Comparative Effect of Motor Control Exercise Using Swiss Ball over Stretching Exercise on Mechanical Low Back Pain
Bibliographic record
Abstract
Background: Trunk muscle coordination can be improved by motor control exercise using Swiss ball exercises, trunk muscles that support the spine's stability and mobility. In this study effect of motor control exercise using Swiss ball is compared with stretching exercise on low back pain. This study is also aimed to find the effect of motor control exercise using Swiss ball and stretching exercise within the group on mechanical low back pain.Methods: A Comparative study was done at ACS Medical College and Hospital, Chennai, with 30 samples. The duration of the treatment was four weeks. Male and female genders were selected for the study with the age of 18-25 yrs. The outcome measures were Low Back Index Scale, Quebec Disability Scale, and Schober Test. Total 30 subjects were randomly allocated, 15 in each group A and B by fulfilling inclusion criteria. Intervention for Group A trained with motor control exercise using Swiss ball and Group B with stretching exercise. Both groups receive treatment for three sessions /week of a total of 12 sessions of treatment.Results: Motor Control Exercises using Swiss Ball found more effective than stretching on reduction of pain with mean difference 2.60 and 2.533 respectively, and stretching found more effective than motor control exercise on disability and improve spinal mobility with a mean difference of 19.00, 19.67, and 22.00, 23.00 respectively among patients mechanical low back pain.Conclusion: Motor Control Exercises using Swiss Ball was found more effective than stretching to reduce pain, disability and improve spinal mobility among patients with mechanical low back pain.
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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) | 0.001 | 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".