Which anthropometric equation to predict body fat percentage is more strongly associated with maximum oxygen uptake in adolescents? A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Identifying the relationship between maximum consumption of oxygen and body fat percentage is important due to increased cardiovascular risk factors. OBJECTIVE: This study aimed to verify the association between body fat percentage determined by three predictive equations using anthropometric measures (Lohman, Boileau, and Slaughter) and maximum oxygen uptake (VO2max). We also aimed to estimate the capacity of these equations for explaining VO2max variations in adolescents according to sex. DESIGN AND SETTING: This was a cross-sectional study conducted in high schools in São José, Southern Brazil. METHODS: This study included 879 adolescents (14-19 years) from Southern Brazil. Aerobic fitness was assessed using the modified Canadian Aerobic Fitness Test. The independent variable was body fat percentage predicted by the Lohman, Boileau, and Slaughter equations. Analyses adjusted for sociodemographic variables, physical activity level, and sexual maturation were performed with P value < 0.05. RESULTS: All anthropometric prediction equations used to estimate body fat percentage explained VO2max variations in adolescents. In male adolescents, both regression models based on the Boileau et al.12 and Lohman10 equations revealed higher explanatory power for VO2max (20%) compared with that based on the Slaughter et al.13 equation (19%). In female adolescents, the model based on the anthropometric equation of Slaughter et al.13 showed the greatest explanatory power for VO2max (18%). CONCLUSION: The inverse relationship between VO2max and body fat intensifies the need for effective intervention programs that prioritize maintenance of appropriate body fat and aerobic fitness levels because inadequate levels of both factors result in negative health consequences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".