Altered multi‐muscle coordination patterns in habitual forefoot runners during a prolonged, exhaustive run
Bibliographic record
Abstract
Abstract Introduction: In response to fatigue during an exhaustive treadmill run, forefoot runner's muscles must adapt to maintain their pace. From a neuromuscular control perspective, certain muscles may not be able to sustain the force to meet the run's demands; thus, there may be alternative muscle coordination in the lower extremity that allows for continued running for an extended period of time. The aim of this study was to quantify the change in muscle coordination during a prolonged run in forefoot runners. Methods: Thirteen forefoot runners performed exhaustive treadmill runs (mean duration: 15.4 ± 2.2 min). The muscle coordination of seven lower extremity muscles was quantified using a high‐resolution time–frequency analysis together with a pattern recognition algorithm. Results: The mean EMG intensity for the lateral and medial gastrocnemius muscles decreased with the run (p = 0.02; 0.06). The weight factors of the second principal pattern decrease by 128.01% by the end of run (p = 0.05, Cohen's d = 0.42) representing a relatively greater biceps femoris activation in midstance but smaller midstance rectus femoris, vastus medialis, triceps surae, and tibialis anterior activation. Discussion: These results suggest that forefoot runners cannot sustain plantar flexor activation throughout an exhaustive run and change their muscle coordination strategy as a compensation. Understanding the underlying compensation mechanisms humans use to cope with fatigue will help to inform training modalities to enhance these late stage muscle activation strategies for athletes with the goal of improving performance and reducing injury.
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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 | 0.000 |
| 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) | 0.001 | 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".