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Clustering of multiple lifestyle behaviors among migrant, left-behind and local adolescents in China: a cross-sectional study

BACKGROUND: Influence of migration on externalized behavioral problems (e.g., aggressive) among adolescents has been well assessed, yet lifestyle behaviors of migrant, left-behind and local adolescents have been largely overlooked by researchers and policy-makers. Therefore, this study aimed to iden...

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Autores principales: He, Li, Li, Xiaoyan, Wang, Weidong, Wang, Youfa, Qu, Haiyan, Zhao, Yang, Lin, Danhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7980326/
https://www.ncbi.nlm.nih.gov/pubmed/33740944
http://dx.doi.org/10.1186/s12889-021-10584-4
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author He, Li
Li, Xiaoyan
Wang, Weidong
Wang, Youfa
Qu, Haiyan
Zhao, Yang
Lin, Danhua
author_facet He, Li
Li, Xiaoyan
Wang, Weidong
Wang, Youfa
Qu, Haiyan
Zhao, Yang
Lin, Danhua
author_sort He, Li
collection PubMed
description BACKGROUND: Influence of migration on externalized behavioral problems (e.g., aggressive) among adolescents has been well assessed, yet lifestyle behaviors of migrant, left-behind and local adolescents have been largely overlooked by researchers and policy-makers. Therefore, this study aimed to identify clustering of multiple lifestyle behaviors and their associations with migrant status among Chinese adolescents. METHODS: A cross-sectional survey was conducted in 2015 in Beijing, and Wuhu city (Anhui province). Adolescents self-reported age, gender, family economic status, migrant situation, and lifestyle behaviors (i.e., physical activity, screen time, sleep, smoke, soft-drink, alcohol, fruit and vegetable consumption) via a battery of validated questionnaires. Latent class analysis was conducted to identify behavioral clusters using Mplus 7.1. ANOVA, and multivariable logistic regression were used to examine associations between migrant situations and behavioral clusters using SPSS 22. RESULTS: Three distinct behavioral clusters were exhibited among 1364 students (mean age: 13.41 ± 0.84 years): “low risk” (N = 847), “moderate risk” (N = 412) and “high risk” (N = 105). The “high-risk” cluster had the highest prevalence of adolescents not meeting healthy behavioral recommendations. There were no significant differences in the prevalence of high-risk lifestyle among migrant, left-behind, rural local and urban local adolescents. But migrant adolescents had the lowest prevalence of low-risk lifestyle, followed by left-behind, rural and urban local adolescents. Moreover, compared with urban local, migrant (OR = 2.72, 95%CI: 1.88,3.94), left-behind (OR = 2.28, 95%CI: 1.46, 3.55), and rural local (OR = 1.76, 95%CI:1.03,3.01) adolescents had a higher risk of moderate-risk lifestyle. CONCLUSIONS: Clustering of assessed lifestyle behaviors differed by the migrant status. Particularly, migrant and left-behind adolescents were more likely to have moderate-risk lifestyle compared with their counterparts. Interventions that promote moderate to vigorous physical activity and consumption of fruits and vegetables simultaneously are needed among them. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-021-10584-4.
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spelling pubmed-79803262021-03-22 Clustering of multiple lifestyle behaviors among migrant, left-behind and local adolescents in China: a cross-sectional study He, Li Li, Xiaoyan Wang, Weidong Wang, Youfa Qu, Haiyan Zhao, Yang Lin, Danhua BMC Public Health Research Article BACKGROUND: Influence of migration on externalized behavioral problems (e.g., aggressive) among adolescents has been well assessed, yet lifestyle behaviors of migrant, left-behind and local adolescents have been largely overlooked by researchers and policy-makers. Therefore, this study aimed to identify clustering of multiple lifestyle behaviors and their associations with migrant status among Chinese adolescents. METHODS: A cross-sectional survey was conducted in 2015 in Beijing, and Wuhu city (Anhui province). Adolescents self-reported age, gender, family economic status, migrant situation, and lifestyle behaviors (i.e., physical activity, screen time, sleep, smoke, soft-drink, alcohol, fruit and vegetable consumption) via a battery of validated questionnaires. Latent class analysis was conducted to identify behavioral clusters using Mplus 7.1. ANOVA, and multivariable logistic regression were used to examine associations between migrant situations and behavioral clusters using SPSS 22. RESULTS: Three distinct behavioral clusters were exhibited among 1364 students (mean age: 13.41 ± 0.84 years): “low risk” (N = 847), “moderate risk” (N = 412) and “high risk” (N = 105). The “high-risk” cluster had the highest prevalence of adolescents not meeting healthy behavioral recommendations. There were no significant differences in the prevalence of high-risk lifestyle among migrant, left-behind, rural local and urban local adolescents. But migrant adolescents had the lowest prevalence of low-risk lifestyle, followed by left-behind, rural and urban local adolescents. Moreover, compared with urban local, migrant (OR = 2.72, 95%CI: 1.88,3.94), left-behind (OR = 2.28, 95%CI: 1.46, 3.55), and rural local (OR = 1.76, 95%CI:1.03,3.01) adolescents had a higher risk of moderate-risk lifestyle. CONCLUSIONS: Clustering of assessed lifestyle behaviors differed by the migrant status. Particularly, migrant and left-behind adolescents were more likely to have moderate-risk lifestyle compared with their counterparts. Interventions that promote moderate to vigorous physical activity and consumption of fruits and vegetables simultaneously are needed among them. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-021-10584-4. BioMed Central 2021-03-19 /pmc/articles/PMC7980326/ /pubmed/33740944 http://dx.doi.org/10.1186/s12889-021-10584-4 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
He, Li
Li, Xiaoyan
Wang, Weidong
Wang, Youfa
Qu, Haiyan
Zhao, Yang
Lin, Danhua
Clustering of multiple lifestyle behaviors among migrant, left-behind and local adolescents in China: a cross-sectional study
title Clustering of multiple lifestyle behaviors among migrant, left-behind and local adolescents in China: a cross-sectional study
title_full Clustering of multiple lifestyle behaviors among migrant, left-behind and local adolescents in China: a cross-sectional study
title_fullStr Clustering of multiple lifestyle behaviors among migrant, left-behind and local adolescents in China: a cross-sectional study
title_full_unstemmed Clustering of multiple lifestyle behaviors among migrant, left-behind and local adolescents in China: a cross-sectional study
title_short Clustering of multiple lifestyle behaviors among migrant, left-behind and local adolescents in China: a cross-sectional study
title_sort clustering of multiple lifestyle behaviors among migrant, left-behind and local adolescents in china: a cross-sectional study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7980326/
https://www.ncbi.nlm.nih.gov/pubmed/33740944
http://dx.doi.org/10.1186/s12889-021-10584-4
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