Exploring demographic-behavioural profiles in a large cohort of Japanese adults: a latent class analysis approach for...

Understanding health behaviour patterns is critical to addressing public health challenges. In Japan, lifestyle factors such as smoking, physical inactivity, poor diet and insufficient sleep significantly contribute to non-communicable diseases (NCDs). Identifying how these behaviours cluster within populations offers valuable insights for effective prevention and intervention strategies. Latent class analysis (LCA) enables the identification of behavioural patterns to inform tailored public health initiatives.

To identify and characterise demographic-behavioural profiles among Japanese adults using LCA.

Data from 137 357 individuals participating in a health promotion programme in Japan (1 January 2022 to 31 December 2022) were analysed. Participants aged 20 years and older with complete records for all relevant health behaviour variables were included. Variables included age, gender, smoking status, alcohol consumption, sleep duration, physical activity, sedentary behaviour and salt intake. LCA was conducted to identify behaviour clusters, with the optimal number of classes determined using Bayesian and Akaike information criteria.

From a cohort of 137 357 individuals, nine clusters were identified: (1) middle-aged women with short sleep duration (20.8%), (2) middle-aged men with elevated health behaviour risks (8.4%), (3) young adults with short sleep duration (18.4%), (4) healthy middle-aged men (2.6%), (5) sedentary middle-aged men with short sleep duration (25.3%), (6) typical middle-aged women (5.2%), (7) physically active young men with poor dietary habits (2.5%), (8) unhealthy middle-aged men with inconsistent behaviours (8.5%) and (9) older adults with moderate health concerns (8.4%).

LCA revealed diverse health behaviour patterns, emphasising the need for tailored health interventions targeting specific subgroups to mitigate NCD risks.