Automated insulin delivery (AID) systems have proven safe and effective in improving glycemic outcomes in individuals with type 1 diabetes (T1D). Clinical evaluation of this technology has progressed to large randomized, controlled outpatient studies and recent commercial approval of AID systems for children and adults. However, several challenges remain in improving these systems for different subpopulations (e.g., young children, athletes, pregnant women, seniors and those with hypoglycemia unawareness). In this review, we highlight the requirements and challenges in AID design for selected subpopulations, and discuss current advances from recent clinical studies.

Background: Suboptimal glycemic control is associated with maternal and neonatal morbidity and mortality in pregnancy complicated by type 1 diabetes (T1D). Prospective analysis of continuous glucose monitoring (CGM) metrics, insulin pump settings, and insulin delivery can better characterize the changes in glycemic levels and insulin use throughout pregnancy with T1D. Materials and Methods: Prescribed parameters, insulin delivery, carbohydrate intake, and CGM data for 25 pregnant women with T1D from three U.S. sites were collected. Participants enrolled before 17 weeks gestation and used personal insulin pumps and study CGM. Mean daily total, basal, and bolus insulin doses (units/kg), CGM time in range (TIR: 63–140 mg/dL), and pump-entered carbohydrates were analyzed for every 2-week gestational interval. Linear mixed-effects regression models were used to evaluate changes across gestational ages compared to 12–14 weeks. Results: Basal insulin was higher during weeks 6–12 and 24–40. Daily bolus and total insulin were higher during weeks 20–40. Pump parameters were adjusted to intensify insulin therapy from 22 weeks onward. Average TIR across pregnancy was 59% ± 14%. Between 18 and 30 weeks, TIR was significantly lower, and time above range was significantly higher compared to the reference biweek. Time below target was lower between 22 and 34 weeks. Seven participants achieved >70% recommended TIR for pregnancy. Participants with maternal complications or infant neonatal intensive care unit admissions had lower TIR. Conclusion: While insulin dosing changed significantly with advancing gestation, most participants did not achieve >70% TIR. Customized anticipatory pump setting adjustments and automated systems aimed toward the designated TIR are needed to improve outcomes for this population. NCT03761615

Objective: Evaluating the feasibility of closed-loop insulin delivery with a zone model predictive control (zone-MPC) algorithm designed for pregnancy complicated by type 1 diabetes (T1D). Research Design and Methods: Pregnant women with T1D from 14 to 32 weeks gestation already using continuous glucose monitor (CGM) augmented pump therapy were enrolled in a 2-day multicenter supervised outpatient study evaluating pregnancy-specific zone-MPC based closed-loop control (CLC) with the interoperable artificial pancreas system (iAPS) running on an unlocked smartphone. Meals and activities were unrestricted. The primary outcome was the CGM percentage of time between 63 and 140 mg/dL compared with participants' 1-week run-in period. Early (2-h) postprandial glucose control was also evaluated. Results: Eleven participants completed the study (age: 30.6 ± 4.1 years; gestational age: 20.7 ± 3.5 weeks; weight: 76.5 ± 15.3 kg; hemoglobin A1c: 5.6% ± 0.5% at enrollment). No serious adverse events occurred. Compared with the 1-week run-in, there was an increased percentage of time in 63–140 mg/dL during supervised CLC (CLC: 81.5%, run-in: 64%, P = 0.007) with less time >140 mg/dL (CLC: 16.5%, run-in: 30.8%, P = 0.029) and time <63 mg/dL (CLC: 2.0%, run-in:5.2%, P = 0.039). There was also less time 180 mg/dL (CLC: 4.9%, run-in: 13.1%, P = 0.032). Overnight glucose control was comparable, except for less time >250 mg/dL (CLC: 0%, run-in:3.9%, P = 0.030) and lower glucose standard deviation (CLC: 23.8 mg/dL, run-in:42.8 mg/dL, P = 0.007) during CLC. Conclusion: In this pilot study, use of the pregnancy-specific zone-MPC was feasible in pregnant women with T1D. Although the duration of our study was short and the number of participants was small, our findings add to the limited data available on the use of CLC systems during pregnancy (NCT04492566).

Objective: In contrast with exercise, or structured physical activity (PA), glycemic disturbances due to daily unstructured PA in patients with type 1 diabetes (T1D) is largely underresearched, with limited information on treatment recommendations. We present results from retrospective analysis of data collected under patients' free-living conditions that illuminate the association between PA, as measured by an off-the-shelf activity tracker, and postprandial blood glucose control. Research Design and Methods: Data from 37 patients with T1D during two clinical studies with identical data collection protocols were analyzed retrospectively: 4 weeks of continuous glucose monitoring, carbohydrate intake, insulin injections, and PA (assessed through wearable activity tracker) were collected in free-living conditions. Five-hour glucose area under curves (GAUCs) following the last-bolused meal of every day were computed to assess postprandial glucose excursions, and their relation with corresponding antecedent PA was analyzed using linear mixed-effects regression models, accounting for meal, insulin, and current glycemic state. Results: Datasets yielded 845 days of data from 37 subjects (22.8 ± 11.6 days/subject); postmeal GAUC was negatively associated with total daily PA measured by step count ( P = 0.025), and total time spent performing higher than light-intensity PA ( P = 0.042). Patients with higher median total daily PA exhibited lower average postprandial GAUC ( P < 0.01). Additional analyses indicated that daily PA likely presents an immediate and delayed impact on glucose control. Conclusion: Daily PA assessed by commonly available sensors is significantly associated with glycemic exposure after an evening meal, indicating that quantitative assessment of PA may be useful in mealtime treatment decisions.

Objective: Suboptimal insulin dosing in type 1 diabetes (T1D) is frequently associated with time-varying insulin requirements driven by various psycho-behavioral and physiological factors influencing insulin sensitivity (IS). Among these, physical activity has been widely recognized as a trigger of altered IS both during and following the exercise effort, but limited indication is available for the management of structured and (even more) unstructured activity in T1D. In this work, we present two methods to inform insulin dosing with biosignals from wearable sensors to improve glycemic control in individuals with T1D. Research Design and Methods: Continuous glucose monitors (CGM) and activity trackers are leveraged by the methods. The first method uses CGM records to estimate IS in real time and adjust the insulin dose according to a person’s insulin needs; the second method uses step count data to inform the bolus calculation with the residual glucose-lowering effects of recently performed (structured or unstructured) physical activity. The methods were tested in silico within the University of Virginia/Padova T1D Simulator. A standard bolus calculator and the proposed “smart” systems were deployed in the control of one meal in presence of increased/decreased IS (Study 1) and following a 1-hour exercise bout (Study 2). Postprandial glycemic control was assessed in terms of time spent in different glycemic ranges and low/high blood glucose indices (LBGI/HBGI), and compared between the dosing strategies. Results: In Study 1, the CGM-informed system allowed to reduce exposure to hypoglycemia in presence of increased IS (percent time < 70 mg/dL: 6.1% versus 9.9%; LBGI: 1.9 versus 3.2) and exposure to hyperglycemia in presence of decreased IS (percent time > 180 mg/dL: 14.6% versus 18.3%; HBGI: 3.0 versus 3.9), tending toward optimal control. In Study 2, the step count-informed system allowed to reduce hypoglycemia (percent time < 70 mg/dL: 3.9% versus 13.4%; LBGI: 1.7 versus 3.2) at the cost of a minor increase in exposure to hyperglycemia (percent time > 180 mg/dL: 11.9% versus 7.5%; HBGI: 2.4 versus 1.5). Conclusions: We presented and validated in silico two methods for the smart dosing of prandial insulin in T1D. If seen within an ensemble, the two algorithms provide alternatives to individuals with T1D for improving insulin dosing accommodating a large variety of treatment options. Future work will be devoted to test the safety and efficacy of the methods in free-living conditions.

Physical activity can cause glucose fluctuations both during and after it is performed, leading to hurdles in optimal insulin dosing in people with type 1 diabetes (T1D). We conducted a pilot clinical trial assessing the safety and feasibility of a physical activity-informed mealtime insulin bolus advisor that adjusts the meal bolus according to previous physical activity, based on step count data collected through an off-the-shelf physical activity tracker. Fifteen adults with T1D, each using a continuous glucose monitor (CGM) and an insulin pump with carbohydrate counting, completed two randomized crossover daily visits. Participants performed a 30 to 45-minute brisk walk before lunch and lunchtime insulin boluses were calculated based on either their standard therapy (ST) or the physical activity-informed bolus method. Post-lunch glycemic excursions were assessed using CGM readings. There was no significant difference between visits in the time spent in hypoglycemia in the post-lunch period (median [IQR] standard: 0 [0]% vs physical activity-informed: 0 [0]%, P = NS). Standard therapy bolus yielded a higher time spent in 70 to 180 mg/dL target range (mean ± standard: 77% ± 27% vs physical activity-informed: 59% ± 31%, P = .03) yet, it was associated with a steeper negative slope in the early postprandial phase (P = .032). Use of step count to adjust mealtime insulin following a walking bout has proved to be safe and feasible in a cohort of 15 T1D subjects. Physical activity-informed insulin dosing of meals eaten soon after a walking bout has a potential of mitigating physical activity related glucose reduction in the early postprandial phase.