Effect of Computer-Generated Tailored Feedback on Glycemic Control in People With Diabetes in the Community
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OBJECTIVE: It is unknown whether computer-generated, patient-tailored feedback leads to improvements in glycemic control in people with type 2 diabetes. RESEARCH DESIGN AND METHODS: We recruited people with type 2 diabetes aged ≥ 40 years with a glycated hemoglobin (A1C) ≥ 7%, living in Hamilton, Canada, who were enrolled in a community-based program (Diabetes Hamilton) that provided regular evidence-based information and listings of community resources designed to facilitate diabetes self-management. After completing a questionnaire, participants were randomly allocated to either receive or not receive periodic computer-generated, evidence-based feedback on the basis of their questionnaire responses and designed to facilitate improved glycemic control and diabetes self-management. The primary outcome was a change in A1C after 1 year. RESULTS: A total of 465 participants (50% women, mean age 62 years, and mean A1C 7.83%) were randomly assigned, and 12-month A1C values were available in 96% of all participants, at which time the A1C level had decreased by an absolute amount of 0.24 and 0.15% in the intervention and control groups, respectively. The difference in A1C reduction for the intervention versus control group was 0.09% (95% CI -0.08 to 0.26; P = 0.3). No between-group differences in measures of quality of life, diabetes self-management behaviors, or clinical outcomes were observed. CONCLUSIONS: Providing computer-generated tailored feedback to registrants of a generic, community-based program that supports diabetes self-management does not lead to lower A1C levels or a better quality of life than participation in the community-based program (augmented by periodic A1C testing) alone.
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