The Effect of Heavy- vs. Moderate-Load Training on the Development of Strength, Power, and Throwing Ball Velocity in Male Handball Players
Bibliographic record
Abstract
The aim was to compare the effect of 2 differing 10-week resistance training programs on the peak power (PP) output, muscle volume, strength, and throwing velocity of the upper limbs in handball players during the competitive season. The subjects were 26 men (age 20.0 +/- 0.6 years, body mass 85.0 +/- 13.2 kg, height 1.86 +/- 0.06 m, and body fat 13.7 +/- 2.4%). They were randomly assigned to 1 of 3 groups: control (C; n = 8), heavy resistance (n = 9), or moderate resistance (MR; n = 9) training, performed twice a week. A force-velocity test on an appropriately modified Monark cycle ergometer determined PP. Muscle volumes were estimated using a standard anthropometric kit. One-repetition maximum (1RM) bench press (1RMBP) and 1RM pull-over (1RMPO) scores assessed arm strength. Handball throwing velocity was measured with (TR) and without run-up (TW). Both training programs enhanced absolute PP relative to controls (p < 0.05), although differences disappeared if PP was expressed per unit of muscle volume. Heavy resistance-enhanced 1RMBP and 1RMPO compared to both MR (p < 0.01 and p < 0.05, respectively) and C (p < 0.001 for both tests). Heavy resistance also increased TR and TW compared to C (p < 0.01 and p < 0.05, respectively). Moderate resistance increased only TR compared to C (p < 0.01). Thus, during the competitive season, the PP, 1RMBP, 1RMPO, and TW of male handball players were increased more by 10 weeks of bench press and pull-over training with suitably adapted heavy loads than with moderate loads. It would seem advantageous to add such resistance exercise before customary technical and tactical handball training sessions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".