Abstract
Abstract
Background
Modifiable risk behaviors, such as smoking, diet, alcohol consumption, physical activity, and sleep, are known to impact health. This study aims toward identifying latent classes of unhealthy lifestyle behavior, exploring the correlations between sociodemographic factors, identifying classes, and further assessing the associations between identified latent classes and all-cause mortality.
Methods
For this study, the data were obtained from a prospective cohort study in Taiwan. The participants’ self-reported demographic and behavioral characteristics (smoking, physical activity, alcohol consumption, fruit and vegetable intake, and sleep) were used. Latent class analysis was used to identify health-behavior patterns, and Cox proportional hazard regression analysis was used to find the association between the latent class of health-behavior and all-cause mortality.
Results
A complete dataset was obtained from 290,279 participants with a mean age of 40 (12.4). Seven latent classes were identified, characterized as having a 100% likelihood of at least one unhealthy behavior coupled with the probability of having the other four unhealthy risk behaviors. This study also shows that latent health-behavior classes are associated with mortality, suggesting that they are representative of a healthy lifestyle. Finally, it appeared that multiple risk behaviors were more prevalent in younger men and individuals with low socioeconomic status.
Conclusions
There was a clear clustering pattern of modifiable risk behaviors among the adults under consideration, where the risk of mortality increased with increases in unhealthy behavior. Our findings can be used to design customized disease prevention programs targeting specific populations and corresponding profiles identified in the latent class analysis.
Publisher
Springer Science and Business Media LLC
Cited by
8 articles.
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1. Co-occurrence of behavioural risk factors for non-communicable diseases among 40-year and above aged community members in three regions of Myanmar;Open Research Europe;2024-01-11
2. Clustering of health risk behaviors in pregnant individuals: Data from the Brazilian risk factor surveillance system for non-communicable chronic diseases;Preventive Medicine;2024-01
3. Intersection of Perceived COVID-19 Risk, Preparedness, and Preventive Health Behaviors: Latent Class Segmentation Analysis;Online Journal of Public Health Informatics;2023-10-24
4. How are different clusters of physical activity, sedentary, sleep, smoking, alcohol, and dietary behaviors associated with cardiometabolic health in older adults? A cross-sectional latent class analysis;Journal of Activity, Sedentary and Sleep Behaviors;2023-08-01
5. Intersection of Perceived COVID-19 Risk, Preparedness, and Preventive Health Behaviors: Latent Class Segmentation Analysis (Preprint);2023-07-18