Vertical Takeoff Acceleration As A Predictor Of Single Leg Jump Performance
Bibliographic record
Abstract
Single leg jump tests are important assessment tools for the evaluation of lower extremity joint function after sports injury. The maximum height or distance achieved can indicate readiness to return to sport after injury or predict performance in some sports. Jump height and distance are often measured in lab settings using force plates. Inertial measurement units (IMUs) are a more practical tool that can be used in clinical and field settings to measure acceleration. To maximize jump height and distance an individual must accelerate their mass vertically. However, it is not known if vertical acceleration at takeoff can predict single leg jump height and distance in a healthy recreational population. PURPOSE: To investigate whether vertical takeoff acceleration (VTA) can predict single leg jump distance and height in a healthy recreational population. METHODS: Healthy participants free of lower extremity injuries completed single-leg jumps for distance (SLJD) and for height (SLJH) in neutral cushioned running shoes with a pair of insole-embedded IMUs. Participants were required to perform each jump three times, aiming to maximize their jump height and distance. VTA was measured with the insole-embedded IMUs at 416 Hz. Primary outcomes were the average jump height and distance over the three jumps. A simple linear regression analysis was conducted to investigate the predictive relationship between VTA and single-leg jump for distance and height. RESULTS: One hundred and eighty-seven participants (89 females, 98 males; 41.8 ± 12.07 years, 69.87 ± 11 kg) completed the jump assessment. The linear regression analysis revealed a strong positive linear relationship between the average vertical takeoff acceleration and SLJH performance (r2 = 0.74, p < 0.01), but no significant association between the average vertical takeoff acceleration and SLJD performance (r2 < 0.01, p = 0.15). There was a positive associations between normalized VTA and SLJH ( r2 ≥ 0.5, p < 0.01), but not for SLJD. CONCLUSION: The vertical takeoff acceleration can be a reliable metric for predicting single leg jump height, but not single leg jump distance in clinical settings. As the SLJD test is a combination of vertical and horizontal trajectories, performance on this test may be better predicted by horizontal acceleration.
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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 | 0.002 |
| 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".