High-Intensity Interval Training for Improvement of Overhand Throwing Velocity
Bibliographic record
Abstract
Context: High intensity interval training (HIIT) has been shown to improve muscle power and endurance, as well as aerobic power. Objective: To assess the effects of HIIT that utilizes resistive elastic bands to improve overhand throwing velocity. Participants: Healthy female volunteers (n = 13) ranging in age from 18–29 years. Interventions: Participants were randomly assigned to either a control group or an experimental group that exercised 3 days per week for 3 weeks. Each training session involved performance of 5 sets of 20 throwing motions against elastic band resistance, which was performed by both extremities. Main Outcome Measures: Maximal oxygen consumption was measured during performance of a graded exercise test that utilized an upper extremity cycle ergometer. A radar gun was used to assess peak throwing velocity and the extent to which throwing velocity was sustained during performance of a 20-throw endurance test. Results: After completing the training, the experimental group exhibited faster peak throwing velocity (61.6 ± 6.6 km/hr to 63.2 ± 8.6 km/hr) and a reduced fatigue index (1.18 ± 0.16 to 1.01 ± 0.02). Training also resulted in a 14% improvement in maximum oxygen consumption (1.40 ± 0.46 L/min to 1.60 ± 0.49 L/ min) and longer time to fatigue (9.99 ± 1.84 min to 11.43 ± 2.29 min). Conclusion: The high-intensity interval training program was effective for improvement of overhand throwing performance.
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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) | 0.000 | 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) | 0.003 | 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".