A Novel Cutting Technique and Resistance Training Intervention to Improve Movement Patterns in Youth Female Handball Players
Bibliographic record
Abstract
OBJECTIVE: To assess whether a 9-week intervention combining cutting technique retraining and hip/calf resistance exercises reduced knee abduction moment (KAM) in young female handball players. STUDY DESIGN: Controlled laboratory study. METHODS: Forty female players (aged 15-18 years) were assigned to an intervention group (IG; n = 20) or control group (CG; n = 20). KAM during sidestep cutting and isometric strength were assessed preintervention and postintervention. The IG completed a twice-weekly program integrated into team practice. RESULTS: Group × time interaction was not significant for KAM ( P = .366) or foot strike angle (FSA; P = .171). Both groups reduced KAM ( P = .002; IG: 0.68 ± 0.16 to 0.59 ± 0.21 Nm/kg*m; CG: 0.68 ± 0.31 to 0.53 ± 0.22 Nm/kg*m) and increased FSA ( P = .040). Knee valgus angle showed no significant interaction ( P = .462) or main effects ( P = .148, P = .080). There was no interaction for hip external rotation, hip abduction, or ankle plantarflexion strength ( P≥.05). Hip abduction strength increased over time ( P = .046) without group differences. CONCLUSION: The intervention did not significantly improve KAM or strength versus standard training, though trends suggest possible effects on cutting mechanics. JOSPT Open 2025;3(4):473-482. Epub 20 August 2025. doi:10.2519/josptopen.2025.0144
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".