Qi Xie

The prevalence of overweight–obesity has increased sharply among undergraduates worldwide. In 2016, approximately 52% of adults were overweight–obese. This cross-sectional study aimed to investigate the prevalence of overweight–obesity and explore in depth the connection between eating habits and overweight–obesity among Chinese undergraduates. The study population included 536 undergraduates recruited in Shijiazhuang, China, in 2017. They were administered questionnaires for assessing demographic and daily lifestyle characteristics, including sex, region, eating speed, number of meals per day, and sweetmeat habit. Anthropometric status was assessed by calculating the body mass index (BMI). The determinants of overweight–obesity were investigated by the Pearson χ² test, Spearman rho test, multivariable linear regression, univariate/multivariate logistic regression, and receiver operating characteristic curve analysis. The prevalence of undergraduate overweight–obesity was 13.6%. Sex [male vs female, odds ratio (OR): 1.903; 95% confidence interval (95% CI): 1.147–3.156], region (urban vs rural, OR: 1.953; 95% CI: 1.178–3.240), number of meals per day (3 vs 2, OR: 0.290; 95% CI: 0.137–0.612), and sweetmeat habit (every day vs never, OR: 4.167; 95% CI: 1.090–15.933) were significantly associated with overweight–obesity. Eating very fast was positively associated with overweight–obesity and showed the highest OR (vs very slow/slow, OR: 5.486; 95% CI: 1.622–18.553). However, the results of multivariate logistic regression analysis indicated that only higher eating speed is a significant independent risk factor for overweight/obesity (OR: 17.392; 95% CI, 1.614–187.363; P = .019). Score_meng = 1.402 × score_sex + 1.269 × score_region + 19.004 × score_{eatin  speed} + 2.546 × score_{number of meals per day} + 1.626 × score_{sweetmeat habit} and BMI = 0.253 × Score_meng + 18.592. These 2 formulas can help estimate the weight status of undergraduates and predict whether they will be overweight or obese.