The influence of locus of control on self-rated health in context of chronic disease: a structural equation modeling approach in a cross sectional study

Abstract

Background: Self-rated health is a robust predictor of several health outcomes, such as functional ability, health care utilization, morbidity and mortality. The purpose of this study is to investigate and explore how health locus of control and disease burden relate to self-rated health among patients at risk for cardiovascular disease.

Methods: In 2009, 414 Swedish patients who were using statins completed a questionnaire about their health, diseases and their views on the three-dimensional health locus of control scale. The scale determines which category of health locus of control - internal, chance or powerful others - a patient most identifies with. The data was analyzed using logistic regression and a structural equation modeling approach.

Results: The analyses showed positive associations between internal health locus of control and self-rated health, and a negative association between health locus of control in chance and powerful others and self-rated health. High internal health locus of control was negatively associated with the cumulative burden of diseases, while health locus of control in chance and powerful others were positively associated with burden of diseases. In addition, age and education level had indirect associations with self-rated health through health locus of control.

Conclusions: This study suggests that self-rated health is positively correlated with internal locus of control and negatively associated with high locus of control in chance and powerful others in patients at high risk for cardiovascular disease. Furthermore, disease burden seems to be negatively associated with self-rated health.

Figure 1

Research framework and model construction. This HDLoC model outlines how the theory was implemented in the empirical pathway analysis.

Figure 2

Structural equation model analysis of data outlined after the theoretical framework generated through a partial least squares estimation technique, with path coefficients of the pathway model (i.e. inner model). The model outlines the hypothesized relationships among the factors in the HDLoC model. Significant direct associations between latent variables are presented in bold.