LaShaundrea Crook

Energy Density, Nutrient Adequacy, and Cost per Serving Can Provide Insight into Food Choices in the Lower Mississippi Delta

OBJECTIVE To compare differences across food groups for food cost, energy, and nutrient profiles of 100 items from a cross-sectional survey of 225 stores in 18 counties across the Lower Mississippi Delta of Arkansas, Louisiana, and Mississippi. METHODS Energy, nutrient, and cost profiles for food items were calculated by using Naturally Nutrient Rich methodology and converting price per 100 g edible portion to price per serving. Foods were grouped into 6 food groups. Mean differences were compared with ANOVA. RESULTS Significant differences existed by food group for each measure. Energy density was highest for fats/oils/sweets, whereas nutrient density was highest for vegetables. Price per serving was lowest for fats/oils/sweets and highest for meats. CONCLUSIONS AND IMPLICATIONS Educational messages focusing on a complete diet should consider the role of food costs and provide specific recommendations for increasing nutrient-dense foods by replacing a portion of the meat serving at meals with culturally acceptable lower-cost nutrient-dense foods.

Mississippi Communities for Healthy Living: Implementing a nutrition intervention effectiveness study in a rural health disparate region

BACKGROUND Intervention research in rural, health disparate communities presents unique challenges for study design, implementation, and evaluation. Challenges include 1) culturally appropriate intervention components, 2) participant recruitment and retention, 3) treatment cross-contamination, 4) intervention delivery and data collection, and 5) potential measurement reactivity. PURPOSE The purposes of this paper are to 1) detail the methods of the MCHL study and 2) report baseline demographic characteristics of study participants. The secondary aim is to determine if study participants were engaging in behavior changes after enrollment and prior to intervention initiation. METHODS MCHL was developed using the RE-AIM planning and evaluation framework (reach, effectiveness, adoption, implementation, maintenance). Intervention components were based on Rogers diffusion of innovation attributes that promote adoption of a new innovation as well as on the psychosocial constructs of social support, self-efficacy and decisional balance. Rolling enrollment data collection was used to acquire sufficient sample size and a second data collection just prior to intervention implementation assessed measurement reactivity effects. Participant outcomes included diet quality, blood pressure, weight status, and quality of life. Cluster stratified assignment to one of two treatment arms was utilized to minimize cross contamination. Generalized linear models were used to compare enrollment measures between the two treatment arms while mixed model linear regression was used to test for changes in diet quality outcomes from enrollment to pre-intervention baseline. RESULTS There were no significant differences in participant demographic, anthropometric or clinical measures between the two treatment arms at enrollment. With the exception of total vegetables, none of the diet quality indicators were significantly different between enrollment and baseline timepoints. CONCLUSIONS Conducting nutrition intervention research in a rural health disparate region requires flexibility in adapting the recruitment, retention, and data collection procedures while maintaining a high level of scientific rigor. Negligible research participation effects, such as measurement reactivity, were noted in this population. However, further research is needed to identify methods to successfully recruit and retain Caucasian females to participate in community-based nutrition interventions in this region.