Predictors of pelvic acceleration during treadmill running across various stride frequency conditions
Bibliographic record
Abstract
Pelvic running injuries often require extensive rehabilitation and pelvic girdle pain is a barrier to running engagement in population sub-groups, such as perinatal women. However, exploration into how external pelvic loading may be altered during running is limited. This study assessed which biomechanical variables influence changes in external peak pelvic acceleration during treadmill running, across various stride frequency conditions. Twelve participants (7 female, 5 male) ran (9 km∙h−1) at their preferred stride frequency, and at ± 5% and ± 10% of their preferred stride frequency. Coordinate and acceleration data were collected using a motion capture system and inertial measurement units. Linear mixed models assessed peak tibial acceleration, displacement from hip to knee and ankle, contact time, and stride frequency as predictors of peak pelvic acceleration. Stride frequency and contact time interacted to predict peak vertical (p = .006) and resultant (p = .009) pelvic acceleration. When modelled, short contact times and low stride frequencies produced higher peak vertical (p = .007) and resultant (p = .016) pelvic accelerations than short contact times and average, or high stride frequencies. Increasing contact time, or increasing stride frequency at shorter contact times, may therefore be useful in reducing pelvic acceleration during treadmill running.
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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 | 0.002 |
| 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".