Affiliation:
1. VA Tennessee Valley Healthcare System, Geriatric Research Education and Clinical Center (GRECC), Nashville, TN, USA
2. Georgia State University, Atlanta, GA, USA
Abstract
Background Black women are diagnosed, disabled, and die from obesity and associated chronic diseases at higher rates than any other sex or race. Advanced practice registered nurses (APRN) can potentially improve culturally relevant health education and counseling by using health literacy communication tools. Objective Explore individualized barriers and APRNs’ role in providing obesity prevention education and counseling by assessing the efficacy of the Teach-Back Method (TBM) to understand health habits and attitudes. Methods Black women aged 18–45, previously diagnosed as overweight or obese, and identified with perceived barriers were recruited from a predominantly Black church in Atlanta. They engaged in weekly, 1-hour educational sessions via Zoom, addressing four common barriers identified in the literature. Sessions ended with a 5–10 minute Teach-Back session. Pre- and post-intervention Readiness to Change Questionnaire (RCQ) were completed. Descriptive statistics and quantitative data from surveys and pre- and post-RCQ were analyzed. Results Twenty women completed the intervention. Paired sample t-test revealed no statistical significance or correlation between pre- and post-RCQ scores after using TBM in educational sessions. However, Pearson’s correlation showed positive associations between elevated body mass index levels as one advances their education and annual income, with a p-value of 0.05. Discussion Increased rates of obesity are experienced despite higher educational attainment or pay. Stress and high-coping mechanisms contributed to disordered eating, decreased physical activity engagement, and decreased motivation toward habit change. Clinicians should be held accountable for delivering culturally sensitive care using the TBM, addressing social determinants of health, performing routine stress assessments, and checking their implicit biases.