Determining the Threshold of Unweighting in Squat Jumps: A Study on Jump Height and Unweighting Amplitude
Bibliographic record
Abstract
ABSTRACT: Agar-Newman, DJ, Funk, S, Cavin, E, Geneau, MC, Tsai, MC, and Klimstra, M. Determining the threshold of unweighting in squat jumps: a study on jump height and unweighting amplitude. J Strength Cond Res 39(3): 295-299, 2025-Squat jumps (SJs), involving only an upward propulsive phase, are commonly used in athletic assessment and research. Unfortunately detecting an unweighting phase before the upward propulsive phase is typically done subjectively by observing the athlete or inspecting the force-time trace, and there is no clearly established threshold of unweighting for a valid SJ. This reliance on subjectivity to determine a valid SJ has the potential to result in misleading findings or incorrect training interventions. Therefore, the aim of this study was to determine at what threshold of unweighting does SJ height increase. To answer this question, 56 female athletes, mean (±SD) body mass (BM) 76.26 ± 12.40 kg, height 1.68 ± 0.06 m, age 22.23 ± 1.47 years performed 936 SJs, under 4 different external loads. Squat jumps were divided into 6 separate groups based on the amplitude of unweighting relative to BM and an analysis of covariance was run with jump height as the dependent variable, unweighting group as the fixed factor, and external load as a covariate. There was a significant difference in jump height (F (5,930) = 13.65, p < 0.01) between unweighting groups while controlling for external load. Post hoc testing using Dunnett test showed that all SJ unweighting thresholds >2% BM (p < 0.01) resulted in an increased jump height from the threshold of ≤1% BM. Therefore, to maintain the validity of SJs as a measure, a threshold of 2% BM for unweighting amplitude is recommended. Adhering to this threshold will eliminate subjectivity in identifying valid SJs and potentially enable practitioners to automate the process using algorithms.
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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 | 0.002 | 0.004 |
| 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) | 0.001 | 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".