Voluntary Muscle Relaxation Can Mitigate Fatigue and Improve Countermovement Jump Performance
Bibliographic record
Abstract
Pinto, BL and McGill, SM. Voluntary muscle relaxation can mitigate fatigue and improve countermovement jump performance. J Strength Cond Res 34(6): 1525-1529, 2020-When muscles contract, they create force and stiffness. Thus, muscle activation and relaxation must be strategically sequenced to coordinate and control movement, to enhance athletic variables such as speed and strength. However, research has favored investigation of muscle activation over relaxation. Athletes such as runners, swimmers, and boxers often shake their limbs to allow the muscle to oscillate freely, immediately before a bout. The purpose was to investigate whether shaking the lower limbs with the intention to voluntarily relax the muscles of the limb has an effect on countermovement jump (CMJ) performance. Subjects performed 10 maximal effort CMJs with 30 seconds of rest between each jump. During the rest period, they either performed the relaxation technique or control condition (standing still). Statistical significance was considered at p < 0.05. Subjects significantly improved jump height, compared with their first jump of the day, when performing the relaxation technique. To further investigate the mechanism of enhancement, subjects were grouped into responders and nonresponders. The responder group significantly decreased their jump height and concentric phase impulse (relative to the first jump) during the control condition compared with the nonresponder group, indicating fatigue. When performing the relaxation technique, the responder group improved their jump height and mitigated fatigue by significantly increasing their unweighting impulse and unweighting force. The relaxation technique improved CMJ performance, specifically in those that fatigue with consecutive bouts, by enhancing unweighting, that requires muscle relaxation, rather than propulsion that requires activation. This technique can be useful for training or competition.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.004 | 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".