Effects of Vibrations on Gastrocnemius Medialis Tissue Oxygenation
Bibliographic record
Abstract
PURPOSE: Whole-body vibrations are known to affect muscle activity and tissue oxygenation, but some energetic aspects are still poorly understood. This study investigates the effects of whole-body vibration on gastrocnemius muscle oxygen utilization rate and tissue oxygenation dynamics during exercise. METHODS: The effects of vibration on gastrocnemius medialis muscle oxygenation were investigated during a dynamic exercise on a sample of 16 active male subjects (age = 26.3 ± 5.1 yr, mass = 71.2 ± 4.8 kg (mean ± SD)). Both arterially occluded (AO) and nonoccluded (N/O) conditions were investigated. Tissue oxygenation was monitored with a near-infrared spectrometer. Oxygen utilization rate and tissue oxygenation recovery were computed as the slopes of the regression line of the oxygenation decay and recovery, respectively. A fast Fourier transform (FFT) was used to determine the frequency spectrum of the oxygen saturation data. EMG activity was monitored using bipolar EMG electrodes. A windowed root mean square analysis was used to monitor the amplitude of the EMG signal. RESULTS: A statistically significant increase of 15% (P < 0.05) in oxygen utilization rate was found for the vibration condition in the AO leg but not in the N/O leg. The oxygenation recovery rate for the vibration condition was 34% higher (P < 0.05) than that for the control condition. A low-frequency periodic oscillation (T ≈ 10 s) in the tissue oxygenation data was determined from the FFT spectrum. A statistically significant decrease in the oscillation frequency was noticed for the vibration condition compared with the control. CONCLUSIONS: Vibrations increased the oxygen utilization rate during a dynamic exercise. The oxygenation recovery rate increased with vibrations. The low-frequency oscillation of the oxygenation was attributed to the periodic changes in tissue blood flow, and this seems to be influenced by vibrations.
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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".