| Diabetes education (DE), the cornerstone of diabetes care, provides education to help persons make daily decisions about their self-care while reducing complications. The problem is that only 45% of the 12 million Americans with diagnosed diabetes receive DE. The purpose of this secondary analysis of the Behavioral Risk Factor Surveillance Survey 2003 data set was to determine the extent to which the relationships derived from the Health Promotion Model predicted the likelihood of participating in diabetes education. The study question was, "To what extent do personal factors of the Health Promotion Model (HPM), biological factors (age, gender, body mass index [BMI], comorbidities, disabilities, insulin use), sociocultural factors (marital status, education, race, ethnicity, employment, income, insurance), and psychological factors (mental health, perceived health status), predict the likelihood of persons with diabetes participating in DE?" The hypothesis, that non-Hispanic married, older, employed females with diabetes, with more education, higher incomes, who have insurance, who use insulin, have lower BMIs, more comorbidities, more disability, poorer self-perceived health status, and poorer mental health are more likely to participate in DE than their counterparts, was partially supported. To test the hypothesis, a binary logistic regression was used resulting in a statistically significant model (p = .000) with a modest predictive value (62.9%) which explained between 10.9% and 14.6% of the variance. The results indicated that younger, employed women who use insulin, have retinopathy, and have limited activities are more likely to participate in DE. The results also identified those persons at a disadvantage of participating in DE: males who did not complete high school, with incomes between {dollar}10,000 to {dollar}15,000 per year, and who never married or were widowed. The proposition of the HPM which states that personal factors directly influence health behaviors was supported. In an era of nationwide health disparities, the inclusion of personal factors in behavioral models can significantly add to their explanatory power and should be included to identify disparate populations so that tailored interventions can be designed. |
