Non-health outcomes affecting self-care behaviors and medical decision-making preference in patients with type 2 diabetes: a cross-sectional study

Abstract

Background: The effects of patient sustained self-care behaviors on glycemic control are even greater than the effects of medical treatment, indicating the value of identifying the factors that influence self-care behaviors. To date, these factors have not been placed in a single model to clarify the critical path affecting self-care behaviors. The aims of this study were to explore the relationships of these factors and the differences in patient preference for medical decision-making.Methods: A cross-sectional study was conducted among outpatients with type 2 diabetes at a regional teaching hospital. Purposive sampling was adopted to recruit 316 eligible patients via self-administered questionnaires. Partial least squares structural equation modeling was used for analysis.Results: Significant direct pathways were identified from health literacy to self-efficacy, patient empowerment, and self-care behaviors; from self-efficacy to self-care behaviors; and from patient empowerment to self-care behaviors. Indirect pathways were from health literacy to self-care behaviors via self-efficacy or patient empowerment. The pathway from health literacy to self-efficacy was significantly stronger in those preferring shared decision-making than in those who preferred physician decision-making. Conclusions: Health literacy is a critical factor in improving self-care behaviors in patients with type 2 diabetes, and the effect of health literacy on self-efficacy was more significant in the shared decision-making than in the physician decision-making. Therefore, developing an effective health strategy to strengthen health literacy awareness and designing friendly, diverse health literacy materials, and application tools is the most important factor to facilitate self-care behaviors in this population.

Effect of maternal and child factors on stunting: partial least squares structural equation modeling

“…The research data were tested using partial least squares structural equation modeling (PLS-SEM), a multivariate statistical technique used to analyze lots of response variables and explanatory variables simultaneously. PLS-SEM has achieved a universal highlight as the A c c e p t e d A r t i c l e instrument of scientific analysis in medical field as it can be applied in Mediation and Moderation analysis 14,15) . This analysis type is not influenced by multicolinearity and free distribution.…”