The effects of shoe upper construction on mechanical ankle joint work during lateral shuffle movements
Bibliographic record
Abstract
Lateral shuffles are common movements in sports and are facilitated by the hip, knee, and ankle joints. Shoe uppers can change ankle kinetics during walking and running. However, it is not known how shoe upper modifications affect ankle kinetics during shuffling. The purpose of this study was to investigate the effects of shoe upper construction on mechanical ankle joint work during shuffling. It was hypothesized that a shoe with a reinforced upper will result in decreased negative ankle joint work. Twenty participants performed Maximal (MLST) and Submaximal Lateral Shuffle Tests (90% of MLST) in footwear with a minimal (MU) and reinforced upper (RU). Ground reaction forces and ankle kinematics were collected to compute ankle joint work. Performing lateral shuffles in the RU condition resulted in significantly reduced positive (MU: 0.62 ± 0.16 J/kg, RU: 0.55 ± 0.16 J/kg; p = 0.001, d = 0.44) and negative (MU: −0.60 ± 0.20 J/kg, RU: −0.53 ± 0.19 J/kg; p = 0.004, d = 0.41) ankle work. A decrease in positive and negative work could be a performance benefit, enabling the athlete to perform the same movement with a lower energy cost. More extreme upper interventions may yield even larger performance benefits.
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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 | 0.001 |
| 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".