Muscle activity in asymmetric bench press among resistance-trained individuals
Bibliographic record
Abstract
PURPOSE: To determine the effects of asymmetric loads on muscle activity with the bench press. METHOD: Seventeen resistance-trained men performed one familiarization session including testing one repetition maximum (1RM) and three 5 repetition maximum (RM) lifts; using symmetric loads, 5% asymmetric loads, and 10% asymmetric loads. The asymmetric loading (i.e., reduced load on one side) was calculated as 5% and 10% of the subject`s 1RM load. In the experimental session, the three conditions of 5RM were conducted with electromyographic activity from the pectoralis major, triceps brachii, biceps brachii, anterior deltoid, posterior deltoid, and external oblique on both sides of the body. RESULTS: On the loaded side, asymmetric loads reduced triceps brachii activation compared to symmetric loads, whereas the other muscles demonstrated similar muscle activity between the three conditions. On the de-loaded side, 10% asymmetry in loading resulted in lower pectoralis major, anterior deltoid, and biceps brachii activation compared to 5% asymmetric and symmetric loading. On the de-loaded side, only pectoralis major demonstrated lower muscle activation than symmetric loads. Furthermore, asymmetric loads increased external oblique activation on both sides compared to symmetric loads. CONCLUSIONS: Asymmetric bench press loads reduced chest and shoulder muscle activity on the de-loaded side while maintaining the muscle activity for the loaded side. The authors recommend resistance-trained participants struggling with strength imbalances between sides, or activities require asymmetric force generation (i.e., alpine skiing or martial arts), to implement asymmetric training as a supplement to the traditional resistance training.
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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) | 0.000 | 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) | 0.003 | 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".