Protocol for the Digital, Individualized, and Collaborative Treatment of Type 2 Diabetes in General Practice Based on Decision Aid (DICTA)-A Randomized Controlled Trial

Background: Despite significant advancements in diabetes care, many individuals with type 2 diabetes (T2D) do not receive optimal care and treatment. Digital interventions promoting behavioral changes have shown promising long-term results in supporting healthier lifestyles but are not implemented in most healthcare offerings, maybe due to lack of general practice support and collaboration. This study evaluates the efficacy of the Digital, Individualized, and Collaborative Treatment of T2D in General Practice Based on Decision Aid (DICTA), a randomized controlled trial integrating a patient-centered smartphone application for lifestyle support in conjunction with a clinical decision support (CDS) tool to assist general practitioners (GPs) in optimizing antidiabetic treatment. Methods: The present randomized controlled trial aims to recruit 400 individuals with T2D from approximately 70 GP clinics (GPCs) in Denmark. The GPCs will be cluster-randomized in a 2:3 ratio to intervention or control groups. The intervention group will receive one year of individualized eHealth lifestyle coaching via a smartphone application, guided by patient-reported outcomes (PROs). Alongside this, the GPCs will have access to the CDS tool to optimize pharmacological decision-making through electronic health records. The control group will receive usual care for one year, followed by the same intervention in the second year. Results: The primary outcome is the one-year change in estimated ten-year cardiovascular risk, assessed by SCORE2-Diabetes calculated from age, smoking status, systolic blood pressure, total and high-density lipoprotein cholesterol, age at diabetes diagnosis, HbA1c, and eGFR. Conclusions: If effective, DICTA could offer a scalable, digital-first approach for improving T2D management in primary care by combining patient-centered lifestyle coaching with real-time pharmacological clinical decision support.