To our knowledge, the present study represents an initial step in understanding how various behaviors blend together to form “overall” healthy or unhealthy lifestyles among Chinese adolescents. And it firstly compares the group differences in behavioral cluster among migrant, left-behind, rural local and urban local adolescents.
Previous studies that explored the relationship between behavioral clusters and sociodemographic correlates usually focused on age, sex, and SES. 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 defined by low levels of PA, high levels of SB or high consumption of beverage drinking [14, 20]. However, studies examining differences in the cluster patterns of behaviors between migrant, left-behind and local adolescents in rural and urban China are limited. Understanding the clustering of “overall” lifestyle behaviors provides a basis to develop tailored interventions that promote effective and sustainable behavior change among this understudied population.
Consistent with previous literature, the present study revealed that adolescents engaged in a variety of lifestyle behaviors, and the cluster patterns were a complex mix of healthy and unhealthy behavioral patterns [14, 18]. All clusters consisted of at least two unhealthy behaviors in current study, which implied that an overall healthy lifestyle was not observed among participants. Therefore, interventions for changing multiple risk lifestyle behaviors simultaneously are needed for adolescents.
Three distinguished behavioral patterns ranging from high risk to low risk were exhibited among young adolescents, with 62% of adolescents having a relatively low-risk lifestyle pattern. The low-risk cluster was characterized by nonsmokers, the lowest levels of screen time, beverage and alcohol consumption, and a moderate level of vegetable consumption and MVPA. One explanation for the lowest levels of screen time in the low-risk cluster may be that only computer time was considered as contributing to screen time in the present study. This is different from other studies which included the time of watching TV or other sedentary behavior-related variables when measuring screen time or overall sedentary time [12, 20, 38]. Therefore, we may have underestimated the screen time of the adolescents in the present study. However, it is worth noting that, with the development of digital technology, computers and mobile phones are increasingly popular for use in adolescent daily life to perform activities unrelated to education. It has been shown that Chinese adolescents usually go online for 22 hours per week to search for information, communicate, and play games [39]. Given the high risk of online information that is unsafe for adolescents, the present study therefore focused on the computer time.
Also, insufficient MVPA and fruit and vegetable consumption were concerns in the high-and moderate-risk clusters. Adolescents in the moderate-risk cluster did not report smoking, but reported the highest level of screen time throughout the week and the lowest level of MVPA and fruit and vegetable consumption, as well as having moderate levels of sleep and alcohol and beverage drinking. The recommendation for Chinese adolescents is one-piece fruit and three times vegetable each day. However, approximately 60–90% of the adolescents in the moderate-risk cluster reported fewer fruit intake (< 7 times/week) and vegetable intake (< 21 times/week). 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.
Importantly, for the impact of migration on behavioral cluster, the present study found that migrant adolescents had the lowest prevalence of low-risk lifestyle patterns, followed by left-behind, rural local, and urban local adolescent. Moreover, migrant and left-behind adolescents had a significantly higher prevalence of moderate-risk lifestyle than the rural and urban local adolescents. This may partly reveal the potential impact of migration on children. Migration means a change of original living surroundings and adaption to new environments for adolescents. For adolescents who move from rural areas to urban areas with their parents, although they can live with one or both parent(s), adapting to a new school, social and physical environment can be challenging. As for the left-behind adolescents, although they have no difficulties in dealing with a new social environment, living without one or both parent(s) may lead to less parental care or supervision, so as to leading to the emergence of behavioral problems. Accordingly, to promote healthy eating, reducing levels of insufficient physical activities, and screen time, so as to achieve the target of a 15% relative reduction in insufficient physical activity among children by 2030, more attention should be paid on the migrant and left-behind adolescents [40]. As previous studies suggest that disadvantaged parents may have lower health literacy, and are unclear about their own or their child’s health risks, so they may not be able to recognize negative changes in their child’s emotional health and lifestyle and provide timely support to their children [41, 42]. Health education about the importance of fruits and vegetables, exercise and reduced screen time, as well as the behavioral guidelines for health could be provided to migrant families or guardian stayed with the left-behind and rural local adolescents.
However, different from our hypothesis, the prevalence of rural local adolescents who had high-risk behavioral pattern is slightly higher than migrant, left-behind, and urban adolescents, but there were no significant differences in four groups of adolescents. High-risk behavioral cluster included multiple risk behaviors (e.g., low levels of vegetable consumption, high levels of screen time on weekends, short sleep time throughout the week, smoking, drinking beverages, and alcohol) in current study. The non-significant difference between four groups might be understandable because of the small number of adolescents who had high-risk behavioral cluster. Only 7.83% of adolescents in our study exhibited the high-risk behavioral cluster. This should be further investigated using a larger nationally representative sample.
Limitations and strengths The utilization of latent cluster analysis appears to be a meaningful and useful technique that advances our understanding of health-related adolescent behavior and may provide information for development of tailored interventions for the first time in migrant and left-behind adolescents. However, participants were recruited only from two cities (Beijing vs. Wuwei County) in China and hence cannot be nationally representative, which limits the generalizability of the findings. A second potential limitation of this study was that we were unable to compare the specific roles of migrant fathers and migrant mothers in behavioral development or to comprehensively examine sex disparities in adolescent behaviors. Moreover, this study uses a cross-sectional design, which provides evidence for associations but not for causation. We were also unable to assess the adolescents’ behaviors objectively using smartphone or ecological momentary assessment or other real-time monitoring methods because of the following reasons: 1) the students were not allowed to carry mobile phones at school; and 2) lack of accelerometers could be applied all study participants.