Predictors of adherence with self-care guidelines among persons with type 2 diabetes: results from a logistic regression tree analysis

Type 2 diabetes is known to contribute to health disparities in the U.S. and failure to adhere to recommended self-care behaviors
is a contributing factor. Intervention programs face difficulties as a result of patient diversity and limited resources.
With data from the 2005 Behavioral Risk Factor Surveillance System, this study employs a logistic regression tree algorithm
to identify characteristics of sub-populations with type 2 diabetes according to their reported frequency of adherence to
four recommended diabetes self-care behaviors including blood glucose monitoring, foot examination, eye examination and HbA1c
testing. Using Andersen’s health behavior model, need factors appear to dominate the definition of which sub-groups were at
greatest risk for low as well as high adherence. Findings demonstrate the utility of easily interpreted tree diagrams to design
specific culturally appropriate intervention programs targeting sub-populations of diabetes patients who need to improve their
self-care behaviors. Limitations and contributions of the study are discussed.