Systematic reduction of leg muscle activity throughout a standard assessment of running footwear
Bibliographic record
Abstract
PURPOSE: This study aimed to investigate whether there is a systematic change of leg muscle activity, as quantified by surface electromyography (EMG), throughout a standard running footwear assessment protocol at a predetermined running speed. METHODS: Thirty-one physically active adults (15 females and 16 males) completed 5 testing rounds consisting of overground running trials at a speed of 3.5 m/s. The level of muscle activity from 6 major leg muscles was recorded using surface EMG. The variables assessed were the EMG total intensity as a function of time and the cumulative EMG overall intensity. Systematic effects of the chronological testing round (independent variable) on the normalized EMG overall intensity (dependent variable) were examined using Friedman analysis of variates and post hoc pairwise Wilcoxon signed-rank tests (α = 0.05). RESULTS: There was a systematic reduction in overall EMG intensity for all 6 muscles over the time course of the running protocol (p < 0.001) until the fourth testing round when EMG intensities reached a steady state. The one exception was the biceps femoris muscle, which showed a significant reduction of EMG intensity during the stance phase (p < 0.001) but not the swing phase (p = 0.16). CONCLUSION: While running at a predetermined speed, the neuromuscular system undergoes an adaptation process characterized by a progressive reduction in the activity level of major leg muscles. This process may represent an optimization strategy of the neuromuscular system towards a more energetically efficient running style. Future running protocols should include a familiarization period of at least 7 min or 600 strides of running at the predetermined speed.
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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 | 0.001 | 0.002 |
| 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".