EATING BEHAVIORS ASSOCIATED WITH THINNESS AMONG 9 TO 12-YEAROLD STUDENTS AT 915 GIA SANG PRIMARY AND SECONDARY SCHOOL, THAI NGUYEN CITY: A DECISION TREE ANALYSIS

DOI: 10.18173/2354-1059.2025-0062

Tóm tắt

Thinness is a persistent but understudied form of undernutrition among school-aged children in Vietnam, especially in peri-urban settings. This study focused on students aged 9 – 12 at Gia Sang Primary and Secondary School in Thai Nguyen, aiming to identify key eating behavior patterns associated with thinness using decision tree analysis as a data-driven approach for nutritional risk classification. A cross-sectional study was conducted with 157 students at Gia Sang Primary and Secondary School in Thai Nguyen, Vietnam. Nutritional status was assessed using body mass index-for-age Z-scores in accordance with WHO (2007) standards. Eating behaviors were measured using the validated Children’s Eating Behavior Questionnaire (CEBQ), comprising eight behavioral subscales. Anthropometric data were collected following standardized procedures. Pearson correlation analysis, logistic regression, and decision tree modeling were applied to examine associations and classify thinness risk. Results showed that 14.0% of participants were classified as thin and 11.5% as overweight, reflecting the double burden of malnutrition. Logistic regression analysis indicated that Satiety Responsiveness (SR) and Slowness in Eating (SE) were significant predictors of thinness (P < 0.05). More notably, decision tree analysis revealed distinct high-risk profiles: children with high Desire for Drinks (DD ≥ 3.8) and low SR (< 2.4) had an 80% probability of being thin, while those with low DD combined with low Food Fussiness (FF), Emotional Overeating (EOE) scores, and SE had a 62% probability. These findings highlight the added value of decision tree modeling in capturing complex interactions among eating behaviors associated with thinness. They also suggest the potential of using behavioral screening tools like the CEBQ for early identification of nutritional risk and for informing targeted, behaviorally tailored interventions within school-based health programs.