Association between Lifestyle Behaviors and Health-Related Quality of Life in a Sample of Brazilian Adolescents
Bibliographic record
Abstract
This study aimed to analyze the association between lifestyle behaviors and health-related quality of life (HRQoL) among Brazilian adolescents. We evaluated 739 adolescents (51.0% girls; mean age, 16.4 ± 1.0 years) from the mesoregion Grande Florianópolis, Brazil. Participants were asked to complete an online questionnaire and sex, age, mother's education, health-related quality of life, physical activity, screen time indicators, sleep duration, diet, cigarette smoking, alcohol drinking, and drug experimentation were retrieved. Health-related quality of life was assessed using the Kidscreen-10 instrument. Measures of body mass and height were taken by trained researchers. Mixed-effects linear regression models were used. Self-reported health-related quality of life was higher in males (β = 3.68, 95%CI: 2.75; 4.61) compared to females, and no association was observed for age and mother's education level. Practicing sports (β = 1.19, 95%CI: 0.29; 2.08) was associated with better HRQoL, while processed food score (β = -0.45, 95%CI: -0.78; -0.13), working using screen devices for more than 4 h/day (β = -2.38, 95%CI: -4.52; -0.25), having experimented illicit drugs (β = -2.05, 95%CI: -3.20; -0.90), and sleeping less than 8 h/night (β = -1.35, 95%CI: -2.27; -0.43) were unfavorably associated with HRQoL. Non-sport physical activities, unprocessed food, studying, watching videos, playing videogames, using social media, alcohol drinking, and smoking were not associated with health-related quality of life. These findings suggest that promoting sports and adequate sleep, and preventing excessive workloads and the use of drugs among adolescents may be effective strategies to improve HRQoL.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".