Effects of Different Magnitudes of Whole-Body Vibration on Dynamic Squatting Performance
Bibliographic record
Abstract
The purpose of this study was to examine the effects (a) of different whole-body vibration (WBV) accelerations when applied simultaneously during a set of squats on performance and perceived exertion and (b) of different linear increases and decreases of vibrations during the squats. It is a randomized, crossover experimental design. Undergraduate students (3 female; 16 male) participated. Each participant completed 5 laboratory sessions in this study (4 familiarization and 1 test session). The test session then had each participant complete one 20-second set of dynamics quarter-squats for 5 separate conditions followed by 5 minutes of rest. Squatting was performed at maximum speed from full extension knee with plantar-flexion ankle to a knee angle of 70° (0° = anatomic position) with dorsiflexion ankle. All sets were performed on the WBV platform in random order, where the 5 different conditions were (a) no WBV-sham, (b) 30 Hz (30 Hz low amplitude), (c) 50 Hz (50 Hz high amplitude), (d) 30-50 Hz (increasing frequency from 30 to 50 Hz; 1 Hz per second with high amplitude), and (e) 50-30 Hz (decreasing frequency from 50 to 30 Hz; 1 Hz per second). There was a significant decrease in the mean velocity of squatting performed during the 30- to 50-Hz condition compared with all other conditions (p ≤ 0.05). There were a significantly lower amount of repetitions performed during the 30- to 50-Hz exposure compared with the no-WBV and 30-Hz conditions. There was a significantly lower Rating of Perceived Exertion (RPE) during the 30-Hz condition compared with the no-WBV, 50-Hz, 30-50-Hz, and 50-30-Hz conditions.
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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) | 0.001 | 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 | 0.001 |
| 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".