Motor unit recruitment patterns of the quadriceps differ between continuous high- and low-torque isometric knee extension to momentary failure
Bibliographic record
Abstract
Abstract The size principle is a theory of motor unit (MU) recruitment that suggests MUs are recruited in an orderly manner from the smallest (lower threshold) to the largest (higher threshold) MUs. A consequence of this biophysical theory is that, for isometric contractions, recruitment is dependent on the intensity of actual effort required to meet task demands. This concept has been supported by modelling work demonstrating that, in tasks performed to momentary failure, full MU recruitment will have occurred upon reaching failure irrespective of the force requirements of the task. However, in vivo studies examining this are limited. Therefore, the aim of the current study was to examine MU recruitment of the quadriceps under both higher- and lower-torque (70% and 30% of MVC, respectively) isometric knee extension, performed to momentary failure. Specifically, we compared surface electromyography (sEMG) frequency characteristics, determined by wavelet analysis, across the two continuous isometric knee extension tasks to identify potential differences in recruitment patterns. A convenience sample of 10 recreationally active adult males (height: 179.6±6.0 cm; mass: 76.8±7.3 kg; age: 26±7 years) with previous resistance training experience (6±3 years) were recruited. Using a within-session, repeated-measures, randomised crossover design participants performed the knee extension tasks whilst sEMG was collected from the vastus medialis (VM), rectus femoris (RF) and vastus lateralis (VL). Myoelectric signals were decomposed into intensities as a function of time and frequency using an EMG-specific wavelet transformation. Our first analysis compared the mean frequency at momentary failure; second, we investigated the effects of load on relative changes in wavelet intensities; finally, we quantified the degree of wavelet similarity over time. Wavelet-based calculation of the mean signal frequency appeared to show similar mean frequency characteristics occurring when reaching momentary failure. However, individual wavelets revealed that different changes in frequency components occurred between the two tasks, suggesting that patterns of recruitment differed. Low-torque conditions resulted in an increase in intensity of all frequency components across the trials for each muscle whereas high-torque conditions resulted in a wider range of frequency components contained within the myoelectric signals at the beginning of the trials. However, as the low-torque trial neared momentary failure there was an increased agreement between conditions across wavelets. Our results corroborate modelling studies as well as recent biopsy evidence, suggesting overall MU recruitment may largely be similar for isometric tasks performed to momentary failure with the highest threshold MUs likely recruited, despite being achieved with differences in the pattern of recruitment over time utilised.
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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) | 0.001 | 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".