The Effect of an Isometric Hip Muscle Strength Training Protocol on Valgus Angle During a Drop Vertical Jump in Competitive Female Volleyball Players
Bibliographic record
Abstract
Background: Hip muscle weakness is associated with higher peak knee valgus angles (VA) during drop vertical jumps (DVJ) and linked to ACL injury risk. Objective: To determine if isometric strengthening (IST) of the hip extensor, abductor, and external rotator muscle groups would reduce VA exhibited during a DVJ. Methodology: Fourteen female volleyball players (7 training (TG), 7 control (CG), VA≥9˚ during DVJ) participated. Pre- and post-test gluteal, quadriceps and hamstring strength were measured with a digital force gauge. Three-dimensional kinematics were collected during 15 DVJ trials. TG participated in a 6-week IST program that targeted the hip extensor, abductor, and external rotator muscle groups. Two-way mixed ANOVAs compared mean differences of VA and strength. Single-participant analyses examined if athlete-specific adaptations went undetected in the analyses of aggregated data. Results: TG hip extension, abduction, and knee flexion strength increased, respectively, by 20.5%, 27.5% and 23.5% (P<0.05). No group-level changes in VA were detected. Unilateral VA decreased for 5 TG participants, and bilateral VA decreased in 2 TG participants. Conclusions: IST increased isometric hip muscle strength, but its effect on VA is inconclusive based on group-level analyses. Using single-participant designs, future studies should assess IST and/or dynamic resistance/neuromuscular training in a larger sample to determine its effect on ACL injury risk factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".