Influence of Sudden Changes in Foot Strikes on Loading Rate Variability in Runners
Bibliographic record
Abstract
Foot strike patterns influence vertical loading rates during running. Running retraining interventions often include switching to a new foot strike pattern. Sudden changes in the foot strike pattern may be uncomfortable and may lead to higher step-to-step variability. This study evaluated the effects of running with an imposed and usual foot strike on vertical loading rate variability and amplitude. Twenty-seven participants (16 men and 11 women; age range: 18-30 years) ran on an instrumented treadmill with their usual foot strike for 10 min. Then, the participants were instructed to run with an unusual foot strike for 6 min. We calculated the vertical instantaneous and vertical average loading rates and their variances over 200 steps to quantify vertical loading rate variability. We also calculated the amplitude and variability of the shank acceleration peak using an inertial measurement unit. The vertical loading rate and shank acceleration peak amplitudes were higher when running with a rearfoot strike, regardless of the foot strike conditions (i.e., usual or imposed). The vertical loading rate and shank acceleration peak variability were higher when running with an imposed rearfoot strike than when running with a usual forefoot strike. No differences were found in the vertical loading rate and shank acceleration peak variabilities between the imposed forefoot strike and usual rearfoot strike conditions. This study offers compelling evidence that adopting an imposed (i.e., unusual) rearfoot strike amplifies loading rate and shank acceleration peak variabilities.
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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".