Reach Height and Jump Displacement: Implications for Standardization of Reach Determination
Bibliographic record
Abstract
Vertical jump performance is often assessed using jump-and-reach tests. The exact procedure used for determining standing reach height and jump height has a large effect on the resultant displacement. The purpose of this investigation was to determine the influence of 4 methods of standing reach height measurement and Vertec jump height measurement against 2 force plate methods of jump displacement determination (impulse and flight-time methods). Fifteen men with various training backgrounds performed 2 each of countermovement, restricted (no arm swing) and static start vertical jumps. Reach height was determined using 4 methods; either a 1- or overlapped 2-hand reach, flat footed or with plantar flexion. All jumps were performed on a force platform. The best jump of each type based on Vertec displacement was used for analysis. Repeated-measures of analysis of variance for each jump type was used for analysis with Bonferroni post hoc for pairwise comparisons of jump measurement style. All jump displacements for similar types were significantly intercorrelated with a minimum r-value of 0.84. Impulse vs. flight time was the only pairwise comparison of measurement type for which similar values were noted. The one-hand reach with plantar flexion was the method of reach that was closest to the impulse and flight-time methods, and thus should be the preferred choice when using jump-and-reach tests to determine jump displacement. In all cases, the Vertec overestimates the displacement of the COM based on force plate methods. When comparing groups of individuals from different data sets, one must consider both the method of reach height (if performed) and jump displacement to make valid comparisons. If plantar flexion with a 1-hand reach is not used during reach measurement, jump displacement will be erroneously high.
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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 | 0.387 | 0.584 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".