Occurrence of Fatigue Induced by a Whole-Body Vibration Session Is Not Frequency Dependent
Bibliographic record
Abstract
The aim of this study was to determine whether neuromuscular adaptations (magnitude and location) induced by isometric exercise performed on an oscillating platform are dependent on whole-body vibration (WBV) frequency. Eleven young men performed 4 separate fatigue sessions of static squatting exercise at 3 frequencies of WBV (V20, V40, and V60) and 1 session without vibration (V0). Isometric torque and electromyographic activity of the vastus lateralis, rectus femoris, and biceps femoris were recorded during maximal voluntary and evoked contractions of the knee extensor muscles before and after each fatigue session to examine both peripheral and central adaptations. Isometric torque decreased significantly after each of the 4 frequency sessions (V0: -9.4 ± 6.1%, p = 0.003; V20: -8.1 ± 9.9%, p = 0.010; V40: -11.9 ± 12.7%, p = 0.011; and V60: -7.8 ± 9.2%, p = 0.001, respectively), but this reduction was not significantly different between frequencies. The torque produced by evoked contraction significantly decreased from pre-exercise values after each session (V0: -14.9 ± 15.6%, p = 0.012; V20: -15.8 ± 16.4%, p = 0.010; V40: -21.0 ± 14.3%, p = 0.004; and V60: -17.3 ± 11.6%, p = 0.005, respectively); however, there was no effect of vibration frequency. In both conditions, the maximal voluntary contraction torque reduction observed was mainly attributable to peripheral fatigue and was not because of central modifications of the neuromuscular system. The present study demonstrates that the frequency of vibration does not significantly influence the magnitude and location of neuromuscular fatigue, suggesting that adding WBV to static squat exercise (on a vertically oscillating platform) does not provide an additional training stimulus.
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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) | 0.002 | 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".