Effect of Remote Glucose Monitoring Utilizing Computerized Insulin Dose Adjustment Algorithms on A1C Levels—A Preliminary Report

Abstract

Adjusting insulin doses is very challenging for primary care physicians because of infrequent office visits and time constraints at the visit. For patients, frequent office visits are a barrier. We have used FDA-approved, computerized insulin dose adjustment algorithms and remote glucose monitoring to circumvent both of these issues. Diabetic patients in a safety net clinic who took insulin for at least 6 months with A1C levels ?8.0% were enrolled in a study in which an iHealth glucose meter was attached to a smartphone containing an app downloaded from the Web that transmitted the glucose values to a secure, HIPAA-approved server. There the values were analyzed by the algorithms and a report with recommendations for dose changes was generated and sent to a clinic nurse practitioner every 2-3 weeks who could accept or modify the recommendations. A clinic staff individual contacted the patient with the insulin dose changes. Twenty-eight diabetic patients (one type 1) have reached 3 months. Their mean age (± SD) was 55.9 ± 8.6 years, 15 were females and 13 were males. There were 20 Latinos, 4 African-Americans, 3 Caucasians, and 1 Asian. Their insulin regimens were: basal alone - 11, basal/bolus - 14 and self-mixed/split - 3. Non-insulin drugs were not changed. Their baseline A1C levels (± SD) of 10.0% ± 1.2 decreased by 1.9% ± 0.9 to 8.1% ± 1.1 after 3 months. Their total insulin dose increased by 24%. There were no episodes of severe hypoglycemia (requiring 2nd party assistance) nor emergency room visits for hypoglycemia. Conclusion: Remote glucose monitoring utilizing computerized insulin dose adjustment algorithms to analyze glucose values and generating recommendations for dose changes at more frequent intervals markedly improved A1C levels in poorly controlled diabetic patients in the absence of office visits. Disclosure M.B. Davidson: Employee; Self; Insulin Algorithms, Inc. J. Davidson: Employee; Self; Insulin Algorithms.