Agonist-Antagonist Paired Set Resistance Training: A Brief Review
Bibliographic record
Abstract
Agonist-antagonist paired set (APS) training refers to the coupling of agonist and antagonist exercises, performed in an alternating manner with rest intervals between each set. The purpose of this review is to identify the proposed benefits and possible underlying mechanisms of APS training and to suggest how APS training may be exploited. Furthermore, areas deserving of further research attention will be presented. This review will also suggest a common terminology (i.e., APS training) for describing training modalities that alternate agonist and antagonist exercises. Although somewhat equivocal, evidence exists supporting the use of APS as a means of enhancing short-term power measures. Evidence also exists suggesting APS training is an efficacious and efficient means of developing strength and power. Time-efficient methods of developing strength and power would have benefits for athletes and the general population. Athletes able to spend less time developing strength and power would have more time to devote to other aspects of performance or other unrelated tasks. The general population may be more willing to adhere to less time-consuming resistance training programs that offer similar results, as compared to more time-consuming programs. This review concludes that the practical applicability of APS training in terms of acute performance enhancement is limited. However, the use of APS training as an efficacious and time-effective method for developing strength and power may hold some merit.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".