Gender differences in the longitudinal association between multilevel latent classes of chronic disease risk behaviours and body mass index in adolescents
Bibliographic record
Abstract
INTRODUCTION: Few studies have assessed the relationship between chronic disease risk behaviours and body mass index (BMI) in a longitudinal, sex/gender-specific context. This study used gender-specific analyses to assess the extent to which chronic disease risk behaviour latent classes are associated with BMI and weight status at follow-up. METHODS: Longitudinal data from 4510 students in Grades 9 to 12, tracked from 2013- 2015, who participated in the COMPASS study were used to assess gender differences in the lagged association between previously determined latent classes (of physical activity and substance use) with BMI using multilevel mixed-effects models. Our multilevel regression models assessed the association between two latent classes, active experimenters and inactive non-using youth, with BMI when stratified by gender. RESULTS: Male inactive non-substance-using youth were associated with a 0.29 higher continuous BMI (95% CI: 0.057, 0.53) and odds of overweight/obesity increased by 72% (OR = 1.72, 95% CI: 1.2, 2.4) for binary BMI at follow-up relative to active youth who experiment with substance use. No significant associations were detected in females. CONCLUSION: Over time, physical activity has a protective role on BMI in male youth. Both substance use and physical inactivity should be addressed in obesity prevention efforts. Gender stratification in analyses is also important since females and males have different contributing factors to increases in BMI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".