Abstract

Healthy lifestyles are considered important means to reduce the burden of diseases. This cross-sectional study was conducted based on the Ecological Model of Health Behavior (EMHB) to analyze the factors associated with the health-promoting lifestyles of Chinese residents. We carried out a cross-sectional investigation in July 2023. Our investigated factors included social-demographic characteristics (including sex, age, education level, employment status, marital status, personal monthly income, and daily behavioral habits [which were measured by a questionnaire)], health literacy [which was measured by the Chinese version of the Health Literacy Scale Short-Form scale (HLS-SF12)], and family health [which was measured by the Chinese version of the Short-Form of the Family Health Scale (FHS-SF)]. Our outcome was health promoting lifestyle, which was measured by a revised version of Health Promoting Lifestyle Profile-II (HPLP-IIR). Data were analyzed using stepwise regression. A total of 1,402 participants were enrolled. Higher scores of HLS-SF12 (β = 0.467), having regular exercise (β = 0.212), and regular physical examination (β = 0.088) were associated with better health-prompting lifestyles. However, older age (≥60 years) (β = -0.046), drinking (β = -0.066), and sleeping time (5-6 h/day) (β = -0.048) were associated lower levels of health-prompting lifestyles. Living with family (β = 0.077), FHS-SF (β = 0.104), and married (β = -0.077) were significant influencers. Unemployed (β = -0.048), receiving retirement pay (β = -0.053), and economic support provided by parents (β = 0.094) were associated with better health-prompting lifestyles. There were multiple influencing factors of the six dimensions of the HPLP-IIR. Our findings indicate that community residents with higher health literacy, better family health, and health-related behaviors tend to have better health-promoting lifestyles. Our findings have confirmed the complex impacts of social-ecological factors on health-promoting lifestyles, which may help policy makers with health-promotion strategies making and also help researchers to control for confounding in study design.

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