Effects of Knee and Ankle Braces on Lower Limb Kinematics During Jump-Heading-Landing in Professional Soccer Players Following ACL Reconstruction
Bibliographic record
Abstract
Objective This study investigated the effects of knee and ankle braces on lower limb kinematics during a jump-landing task incorporating heading in elite soccer players with a history of anterior cruciate ligament reconstruction (ACLR).Methods Twelve male soccer players with ACLR and 12 healthy controls performed the task under three experimental conditions: without braces (WS), with a knee brace (KS), and with both knee and ankle braces (KAS).Kinematic data were collected using the Vicon motion analysis system, and hip, thigh, knee, and ankle angles were analyzed across five jump-landing phases. ResultsThe control group exhibited greater hip flexion during the take-off and peak phases in the WS condition (P=0.096,P=0.025).The ACLR group demonstrated greater hip flexion during take-off in the WS condition (P=0.035).Within-group comparisons revealed no significant differences during the landing and post-landing phases.Between-group comparisons showed that the ACLR group exhibited significantly greater pelvic tilt across all conditions (WS, KS, KAS) compared to controls.Additionally, the ACLR group had greater hip flexion during landing in the WS condition (P=0.033) and greater ankle plantar flexion in the KS condition (P=0.052)compared to controls.In the post-landing phase, the ACLR group exhibited greater hip flexion in the WS and KAS conditions (P=0.038).Conclusion Knee and ankle braces can alter lower limb joint movement patterns in ACLR athletes, which may help reduce injury risk.However, they may also impose movement restrictions that could impact athletic performance.These findings underscore the importance of balancing injury prevention with performance optimization when using braces in soccer players with ACLR.
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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) | 0.001 | 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".