Pelvic Stabilization and Semisitting Position Increase the Specificity of Back Exercises
Bibliographic record
Abstract
PURPOSE: To examine the effect of pelvic stabilization and two lower-limb configurations on the EMG activity of back and hip extensor muscles during a dynamic back extension exercise on a machine and to compare them between sexes. METHODS: Twenty-two healthy volunteers (11 men and 11 women) performed five trunk flexion-extension cycles at 40% of their maximal voluntary contraction (MVC) in a machine designed for back exercise. Two different positions were used: 1) seated position (seat horizontal, knees at 90 degrees ) and 2) semisitting position (seat slightly tilted forward at 15 degrees , knees at 45 degrees of flexion). In each position, three pelvic stabilization conditions were tested: 1) unrestrained (control condition), 2) partially restrained (posteriorly), and 3) totally restrained (posteriorly and anteriorly). EMG signals were recorded bilaterally with 12 pairs of electrodes placed on back muscles as well as on the gluteus maximus and biceps femoris. The muscular activation level, that is, the percentage of EMG amplitude relative to the maximal EMG obtained from MVC, was used to asses the relative contribution of each muscle group across exercises. RESULTS: In both sexes, two main results were found: 1) pelvic stabilization (partially and totally restrained) significantly (P < 0.05) increased the activity of all back muscles (average of 12%) and 2) semisitting position significantly decreased (range = 8-27%) the activity of two hip extensors compared with the seated position. Sex differences were also observed relative to the activity of some back muscles as well as the biceps femoris. CONCLUSIONS: Combining pelvic stabilization and semisitting position in back exercise machines might be a useful way to localize the effects of endurance training at the back muscles and this in both the sexes.
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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) | 0.003 | 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".