Instructional Cueing Alters Upper Limb Muscle Activity and Kinematics During Elastic Resistance Exercise
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to quantify the differences in upper limb muscle activity and kinematics when performing shoulder elastic resistance exercises with no cue, slouched posture, and corrected posture. DESIGN: Fifteen healthy participants completed four shoulder elastic resistance exercises (unilateral flexion, bilateral flexion, external rotation, and external rotation with towel) across three simulated body postures (no cue, corrected posture, and slouched posture). Surface electromyography was measured on 16 upper limb muscles and kinematics were collected. Two-way repeated-measures analyses of variance examined differences in muscle activation and kinematics across postures and exercises. RESULTS: Interactions between exercise and posture were found for most muscles. Muscle activity interactions existed in 14 of the 16 muscles examined, with 8 muscles having the greatest activity in the unilateral flexion, slouched condition (P < 0.0001). The slouched posture generated activity up to 88.4 ± 5.1 %MVC in the cervical extensors. Completing flexion or external rotation exercises with a slouched posture led to increased glenohumeral range of motion (P < 0.0001), but these differences were less than 5 degrees between the greatest and smallest ranges of motion (85.8 vs. 81.0 degrees). CONCLUSION: Posture influenced muscle activation and kinematics, with slouched postures increasing muscle activity and range of motion. There was little to no difference between the no cue and corrected cue conditions, suggesting that perhaps a clinician's time may be better spent focusing on avoiding slouched postures rather than ensuring mastering technique.
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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 | 0.003 |
| 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) | 0.002 | 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".