Longitudinal Effects of Sex-Based Shoe Design on the Biomechanics of Basketball Specific Movements
Bibliographic record
Abstract
Basketball's high-intensity, multi-directional nature results in twice as many lower extremity (LE) injuries in females compared to males due to anthropometric and movement differences. Rising popularity of women's basketball has led to new designs of shoes specifically for females, though the benefits are yet to be proven. Additionally, shoe degradation has been linked to altered LE biomechanics and reduced cushioning. This study aimed to quantify biomechanical demands of basketball movements in female athletes wearing female-designed (FS) versus unisex-designed (US) basketball shoes. Twenty female basketball players performed four common maneuvers—cuts, jumps (CMJ), jogs, and sprints—in both shoe types across two sessions separated by six weeks. Participants wore the shoes for about one hour, three times per week between sessions. Ground reaction forces (GRFs) and tibial shock (TS) were measured using an embedded force plate and accelerometer, respectively. Participants provided subjective ankle stability (PAS) ratings via a 4-point Likert scale. Shoe material hardness testing was conducted via Shore-A durometer. Initial results showed increased TS and anterior-posterior GRFs with higher PAS during cutting in the FS compared to the US shoe. Long-term usage of both shoes resulted in increased TS but decreased take-off velocity during CMJs, indicating a possible decline in performance and increased LE injury risk. Future developers of FS shoes should modify the shock absorption technology to increase force dissemination and mitigate TS with the increased feeling of stability, allowing athletes to put in full effort without worry. All basketball shoe developers should improve longevity of the insoles. This study contributes to limited research on female basketball biomechanics, suggesting that improved footwear design for females may minimize injuries and enhance 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 | 0.001 | 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".