Estimated moments at L5/S1 level and muscular activation of back extensors for six prone back extension exercises in healthy individuals
Bibliographic record
Abstract
The purpose of this study was to identify the prone back extension (PBE) exercises that offer the most resistance to the back extensor muscles. Twenty males with no previous history of low back injury performed two repetitions of eight exercises. These consisted of two maximal isometric voluntary activations (MVA) and six different PBE exercises. The participants, while lying prone on a bench, were asked to raise either their trunk or legs to the horizontal position and hold for one second (static phase) before returning to the original position. The exercises differed according to the particular segments that were raised into extension. To assess the PBE exercises, a biomechanical model was used to estimate the level of load resistance (%MVA) relative to the maximum voluntary activation strength (MVA). In addition, electromyography (EMG) was included to measure the level of muscle activity (%MVE) relative to the maximum voluntary activation EMG (MVE) value reached during the MVA. A significant relationship of 0.86 was found between the mass of the subject and the peak reaction moment during the MVA. Peak level of load resistance averaged around 50% MVA with the exception of exercise no. 5 where the subject had to raise in extension his trunk, legs and arms (65% MVA). The erector spinae (ES) activity level reached a peak value of 61% MVE for exercise no. 5. In the exercises where only the trunk was lifted, the peak level of ES was below 47% MVE and the average activity during the static phase reached approximately 26% MVE. For healthy individuals PBE exercises can be considered low intensity exercises (< 50% MVA; except exercise no. 5) and can be used to improve the endurance of the back muscles.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".