Child eating behavior predicts body mass index after 1 year: results from the Swiss Preschooler’s Health Study (SPLASHY) - Mihov, Yoan; Meyer, Andrea H.; Kakebeeke, Tanja H.; Stülb, Kerstin; Arhab, Amar; Zysset, Annina E.; Leeger-Aschmann, Claudia S.; Schmutz, Einat A.; Kriemler, Susi; Jenni, Oskar G.; Puder, Jardena J.; Messerli-Bürgy, Nadine; et al. (Frontiers in Psychology, 2024)
Child obesity is a growing global issue. Preventing early development of overweight and obesity requires identifying reliable risk factors for high body mass index (BMI) in children. Child eating behavior might be an important and malleable risk factor that can be reliably assessed with the parent-report Child Eating Behavior Questionnaire (CEBQ).Using a hierarchical dataset (children nested within child care centers) from a representative cohort of Swiss preschool children, we tested whether eating behavior, assessed with a 7-factor solution of the CEBQ, and BMI at baseline predicted the outcome BMI after one year, controlling for socioeconomic status (n=555; 47% female; mean age=3.9 years, range: 2.2-6.6; mean BMI=16 kg/m^2 , range:11.2-23; mean age-and sex-corrected z-transformed BMI, zBMI=0.4, range -4 to+4.7). The statistical model explained 65.2% of zBMI at follow-up. Baseline zBMI was a strong positive predictor, uniquely explaining 48.8% of outcome variance. A linear combination of all CEBQ scales, taken together, explained 10.7% of outcome variance. Due to their intercorrelations, uniquely explained variance by any individual scale was of negligible clinical relevance. Only food responsiveness was a significant predictor, when accounting for all other predictors and covariates in the model, and uniquely explained only 0.4% of outcome variance.Altogether, our results confirm, extend, and refine previous research on eating behavior and zBMI in preschool children, by adjusting for covariates, accounting for intercorrelations between predictors, partitioning explained outcome variance, and providing standardized beta estimates. Our findings show the importance of carefully examining the contribution of predictors in multiple regression models for clinically relevant outcomes.