Vibration Training: An Overview of the Area, Training Consequences, and Future Considerations
Bibliographic record
Abstract
The effects of vibration on the human body have been documented for many years. Recently, the use of vibration for improving the training regimes of athletes has been investigated. Vibration has been used during strength-training movements such as elbow flexion, and vibration has also been applied to the entire body by having subjects stand on vibration platforms. Exposure to whole-body vibration has also resulted in a significant improvement in power output in the postvibratory period and has been demonstrated to induce significant changes in the resting hormonal profiles of men. In addition to the potential training effects of vibration, the improvement in power output that is observed in the postvibratory period may also lead to better warm-up protocols for athletes competing in sporting events that require high amounts of power output. These observations provide the possibility of new and improved methods of augmenting the training and performance of athletes through the use vibration training. Despite the potential benefits of vibration training, there is substantial evidence regarding the negative effects of vibration on the human body. In conclusion, the potential of vibration treatment to enhance the training regimes of athletes appears quite promising. It is essential though that a thorough understanding of the implications of this type of treatment be acquired prior to its use in athletic situations. Future research should be done with the aim of understanding the biological effects of vibration on muscle performance and also the effects of different vibration protocols on muscle performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".