Latent Class Analysis of Lifestyle Risk Factors and Association with Overweight and/or Obesity in Children and Adolescents: Systematic Review
Bibliographic record
Abstract
Background: Multiple modifiable lifestyle factors are well-known contributors to many health problems. Objectives: This study aims to determine the association between latent class analysis (LCA) of modifiable lifestyle risk factors with being overweight and/or obese for children and/or adolescents. Methods: Articles were selected from six databases, without limitation regarding language or date. The review included studies that identify latent classes of modifiable lifestyle risk factors [e.g., physical activity (PA), diet, sedentary behavior (SB), and/or unhealthy behavior] by LCA to determine the association between latent classes with being overweight and/or obese. The methodology of the selected studies was evaluated using the JBI Critical Appraisal Checklist for Analytical Cross-Sectional Studies. Owing to the heterogeneity between latent classes of modifiable lifestyle risk factors with obesity and/or being overweight, the results are described narratively. Results: Using a selection process in two phases, nine articles were included. All of the included studies were of high methodological quality. The studies were conducted in six different countries: the USA, Brazil, Canada, Portugal, Italy, and Australia. Sample sizes ranged from 166 to 18.587 children and adolescents, and in terms of age (range 5–19 years). Across study clusters characterized by low consumption of fruit and vegetables, and high consumption of fatty foods, sugar snack foods, sweets, chips and fries, low PA (<1 hour each day), and high SB (screen time and TV >2 hours/day), sleep time (<10 hours/day) were positively associated with being overweight and/or obese. Conclusion: Overall there is good evidence to support that the modifiable lifestyle risk factors clustered together by LCA should be novel targets for the treatment of obesity and its associated comorbidities.
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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 | 0.019 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".