Altering muscle activity in the lower extremities by running with different shoes
Bibliographic record
Abstract
PURPOSE: To provide evidence that lower-extremity muscle activity during running is tuned in response to the loading rate of the impact forces at heel-strike. METHODS: Six runners ran two 30-min trials per week for 4 wk. The trials tested two shoes which differed only in the material hardness of the midsole. The shoes were tested in a randomized sequence. Bipolar surface EMG was recorded from the muscles of the rectus femoris, biceps femoris, medial gastrocnemius, and tibialis anterior. EMG was resolved into time-frequency space using wavelet techniques. EMG was analyzed for the 150 ms time window immediately before heel-strike. RESULTS: The intensity of the EMG and the ratio of the EMG intensity between high and low frequency components both showed significant changes between shoes, subjects, and muscles. Additionally, the intensity ratio showed a significant change over the course of each 30-min run. CONCLUSIONS: Lower-extremity muscle activity used to tune the muscles for the impact task can be altered by changing the material hardness of the shoe. The changes in the EMG frequency ratio suggest that muscle fiber-type recruitment patterns can also be altered by the choice of midsole material.
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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) | 0.002 | 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".