Footwear Traction and Lower Extremity Joint Loading
Bibliographic record
Abstract
BACKGROUND: Traction is influenced by the sole architecture and playing surface, with increases in traction potentially leading to injury. The mechanism as to how or why increased traction could lead to injury remains unknown. PURPOSE: This study was undertaken to determine how shoes of different sole designs and traction influence knee and ankle joint moments. STUDY DESIGN: Controlled laboratory study. METHODS: Traction testing was performed on 2 shoes of varying sole designs (tread vs smooth) using a robotic testing machine. All testing was conducted on a 60-cm x 90-cm piece of sample track surface. Kinematic and kinetic data were then collected on 13 recreational athletes performing running V-cuts in the 2 different shoe conditions. Five trials per condition were collected with reflective markers placed on the right shank and shoe of each participant. Kinematic and kinetic data were collected using an 8-high-speed camera system and force plate. RESULTS: The coefficient of translational traction and the peak moment of rotation were both significantly higher in the tread shoe compared with the smooth shoe (1.00 vs 0.87 and 23.87 N.m vs 16.12 N.m, respectively). The high-traction shoe had significantly higher peak ankle external rotation moments (89.58 N.m vs 80.17 N.m), peak knee external rotation moments (36.23 N.m vs 32.02 N.m), peak knee adduction moments (224.0 N.m vs 186.8 N.m), and knee adduction angular impulse (2.10 Nms vs 1.83 Nms) compared with the low-traction shoe. CONCLUSION: Increased shoe traction significantly increased ankle and knee joint moments during a V-cut. Despite the significant difference in traction, no difference in performance was observed. These changes could have an effect on ankle and knee joint injury. CLINICAL RELEVANCE: Shoes with decreased traction could be used in sports to reduce the joint moments in the knee and ankle and potentially reduce injury without a loss in performance.
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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".