Engaging Patient Advisory Boards of African American Community Members with Type 2 Diabetes in Implementing and Refining a Peer-Led Medication Adherence Intervention.
Martha A MaurerOlayinka O ShiyanbolaMattigan L MottJulia MeansPublished in: Pharmacy (Basel, Switzerland) (2022)
African Americans are more likely than non-Hispanic whites to be diagnosed with and die from diabetes. A contributing factor to these health disparities is African Americans' poor diabetes medication adherence that is due in part to sociocultural barriers (e.g., medicine and illness misperceptions), which negatively affect diabetes management. In our prior work, we engaged with community stakeholders to develop and test a brief version of a culturally adapted intervention to address these barriers to medication adherence. The objective of this study was to elicit feedback to inform the refinement of the full 8-week intervention. We utilized a community-engaged study design to conduct a series of meetings with two cohorts of patient advisory boards of African Americans with type 2 diabetes who were adherent to their diabetes medicines (i.e., peer ambassadors). In total, 15 peer ambassadors were paired with 21 African American participants (i.e., peer buddies) to provide specific intervention support as peers and serve in an advisory role as a board member. Data were collected during nine board meetings with the patient stakeholders. A qualitative thematic analysis of the data was conducted to synthesize the findings. Feedback from the patient advisory board contributed to refining the intervention in the immediate-term, short-term, and long-term. The inclusion of African American community members living with type 2 diabetes on the advisory board contributed to further tailoring the intervention to the specific needs of African Americans with type 2 diabetes in the community.
Keyphrases
- african american
- randomized controlled trial
- healthcare
- mental health
- type diabetes
- cardiovascular disease
- glycemic control
- public health
- metabolic syndrome
- big data
- clinical trial
- machine learning
- adipose tissue
- weight loss
- health information
- insulin resistance
- climate change
- artificial intelligence
- gestational age
- preterm birth
- psychometric properties