Vibration Training: An Overview of the Area, Training Consequences, and Future Considerations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it