The Effects of a 9-Week Hip Focused Weight Training Program on Hip And Knee Kinematics and Kinetics in Experienced Female Dancers
Bibliographic record
Abstract
Increased involvement of the hip musculature during some movements is associated with enhanced performance and reduced injury risk. However, the impact of hip dominant weight training methods on movement strategy has seen limited attention within the literature. The aim of this study was to evaluate if a 9-week hip dominant weight training intervention promotes a more hip dominant movement strategy leading to an improvement in countermovement jump performance. Twenty-two experienced female dancers were recruited and separated into an intervention (age 24.4 ± 6.3 years, body height 165.5 ± 5.8 cm, body mass 65.9 ± 5.6 kg) and a control (age 22.9 ± 5.6 years, body height 163.3 ± 5.4 cm, body mass 57.4 ± 6.8 kg) group. The intervention group participated in a 9-week hip dominant training intervention, which consisted of a wide stance back squat, Romanian deadlift, hip thrusters, and a bent over row. Hip and knee kinematics and kinetics, and countermovement jump performance were assessed pre and post training. Significant interaction effects were found for peak hip joint moment (p = 0.030, η2 = 0.214) and countermovement jump performance (p = 0.003, η2 = 0.356), indicating an increase in peak hip joint moment and countermovement jump performance for the intervention group. Specifically, the intervention group showed a mean increase in jump height of 11.5%. The data show that the use of a hip dominant weight training strategy can improve hip contribution in the propulsion phase of the countermovement jump. Strength and conditioning specialists should incorporate hip dominant weight training exercises to increase hip strength and improve 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".