Verbal Instructions Acutely Affect Drop Vertical Jump Biomechanics—Implications for Athletic Performance and Injury Risk Assessments
Bibliographic record
Abstract
Biomechanical quantities acquired during the drop vertical jump (DVJ) are used in the assessment of athletic performance and injury risk. The objective was to examine the impact of different verbal instructions on spatiotemporal, kinematic, and kinetic variables commonly included in such assessments. Ten men and 10 women from local varsity and club volleyball, basketball, figure skating, and track and field teams volunteered to participate. The athletes performed DVJs after given instructions to minimize ground contact time (CT), maximize jump height (HT), and synchronously extend the lower extremity joints (EX). Between the CT, HT, and EX conditions, body segment and joint angles were compared together with characteristics of vertical ground reaction force (GRF), whole-body power output, stiffness, and center-of-mass displacement time histories. Verbal instructions were found to influence nearly all of the spatiotemporal, body segment and joint kinematic, and kinetic variables that were statistically analyzed. Particularly noteworthy was the finding that athletic performance indices (e.g., jump height, power output, vertical stiffness, and reactive strength index) and lower extremity injury risk markers (e.g., peak vertical GRF and frontal plane knee angle) were significantly different (p ≤ 0.05) between the CT, HT, and EX conditions. The findings of this study suggest that verbal instructions should be controlled and/or clearly documented when using the DVJ to assess athletic performance potential and injury risk. Moreover, practitioners who devise performance enhancement and injury prevention strategies based on DVJ assessments are advised to consider that "coaching" or "cueing" during the task execution could impact conclusions drawn.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".