Effects of Interval Sprint Trainings on Heart Rate and 50 m Swimming Performances of Young Male Swimmers
Bibliographic record
Abstract
Achieving superior performance in swimming, as in other sports, depends on the customization of training for young athletes. The aim of this study was to investigate the effects of sprint interval trainings on heart rate and 50 m swimming performances of young male swimmers. 24 young male swimmers joined to the study by having their parents confirm the “Parental Permission Form”. Swimmers randomly divided into two groups as normal training group and interval sprint training group. As normal training group continued the routine swimming training, interval sprint trainings (8 x 50 interval repeats in crawl and backstroke styles) were applied to the other group additionally for 8 weeks, 3 days a week, at least 30 min a day. Rested-maximum heart rate, 50 m sprint swimming test in crawl and backstroke styles were applied to the swimmers in 1st and 8th weeks of the period. The analysis of data was made in the statistical package program by using “Descriptive statistics”, “Paired t Test” and “Independent t Test” for comparison. Results of pre- and post-test comparison of each group, significant differences were found in resting heart rate values of normal training group and all values of interval sprint training group (p < .05). Results of comparison between groups, differences found statistically significant in 8th week maximum heart rate, crawl and backstroke performances (p < .05). To conclude, we could say that the reason of finding significant differences in rested and maximum heart rate is the positive effects of physical activity on the cardio-vascular system (adaptation). And, the reason for the positive effects on sprint interval performance is depended on sprint interval swimming was acute origination of body’s physiological reaction to rising energy need during short time and intensive physical activity even in micro plan.
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 | 0.001 |
| 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".