Unhealthy lifestyles and clusters status among 3637 adolescents aged 11-23 years: a school-based cross-sectional study in China

• Main Authors: Yalin Song (Author), Jingru Liu (Author), Yize Zhao (Author), Lu Gong (Author), Qiuyuan Chen (Author), Xili Jiang (Author), Jiangtao Zhang (Author), Yudan Hao (Author), Huijun Zhou (Author), Xiaomin Lou (Author), Xian Wang (Author)
• Format: Book
• Published: BMC, 2023-07-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s12889-023-16197-3; 1471-2458
• Keywords: Life style; Adolescent; Cluster; Public aspects of medicine; RA1-1270
• Source: BMC Public Health, Vol 23, Iss 1, Pp 1-18 (2023)

Abstract Background Unhealthy lifestyles are risk factors for non-communicable diseases (NCDs) and tend to be clustered, with a trajectory that extends from adolescence to adulthood. This study investigated the association of diets, tobacco, alcohol, physical activity (PA), screen time (ST) and sleep duration (SD) in a total of six lifestyles, separately and as cumulative lifestyle scores, with sociodemographic characteristics among school-aged adolescents in the Chinese city of Zhengzhou. Methods In the aggregate, 3,637 adolescents aged 11-23 years were included in the study. The questionnaire collected data on socio-demographic characteristics and lifestyles. Healthy and unhealthy lifestyles were identified and scored, depending on the individual score (0 and 1 for healthy and unhealthy lifestyles respectively), with a total score between 0 and 6. Based on the sum of the dichotomous scores, the number of unhealthy lifestyles was calculated and divided into three clusters (0-1, 2-3, 4-6). Chi-square test was used to analyze the group difference of lifestyles and demographic characteristics, and multivariate logistic regression was used to explore the associations between demographic characteristics and the clustering status of unhealthy lifestyles. Results Among all participants, the prevalence of unhealthy lifestyles was: 86.4% for diet, 14.5% for alcohol, 6.0% for tobacco, 72.2% for PA, 42.3% for ST and 63.9% for SD. Students who were in university, female, lived in country (OR = 1.725, 95% CI: 1.241-2.398), had low number of close friends (1-2: OR = 2.110, 95% CI: 1.428-3.117; 3-5: OR = 1.601, 95% CI: 1.168-2.195), and had moderate family income (OR = 1.771, 95% CI: 1.208-2.596) were more likely to develop unhealthy lifestyles. In total, unhealthy lifestyles remain highly prevalent among Chinese adolescents. Conclusion In the future, the establishment of an effective public health policy may improve the lifestyle profile of adolescents. Based on the lifestyle characteristics of different populations reported in our findings, lifestyle optimization can be more efficiently integrated into the daily lives of adolescents. Moreover, it is essential to conduct well-designed prospective studies on adolescents.