ABSTRACT
Introduction: Food insecurity means lacking access to adequate, nutritious, and safe food. Collegiate food insecurity rates at ten Appalachian campuses range from 22.4% to 51.8% and have been associated with unfavorable health and academic outcomes. Purpose: This study compared cooking, dietary, and food safety characteristics of food secure (FS) and food insecure (FI) sophomores at a university in Appalachia in the context of the USDA definition of food security. Methods: Data were collected using an online questionnaire. Descriptive and inferential procedures compared FS and FI sophomores (p < 0.05). Results: Participants (n = 226) were 65.0% females, 76.1% whites, and 46% FI. About 40% of on-campus and 50% of off-campus residents were FI, and 70% of FI students reported needing help accessing food. Cooking was undertaken "less often" by 61.5% of FS and 55.8% of FI sophomores. Mean cooking self-efficacy scores for FS and FI students were 44.9, vs 43.4, (p > 0.05) out of 52 points. Grains were consumed most often by 40% of FS and FI students and vegetables were consumed least often by 70% of both groups. Mean food safety test scores for FS and FI students were 6.2 1.60 vs 6.6 1.52 (p > 0.05) out of 11 points. Requested educational activities included making a budget and planning balanced meals. Implications: The high rate of food insecurity reflects an ongoing need among sophomores for campus and community food assistance and for educational activities that teach purchasing and preparation of affordable, healthy and safe foods.
ABSTRACT
OBJECTIVES: To characterize dietary patterns using two different cluster analysis strategies. DESIGN: In this cross-sectional study, diet information was assessed by five 24-hour recalls collected over 10 months. All foods were classified into 24 food subgroups. Demographic, health, and anthropometric data were collected via home visit. SUBJECTS: One hundred seventy-nine community-dwelling adults, aged 66 to 87 years, in rural Pennsylvania. STATISTICAL ANALYSIS: Cluster analysis was performed. RESULTS: The methods differed in the food subgroups that clustered together. Both methods produced clusters that had significant differences in overall diet quality as assessed by Healthy Eating Index (HEI) scores. The clusters with higher HEI scores contained significantly higher amounts of most micronutrients. Both methods consistently clustered subgroups with high energy contribution (eg, fats and oils and dairy desserts) with a lower HEI score. Clusters resulting from the percent energy method were less likely to differentiate fruit and vegetable subgroups. The higher diet quality dietary pattern derived from the number of servings method resulted in more favorable weight status. CONCLUSIONS: Cluster analysis of food subgroups using two different methods on the same data yielded similarities and dissimilarities in dietary patterns. Dietary patterns characterized by the number of servings method of analysis provided stronger association with weight status and was more sensitive to fruit and vegetable intake with regard to a more healthful dietary pattern within this sample. Public health recommendations should evaluate the methodology used to derive dietary patterns.
