OBJECTIVETo demonstrate that Diabeo software enabling individualized insulin dose adjustments combined with telemedicine support significantly improves HbA1c in poorly controlled type 1 diabetic patients.RESEARCH DESIGN AND METHODSIn a six-month open-label parallel-group, multicenter study, adult patients (n = 180) with type 1 diabetes (>1 year), on a basal-bolus insulin regimen (>6 months), with HbA1c ≥8%, were randomized to usual quarterly follow-up (G1), home use of a smartphone recommending insulin doses with quarterly visits (G2), or use of the smartphone with short teleconsultations every 2 weeks but no visit until point end (G3).RESULTSSix-month mean HbA1c in G3 (8.41 ± 1.04%) was lower than in G1 (9.10 ± 1.16%; P = 0.0019). G2 displayed intermediate results (8.63 ± 1.07%). The Diabeo system gave a 0.91% (0.60; 1.21) improvement in HbA1c over controls and a 0.67% (0.35; 0.99) reduction when used without teleconsultation. There was no difference in the frequency of hypoglycemic episodes or in medical time spent for hospital or telephone consultations. However, patients in G1 and G2 spent nearly 5 h more than G3 patients attending hospital visits.CONCLUSIONSThe Diabeo system gives a substantial improvement to metabolic control in chronic, poorly controlled type 1 diabetic patients without requiring more medical time and at a lower overall cost for the patient than usual care.

Integrated closed-loop control (CLC), combining continuous glucose monitoring (CGM) with insulin pump (continuous subcutaneous insulin infusion [CSII]), known as artificial pancreas, can help optimize glycemic control in diabetes. We present a fundamental modular concept for CLC design, illustrated by clinical studies involving 11 adolescents and 27 adults at the Universities of Virginia, Padova, and Montpellier. We tested two modular CLC constructs: standard control to range (sCTR), designed to augment pump plus CGM by preventing extreme glucose excursions; and enhanced control to range (eCTR), designed to truly optimize control within near normoglycemia of 3.9–10 mmol/L. The CLC system was fully integrated using automated data transfer CGM→algorithm→CSII. All studies used randomized crossover design comparing CSII versus CLC during identical 22-h hospitalizations including meals, overnight rest, and 30-min exercise. sCTR increased significantly the time in near normoglycemia from 61 to 74%, simultaneously reducing hypoglycemia 2.7-fold. eCTR improved mean blood glucose from 7.73 to 6.68 mmol/L without increasing hypoglycemia, achieved 97% in near normoglycemia and 77% in tight glycemic control, and reduced variability overnight. In conclusion, sCTR and eCTR represent sequential steps toward automated CLC, preventing extremes (sCTR) and further optimizing control (eCTR). This approach inspires compelling new concepts: modular assembly, sequential deployment, testing, and clinical acceptance of custom-built CLC systems tailored to individual patient needs.