Rate of Muscle Activation in Power-and Endurance-Trained Boys
Bibliographic record
Abstract
UNLABELLED: Previous studies in adults have demonstrated power athletes as having greater muscle force and muscle activation than nonathletes. Findings on endurance athletes are scarce and inconsistent. No comparable data on child athletes exist. PURPOSE: This study compared peak torque (Tq), peak rate of torque development (RTD), and rate of muscle activation (EMG rise, Q30), in isometric knee extension (KE) and flexion (KF), in pre- and early-pubertal power- and endurance-trained boys vs minimally active nonathletes. METHODS: Nine gymnasts, 12 swimmers, and 18 nonathletes (7-12 y), performed fast, maximal isometric KE and KF. Values for Tq, RTD, electromechanical delay (EMD), and Q30 were calculated from averaged torque and surface EMG traces. RESULTS: No group differences were observed in Tq, normalized for muscle cross-sectional area. The Tq-normalized KE RTD was highest in power athletes (6.2 ± 1.9, 4.7 ± 1.2, 5.0 ± 1.5 N·m·s-1, for power, endurance, and nonathletes, respectively), whereas no group differences were observed for KF. The KE Q30 was significantly greater in power athletes, both in absolute terms and relative to peak EMG amplitude (9.8 ± 7.0, 5.9 ± 4.2, 4.4 ± 2.2 mV·ms and 1.7 ± 0.8, 1.1 ± 0.6, 0.9 ± 0.5 (mV·ms)/(mV) for power, endurance, and nonathletes, respectively), with no group differences in KF. The KE EMD tended to be shorter (P = .07) in power athletes during KE (71.0 ± 24.1, 87.8 ± 18.0, 88.4 ± 27.8 ms, for power, endurance, and nonathletes), with no group differences in KF. CONCLUSIONS: Pre- and early-pubertal power athletes have enhanced rate of muscle activation in specifically trained muscles compared with controls or endurance athletes, suggesting that specific training can result in muscle activation-pattern changes before the onset of puberty.
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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 | 0.001 | 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) | 0.004 | 0.001 |
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".