Association Between Aerobic Fitness and High Blood Pressure in Adolescents in Brazil: Evidence for Criterion-Referenced Cut-Points
Bibliographic record
Abstract
PURPOSE: Criterion-referenced cut-points for health-related fitness measures are lacking. This study aimed to determine the associations between aerobic fitness and high blood pressure levels (HBP) to determine the cut-points that best predict HBP among adolescents. METHOD: This cross-sectional school-based study with sample of 875 adolescents aged 14-19 years was conducted in southern Brazil. Aerobic fitness was assessed using the modified Canadian Aerobic Fitness Test (mCAFT). Systolic and diastolic blood pressure were measured by the oscillometric method with a digital sphygmomanometer. Analyses controlled for sociodemographic variables, physical activity, body mass and biological maturation. RESULTS: Receiver Operating Characteristic (ROC) curves demonstrated that mCAFT measures could discriminate HBP in both sexes (female: AUC = 0.70; male: AUC = 0.63). The cut-points with the best discriminatory power for HBP were 32 mL·kg-1·min-1 for females and 40 mL·kg-1·min-1 for males. Females (OR = 8.4; 95% CI: 2.1, 33.7) and males (OR: 2.5; CI 95%: 1.2, 5.2) with low aerobic fitness levels were more likely to have HBP. CONCLUSION: mCAFT measures are inversely associated with BP and cut-points from ROC analyses have good discriminatory power for HBP.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".