The clustering of multiple health and lifestyle behaviours among Swedish adolescents: A person-oriented analysis

Abstract

ABSTRACTBackgroundKnowledge of the distribution, prevalence, and clustering of multiple health and lifestyle related behaviours (HLBs) among adolescents can inform the development of more effective health-promoting policies and interventions. We therefore assessed the clustering of multiple HLBs among 11, 13 and 15-year-old Swedish adolescents and examined the socioeconomic and demographic correlates of each cluster.MethodsWe used data from the 2017/2018 Swedish Health Behaviour in School-aged Children (HBSC) study to conduct sex and age-stratified latent class analysis (LCA). The LCA was based on five HLBs: eating behaviour and habits (EBH), physical activity (PA), tobacco usage (TU), alcohol consumption (AC) and sleeping habits and patterns (SHPs). Multinomial logistic regression models were used to assess the associations between the identified clusters and the socioeconomic and demographic characteristics of adolescents and their parents.ResultsHealth behaviours varied by age and sex. Four distinct clusters were identified based on sex: cluster 1 (Mixed eating behaviours and habits, physical activity and low alcohol consumption), cluster 2 Healthy lifestyle behaviours), cluster 3 (Unhealthy lifestyle behaviours), and cluster 4 (Breakfast, low alcohol consumption and tobacco usage). In the age-stratified analyses, three clusters were identified: cluster 1 (Unhealthy lifestyle behaviours), cluster 2 (Moderately healthy lifestyle behaviours) and cluster 3 (Healthy lifestyle behaviours). The multinomial analysis showed that sex, age, family situation and perceived family wealth were strong predictors of health behaviours. In particular, unhealthy behaviours showed the most frequent associations with socioeconomic disadvantage, having a migrant background and living in reconstructed families or single parent households.ConclusionsHealth behaviours vary significantly based on socioeconomic and demographic factors. Targeted policy and interventions programmes can effectively improve HLBs among vulnerable and at-risk adolescents.