ArticlePloS one2026
Broader factors associated with current adolescent overweight/obesity status in YRBSS 2023: Survey-aware analysis and calibrated AI-assisted classification.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Adolescent overweight and obesity are shaped by demographic, behavioral and psychosocial factors, but it remains unclear whether broader predictor information improves cross-sectional classification of current overweight/obesity status more consistently than changing the machine-learning algorithm. This cross-sectional secondary analysis used 2023 Youth Risk Behavior Surveillance System data from 17,814 United States adolescents aged 12-18 years. Survey-aware multivariable logistic regression was used to identify independent correlates, while Random Forest and XGBoost were evaluated under base and extended predictor frameworks. Models were assessed on a held-out test set for discrimination, threshold-dependent performance, calibration and interpretability. The prevalence of overweight/obesity was 31.8%. Male sex and Black or African American and Hispanic/Latino race/ethnicity were associated with higher odds of overweight/obesity, whereas higher moderate-to-vigorous physical activity and more frequent breakfast intake were associated with lower odds. Extended models outperformed their corresponding base models: ROC-AUC increased from 0.6241 to 0.6894 for Random Forest and from 0.6217 to 0.6724 for XGBoost. RF-Extended was selected for calibration and interpretation. Isotonic recalibration preserved discrimination while improving probability reliability, reducing the Brier score from 0.2126 to 0.1960 and expected calibration error from 0.1160 to 0.0213. Exploratory SHAP analysis identified sex, race/ethnicity, physical activity, sleep duration, age and breakfast frequency among the features contributing most strongly to RF-Extended output. Because predictors and BMI status were measured during the same survey cycle, the models classify current status and should not be interpreted as predicting future obesity. Any screening or prevention application, including potential use in Vietnamese adolescent populations, would require prospective and external validation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.