Embedding Translational Science Strategies in a Community-Based Diabetes Management Pilot Program to Inform Programmatic Adaptations and Maximize Impact

INTRODUCTION: Community health programs often require contextual adaptation in development and implementation to ensure appropriate feasibility and acceptability levels are achieved. Using RE-AIM, SC CTSI - CE developed structured team-based assessment tools to optimize program refinement and impact. METHODS: We implemented a translational science procedural feedback loop to adapt a clinically oriented diabetes management program for community settings. Surveys collected throughout the training program provided program outcome data. Team-based assessments with training facilitators and independent observers focused on feasibility, fidelity, session flow, and implementation barriers to inform quality improvement. The program team jointly reviewed quantitative and qualitative team-based assessment findings to identify converging feedback, prioritize refinements, and maximize impacts. Refinements were agreed upon, documented, and implemented in phases through a replicable procedural feedback loop. RESULTS: A procedural feedback loop was applied to inform adaptations of a 5-week diabetes management pilot program. Team-based assessments drove targeted refinements to curriculum pacing, clarity of core concepts, interactive activities, and delivery processes. Continuous refinement strengthened feasibility and acceptability across three implementation phases, along with improved participant outcomes. Facilitator fidelity ratings improved over time indicating stronger alignment with training objectives and delivery expectations. Facilitator and observer feedback showed fewer and less substantial barriers, smoother session flow, and improved logistical feasibility with each passing phase. DISCUSSION: A structured, evidence-based procedural feedback loop is a generalizable model for adapting and refining CE programs. Using team-based assessments supports continuous improvement. This model can be replicated across program contexts to strengthen feasibility, acceptability, and translational impact.