Assessment Of Cardiovascular Endurance And Stress Levels Of Female Sportspersons During The Menstrual Cycle
Bibliographic record
Abstract
The purpose of this study was to examine the impact of different menstrual cycle phases on cardiovascular endurance and stress levels in female athletes.Thirty female athletes, aged 21-25, from the Physical Education Department of Kuvempu University, Shankaraghatta, were selected through purposive sampling.The research focused on two key phases of the menstrual cycle: the early follicular phase (Day 2) and the luteal phase (Day 15).Cardiovascular endurance was assessed using the Harvard Step Test, while stress levels were measured using a standardized questionnaire.Data collection involved pre-tests on Day 2 and post-tests on Day 15.Descriptive statistics, including mean and standard deviation, were employed, and paired samples t-tests were conducted to compare the results between the two phases.The results indicated significant differences in cardiovascular endurance and stress levels between the early follicular and luteal phases.Participants showed reduced cardiovascular endurance and heightened stress levels during the luteal phase compared to the early follicular phase.These variations are likely due to hormonal changes, particularly increased progesterone during the luteal phase, which is known to influence physical and psychological performance.This study underscores the need to consider menstrual cycle phases in the design of training programs for female athletes.Understanding how hormonal fluctuations affect performance can help coaches and trainers create more effective training and recovery plans that align with the athlete's physiological conditions.
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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 | 0.001 |
| 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".