Scapular Muscle Activity During Static Yoga Postures
Bibliographic record
Abstract
Study Design Controlled, cross-sectional laboratory study. Background Despite the growing popularity of yoga, little is known about the muscle activity of the scapular stabilizers during isometric yoga postures and their potential utility in shoulder rehabilitation. Objectives To examine scapular stabilizer muscle activation during various yoga postures. Methods Twenty women with yoga experience and no shoulder pain or injury participated. Electromyography was used to record the muscle activity of the upper, middle, and lower trapezius, as well as of the serratus anterior, during 15 yoga postures. Results Muscle activity varied between yoga postures (3%-57% maximum voluntary isometric contraction [MVIC]). Overall, the "locust arms forward" posture elicited the highest activity from the upper (22.4% MVIC), middle (41.8% MVIC), and lower (56.8% MVIC) trapezius, while several postures elicited moderate activity (greater than 20% MVIC) from the serratus anterior. Conversely, the "dancer's pose right," "reverse tabletop," and "warrior II" postures demonstrated low activity (less than or equal to 15.7% MVIC) of the scapular stabilizers. Conclusion Strengthening the scapular stabilizer muscles is an important component of shoulder rehabilitation. Yoga postures have been identified that activate the scapular stabilizer muscles at varying levels of activity. J Orthop Sports Phys Ther 2018;48(6):504-509. Epub 6 Apr 2018. doi:10.2519/jospt.2018.7311.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".