Adapting a cooking, food budgeting and nutrition intervention for a rural community of American Indians with type 2 diabetes in the North-Central United States. Issue 1 (7th November 2022)

• Record Type: Journal Article
• Authors: Brown, Meagan C; Hawley, Caitie; Ornelas, India J; Huber, Corrine; Best, Lyle; Thorndike, Anne N; Beresford, Shirley; Howard, Barbara V; Umans, Jason G; Hager, Arlette; Fretts, Amanda M
• Is Part Of: Health education research. Volume 38:Issue 1(2023)
• Page Start: 13
• Page End: 27
• Publication Date: 2022-11-07
• Subjects: Health education -- Periodicals; Health education -- Study and teaching -- Periodicals; 613.071
• DOI: 10.1093/her/cyac033
• Languages: English
• ISSNs: 0268-1153

Abstract

Abstract: American Indian (AI) communities experience persistent diabetes-related disparities, yet few nutrition interventions are designed for AI with type 2 diabetes or address socio-contextual barriers to healthy eating. We describe our process of adapting the evidence-based Cooking Matters® program for use by AI adults with type 2 diabetes in a rural and resource-limited setting in the North-Central United States. We conducted three focus groups with AI adults with diabetes to (i) identify Cooking Matters® adaptations and (ii) gather feedback on appropriateness of the adapted intervention using Barrera and Castro's cultural adaptation framework. Transcripts were coded using an inductive, constant comparison approach. Queries of codes were reviewed to identify themes. Contextual considerations included limited access to grocery stores and transportation barriers, reliance on government food assistance and the intergenerational burden of diabetes. Adaptations to content and delivery included incorporating traditional and locally available foods; appealing to children or others in multigenerational households and prioritizing visual over written content. Our use of Barrera and Castro's framework adds rigor and structure to the cultural adaptation process and increases the likelihood of future intervention success. Other researchers may benefit from using this framework to guide the adaptation of evidence-based interventions in AI communities.