The reliability and sensitivity of indices related to cardiovascular fitness evaluation
Bibliographic record
Abstract
Determining the recovery heart rate (RHR) index after various submaximal exercises is a popular and practical way for a population's cardiovascular fitness evaluation.These evaluations are based on the regression among RHR, load intensity, and maximal oxygen uptake.However, little work has been done to 1) explore the influences of body weight and height on these tests' reliability and sensitivity, and 2) compare the reliability among the tests.As a result, practitioners often choose tests without the appropriate criteria.This study researched the mentioned two aspects by evaluating 30 male college students via three common tests -30 cm step test, 40 cm step test and squat-up-down test.The results showed that the reliability and sensitivity of the three tests were remarkably different.Adding body weight into the evaluation would improve both reliability and sensitivity.Considering all the influence factors, 30 cm step test was the best one.These findings suggested that applying the relative RHR index (normalized by body weight) should be considered for a population's cardiovascular fitness evaluation in the future.
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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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".