity, and peak power in the bench press throw and one-repetition maximum (1-RM) in the bench press and bench pull exercises, before and after the eight-week programme. The traditional set group performed the pulling before the pushing exercise sets, whereas the complex set group alternated pulling and pushing sets. The complex set training sessions were completed in approximately half the time. Electromyographic (EMG) activity was monitored during both test sessions in an attempt to determine if it was affected as a result of the training programme. Although there were no differences in the dependent variables between the two conditions, bench pull and bench press 1-RM increased significantly under the complex set condition and peak power increased significantly under the traditional set condition. Effect size statistics suggested that the complex set was more time-efficient than the traditional set condition with respect to development of 1-RM bench pull and bench press, peak velocity and peak power. The EMG activity was not affected. Complex set training would appear to be an effective method of exercise with respect to efficiency and strength development.
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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) | 0.000 | 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) | 0.002 | 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".