How do energy balance-related behaviors cluster in adolescents?

Collese, Tatiana Sadalla; Moraes, Augusto César Ferreira de; Fernández-Alvira, Juan Miguel; Michels, Nathalie; Henauw, Stefaan De; Μanios, Yannis; Androutsos, Odysseas; Kafatos, Anthony; Widhalm, Kurt; Galfo, Myriam; Béghin, Laurent; Sjöstróm, Michael; Pedrero‐Chamizo, Raquel; Carvalho, Heráclito Barbosa; Moreno, Luís A.

doi:10.1007/s00038-018-1178-3

Cited by 12 publications

(24 citation statements)

References 41 publications

(46 reference statements)

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“…Cluster patterns were different by age, sex and SES in previous studies [14,16,17,23,36,37]. A higher proportion of girls, older children or adolescents, and children/adolescents from the lowest SES in the clusters de ned by low levels of PA, high levels of SB or high consumption of beverage drinking [14,20].…”

Section: Discussionmentioning

confidence: 80%

“…This implies a critical need to promote fruit and vegetable consumption among young adolescents. In previous studies, the majority of adolescents in Western countries also failed to meet guidelines for one or more health behavior(s); for example, high levels of screen time, low fruit and vegetable consumption, and inactivity tend to cluster in this age group [16,19,23]. Although there were differences in the numbers of health-related behaviors, measurements, analytical approaches and countries between the current study and other studies of adolescents, they consistently suggest that behavioral interventions to promote healthy behaviors are challenging, and more studies are required to promote a healthy lifestyle in adolescent population.…”

Section: Discussionmentioning

confidence: 98%

“…Although there has been a growing number of studies investigating the clustering or cooccurrence of multiple modi able behaviors among adolescents, few of them were conducted in China [14][15][16][17][18][19][20][21][22][23]. Parker et al [14] systematically reviewed studies published up to May 2018, and identi ed 36 studies examining clusters of activity-related behaviors among adolescents, as well as socio-demographic characteristics and the modi able correlates associated with behaviors.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

et al. 2020

Preprint

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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 City and Wuwei County. Adolescents self-reported age, gender, family economic status, migrant situation, and lifestyle behaviors (physical activity, sleep, smoke, drink, 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 1,364 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 different lifestyles was observed. To achieve the target of a 15% relative reduction in insufficient physical activity by 2030 and to promote health eating, more attention should be paid on the migrant and left-behind adolescents in China.

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Section: Discussionmentioning

confidence: 80%

Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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

Wang

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Although there has been a growing number of studies investigating the clustering or cooccurrence of multiple modifiable behaviors among adolescents, few of them were conducted in China [14][15][16][17][18][19][20][21][22][23]. Parker et al [14] systematically reviewed studies published up to May 2018, and identified 36 studies examining clusters of activity-related behaviors among adolescents, as well as socio-demographic characteristics and the modifiable correlates associated with behaviors.…”

Section: Introductionmentioning

confidence: 99%

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

Wei-dong

et al. 2021

BMC Public Health

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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.

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“…Similarly, the studies by Hyun-soo Kim (2018) and Farrell et al (2018) also identified gender differences in risk factors for suicide behaviours across diverse sample populations. In relation to other health outcomes, Moreau et al (2018) report higher prevalence of negative feelings about timing of first sexual intercourse among girls than boys in a European sample, Sadalla Collese et al (2018) found gender differences in clustering of energy-related behaviours among both Brazilian and European adolescents, and Paclikova et al (2018) found a moderating effect of gender on the relationship between family communication and emotional and behaviour problems in Slovak adolescents.…”

mentioning

confidence: 99%