The Validity of Applying a Simple Three-Factor Computational Model to Calculate Force, Power, and Speed Using Hexagonal Bar Jumps
Bibliographic record
Abstract
Abstract Agar-Newman, DJ, Tsai, MC, and Klimstra, M. The validity of applying a simple three-factor computational model to calculate force, power, and speed using hexagonal bar jumps. J Strength Cond Res 36(8): 2108–2114, 2022—The development of athlete specific force–speed profiles can be accomplished through testing ballistic movements, enabling athlete comparisons and to direct training interventions. However, field-based assessments relying on the squat jump or countermovement jump may lack specificity for some sports or be contraindicated for some athletes. Therefore, the purpose of this study was to assess the validity of a three-factor computational model using system mass, push-off distance, and jump height to calculate force, speed, and power for the hexagonal bar (hex-bar) jump. Twenty-one university varsity rowing athletes (12 females and 9 males, 20.40 ± 2.60 years, 78.56 ± 13.68 kg, 1.77 ± 0.08 m, and strength training history of 3.57 ± 2.69 years) were purposefully sampled. Testing consisted of jumps at loads starting at 28.55 kg and increasing by 10-kg increments to 78.55 kg or until technical failure occurred. Validity was assessed by comparing the three-factor computational model to the criterion force–time measures from a force plate. The results show force (mean bias = 85.38 N, SE = 5.41, 95% confidence limit 1,576.85–1,598.19), speed (mean bias = 0.00 m·s −1 , SE = , 95% confidence limit 0.72–0.72), and power (mean bias = 73.36 W, SE = 3.90, 95% confidence limit 1,166.61–1,181.97) can be computed using a three-factor computational model using the hex-bar jump. In conclusion, jump height from a hex-bar jump can be used with a simple three-factor computational model to calculate valid measures of force, speed, and power. This allows practitioners in the field to use a movement that may be more sport-specific or safe, to calculate kinetic and kinematic measures without encountering the issues of cost and portability associated with force plates.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".