The influence of compression apparel on soft tissue vibrations during running
Bibliographic record
Abstract
The impact at heel-strike during running initiates soft tissue vibrations in theleg, which were suggested to negatively affect muscle fatigue1 and performance2.Tight fitting compression apparel has previously been shown to influence soft tissuevibration2,3, however, very little is known about the influence of running speed andthe level of compression on muscle vibrations. Therefore, the aim of this study was toinvestigate the influence of different levels of compression and 7 running speeds onmuscle vibrations. 14 trained, male athletes participated in the study. The conditionstested included 3 compression suits with different levels of compression as well astwo non-compressive (control) suits in randomized order. Vertical muscle vibrations ofthe gastrocnemius medialis and the vastus lateralis muscles were measured using askin-mounted accelerometer during treadmill running at speeds ranging from 2.68 to5.36 m*s1. Peak power and damping coefficient of the measured vibration data werecalculated using a wavelet-based method and compared between the suits. Correlationbetween speed and peak power as well as between speed and damping coefficient wasanalyzed for all compression conditions normalized to the control conditions. Peakpower decreased significantly for the gastrocnemius medialis at speeds equal or higherthan 3.58 m*s1 (p < 0.05) but not for the vastus lateralis when wearing compressiveapparel. No significant changes were found in the damping coefficient of either of themuscles. Peak power and damping were significantly affected by running speed whenwearing compression apparel versus control. This study showed that higher levels ofcompression do not systematically reduce muscle vibrations more effectively but thatrunning speed and the investigated muscles are more critical factors. Vibrations in allthree planes of motion and more muscles need to be further investigated.
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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".