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Systematic estimation of BMI: A novel insight into predicting overweight/obesity in undergraduates

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 ov...

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Autores principales: Shan, Meng-Jie, Zou, Yang-Fan, Guo, Peng, Weng, Jia-Xu, Wang, Qing-Qing, Dai, Ya-Lun, Liu, Hui-Bin, Zhang, Yuan-Meng, Jiang, Guan-Yin, Xie, Qi, Meng, Ling-Bing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer Health 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6571404/
https://www.ncbi.nlm.nih.gov/pubmed/31124981
http://dx.doi.org/10.1097/MD.0000000000015810
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author Shan, Meng-Jie
Zou, Yang-Fan
Guo, Peng
Weng, Jia-Xu
Wang, Qing-Qing
Dai, Ya-Lun
Liu, Hui-Bin
Zhang, Yuan-Meng
Jiang, Guan-Yin
Xie, Qi
Meng, Ling-Bing
author_facet Shan, Meng-Jie
Zou, Yang-Fan
Guo, Peng
Weng, Jia-Xu
Wang, Qing-Qing
Dai, Ya-Lun
Liu, Hui-Bin
Zhang, Yuan-Meng
Jiang, Guan-Yin
Xie, Qi
Meng, Ling-Bing
author_sort Shan, Meng-Jie
collection PubMed
description 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 χ(2) 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.
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spelling pubmed-65714042019-07-22 Systematic estimation of BMI: A novel insight into predicting overweight/obesity in undergraduates Shan, Meng-Jie Zou, Yang-Fan Guo, Peng Weng, Jia-Xu Wang, Qing-Qing Dai, Ya-Lun Liu, Hui-Bin Zhang, Yuan-Meng Jiang, Guan-Yin Xie, Qi Meng, Ling-Bing Medicine (Baltimore) Research Article 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 χ(2) 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. Wolters Kluwer Health 2019-05-24 /pmc/articles/PMC6571404/ /pubmed/31124981 http://dx.doi.org/10.1097/MD.0000000000015810 Text en Copyright © 2019 the Author(s). Published by Wolters Kluwer Health, Inc. http://creativecommons.org/licenses/by-nc/4.0 This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc/4.0
spellingShingle Research Article
Shan, Meng-Jie
Zou, Yang-Fan
Guo, Peng
Weng, Jia-Xu
Wang, Qing-Qing
Dai, Ya-Lun
Liu, Hui-Bin
Zhang, Yuan-Meng
Jiang, Guan-Yin
Xie, Qi
Meng, Ling-Bing
Systematic estimation of BMI: A novel insight into predicting overweight/obesity in undergraduates
title Systematic estimation of BMI: A novel insight into predicting overweight/obesity in undergraduates
title_full Systematic estimation of BMI: A novel insight into predicting overweight/obesity in undergraduates
title_fullStr Systematic estimation of BMI: A novel insight into predicting overweight/obesity in undergraduates
title_full_unstemmed Systematic estimation of BMI: A novel insight into predicting overweight/obesity in undergraduates
title_short Systematic estimation of BMI: A novel insight into predicting overweight/obesity in undergraduates
title_sort systematic estimation of bmi: a novel insight into predicting overweight/obesity in undergraduates
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6571404/
https://www.ncbi.nlm.nih.gov/pubmed/31124981
http://dx.doi.org/10.1097/MD.0000000000015810
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