Training induced improvements in knee extensor force accuracy are associated with reduced vastus lateralis motor unit firing variability
Bibliographic record
Abstract
Abstract Background Muscle force output during sustained submaximal isometric contractions fluctuates around an average value and is known to be influenced by variation in motor unit (MU) firing rates. MU firing rate variability seemingly reduces following exercise training interventions, however, much less is known with respect to peripheral MU properties. We therefore investigated whether targeted force accuracy training could lead to improved muscle functional capacity and control, in addition to determining any alterations of individual MU features. Methods Ten healthy participants (7 females, 3 males, 27±6 years, 170±8 cm, 69±16kg) underwent a 4-week supervised, unilateral, force accuracy training intervention. The coefficient of variation for force (FORCE CoV ) and sinusoidal wave force tracking accuracy (FORCE Sinu ) were determined at 25% maximal voluntary contraction (MVC) pre- and post-training. Intramuscular electromyography was utilised to record individual MU potentials from the vastus lateralis (VL) muscles at 25% MVC during sustained contractions, pre- and post-training. Results Knee extensor muscle strength remained unchanged following training, with no improvements in unilateral leg-balance. FORCE CoV and FORCE Sinu significantly improved in only the trained knee extensors by ~13% ( p =0.01) and ~30% ( p <0.0001) respectively. MU firing rate variability significantly reduced in the trained VL by ~16% ( n =8; p =0.001), with no further alterations to MU firing rate or neuromuscular junction transmission instability. Conclusion Our results suggest muscle force control and tracking accuracy is a trainable characteristic in the knee extensors, which is likely explained by the reduction in MU firing rate variability apparent in the trained limb only.
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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".