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

BACKGROUND

There is currently no consensus on the number and domains of lifestyle factors to incorporate in research examining lifestyle combinations. A common approach in this field involves creating unweighted lifestyle scores by summing healthy/unhealthy lifestyle scores. However, further exploration and comparison are needed to advance beyond simple summation and investigate more nuanced lifestyle patterns.

METHODS

Latent class analysis was performed to identify lifestyle patterns among 112,842 participants aged 18 or older from the Dutch Lifelines cohort (baseline data, collected from 2007 to 2013). 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). Socio-demographic characteristics including age distribution, sex, education level, income and employment status differed significantly (nominal  < 0.05) across lifestyle patterns. Multiple comparison analysis showed that healthy lifestyle scores differed within lifestyle pattern pairs. Proportions of unhealthy lifestyle scores are comparable between the “Healthy but physically inactive” and the “Healthy in a balanced way” patterns as well as the “Unhealthy but no substance use” and “Unhealthy but light drinking and never smoked” patterns.

CONCLUSION

The five identified lifestyle patterns exhibit distinct, non-random clustering of behaviours, each linked to specific socio-demographic characteristics. Understanding these clustering tendencies can help identify target populations and uncover barriers to unhealthy behaviours, aiding the development of tailored health interventions. The overlap in the distribution of unhealthy lifestyle scores between two lifestyle patterns suggests that the latter may provide a more comprehensive perspective on habitual behaviours.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s13690-025-01678-1.