Acute effects of whole‐body vibration on peak isometric torque, muscle twitch torque and voluntary muscle activation of the knee extensors
Bibliographic record
Abstract
The purpose of this investigation was to compare the acute effects of whole-body vibration (WBV) with a static squat on resting muscle twitch torque, peak isometric torque and voluntary muscle activation of the knee extensors during an isometric maximal voluntary contraction (MVC). Twenty-four healthy, strength-trained males were recruited for this randomized, cross-over design investigation. The WBV treatment consisted of three sets of 60 s of vibration (30 Hz, +/-4 mm) while standing in a semi-squat position. Voluntary muscle activation, peak isometric torque during MVC and resting muscle twitch torque (RTT) through percutaneous femoral nerve stimulation were obtained before and following the treatment. Change in peak isometric torque, voluntary muscle activation and the RTT were calculated as the difference between pre- and post-treatment values. There was no observable post-activation potentiation of muscle twitch torque or enhancement in voluntary muscle activation or peak isometric torque. However, decreases in the peak isometric torque (P=0.0094) and voluntary muscle activation (P=0.0252) were significantly smaller post WBV interventions compared with the control treatment. Based on the current data, it is unclear whether or not this was attributable to the effects of WBV but further research into this possibility is warranted.
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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) | 0.001 | 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) | 0.002 | 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".