Effect of Strength Training on Jump Landing Biomechanics
Bibliographic record
Abstract
Previous research suggests that neuromuscular training programs can be effective in preventing anterior cruciate ligament (ACL) injury, as well as the movement patterns associated with injury such as landing awkwardly from a jump. However it is not known which components of the exercise programs are most effective in eliciting improvement. PURPOSE: To determine whether a leg focused strength training program would result in a safer jump landing movement pattern as measured by the Landing Error Scoring System (LESS) compared to a placebo control group who strength trained their arms. METHODS: Thirty-six girls aged 10-14 years who were involved in organized sport were randomly assigned to a 12 week strength training program focused on the legs (n=19) or arms (n=17). The girls were blinded as to which was the experimental group. Athletes trained twice a week for an hour each time with free weights, under the supervision of a certified personal trainer and/or a physical therapist. The girls in the leg group performed squats, lunges, side-lying hip abduction and supine hamstring curls on a ball. Exercises for the active control group included chest press, shoulder press, bicep curls and dumbbell rows. For the main outcome variable (LESS score), all participants were videotaped from the sagittal and frontal views while jumping off a 30 cm high box, before and after the exercise intervention. When all testing for all participants was complete, each videotaped jump was scored by a blinded assessor using the LESS and the mean of 3 trials for each athlete was used in analysis. RESULTS: There was no difference in LESS score between the leg group and control group post-intervention [leg group adjusted mean LESS score, 6.0 ± standard error (SE) 0.3 vs. control group adjusted mean 6.1 ± SE 0.3; p = 0.85] when controlling for baseline LESS score. CONCLUSION: Twelve weeks of leg focused strength training did not lead to a significantly better jump landing score in young female athletes compared to the control group. Future studies should explore other resistance training procedures to determine the elements that are required to improve jump landing performance in young female athletes. Supported by the Canadian Institutes of Health Research; Research Manitoba; and the Canadian Athletic Therapists Association
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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) | 0.003 | 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".