The Effect of a Complex Agonist and Antagonist Resistance Training Protocol on Volume Load, Power Output, Electromyographic Responses, and Efficiency
Bibliographic record
Abstract
The objective of this study was to investigate the acute effects of performing traditional set (TS) vs. complex set (CS) agonist-antagonist training over 3 consecutive sets, on bench press throw (BPT) throw height (TH), peak velocity (PV), peak power (PP), bench pull volume load (VL), and electromyographic (EMG) activity. Eighteen trained men performed 2 testing protocols: TS comprising 3 sets of Bpull followed by 3 sets of BPT performed in approximately 20 minutes or CS comprising 3 sets of both Bpull and BPT performed in an alternating manner in approximately 10 minutes. Throw height, PV, PP, and EMG activity were not different within, or between, the 2 conditions. Bench pull VL decreased significantly from set 1 to sets 2 and 3, under both conditions. Decreases from set 1 to set 2 were 14.55 +/- 26.11 and 9.07 +/- 13.89% and from set 1 to set 3 were 16.87 +/- 29.90 and 14.17 +/- 18.37% under CS and TS, respectively. There was no difference in VL per set, or session, between the conditions. Although there was no augmentation of the power measures, CS was determined to have approximately twice the efficiency (ouput/time) as compared to TS. Efficiency calculations for VL, TH, PV, and PP are 103.47 kg.min, 26.25 cm.min, 1.98 m.s.min, 890.39 W.min under CS and 54.71 kg.min, 13.02 cm.min, 0.99 m.s.min, 459.28 W.min under TS. Comparison of EMG activity between the protocols suggests the level of neuromuscular fatigue did not differ under the 2 conditions. Complex set training would appear to be an effective method of exercise with respect to efficiency and the maintenance of TH, PV, PP, and VL.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".