Clustering of health behaviours and association with sociodemographic factors among adults in Japan. (20th October 2021)

• Record Type: Journal Article
• Authors: Satoh, M
• Is Part Of: European journal of public health. Volume 31(2021)Supplement 3
• Publication Date: 2021-10-20
• Subjects: Epidemiology -- Europe -- Periodicals; Public health -- Europe -- Periodicals; 362.109405
• DOI: 10.1093/eurpub/ckab165.370
• Languages: English
• ISSNs: 1101-1262

Abstract

Abstract: Background: Health behaviours (HBs) commonly co-occur, acting synergistically. Little is known about the clustering of HBs in the Japanese population. Therefore, this study aimed to investigate the clusters of HB patterns and sociodemographic factors related to the clusters in the Japanese population. Methods: Data used in this study was obtained from the Japanese and Keio Household Panel Surveys. The responses of 1, 554 questionnaires (aged 27 − 65 years) were analysed. The surveys probed HBs (alcohol consumption, smoking, vegetable and fruit consumption, breakfast consumption habits, and physical activity) and sociodemographic characteristics. Latent class analysis was used to identify the clusters, and latent regression was used to investigate sociodemographic characteristics related to the clusters by gender. Results: Two HB clusters were identified: 'inactive, moderately healthy' and 'inactive, high alcohol consumption, poor nutrition'. Age in the 60s indicated significantly higher odds ratios (ORs) of the 'inactive, moderately healthy' cluster in men and women [OR = 1.820, 95% confidence interval (CI)=1.109 − 2.985, p = 0.018; OR = 3.865, CI = 1.830 − 8.163, p = 0.000]. In men, living with families and higher education levels indicated significantly higher ORs of the 'inactive, moderately healthy' cluster (OR = 1.812, CI = 1.078 − 3.047, p = 0.025; OR = 2.236, CI = 1.698 − 2.943, p = 0.000). In women, being married, higher socioeconomic status, and higher education levels indicated significantly higher ORs of the 'inactive, moderately healthy' cluster (OR = 2.097, CI = 1.309 − 3.359, p = 0.002; OR = 2.068, CI = 1.092 − 3.917, p = 0.026; OR = 2.516, CI = 1.563 − 4.051, p = 0.000). Conclusions: Policymakers must recognise HB clusters unique to Japanese adults and have to build effective strategies based on understanding gender differences and similarities in sociodemographic factors associated with HB clusters. Key messages: Interventions for multiple HB simultaneously should be designed and developed. Health behavior patterns have to be clarified for targeting population when interventions are built.