Trunk Muscle Electromyographic Activity With Unstable and Unilateral Exercises
Bibliographic record
Abstract
The purpose of this cross-sectional study was to evaluate the effect of unstable and unilateral resistance exercises on trunk muscle activation. Eleven subjects (6 men and 5 women) between 20 and 45 years of age participated. Six trunk exercises, as well as unilateral and bilateral shoulder and chest presses against resistance, were performed on stable (bench) and unstable (Swiss ball) bases. Electromyographic activity of the upper lumbar, lumbosacral erector spinae, and lower-abdominal muscles were monitored. Instability generated greater activation of the lower-abdominal stabilizer musculature (27.9%) with the trunk exercises and all trunk stabilizers (37.7-54.3%) with the chest press. There was no effect of instability on the shoulder press. Unilateral shoulder press produced greater activation of the back stabilizers, and unilateral chest press resulted in higher activation of all trunk stabilizers, when compared with bilateral presses. Regardless of stability, the superman exercise was the most effective trunk-stabilizer exercise for back-stabilizer activation, whereas the side bridge was the optimal exercise for lower-abdominal muscle activation. Thus, the most effective means for trunk strengthening should involve back or abdominal exercises with unstable bases. Furthermore, trunk strengthening can also occur when performing resistance exercises for the limbs, if the exercises are performed unilaterally.
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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 | 0.002 |
| 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) | 0.003 | 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".