Specificity of a Back Muscle Roman Chair Exercise in Healthy and Back Pain Subjects
Bibliographic record
Abstract
PURPOSE: Roman chair exercises are popular for improving back muscle endurance but do not specifically target back muscles. This study aimed to determine whether an adaptation of the Roman chair exercise would induce more fatigue in back muscles than in hip extensors. METHODS: For this study, 16 healthy subjects and 18 patients with nonspecific chronic low back pain performed trunk flexion-extension cycles until exhaustion in a Roman chair with hips flexed at 40°. Surface EMG signals were recorded bilaterally on four back muscles and two hip extensors (gluteus maximus and biceps femoris). Motion analysis of the trunk segments (pelvis, lumbar, and thoracic spines) was also carried out. RESULTS: In both groups, EMG revealed clear evidence of muscle fatigue for the gluteus maximus, less clear evidence of fatigue for the lower back muscles, and motor unit recruitment (without fatigue) for the upper back muscles and biceps femoris. A change of muscle activation pattern was emphasized throughout the exercise bout, with some lower back muscles showing an increase followed by a decrease or leveling off of activation and with upper back muscles showing an increased activation at the end. Kinematic analyses revealed a progressive decrease (11°) in the lumbar range of motion (ROM) and a progressive increase in hip (2°) and thoracic (7°) ROM during the exercise bout. CONCLUSIONS: Roman chairs allow more freedom to change the kinematics of the spine during the exercise (less lumbar and more thoracic motion) to delay lower back muscle fatigue by sharing the load between the lower and upper back muscles. Even with adaptations to reduce hip extensors fatigue, this may make this exercise not as specific as wanted for fatiguing lower 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) | 0.000 | 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) | 0.002 | 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".