Reliability of Soft Tissue Vibration Measurement and Number of Steps Demanded during Treadmill Running
Bibliographic record
Abstract
The present study aims to determine the test-retest reliability of the input signal (INPUT) of foot impact and soft tissue vibration (STV) of the lower limb muscles during treadmill running. Twenty-six recreational runners participated in three running trials at constant velocity (10 km/h) within two days. The INPUT and STV of gastrocnemius medialis (GAS) and vastus lateralis (VL) were extracted from 100 steps measured by three triaxial accelerometers. The Intraclass Correlation Coefficient (ICC) was calculated to determine the Intra-trial and Inter-day reliability of the different variables. Intra-trial reliability results indicated that most of the INPUT and GAS STV parameters, except for damping coefficient and setting time, have good to excellent reliability (0.75 < ICC < 0.9) from the beginning of the run (10 steps) to the end. In contrast, only 4 VL STV parameters showed good reliability. Furthermore, inter-trial reliability measured on day one showed that the number of reliable parameters reduced, especially for VL STV, and more steps were required (20 < steps < 80) to achieve good reliability. Inter-day reliability results showed that only one VL STV parameter reached good reliability. Therefore, the present results show that the measurement of the foot impact and the calf muscle vibrations present a good to excellent reliability measured on a single trial and two trials carried out on the same day. The reliability of these parameters remains good when comparing two days of experimentation. We recommend measuring impact and STV parameters during treadmill running in the same session.
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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 | 0.002 | 0.000 |
| 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) | 0.000 | 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".