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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wolters Kluwer Health
2019
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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. |
format | Online Article Text |
id | pubmed-6571404 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Wolters Kluwer Health |
record_format | MEDLINE/PubMed |
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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