Do Exercise Balls Provide a Training Advantage for Trunk Extensor Exercises? A Biomechanical Evaluation
Bibliographic record
Abstract
OBJECTIVE: Only a few abdominal muscle exercises have been quantitatively evaluated on both a mat and exercise ball, but the benefits reported for the ball have been equivocally applied to all exercises. The purpose of this study is to evaluate differences in the biological response of muscle activation, lumbar spine posture, and loading variables for extensor exercises performed on 2 surfaces. METHODS: Bilateral muscle activation was recorded from 7 sites (rectus abdominis, external/internal obliques, latissimus dorsi, thoracic/lumbar erector spinae, and multifidus) on 8 subjects. Three-dimensional lumbar spine postures and upper body kinematics were recorded while the participants performed the exercises. An electromyography-driven model was used to calculate spinal loading. RESULTS: Cocontraction of trunk flexor and extensor muscles was reduced by up to 30% for the extension exercises when performed on the ball. Peak muscle activation remained unchanged or decreased, and spinal loading (compression and anterior-posterior shear) decreased when the extension exercises were performed on the ball. The lumbar spine postures attained during the exercises did not differ between surfaces. CONCLUSIONS: The assumption that the use of an exercise ball will always create a greater challenge for the musculoskeletal system was not supported by the findings of this study. Likewise, in a healthy, young population, there does not appear to be any training advantage to performing extensor exercises on a ball versus a mat. However, in a rehabilitation scenario, these exercises performed on a ball could reduce low back loading and hence reduce the potential for reinjury.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".