Muscle activity and spine load during anterior chain whole body linkage exercises: the body saw, hanging leg raise and walkout from a push-up
Bibliographic record
Abstract
This study examined anterior chain whole body linkage exercises, namely the body saw, hanging leg raise and walkout from a push-up. Investigation of these exercises focused on which particular muscles were challenged and the magnitude of the resulting spine load. Fourteen males performed the exercises while muscle activity, external force and 3D body segment motion were recorded. A sophisticated and anatomically detailed 3D model used muscle activity and body segment kinematics to estimate muscle force, and thus sensitivity to each individual's choice of motor control for each task. Gradations of muscle activity and spine load characteristics were observed across tasks. On average, the hanging straight leg raise created approximately 3000 N of spine compression while the body saw created less than 2500 N. The hanging straight leg raise created the highest challenge to the abdominal wall (>130% MVC in rectus abdominis, 88% MVC in external oblique). The body saw resulted in almost 140% MVC activation of the serratus anterior. All other exercises produced substantial abdominal challenge, although the body saw did so in the most spine conserving way. These findings, along with consideration of an individual's injury history, training goals and current fitness level, should assist in exercise choice and programme design.
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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) | 0.000 | 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".