Addition of Glute-Ham-Gastroc Raise to a Resistance Training Program: Effect on Jump Propulsion and Landing
Bibliographic record
Abstract
Chiu LZF, Yaremko A, and vonGaza GL. Addition of glute-ham-gastroc raise to a resistance training program: effect on jump propulsion and landing. J Strength Cond Res 31(9): 2562-2571, 2017-Exercises such as squats and cleans are commonly used in resistance training programs to enhance athletic performance. However, these exercises may not effectively train the gastrocnemius, an important muscle for energy generation and absorption. The purpose of this research was to examine the effects of adding glute-ham-gastroc raise exercise to target the gastrocnemius to a traditional resistance training program involving squats and cleans. Vertical jump height, weight-bearing ankle dorsiflexion, and jump propulsion and landing mechanics were examined before and after an 8-week training intervention in female youth volleyball players. Approach (with: [INCREMENT] = 2.6 ± 1.7 cm; 90% confidence interval [CI] [1.8-3.6 cm] vs. without: [INCREMENT] = 1.8 ± 1.9 cm; 90% CI [0.8-2.8 cm]) and standing (with: [INCREMENT] = 2.7 ± 1.7 cm; 90% CI [1.7-3.6 cm] vs. without: [INCREMENT] = 1.6 ± 1.5 cm; 90% CI [0.8-2.4 cm]) vertical jump height increased more in the group performing glute-ham-gastroc raise. Weight-bearing ankle dorsiflexion increased when glute-ham-gastroc raise was included (left: [INCREMENT] = 4.1 ± 4.1°; 90% CI [1.9-6.4°] and right: [INCREMENT] = 4.1 ± 3.9°; 90% CI [1.9-6.2°]) but did not appear to change with resistance training only (left: [INCREMENT] = 1.4 ± 4.5°; 90% CI [-1.0 to 3.9°] and right: [INCREMENT] = 2.5 ± 4.4°; [-0.3 to 4.5°]). No discernible differences were observed for changes in jump propulsion and landing mechanics between groups. Glute-ham-gastroc raise may have a beneficial effect with young athletes when added to squat- and clean-based resistance training programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".