The Association Between Lower Extremity Kinematics And Impact Load During The Drop Vertical Jump Task
Bibliographic record
Abstract
Greater impact loads during running have been associated with risk of injury in runners. However, there are limited studies of impact load during dynamic tasks, such as the drop vertical jump (DVJ), in athletes that participate in sports that require repetitive jumping. Further, an understanding of the kinematic factors that influence impact loads during jumping tasks can provide a basis for modifying high-impact loads in these athletes. PURPOSE: To explore the association between lower extremity (LE) kinematics and impact load during a drop vertical jump (DVJ) task in collegiate athletes. METHODS: This study included 39 Division I collegiate athletes who participated in basketball (22 male) and volleyball (17 female). A 16-camera optical motion capture system was used for all motion capture testing. Reflective markers were placed on anatomical landmarks to define pelvis, thigh, shank, and foot segments and hip, knee, and ankle joints. The DVJ task began with participants stepping off a box, landing with each foot on a separate force plate, followed by a maximal countermovement jump and then a second landing on the force plates. Impact load was measured using average load rate (ALR), the average slope of the vGRF between 20%-80% of the peak vGRF, during the second landing. Maximal hip, knee, and ankle angles in the sagittal and frontal plane were calculated for the second landing. Bivariate correlations were conducted between maximal LE angles and ALR; angles correlated with ALR (P < 0.15) were included in a linear regression analysis to assess the combined influence of LE kinematics on impact load (P < .05). RESULTS: On the left LE, ankle dorsiflexion (p < 0.001), ankle plantarflexion (p < 0.001), and knee abduction (p < 0.006) were significantly associated with ALR. For left ankle movements, a one-degree increase in movement was associated with a decrease in ALR of >2 body weights per second. On the right LE, ankle plantarflexion (p < 0.001) was significantly associated with ALR. A one degree increase in movement was associated with a decrease in ALR of 2 body weights per second. CONCLUSIONS: Findings suggest that ankle kinematics were the kinematic measures most consistently associated with impact load bilaterally. Greater ankle movement was associated with a decrease in ALR.
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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 | 0.001 | 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".