1. Impact of Surface Stability and Hand Separation on Activation of Agonist and Stabilizing Muscles During Push-Up Exercise
Bibliographic record
Abstract
Stability is an important factor to consider when designing an exercise program as recent studies have suggested that there can be benefits to performing exercises on unstable surfaces compared to rigid ones. Furthermore, to increase strength of the agonist muscles, pectoralis major and triceps brachii, various hand positions can also be implemented to increase stress on these muscles during an exercise. Thus, our goal was to test the impact that surface stability and hand position can have on muscle activity of stabilizing and agonist muscles during a push-up exercise. Surface electrodes were attached to specific stabilizing and agonist muscles and electromyography (EMG) data was used to measure muscle activity. When compared to a rigid surface, a push up exercise performed on an unstable surface of a BOSU ball resulted in higher muscle activity for all the stabilizing muscles, while only the pectoralis major had an increase in activity for the agonist muscle group. In regards to hand position, a narrow hand spacing resulted in greater activation of all the stabilizing muscles and was able to specifically target the triceps brachii, while the use of wide hand spacing only isolated the pectoralis major. Hence an exercise program consisting of push-ups can target the stabilizing muscles further by using an unstable surface and a narrow hand position. In regards to the agonist muscles, the use of a specific surface and hand position can allow the user to target the agonist muscle they desire in a more efficient manner.
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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 | 0.000 | 0.002 |
| 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) | 0.004 | 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".