ECPN Student Poster Contestant: Assessing Nutrition Knowledge in Rural Communities: Examining the Association between Sociodemographic Factors and Nutrition Knowledge.

Introduction: Chronic health conditions, such as obesity and type 2 diabetes, are disproportionately higher among rural populations than urban populations. One significant contributor to these health inequities is lower levels of nutrition knowledge, particularly incorrectly estimating portion sizes among rural populations than urban populations. Sociodemographic factors (e.g., income, education) have been found to be associated with better nutritional knowledge as well as improved diets, with individuals lower in these indices overestimating federal guidelines of food servings. Previous research also suggests that males have lower nutrition knowledge than females. While the associations between sociodemographic factors and nutrition knowledge have been examined, there is scarce research examining these associations in rural populations. The purpose of this study was to examine if sociodemographic variables (i.e., gender, income, education level, and employment status) were associated with knowledge of appropriate portion sizes in a sample of rural adults.

Method: The sample included 121 adults from a small town in rural Oregon (M_age = 54.30±16.03). The majority of participants identified as Caucasian/White (93%) and female (64 %). Participants completed a survey and reported sociodemographic variables (including gender, income, highest level of education completed, and employment status) and responded to questions about appropriate portion sizes of proteins, grains, and dairy. Three logistic regressions were conducted to examine the associations between these sociodemographic variables and knowledge of appropriate portion sizes. Categorical variables were dummy-coded and outcomes were coded as right or wrong to fit the parameters of binary logistic regression analyses.

Results: Income was significantly associated with protein portion size, such that those with incomes between $10,000 to $19,999 (b = 1.48, p = .04, OR = 4.39, 95% CI [1.06, 21.28]) and $20,000 to $29,999 (b = 1.69, p = .04, OR = 5.31, 95% CI [1.11, 29.41]) were more likely to answer correctly than those with incomes less than $10,000. The model accounted for 18% of the variance (McFadden R²). There were no significant associations found for grains and dairy knowledge outcomes.

Conclusions: These findings suggest that income may be an important contributor to lower levels of nutrition knowledge in rural populations. These significant findings provide important implications for future research focused on nutrition knowledge in very low income rural communities. These findings support the need to increase nutrition education in rural regions where health disparities are more challenging in comparison to urban regions.

In remembrance of Dr. Tasia M. Smith