Electromyography of scapular stabilizers in people without scapular dyskinesis during push-ups
Bibliographic record
Abstract
Background: Push-up (PU) is widely considered an effective exercise to stabilize the scapular, especially if performed on unstable surfaces. However, available studies cover a wide range of exercise variations and differ according to exercise prescription, muscle selection and study design. Therefore, findings are contradictory, and conclusions for a proper application of the PU are difficult to draw. Objective: To synthesize the available literature on the changes in the activity of the periscapular muscles in individuals without scapular dyskinesis while performing different types of PU on unstable surfaces. Search procedure: Four online databases were searched from the earliest publications to 9 August 2023, using predefined keywords. Out of the 2,850 potential references identified in the primary search, 92 studies were reviewed in detail, of which 38 met the inclusion criteria and were included. Methodological quality was evaluated using a standardized form based on the Newcastle‒Ottawa scale for observational studies. Data combination was performed using CMA (v3), and the random-effects model was used to calculate the standardized mean difference (SMD) with a 95% confidence interval (CI). Results: The use of unstable surfaces in people without scapular dyskinesis led to increased activity of the upper trapezius during the PU (p = 0.017; I2 = 84.95%; SMD = 0.425 [95% CI 0.077, 0.773]) and knee PU (p = 0.023; I2 = 70.23%; SMD = 0.474 [95% CI 0.066, 0.882]) exercises and increased activity of the middle trapezius (MT) (p = 0.003; I2 = 64.50%; SMD = 0.672 [95% CI 0.225, 1.119]) and serratus anterior (SA) (p = 0.039; I2 = 4.25%; SMD = 0.216 [95% CI 0.011, 0.420]) muscles during the push-up plus (PUP) exercise. Conclusion: Using an unstable support base during PU does not necessarily increase the activity of all scapular stabilizers. The amount of muscle activity depends on the type of PU other than the type of support base. If an unstable surface is used, PUP exercise appears to be the most effective modality to increase the quality of training, improve performance, and prevent the occurrence of scapular dyskinesis due to the increase in the activity of the MT and SA muscles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".