Factors associated with low levels of aerobic fitness among adolescents
Bibliographic record
Abstract
Abstract Objective: To evaluate the prevalence of low aerobic fitness levels and to analyze the association with sociodemographic factors, lifestyle and excess body fatness among adolescents of southern Brazil. Methods: The study included 879 adolescents aged 14-19 years the city of São José/SC, Brazil. The aerobic fitness was assessed by Canadian modified test of aerobic fitness. Sociodemographic variables (skin color, age, sex, study turn, economic level), sexual maturation and lifestyle (eating habits, screen time, physical activity, consumption of alcohol and tobacco) were assessed by a self-administered questionnaire. Excess body fatness was evaluated by sum of skinfolds triceps and subscapular. We used logistic regression to estimate odds ratios and 95% confidence intervals. Results: Prevalence of low aerobic fitness level was 87.5%. The girls who spent two hours or more in front screen, consumed less than one glass of milk by day, did not smoke and had an excess of body fatness had a higher chance of having lower levels of aerobic fitness. White boys with low physical activity had had a higher chance of having lower levels of aerobic fitness. Conclusions: Eight out of ten adolescents were with low fitness levels aerobic. Modifiable lifestyle factors were associated with low levels of aerobic fitness. Interventions that emphasize behavior change are needed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.005 | 0.001 |
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".