The Effects of Local Footwear Traction on Ground Reaction Impulse During Sprinting
Bibliographic record
Abstract
During sprinting, the anterior-posterior force can be split into two phases; an initial braking phase followed by a propulsive phase. The cumulative effect of this force, the net horizontal impulse, is determined by integrating the force curve with respect to time. The braking impulse works to slow the athlete down whereas the propulsive impulse speeds the athlete up. The purpose of this study was to determine if and how braking forces could be reduced through manipulating traction and the effect on performance. Previous research results from internal testing showed that braking impulses occur on the lateral portion of sprint shoes, so the first part of the study sought to reduce the traction in this area. Five athletes performed three 10m sprints on a sample track surface, in two different sprint shoe conditions; a control shoe and a shoe with reduced lateral traction. Force plate data (Kistler, 2400Hz) and pressure insole data (Pedar, 100Hz) were used to determine impulse and regional traction. Results showed no significant differences between conditions in terms of horizontal force or impulse. There was also no change in regional utilized traction. No difference was found when comparing traction coefficients of the two groups concluding that available traction between the shoes and track surface was not low enough. Further attempts to reduce available traction included lateral taping of a running shoe as well as lateral and midfoot taping on a lab surface. These modifications appeared to show shifting of where traction was developed under the shoe. They also showed that with decreasing braking impulse, propulsive impulse also decreased. Further testing included removal of primary and secondary traction elements from the sprint shoe followed by adhering a directional traction element that provides minimal traction in terms of braking yet sufficient traction in the propulsive phase.
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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) | 0.002 | 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".