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TeleDiab‐2 was a 13‐month randomized controlled trial evaluating the efficacy and safety of two telemonitoring systems to optimize basal insulin (BI) initiation in subjects with inadequately controlled type 2 diabetes (HbA1c, 7.5%‐10%). A total of 191 participants (mean age 58.7 years, mean HbA1c 8.9%) were randomized into three groups: group 1(G1, standard care, n = 63), group 2 (G2, interactive voice response system, n = 64) and group 3 (G3, Diabeo‐BI app software, n = 64). The two telemonitoring systems proposed daily adjustments of BI doses, in order to facilitate the achievement of fasting blood glucose (FBG) values targeted at ~100 mg/dL. At 4 months follow‐up, HbA1c reduction was significantly higher in the telemonitoring groups (G2: −1.44% and G3: −1.48% vs. G1: −0.92%; P < 0.002). Moreover, target FBG was reached by twice as many patients in the telemonitoring groups as in the control group, and insulin doses were also titrated to higher levels. No severe hypoglycaemia was observed in the telemonitoring groups and mild hypoglycaemia frequency was similar in all groups. In conclusion, both telemonitoring systems improved glycaemic control to a similar extent, without increasing hypoglycaemic episodes.

OBJECTIVEWe investigated the relationship between carbohydrate intake and postprandial blood glucose (BG) levels to determine the most influential meal for type 2 diabetic subjects treated with basal insulin and needing prandial insulin.RESEARCH DESIGN AND METHODSThree-day BG profiles for 37 type 2 diabetic subjects, with A1C levels of 7.7%, treated with sulfonylurea and metformin, and well titrated on insulin glargine, were analyzed using a continuous glucose monitoring system. Food intake from 680 meals was recorded and quantified during continuous glucose monitoring.RESULTSThe median BG excursion (ΔBG) was higher at breakfast than at lunch or dinner (111 [81; 160] vs. 69.5 [41.5; 106] and 82.5 mg/dl [53; 119] mg/dl, P < 0.0001). There was a weak overall correlation between ΔBG and carbohydrate intake. Correlation improved when mealtime was taken into account. Simple relationships were established: ΔBG (mg/dl) = 65 × carbohydrate/body weight + 73 for breakfast (R2 = 0.20, P < 0.0001); the slope was reduced by half at lunch and by one-third at dinner. Twelve relevant variables likely to affect ΔBG were integrated into a polynomial equation. This model accounted for 49% of ΔBG variability. Two groups of patients were identified: responders, in whom ΔBG was well correlated with carbohydrate intake (R2 ≥ 0.30, n = 8), and nonresponders (R2 < 0.30, n = 29). Responders exhibited a greater insulinopenic profile than nonresponders.CONCLUSIONSThe carbohydrate intake in responders clearly drives ΔBG, whereas, in nonresponders, other factors predominate. This sort of characterization should be used to guide therapeutic choices toward more targeted care with improved type 2 diabetes management.