Non-random aggregations of healthy and unhealthy lifestyles and their population characteristics - pattern recognition in a large population-based cohort

Background: Studies showed that multiple healthy and unhealthy lifestyle factors coexist within an individual as a lifestyle pattern. However, there is currently no consensus on the number and domains of lifestyle factors to include in such studies. How combinations of lifestyle factors occur non-randomly remains unclear. Moreover, few studies have assessed whether lifestyle patterns provide insights comparable to aggregated (un)healthy lifestyle scores in evaluating lifestyle health levels.
Methods: Latent class analysis was performed to identify lifestyle patterns among 112,842 participants aged >=18 years from the Dutch Lifelines cohort. Ten lifestyle factors were selected based on the six pillars of Lifestyle Medicine: smoking habits, binge drinking, daily alcohol intake, diet quality, ultra-processed food consumption, long-term stress, physical (in)activity, sleeping, TV watching time and social connections. Lifestyle factors were assessed using validated self-report questionnaires. 
Results: We identified five lifestyle patterns: “Healthy but physically inactive” (8.6% of the total population, class 1), “Unhealthy but no substance use” (8.5%, class 2), “Healthy in a balanced way” (37.2%, class 3), “Unhealthy but light drinking and never smoked” (31.6%, class 4) and “Unhealthy” (14.2%, class 5). These patterns primarily differed in smoking habits, binge drinking, daily alcohol intake, diet quality, ultra-processed food consumption, long-term stress and physical (in)activity. Sleep, TV watching time and social connections lacked strong clustering properties. Socio-demographic characteristics including age distribution, sex, education level, income and employment status differed significantly (nominal p<0.05) across lifestyle patterns. Healthy lifestyle scores decreased from “class 1” to “class 5” patterns gradually and no clear gradients were observed from unhealthy lifestyle scores across five patterns.  
Conclusion: The five identified lifestyle patterns reveal distinct, non-random clustering of lifestyle behaviours, each associated with different socio-demographic characteristics. Understanding these clustering tendencies can help identify target populations and understand barriers of unhealthy lifestyle behaviours, facilitating the design of tailored health interventions.
Keywords: Combined lifestyle factors, Lifestyle Medicine, Lifestyle clusters, Tailored lifestyle intervention, Healthy lifestyle scores