Effects of Drop-height and Surface Instability on Jump Performance and Knee Kinematics
Bibliographic record
Abstract
The purpose of this study was to examine the combined effects of drop-height and surface condition on drop jump (DJ) performance and knee joint kinematics. DJ performance, sagittal and frontal plane knee joint kinematics were measured in jump experienced young male and female adults during DJs on stable, unstable and highly unstable surfaces using different drop-heights (20, 40, 60 cm). Findings revealed impaired DJ performance (Δ5-16%; p<0.05; 1.43≤d≤2.82), reduced knee valgus motion (Δ33-52%; p<0.001; 2.70≤d≤3.59), and larger maximum knee flexion angles (Δ13-19%; p<0.01; 1.74≤d≤1.75) when using higher (60 cm) compared to lower drop-heights (≤40 cm). Further, lower knee flexion angles and velocity were found (Δ8-16%; p<0.01; 1.49≤d≤2.38) with increasing surface instability. When performing DJs from high (60 cm) compared to moderate drop-heights (40 cm) on highly unstable surfaces, higher knee flexion velocity and maximum knee valgus angles were found (Δ15-19%; p<0.01; 1.50≤d≤1.53). No significant main and/or interaction effects were observed for the factor sex. In conclusion, knee motion strategies were modified by the factors 'drop-height' and/or 'surface instability'. The combination of high drop-heights (>40 cm) together with highly unstable surfaces should be used cautiously during plyometrics because this may increase the risk of injury due to higher knee valgus stress.
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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) | 0.002 | 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".