Soft-tissue vibration and damping response to footwear changes across a wide range of anthropometrics in running
Bibliographic record
Abstract
Different factors were shown to alter the vibration characteristics of soft-tissue compartments during running. Changing pre-heel strike muscle activation or changing footwear conditions represents two possibilities to influence the vibration response via frequency shift or altered damping. Associated with the study of muscle pre-tuning is the difficulty in quantifying clean experimental data for the acceleration of soft-tissue compartments and muscle activities in heterogeneous populations. The purpose of this study was to determine the vibration and pre-tuning response to footwear across a wide range of participants during running and establish and describe groups formed according to the damping coefficient. 32 subjects were used for further analysis. The subjects ran at a self-selected speed (5 min) on a treadmill in two different shoes (soft & hard), while soft-tissue accelerations and muscle activation at the gastrocnemius medialis were quantified. Damping coefficients, total muscle intensity and dominant vibration frequencies were determined. Anthropometrics and skinfold measurements of the lower limbs were obtained. According to the damping coefficient response to the footwear intervention, three groups were formed, with most runners (n = 20) showing less damping in the hard shoe. Total muscle intensity, anthropometrics, and dominant vibration frequency across footwear were not different for these three groups. Most runners (84.4%) used the strategy of adjusting the damping coefficients significantly when switching footwear. Despite damping being the preferred adjustment to changes in footwear, muscle pre-tuning might not be the only mechanism to influence damping as previously suggested. Future studies should focus on the subject-specific composition of soft-tissue compartments to elucidate their contribution to vibrations.
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How this classification was reachedexpand
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".