In-Season Weightlifting Training Exercise in Healthy Male Handball Players: Effects on Body Composition, Muscle Volume, Maximal Strength, and Ball-Throwing Velocity
Bibliographic record
Abstract
This study assessed the impact of 8 weeks biweekly in-season weightlifting training on the strength, throwing ability, and body composition of healthy male handball players. Twenty players (age: 21.2 ± 0.7 years, height: 1.83 ± 0.08 m, body mass: 83.3 ± 7.5 kg, body fat: 13.2 ± 1.4%, upper limb muscle volume: 3.16 ± 0.16 L) were randomly allocated between experimental (EG) and control (CG) groups. Measures of one-repetition maximal strength included bench press, pull-over, snatch, and clean and jerk. Throwing velocity was investigated by standing, running, and jump throws, and the power of the upper limbs was estimated from the total distance of a 3-kg medicine ball overhead throw. Muscle volumes were estimated anthropometrically. Training sessions comprised 3–4 sets of explosive weightlifting exercise at 75%–90% of 1RM (repetition maximum). Significant interaction effects (time x group) were found for all strength and throwing variables, ranging from ηp2 = 0.595 (pull-over) to ηp2 = 0.887 (medicine ball throw), with the largest between-group difference (more than 40%, Δd = 6.65) and effect size (d = 6.44) for the medicine ball throw, and the smallest (about 23%, Δd = 1.61) for the standing shot performance. Significant interaction effects were also detected for all anthropometric parameters (body mass: ηp2 = 0.433; body fat: ηp2 = 0.391; upper limb muscle volume: ηp2 = 0.920, with an almost 20% gain of muscle volume). It can be concluded that 8 weeks of biweekly in-season weightlifting training yielded substantial increases of muscle volume, maximal strength of the upper limbs, and ball throwing velocity in healthy handball players relative to their standard training program.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".