Trunk Muscle Activation During Dynamic Weight-Training Exercises and Isometric Instability Activities
Bibliographic record
Abstract
The purpose of this study was to examine the extent of activation in various trunk muscles during dynamic weight-training and isometric instability exercises. Sixteen subjects performed squats and deadlifts with 80% 1 repetition maximum (1RM), as well as with body weight as resistance and 2 unstable calisthenic-type exercises (superman and sidebridge). Electromyographic (EMG) activity was measured from the lower abdominals (LA), external obliques (EO), upper lumbar erector spinae (ULES), and lumbar-sacral erector spinae (LSES) muscle groups. Results indicated that the LSES EMG activity during the 80% 1RM squat significantly exceeded 80% 1RM deadlift LSES EMG activity by 34.5%. The LSES EMG activity of the 80% 1RM squat also exceeded the body weight squat, deadlift, superman, and sidebridge by 56, 56.6, 65.5, and 53.1%, respectively. The 80% 1RM deadlift ULES EMG activity significantly exceeded the 80% 1RM squat exercise by 12.9%. In addition, the 80% 1RM deadlift ULES EMG activity also exceeded the body weight squat, deadlift, superman, and sidebridge exercises by 66.7, 65.5, 69.3, and 68.6%, respectively. There were no significant changes in EO or LA activity. Therefore, the augmented activity of the LSES and ULES during 80% 1RM squat and deadlift resistance exercises exceeded the activation levels achieved with the same exercises performed with body weight and selected instability exercises. Individuals performing upright, resisted, dynamic exercises can achieve high trunk muscle activation and thus may not need to add instability device exercises to augment core stability 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 | 0.000 |
| 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) | 0.001 | 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".