Carryover effects of treadmill-based footstrike modification gait retraining on overground running biomechanics
Bibliographic record
Abstract
Gait retraining has gained attention as a practical intervention to improve running biomechanics and reduce injury risk. This study investigated the carryover effects of treadmill-based gait retraining on footstrike pattern, cadence, and vertical loading rate during overground running. Twelve recreational runners who habitually adopted a rearfoot strike (RFS) pattern participated in an eight-session treadmill-based gait retraining programme aimed at footstrike transition to a midfoot strike (MFS). The programme utilised real-time visual feedback and progressively reduced guidance to encourage sustainable biomechanical adaptations. Biomechanical assessments were conducted on both treadmill and overground surfaces before and after training. Results demonstrated significant reductions in footstrike angle (FSA) (95%CI interval -13.9 to -5.1; Cohen's d = 2.22), vertical loading rate (95%CI -0.49 to -41.56; Cohen's d = 0.76), and increased cadence (95%CI 2.47 to 14.06; Cohen's d = 0.87) during treadmill running. However, only the reduction in FSA transferred to overground running, with only 33% of participants exhibiting an MFS pattern during overground running after training, suggesting limited carryover of other biomechanical changes and highlighting discrepancies between trained and untrained conditions. These findings underscore the potential of gait retraining to modify running biomechanics while emphasising the need for overground-specific protocols to ensure effective transfer of improvements to real-world running environments.
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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 | 0.003 |
| 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".