The effects of whole-body vibration on upper- and lower-body EMG during static and dynamic contractions
Bibliographic record
Abstract
Whole-body vibration (WBV) training uses a vertically oscillating platform and reports suggest that this perturbation elicits reflexive muscle contractions that augment muscle activity and contribute to increased strength. No WBV study has measured both upper- and lower-body muscle activation. The purpose of this study was to determine the optimal WBV stimulus (frequency x amplitude) to increase electromyography (EMG) in upper- and lower-body muscles for three distinctive unloaded actions: isometric semi-squat, dynamic leg squats, and static and dynamic bilateral bicep curls. Surface EMG was measured for the vastus lateralis (VL), biceps femoris (BF), biceps brachii (BB), and triceps brachii (TB) in 10 recreationally active male university students (24.4+/-2.0 years; mean+/-SD) when WBV was administered at 2 and 4 mm and at 25, 30, 35, 40, and 45 Hz. EMG changes are reported as the difference between WBV and no WBV EMG root mean square expressed as a percentage of maximum voluntary exertion (%MVE). In static semi-squat, WBV increased muscle activity 2.9%-6.7% in the VL and 0.8%-1.2% in the BF. During dynamic squatting, WBV increased muscle activity in the VL by 3.7%-8.7% and in the BF by 0.4%-2.0%. In a static biceps curl, WBV had no effect on BB EMG, but did increase TB activity 0.3%-0.7%. During dynamic biceps curls, WBV increased BB EMG activity by 0.6%-0.8% and TB activity by 0.2%-1.0%. The higher WBV amplitude (4 mm) and frequencies (35, 40, 45 Hz) resulted in the greatest increases in EMG activity.
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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 | 0.000 | 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".