Maximal motor unit firing rates during isometric resistance training in men
Bibliographic record
Abstract
This study measured changes in maximal voluntary contraction (MVC) force, percentage maximal activation, maximal surface EMG, M-wave amplitude and average motor unit firing rates during the initial 3 weeks of isometric resistance training of the quadriceps muscle. Ten men participated in a resistance training programme three times a week for 3 weeks and 10 men participated as a control group. In the training group, MVC increased by 35% (from 761 +/- 77 to 1031 +/- 78 N) by the end of the 3 weeks. There were no changes in mean motor unit firing rates during submaximal or maximal voluntary contractions of 50 (15.51 +/- 1.48 Hz), 75 (20.23 +/- 1.85 Hz) or 100% MVC (42.25 +/- 2.72 Hz) with isometric resistance training. There was also no change in maximal surface EMG relative to the M-wave amplitude. However, there was a small increase in maximal activation (from 95.7 +/- 1.83 to 98.44 +/- 0.66%) as measured by the twitch interpolation technique. There were no changes in any of the parameters measured in the control group. It is suggested that mechanisms other than increases in average motor unit firing rates contributed to the increase in maximal force output with resistance training. Such mechanisms may include a combination of increased motor unit recruitment, enhanced protein synthesis, and changes in motor unit synchronization and muscle activation patterns across the quadriceps synergy.
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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) | 0.001 | 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".