The Effect of Trunk Stability Training on Vertical Takeoff Velocity
Bibliographic record
Abstract
STUDY DESIGN: Randomized controlled trial with repeated measures. OBJECTIVES: To determine the effect of trunk stability training on vertical takeoff velocity. BACKGROUND: Trunk stability training is commonly used in sports training programs; however, the effects of stability training on performance enhancement are not known. Trunk stability training may provide a more stable pelvis and spine from which the leg muscles can generate action, may better link the upper body to the lower body, or may enhance leg muscle activation, thus promoting optimal force production during sporting activities such as a vertical jump. METHODS AND MEASURES: Fifty-five athletes were randomly assigned to 1 of 4 training groups: trunk stability (TS), leg strength (LS), trunk stability and leg strength (TL), and control (CO). Subjects were tested 3 times: at pretraining, after 3 weeks of training, and after 9 weeks of training. A repeated-measures analysis of covariance (ANCOVA) was used to examine differences among groups for vertical takeoff velocity measured indirectly using a force plate. Pretraining takeoff velocity and body mass were used as covariates. RESULTS: After 3 and 9 weeks, the training groups were not different from each other. After 9 weeks of training, all 3 training groups had a greater takeoff velocity than the control group (P<.05). After 3 weeks of training only the TS group had a greater takeoff velocity than the control group (P<.05). Only the TL group increased significantly in vertical takeoff velocity between the third- and ninth-week testing periods (P<.05). CONCLUSIONS: Nine weeks of trunk stability training was similarly effective in enhancing vertical takeoff velocity as leg strength training or the combination of trunk stability and leg strength training.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".