A Comparative Study of Muscle Energy Technique and Dynamic Stretching on Calf Muscle Group for Speed and Physical Endurance on Healthy Sprint Runners in School Level
Bibliographic record
Abstract
Backgroud: Athletes of all ages and skill levels use running as a popular form of exercise all over the world. The calf muscle complex is primarily responsible for propulsion during running gait. The gastrocnemius appears to be vulnerable to injury because of the strong stresses generated in this muscle during the push-off phase of running. According to reports, up to 30% of running-related injuries occur in the calf muscle region each year. Furthermore, lower leg soreness, gastrocnemius pain or strain, calf pain, calf spasm, and Achilles tendon injuries have all been reported as symptoms of calf injuries. Aims of The Study: Aim of the study is to evaluate the effect of MET and Dynamic Stretching on calf muscle group for speed and physical endurance on healthy sprint runners in school level Methods: Thirty-two subjects were divided into two groups. Group-A received Muscle energy technique (n=16) and Group-B trained with Dynamic stretching (n=16). Both groups received training of 5 sessions per week for 6 weeks. Outcomes were assessed by Bruce treadmill test and 40-yard sprint test before and after treatment. Results: The study shows statistically significant improvement (p<0.05) in both groups for all the outcomes. After 6 weeks of training period, the group trained with muscle energy technique scored significantly higher in improving the endurance and speed than the group trained with dynamic stretching when the pre & post test values of Bruce treadmill Test and 40 yard sprint test were statistically analyzed using an independent ‘t’ test. Conclusions: Muscle energy technique was found to be much effective in improving the endurance and speed of sprint runners with improving flexibility of calf muscles than dynamic stretching technique.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".