The Effects of Posture and Dynamic Stretching on the Electromechanical Delay of the Paraspinal Muscles
Bibliographic record
Abstract
Electromechanical delay (EMD) of muscle is influenced in part by its in-series arrangement with connective tissue. Therefore, studying EMD might provide a better understanding of the muscle-connective tissue interaction. Here, EMD of the thoracic and lumbar erector spinae muscles were investigated under conditions that could influence muscle-connective tissue interaction. A total of 19 participants performed isometric back extension contractions in 3 different postures that influence lumbar spine angle: sitting, standing, and kneeling. They then performed a 15-minute dynamic stretching routine and repeated the standing contractions. Mean lumbar flexion angles of 0.5°, 9.9°, and 19.8° were adopted for standing, kneeling, and sitting, respectively. No statistically significant differences in the thoracic erector spinae EMD were found between the different postures. Lumbar erector spinae EMD was significantly longer in the sitting (94.1 ms) compared to the standing (69.9 ms) condition, with no differences compared to kneeling (79.7 ms). There were no statistically significant differences of the thoracic or lumbar erector spinae EMDs before and after dynamic stretching. These results suggest that dynamic stretching does not affect the mechanical behavior of the muscle-tendon-aponeurosis units in a way that alters force generation and transmission, but a sitting posture can alter how force is transmitted through the musculotendinous complex of the lumbar erector spinae.
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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 | 0.001 |
| 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) | 0.002 | 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".