Pre-season Training And Its Effect On Altering Cardiovascular Function, Anthropometry And Performance In Female Collegiate Soccer Players
Bibliographic record
Abstract
Competition and training within sport enhances athletic performance over time. Furthermore, performing regular exercise can elicit positive adaptations in both resting cardiovascular function and anthropometry. However, the association between accumulated exercise stress throughout specific training periods, such as pre-season, has yet to be examined for its influence in altering such variables in female collegiate soccer players. PURPOSE: To examine the association between accumulated exercise stress over a 5-week pre-season period and change in resting cardiovascular function, anthropometry and athletic performance in female collegiate soccer players. METHODS: Seventeen healthy female collegiate soccer players with an age of (mean ± SD) 18.5 ± 0.75 yr, a weight of 57.3 ± 9.2 kg, a height of 165.4 ± 7.2 cm, a body fat % of 23.2 ± 7.4 and a BMI of 20.9 ± 2.2 volunteered as participants for this study. Resting cardiovascular function was examined using a heart rate chest strap and an automated blood pressure monitor after a 10-min stabilization period in the supine position. Measurements included cardiac output, stroke volume, systemic vascular resistance, resting heart rate, ln rMSSD, systolic and diastolic blood pressure and mean arterial pressure. Athletic performance was examined using vertical jump, 5 m & 10 m speed and the YoYo IRT-1. Indices of accumulated exercise stress were recorded using the Polar Team Pro ® system and included total distance covered, number of sprints, estimated caloric expenditure, TRIMP and the Polar training load. Linear regression was used to examine the association between indices of accumulated exercise and cardiovascular function, anthropometry and athletic performance. This study was approved by and followed the recommendation of the Research Ethics Review board at Langara College. RESULTS: Significant inverse associations were observed between change in body weight (kg) and total distance covered (km) (r = -0.56, p < 0.05), as well as number of sprints performed (r = -0.59, p < 0.05). CONCLUSION: Change in body weight was associated with accumulated exercise over a 5-week pre-season period and may be used by coaches to monitor the anthropometric response to accumulated exercise. This investigation was funded by the Langara College ARC-1 Grant.
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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) | 0.002 | 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".