Clustering of lifestyle and health behaviours in Australian adolescents and associations with obesity, self-rated health and quality of life

Abstract

Abstract Objective The primary aim of this study was to identify clusters of lifestyle and health behaviours and explore their associations with health outcomes in a nationally representative sample of Australian adolescents. Methods The study participants were 3127 adolescents aged 14–15 years who participated in the eighth wave of the birth cohort of the Longitudinal Study of Australian Children (LSAC). A latent class analysis (LCA) was performed to identify clusters based on the behaviours of physical activity, alcohol consumption, smoking, diet, eating disorders, sleep problems and weight consciousness. Multinomial logistic regression models were fitted to the following health outcome variables: obesity, self-rated general health and pediatric health-related quality of life, to investigate their associations with LCA clusters. Results Based on the prevalence of health behaviour related characteristics, LCA identified gender based distinct clusters of adolescents with certain outward characteristics. There were five clusters for male and four clusters for female participants which are named as: healthy lifestyle, temperate, mixed lifestyle, multiple risk factors, and physically inactive (male only). Adolescents in the healthy lifestyle and temperate clusters reported low and moderately active health risk behaviours, for example, low physical activity, inadequate sleep and so on, while these behaviours were prevailing higher among adolescents of other clusters. Compared to adolescents of healthy lifestyle clusters, male members of physically inactive (OR = 3.87, 95% CI: 1.12 – 13.33) or mixed lifestyle (OR = 5.57, 95% CI: 3.15 – 9.84) clusters were over three to five times more likely to have obesity; while for female adolescents, members of only multiple risk factors clusters (OR = 3.61, 95% CI: 2.00 – 6.51) were over three time more likely to have obesity compared to their counterpart of healthy lifestyle clusters. Adolescents of physically inactive (b = -9.00 for male only), mixed lifestyle (b = -2.77 for male; b = -6.72 for female) or multiple risk factors clusters (b = -6.49 for male; b = -6.59 for female) had a stronger negative association with health-related quality of life scores compared to adolescents of healthy lifestyle clusters. Conclusion The study offers novel insights into latent class classification through the utilisation of different lifestyles and health-related behaviours of adolescents to identify characteristics of vulnerable groups concerning obesity, general health status and quality of life. This classification strategy may help health policy makers to target vulnerable groups and develop appropriate interventions.

Keywords

• LSAC
• Adolescents
• Latent class analysis
• Cluster analysis
• Health-related behaviours
• Obesity