Peak Impact Accelerations In Elite Female Runners: Super Shoes Vs. Female-Specific Shoes
Bibliographic record
Abstract
Women are underrepresented in running footwear research, highlighting the need for more detailed exploration of the performance of footwear designed specifically for women. Peak impact accelerations offer insights into the biomechanical stress experienced during running and the effectiveness of footwear cushioning. These factors may also influence both running economy and injury risk. PURPOSE: To compare the peak impact accelerations measured at the sacrum and tibia in elite female runners wearing female-specific shoes and supershoes. METHODS: Nineteen elite female runners (age: 29.69 ± 7.09 years; BMI 19.74 ± 1.62 kg/m2; average 10 km personal best: 35:59) completed eight 5-minute intervals at 14 km/h on an instrumented treadmill with 5-minutes rest in-between. Participants wore each shoe for two intervals in a randomized and mirrored order (ABCDDCBA). Inertial measurement units (IMUs) were attached to the distal portion of the tibia on both legs and on the sacrum at the level of S2. Raw acceleration data collected at 1125 Hz were processed using a 4th-order Butterworth filter with a low-pass frequency of 75 Hz, implemented through custom MATLAB code. The average peak acceleration values in the final 2 minutes of each 5-minute trial were identified using a custom MATLAB script. A two-way ANOVA analyzed the effects of sensor location and shoe type on the mean peak accelerations with post-hoc analyses for any significant main effects. RESULTS: A significant main effect was observed for sensor location (p < 0.001), indicating higher peak accelerations in the tibial sensors (right: 10.64 ± 1.17 g, left: 11.58 ± 1.17 g) compared to the sacral sensor (5.13 ± 1.17 g). However, no significant main or interaction effects were observed between shoe types. CONCLUSIONS: There were no differences in peak impact accelerations between supershoes and female-specific shoes. Overall, our results advance the understanding of female-specific footwear and provide a foundation for future research aiming to refine and optimize footwear design for enhanced running performance and injury prevention.
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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) | 0.003 | 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".