Effectiveness of Lumbar Motor Control Exercises in Improving Lumbar Stability among Bharatanatyam Dancers
Bibliographic record
Abstract
Background and purpose: Low back injuries among Bharatanatyam dancers are said to be over use relatedand secondary to biomechanical alignment defects. This causes weakness of the deep core muscles resultingin lumbar instability and low back pain. Motor control exercises aim to retrain the optimal control of thedeep spinal muscle and maintain normal control during physical and functional tasks. The study is designedto assess the effectiveness of motor control exercises in improving lumbar stability among Bharatanatyamdancers.Objectives: To find out the strength of the deep abdominal muscle function pre and post intervention. Tofind out the correlation between lumbar spine curvature in standing and the deep abdominal muscle strength.To assess the effectiveness of lumbar motor control exercises in improving lumbar stability.Design: Quasi experimental one group pre and post-test designMethods: A total of 85 subjects (females) aged between 18 to 30 years with Quebec score >20 were recruitedusing purposive sampling method. Subjects were examined for core muscle activity using Stabilizer pressurebiofeedback and were assessed for Lumbar spine curvature using Flexible curve ruler. Pain and disabilitywere scored using Modified Oswestry disability questionnaire (MODQ). The intervention consisted of motorcontrol exercises for 6 weeks. Pre and post-test intervention scores were recorded and analysed. Results:The core muscle activity and Modified Oswestry disability score significantly (p<0.001) improved after theintervention and showed strong significance. The correlation between lumbar lordosis angle and core muscleactivity showed positive relation (r = 0.859) and statistically significant (p<0.001). Conclusion: The resultssuggest that, motor control exercises may be significantly effective in improving lumbar stability amongBharatanatyam dancers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".