Individual Control Strategies in Training: Myoelectric activity and recruitment strategies in the co-contraction training
Bibliographic record
Abstract
BACKGROUND: The literature in motor control has abundant evidence on within-/between-person variability when it comes to control strategies in several behaviors - which largely influences intervention outcomes. Despite being an intervention itself, strength training paradigms are yet to be analyzed beyond the average behavior. Based on myoelectric activity (EMG) analyses, this study emerges as a descriptive analysis on how the co-contraction training paradigm provides stimuli for strength training of knee extensors and flexors. AIM: Considering the potential large interindividual variability in muscle activation patterns during resistance training, we explored the co-contraction paradigm considering the individual characteristics. METHOD: Ten active male adults participated in two days of co-contraction training paradigm with their EMG activity collected (sartorius, biceps femoris long and short heads, semitendinosus, semimembranosus, rectus femoris, vastus lateralis and medialis and tensor fascia-latae). RESULTS: On average, participants recruit 36% of their maximum EMG amplitude, decay 0.41% per repetition but increase 7.45% between sessions. The training stimulated similarly the knee flexors and extensors EMG ratio of all participants. However, participants demonstrated different average muscle recruitment patterns with few individuals modifying, largely, their recruitment over repetitions/days. Between and within-variability in recruitment pattern was maintained throughout repetitions and days. CONCLUSION: Thus, the co-contraction training demonstrated sufficient muscle activation to be employed and evoked similar muscular recruitment between agonists and antagonists. To the best of our knowledge, this is a pioneer study encompassing the complexity of movement control in evaluating a strength training protocol.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".