Changes in joint coupling and variability during walking following tibialis posterior muscle fatigue
Bibliographic record
Abstract
BACKGROUND: The tibialis posterior muscle is believed to play a key role in controlling foot mechanics during the stance phase of gait. However, an experiment involving localised tibialis posterior muscle fatigue, and analysis of discrete rearfoot and forefoot kinematic variables, indicated that reduced force output of the tibialis posterior muscle did not alter rearfoot and forefoot motion during gait. Thus, to better understand how muscle fatigue affects foot kinematics and injury potential, the purpose of this study was to reanalyze the data and investigate shank, rearfoot and forefoot joint coupling and coupling variability during walking. METHODS: Twenty-nine participants underwent an exercise fatigue protocol aimed at reducing the force output of tibialis posterior. An eight camera motion analysis system was used to evaluate 3 D shank and foot joint coupling and coupling variability during treadmill walking both pre- and post-fatigue. RESULTS: The fatigue protocol was successful in reducing the maximal isometric force by over 30% and a concomitant increase in coupling motion of the shank in the transverse plane and forefoot in the sagittal and transverse planes relative to frontal plane motion of the rearfoot. In addition, an increase in joint coupling variability was measured between the shank and rearfoot and between the rearfoot and forefoot during the fatigue condition. CONCLUSIONS: The reduced function of the tibialis posterior muscle following fatigue resulted in a disruption in typical shank and foot joint coupling patterns and an increased variability in joint coupling. These results could help explain tibialis posterior injury aetiology.
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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) | 0.001 | 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".