Comparison of Different Rowing Exercises: Trunk Muscle Activation and Lumbar Spine Motion, Load, and Stiffness
Bibliographic record
Abstract
The objective of this study was to investigate 3 different rowing exercises and quantify the muscle activation of the torso and the hip musculature, together with the corresponding spinal loading and stiffness. Seven healthy men from a university population were instrumented to obtain surface electromyography of selected trunk and hip muscles and to obtain spine position using an electromagnetic spine position sensor, together with video analysis to calculate joint moments. The 3 rowing exercises investigated were the inverted row, standing bent-over row, and standing 1-armed cable row. The inverted row elicited the highest activation of the latissimus dorsi muscles, upper-back, and hip extensor muscles. The lower activation of the lumbar erector spinae muscles during the inverted row corresponded to the lower spine load measured. The standing bent-over row produced large activation symmetrically across the back, but it produced the largest lumbar spine load. The 1-armed cable row challenged the torsional capabilities of the trunk musculature. Some core exercises may be better for rehabilitation (e.g., having the training goals of modest muscle activation with low spine load), whereas other exercises may be better for athletic training (e.g., resulting in higher muscle activation and larger spine load). When prescribing core exercises, those wishing to spare the low back may choose the inverted row, given the lowest spine load exercise. The standing bent-over row elicited large muscle activation symmetrically from the upper to lower back; it induced larger spine loads but also, not surprisingly, the highest spine stiffness. If torsional endurance or strength is the training goal, the 1-armed cable row might be considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".