To explore the heterogenous subtypes and the associated factors of health literacy among patients with metabolic syndrome. A cross-sectional study was conducted, and 337 patients with metabolic syndrome were recruited from Sir Run Run Shaw Hospital in Zhejiang Province from December 2021 to February 2022. The Social Support Questionnaire, Short version of the Health Literacy Scale European Questionnaire (HLS-EU-Q16), and MacArthur Scale of Subjective Social Status were used for investigation. Latent class analysis (LCA) was performed to explore the heterogenous subtypes of health literacy among Metabolic syndrome patients. Univariate analysis and logistic regression were used to identify the predictors of the latent classes. The findings of LCA suggested that three heterogeneous subtypes of health literacy among individuals with metabolic syndrome were identified: high levels of health literacy, moderate levels of health literacy, and low levels of health literacy. The multinomial logistic regression results indicated that compared with low levels of health literacy class, the high levels of health literacy class were predicted by age (OR 0.932, 95%CI[0.900-0.966]), socio-economic status (OR 1.185, 95%CI[1.058-1.328]), and social support (OR 1.065, 95%CI[1.012-1.120]). Compared with low levels of health literacy class, the moderate levels of health literacy class were predicted by age (OR 0.964, 95%CI[0.934-0.995]), socio-economic status (OR 1.118, 95%CI[1.006-1.242]), male (OR 0.229, 95%CI[0.092-0.576]). The levels of health literacy among patients with metabolic syndrome can be divided into three heterogenous subtypes. The results can inform policy-makers and care professionals to design targeted interventions for different subgroups among patients with metabolic syndrome who are male, at older age, have less social support, and with disadvantaged socio-economic status to improve health literacy.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10586064PMC
http://dx.doi.org/10.1080/07853890.2023.2268109DOI Listing

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