The influence of preceding activity and muscle length on voluntary and electrically evoked contractions
Bibliographic record
Abstract
Muscle length and preceding activity independently influence rate of torque development (RTD) and electromechanical delay (EMD), but it is unclear whether these parameters interact to optimize RTD and EMD. The purpose of this study was to determine the influence of muscle length and preceding activity on RTD and EMD during voluntary and electrically stimulated (e-stim) contractions. Participants (n = 17, males, 24 ± 3 years) performed isometric knee extensions on a dynamometer. Explosive maximal contractions were performed at 2 knee angles (35° and 100° referenced to a 0° straight leg) without preceding activity (unloaded, UNL) and with preceding activities of 20%, 40%, 60%, and 80% of maximal voluntary contraction (MVC) torque. Absolute and normalized voluntary RTD were slowed with preceding activities ≥40% MVC for long muscle lengths and all preceding activities for short muscle lengths compared with UNL (p < 0.001). Absolute and normalized e-stim RTD were slower with preceding activities ≥40% MVC compared with UNL (p < 0.001) for both muscle lengths. Normalized RTD was faster at short muscle lengths than at long muscle lengths (p < 0.001) for e-stim (∼50%) and voluntary (∼32%) UNL contractions, but this effect was not present for absolute RTD. Muscle length did not affect EMD (p > 0.05). EMD was shorter at 80% MVC compared with UNL (∼35%; p < 0.001) for both muscle lengths during voluntary but not e-stim contractions. While RTD is limited by preceding activity at both muscle lengths, long muscle lengths require greater preceding activity to limit RTD than short muscle lengths, which indicates long muscle lengths may offer a "protective effect" for RTD against preceding activity.
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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 | 0.001 | 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) | 0.002 | 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".