Shoulder muscle EMG activity during push up variations on and off a Swiss ball
Bibliographic record
Abstract
BACKGROUND: Surface instability is a common addition to traditional rehabilitation and strength exercises with the aim of increasing muscle activity, increasing exercise difficulty and improving joint proprioception. The aim of the current study was to determine if performing upper body closed kinetic chain exercises on a labile surface (Swiss ball) influences myoelectric amplitude when compared with a stable surface. METHODS: Thirteen males were recruited from a convenience sample of college students. Surface electromyograms were recorded from the triceps, pectoralis major, latissimus dorsi, rectus abdominis and external oblique while performing push up exercises with the feet or hands placed on a bench and separately on a Swiss ball. A push up plus exercise was also evaluated with hands on the support surface. RESULTS AND DISCUSSION: Not all muscles responded with an increase in muscle activity. The pectoralis major muscle was not influenced by surface stability. The triceps and rectus abdominis muscles showed increases in muscle activity only when the hands were on the unstable surface. The external oblique muscle was only influenced by surface stability during the performance of the push up plus exercise. No muscle showed a change in activation level when the legs were supported by the Swiss ball instead of the bench. CONCLUSION: Muscle activity can be influenced by the addition of surface instability however an increase in muscle activity does not influence all muscles in all conditions. The relationship between the participant's center of mass, the location of the unstable surface and the body part contacting the Swiss ball may be important factors in determining the muscle activation changes following changes in surface stability.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".