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Current guidelines recommend 15-20 g of carbohydrate (CHO) for treatment of mild to moderate hypoglycemia. However, these guidelines do not account for reduced insulin during suspensions with predictive low-glucose suspend (PLGS). We assessed insulin suspensions, hypoglycemic events, and CHO treatment during a 20-h inpatient evaluation of an investigational system with a PLGS feature, including an overnight basal up-titration period to activate the PLGS. Among 10 adults with type 1 diabetes, there were 59 suspensions; 7 suspensions were associated with rescue CHO and 5 with hypoglycemia. Rescue treatment consisted of median 9 g CHO (range: 5-16 g), with no events requiring repeat CHO. No rescue CHO were given during or after insulin suspension for the overnight basal up-titration. To minimize rebound hyperglycemia and needless calorie intake from hypoglycemia overtreatment, updated guidance for PLGS systems should reflect possible need to reduce CHO amounts for hypoglycemia rescue associated with an insulin suspension. The clinical trial was registered with ClinicalTrials.gov (NCT03890003).

Background: Personal insulin pumps have shown to be effective in improving the quality of therapy for people with type 1 diabetes (T1D). However, the safety of this technology is limited by the possible infusion site failures, which are linked with hyperglycemia and ketoacidosis. Thanks to the large availability of collected data provided by modern therapeutic technologies, machine learning algorithms have the potential to provide new way to identify failures early and avert adverse events. Methods: A clinical dataset ( N = 20) is used to evaluate a novel method for detecting real-time infusion site failures using unsupervised anomaly detection algorithms, previously proposed and developed on in-silico data. An adapted feature engineering procedure is introduced to make the method able to operate in the absence of a closed-loop (CL) system and meal announcements. Results: In the optimal configuration, we obtained a performance of 0.75 Sensitivity (15 out of 20 total failures detected) and 0.08 FP/day, outperforming previously proposed literature algorithms. The algorithm was able to anticipate the replacement of the malfunctioning infusion sets by ~2 h on average. Conclusions: On the considered dataset, the proposed algorithm showed the potential to improve the safety of patients treated with sensor-augmented pump systems.

Objective: In this work, we design iterative algorithms for the delivery of long-acting (basal) and rapid-acting (bolus) insulin, respectively, for people with type 1 diabetes (T1D) on multiple-daily-injections (MDIs) therapy using feedback from self-monitoring of blood glucose (SMBG) measurements. Methods: Iterative learning control (ILC) updates basal therapy consisting of one long-acting insulin injection per day, while run-to-run (R2R) adapts meal bolus therapy via the update of the mealtime-specific insulin-to-carbohydrate ratio (CR). Updates are due weekly and are based upon sparse SMBG measurements. Results: Upon termination of the 20 weeks long in-silico trial, in a scenario characterized by meal carbohydrate (CHO) normally distributed with mean $\mu$ = [50, 75, 75] grams and standard deviation $\sigma$ = [5, 7, 7] grams, our strategy produced statistically significant improvements in time in range (70--180) [mg/dl], from 66.9(33.1) $\%$ to 93.6(6.7) $\%$ , $p$ = 0.02. Conclusions: Iterative learning shows potential to improve glycemic regulation over time by driving blood glucose closer to the recommended glycemic targets. Significance: Decision support systems (DSSs) and automated therapy advisors such as the one proposed here are expected to improve glycemic outcomes reducing the burden on patients on MDI therapy.

Background: Automated Insulin Delivery (AID) hybrid closed-loop systems have not been well studied in the context of prescribed meals. We evaluated performance of our interoperable artificial pancreas system (iAPS) in the at-home setting, running on an unlocked smartphone, with scheduled meal challenges in a randomized crossover trial. Methods: Ten adults with type 1 diabetes completed 2 weeks of AID-based control and 2 weeks of conventional therapy in random order where they consumed regular pasta or extra-long grain white rice as part of a complete dinner meal on six different occasions in both arms (each meal thrice in random order). Surveys assessed satisfaction with AID use. Results: Postprandial differences in conventional therapy were 10,919.0 mg/dL × min (95% confidence interval [CI] 3190.5–18,648.0, P = 0.009) for glucose area under the curve (AUC) and 40.9 mg/dL (95% CI 4.6–77.3, P = 0.03) for peak continuous glucose monitor glucose, with rice showing greater increases than pasta. White rice resulted in a lower estimate over pasta by a factor of 0.22 (95% CI 0.08–0.63, P = 0.004) for AUC under 70 mg/dL. These glycemic differences in both meal types were reduced under AID-based control and were not statistically significant, where 0–2 h insulin delivery decreased by 0.45 U for pasta (P = 0.001) and by 0.27 U for white rice (P = 0.01). Subjects reported high overall satisfaction with the iAPS. Conclusions: The AID system running on an unlocked smartphone improved postprandial glucose control over conventional therapy in the setting of challenging meals in the outpatient setting. Clinical Trial Registry: clinicaltrials.gov NCT03767790.

We propose a Kalman filter-based observer utilizing noisy remote compartment insulin measurements to estimate plasma insulin concentration. The design considers plant-model mismatch, sensor noise, as well as both uniform sampling intervals, mimicking infrequent continuous measurements, and non-uniform sampling intervals, mimicking infrequent on-demand measurements. The performance of the observer is demonstrated on ten in-silico subjects from the UVA/Padova simulator using real-life scenarios, including variability in sensor noise and variability in insulin pharmacokinetics. The proposed observer provides insight into the future use of insulin measurements for diabetes management.

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

This paper introduces methods to estimate aspects of physical activity and sedentary behavior from three-axis accelerometer data collected with a wrist-worn device at a sampling rate of 32 [Hz] on adults with type 1 diabetes (T1D) in free-living conditions. In particular, we present two methods able to detect and grade activity based on its intensity and individual fitness as sedentary, mild, moderate or vigorous, and a method that performs activity classification in a supervised learning framework to predict specific user behaviors. Population results for activity level grading show multi-class average accuracy of 99.99%, precision of 98.0 ± 2.2%, recall of 97.9 ± 3.5% and F1 score of 0.9 ± 0.0. As for the specific behavior prediction, our best performing classifier, gave population multi-class average accuracy of 92.43 ± 10.32%, precision of 92.94 ± 9.80%, recall of 92.20 ± 10.16% and F1 score of 92.56 ± 9.94%. Our investigation showed that physical activity and sedentary behavior can be detected, graded and classified with good accuracy and precision from three-axial accelerometer data collected in free-living conditions on people with T1D. This is particularly significant in the context of automated glucose control systems for diabetes, in that the methods we propose have the potential to inform changes in treatment parameters in response to the intensity of physical activity, allowing patients to meet their glycemic targets.

Aim: Overnight insulin dosing relies on glucose level and evening food intake, although PA also impacts glycemic levels. We assessed associations between antecedent energy expenditure (EE) from PA and nocturnal glycemia in youth with T1D. Methods: Youth completed 3-day PA logs (PA type, duration, intensity) and wore 3-day masked CGM (iPro™) quarterly for 18 months. We assigned a MET value to PA and calculated EE (kcal/day), using only complete 24-hour days. CGM metrics were calculated for days (6 AM-11:59 PM) and nights (12 AM-5:59 AM) when ≥50% of CGM data were available. Results: Youth (N=136, 48% male) were ages 8-17 years (M±SD 12.8±2.5), with T1D duration 5.9±3.1 years and daily insulin dose 0.9±0.3 U/kg; 73% used insulin pumps. Median (IQR) PA duration and EE were 60 (10-120) minutes/day and 287 (0-695) kcal/day, respectively. CGM metrics by day/night appear in Table. In partial correlations adjusted for multiple observations/person, higher EE was associated with lower mean glucose (r=-.12, p=.0001), more %time 180 (r=-.09, p=.006) that night. Time in range was unchanged. When youth EE was 180 (p=.03) that night. Conclusion: In youth with T1D, PA can reduce nocturnal hyperglycemia and increase nocturnal hypoglycemia. These findings can help tailor automated insulin delivery algorithms according to PA. Disclosure R. O. La banca: None. L. K. Volkening: None. K. Milaszewski: Consultant; Self; Lilly USA, LLC. E. Dassau: Consultant; Self; Eli Lilly and Company, Employee; Self; Eli Lilly and Company, Research Support; Self; Dexcom, Inc., Tandem Diabetes Care, Speaker’s Bureau; Self; Dexcom, Inc., Roche Diabetes Care, Stock/Shareholder; Self; Eli Lilly and Company. S. N. Mehta: None. L. M. Laffel: Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Dexcom, Inc., Dompe, Insulogic LLC, Janssen Pharmaceuticals, Inc., Laxmi Therapeutic Devices, LifeScan, Lilly Diabetes, Medtronic, Provention Bio, Inc. Funding National Institutes of Health (K12DK094721, P30DK036836); Iacocca Family Foundation

Aim: PA is an important part of diabetes management. ADA recommends that youth with T1D participate daily in ≥60 minutes of moderate to vigorous PA. We prospectively assessed daily PA in youth with T1D and compared demographic and diabetes variables according to frequency of PA ≥60 minutes/day. Methods: Youth (N=125, ages 8-17) with T1D completed 3-day PA records every 3 months for 18 months. Diabetes treatment data and A1c were collected at the same time. For each youth, we calculated the % of days with ≥60 minutes PA and compared those with ≥60 minutes PA on Results: Youth (50% male, 90% white, 74% pump-treated) had a mean±SD age 12.8±2.5 years, T1D duration 6.0±3.2 years, and A1c 8.2±0.9%. Youth had a median of 16 (IQR 12-17) PA record days. The % of days with ≥60 minutes PA ranged from 0 to 100% (median 53% [IQR 35-71%]), with 59% of youth reporting ≥60 minutes PA on ≥50% of days. Youth with ≥60 minutes PA on ≥50% of days were younger (12.4 vs. 13.5 years, p=.02), had shorter T1D duration (5.2 vs. 7.0 years, p=.002), lower daily insulin dose (0.86 vs. 1.00 u/kg, p=.002), and lower A1c (8.1 vs. 8.4%, p=.03) than youth with ≥60 minutes PA on Conclusion: A substantial proportion of youth with T1D do not exercise per recommendations. Given associations of PA with lower A1c, additional efforts to support exercise management in T1D are needed along with support for PA throughout the year. Disclosure R. O. La banca: None. L. K. Volkening: None. H. Desrochers: None. E. Dassau: Consultant; Self; Eli Lilly and Company, Employee; Self; Eli Lilly and Company, Research Support; Self; Dexcom, Inc., Tandem Diabetes Care, Speaker’s Bureau; Self; Dexcom, Inc., Roche Diabetes Care, Stock/Shareholder; Self; Eli Lilly and Company. S. N. Mehta: None. L. M. Laffel: Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Dexcom, Inc., Dompe, Insulogic LLC, Janssen Pharmaceuticals, Inc., Laxmi Therapeutic Devices, LifeScan, Lilly Diabetes, Medtronic, Provention Bio, Inc. Funding National Institutes of Health (K12DK094721, P30DK036836); Iacocca Family Foundation

Automated Insulin Delivery (AID) hybrid closed-loop systems have not been well studied in the context of psychological and physiological stress. We evaluated our interoperable artificial pancreas system with in-clinic stress challenges in a randomized crossover trial. Fourteen adults with type 1 diabetes were randomized to 2 weeks of AID-based control and two weeks of sensor augmented pump (SAP)l therapy at home in random order. The AID system used the Zone-Model Predictive Control algorithm with a continuous function of glucose velocity and insulin-on-board to gradually increase insulin infusion under conditions of sustained hyperglycemia. During each two-week period, in-clinic stress assessments for psychological stress (TSST and SECPT) and pharmacological stress (hydrocortisone 40-20-20 mg) were performed. Ten subjects completed the study per protocol, with 48 stress sessions completed in 12 subjects. AID increased time 70-180mg/dL from 63.1 to 74.4% (p=0.001), decreased time 180mg/dL from 35.4 to 24.8% (p=0.001) during the 2 weeks outpatient use. AID decreased glycemic variability as measured by CV (p=0.037) and time Disclosure R. Kaur: None. Y. C. Kudva: Research Support; Self; Dexcom, Inc. S. Deshpande: None. J. E. Pinsker: Advisory Panel; Self; Medtronic, Consultant; Self; Eli Lilly and Company, Tandem Diabetes Care, Research Support; Self; Dexcom, Inc., Insulet Corporation, Medtronic, Tandem Diabetes Care. S. K. Mccrady-spitzer: None. D. Desjardins: None. M. Church: None. W. K. Kremers: Research Support; Self; AstraZeneca, Biogen, Roche Pharma. F. J. Doyle: Advisory Panel; Self; Mode AGC, Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. E. Dassau: Consultant; Self; Eli Lilly and Company, Employee; Self; Eli Lilly and Company, Research Support; Self; Dexcom, Inc., Tandem Diabetes Care, Speaker’s Bureau; Self; Dexcom, Inc., Roche Diabetes Care, Stock/Shareholder; Self; Eli Lilly and Company. Funding JDRF (2-SRA-2017-503-M-B); The Leona M. and Harry B. Helmsley Charitable Trust; Dexcom, Inc. (IIS-2019-017)

Pregnancy complicated by type 1 diabetes (T1D) is associated with increased maternal and fetal morbidity and mortality. We report results from the first clinical trial testing a closed-loop control (CLC) system specifically designed for pregnant women with T1D in the United States. Eight pregnant women with T1D participated in a 48-hour clinical trial at three US sites (NCT04492566). Women were enrolled in the second trimester after organogenesis was completed. CLC sessions used the interoperable artificial pancreas system (iAPS) running a Zone-Model Predictive Control algorithm designed for stricter glycemic targets for pregnancy. Glycemic target zones were 80-110 mg/dL during the day and 80-100 mg/dL overnight, with assertive insulin delivery in the postprandial period. The primary outcome was sensor glucose time in range (TIR) for pregnancy 63-140 mg/dL as per international consensus guidelines. All enrolled subjects completed the trial. Participants had a mean age of 29.3±3.5 years, gestational age of 22.2±3.6 weeks, weight of 77±12.2 kg, and HbA1c of 5.7±0.5%. Mean sensor TIR 63-140 mg/dL was 79.3±12.8% during CLC, compared to 60.8±18.8% for the week prior in open loop at home (p=0.03, Table 1). Unrestricted carbohydrate intake at meals ranged from 7 to 78 grams during the CLC sessions. No severe hypoglycemia or adverse events occurred. CLC in pregnant women with T1D is safe and effective. Disclosure B. Ozaslan: None. M. Church: None. M. Plesser: None. S. K. Mccrady-spitzer: None. C. Bakus: None. S. J. Ogyaadu: None. K. Nelson: None. C. Reid: None. S. Deshpande: None. W. K. Kremers: Research Support; Self; AstraZeneca, Biogen, Roche Pharma. F. J. Doyle: Advisory Panel; Self; Mode AGC, Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. C. J. Levy: Advisory Panel; Self; Dexcom, Inc., Eli Lilly and Company, Employee; Spouse/Partner; AbbVie Inc., Other Relationship; Self; Dexcom, Inc., Research Support; Self; Abbott Diabetes, Dexcom, Inc., Insulet Corporation, Tandem Diabetes Care. B. Rosenn: None. Y. C. Kudva: Research Support; Self; Dexcom, Inc. E. Dassau: Consultant; Self; Eli Lilly and Company, Employee; Self; Eli Lilly and Company, Research Support; Self; Dexcom, Inc., Tandem Diabetes Care, Speaker’s Bureau; Self; Dexcom, Inc., Roche Diabetes Care, Stock/Shareholder; Self; Eli Lilly and Company. J. E. Pinsker: Advisory Panel; Self; Medtronic, Consultant; Self; Eli Lilly and Company, Tandem Diabetes Care, Research Support; Self; Dexcom, Inc., Insulet Corporation, Medtronic, Tandem Diabetes Care. G. O’malley: Research Support; Self; Abbott Diabetes, Dexcom, Inc., Horizon Therapeutics plc, Tandem Diabetes Care. R. Kaur: None. K. N. Castorino: Consultant; Self; Dexcom, Inc., Research Support; Self; Abbott Diabetes, Abbott Laboratories, Dexcom, Inc., Drawbridge Health, Inc., Lilly Diabetes, Medtronic, Novo Nordisk Inc. C. Levister: Research Support; Self; Abbott Diabetes, Insulet Corporation. M. Trinidad: None. D. Desjardins: None. Funding National Institutes of Health (R01DK120358); Dexcom, Inc. (AP-2020-014)

Background As new diabetes technologies improve to better manage glucose levels, users' priorities for future technologies may shift to prioritize burden reduction and ease of use. We used qualitative methods to explore youth and parent desired features of an "ideal" artificial pancreas (AP) system. Methods We conducted semi-structured interviews with 39 youth, ages 10-25 years, and 44 parents. Interviews were audio-recorded, transcribed, and coded using thematic analysis. Results Youth (79% female, 82% non-Hispanic white) were (M±SD) ages 17.0±4.7 years, with diabetes for 9.4±4.9 years, and HbA1c of 8.4±1.1%; 79% were pump-treated and 82% used CGM. Of parents, 91% were mothers and 86% were non-Hispanic white. Participants suggested various ways in which an ideal AP system could reduce physical and emotional burdens of diabetes. Physical burdens could be reduced by lessening user responsibilities to manage glucose for food and exercise, and wear or carry devices. Emotional burden could be reduced by mitigating negative emotional reactions to sound and frequency of alerts, while increasing feelings of normalcy. Youth and parents differed in their suggestions to reduce emotional burden. Participants suggested features that would improve glycemia, but nearly always in the context of how the feature would directly reduce their diabetes-specific burden. Conclusions Although participants expressed interest in improving glucose levels, the pervasive desire among suggested features of an ideal AP system was to minimize the burden of diabetes. Understanding and addressing users' priorities to reduce physical and emotional burden will be necessary to enhance uptake and maintain use of future AP systems. This article is protected by copyright. All rights reserved.

As wearable healthcare monitoring systems advance, there is immense potential for biological sensing to enhance the management of type 1 diabetes (T1D). The aim of this work is to describe the ongoing development of biomarker analytes in the context of T1D. Technological advances in transdermal biosensing offer remarkable opportunities to move from research laboratories to clinical point‐of‐care applications. In this review, a range of analytes, including glucose, insulin, glucagon, cortisol, lactate, epinephrine, and alcohol, as well as ketones such as beta‐hydroxybutyrate, will be evaluated to determine the current status and research direction of those analytes specifically relevant to T1D management, using both in‐vitro and on‐body detection. Understanding state‐of‐the‐art developments in biosensing technologies will aid in bridging the gap from bench‐to‐clinic T1D analyte measurement advancement.

Background: The objective of this study was to assess the safety and performance of the Omnipod® personalized model predictive control (MPC) algorithm with variable glucose setpoints and moderate intensity exercise using an investigational device in adults with type 1 diabetes (T1D). Materials and Methods: A supervised 54-h hybrid closed-loop (HCL) study was conducted in a hotel setting after a 7-day outpatient standard treatment phase. Adults aged 18–65 years with T1D and HbA1c between 6.0% and 10.0% were eligible. Subjects completed two moderate intensity exercise sessions of >30 min duration on consecutive days: the first with the glucose set point increased from 130 to 150 mg/dL and the second with a temporary basal rate of 50%, both started 90 min pre-exercise. Primary endpoints were percentage time in hypoglycemia

The human insulin-glucose metabolism is a time-varying process, which is partly caused by the changing insulin sensitivity of the body. This insulin sensitivity follows a circadian rhythm and its effects should be anticipated by any automated insulin delivery system. This paper presents an extension of our previous work on automated insulin delivery by developing a controller suitable for humans with Type 1 Diabetes Mellitus. Furthermore, we enhance the controller with a new kernel function for the Gaussian Process and deal with noisy measurements, as well as, the noisy training data for the Gaussian Process, arising therefrom. This enables us to move the proposed control algorithm, a combination of Model Predictive Controller and a Gaussian Process, closer towards clinical application. Simulation results on the University of Virginia/Padova FDA-accepted metabolic simulator are presented for a meal schedule with random carbohydrate sizes and random times of carbohydrate uptake to show the performance of the proposed control scheme.

Background: People with type 1 diabetes estimate meal carbohydrate content to accurately dose insulin, yet, protein and fat content of meals also influences postprandial glycemia. We examined accuracy of macronutrient content estimation via a novel phone app. Participant estimates were compared with expert nutrition analyses performed via the Remote Food Photography Method© (RFPM©). Methods: Data were collected through a novel phone app. Participants were asked to take photos of meals/snacks on the day of and day after scheduled exercise, enter carbohydrate estimates, and categorize meals as low, typical, or high protein and fat. Glycemia was measured via continuous glucose monitoring. Results: Participants (n = 48) were 15–68 years (34 ± 14 years); 40% were female. The phone app plus RFPM© analysis captured 88% ± 29% of participants' estimated total energy expenditure. The majority (70%) of both low-protein and low-fat meals were accurately classified. Only 22% of high-protein meals and 17% of high-fat meals were accurately classified. Forty-nine percent of meals with

AIMS: Participant-driven solutions may help youth and families better engage and maintain use of diabetes technologies. We explored innovative features and functionalities of an ideal artificial pancreas (AP) system suggested by youth with type 1 diabetes and parents. METHODS: Semi-structured interviews were conducted with 39 youth, ages 10–25 years, and 44 parents. Interviews were recorded, transcribed, and coded using thematic analysis. RESULTS: Youth (72% female, 82% non-Hispanic white) were (M±SD) ages 17.0±4.7 years, with diabetes for 9.4±4.9 years, and HbA1c of 68±11 mmol/mol (8.4±1.1%); 79% were pump-treated and 82% were CGM users. Of parents, 91% were mothers and 86% were non-Hispanic white, with a child 10.6±4.5 years old.Youth and parents suggested a variety of innovative features and functionalities for an ideal artificial pancreas system related to: 1) enhancing the appeal of user interface, 2) increasing automation of new glucose management functionalities, and 3) innovative and commercial add-ons for greater convenience. Youth and parents offered many similar suggestions, including integration of ketone testing, voice activation, and location-tracking into the system. Youth seemed more driven by increasing convenience and normalcy, while parents expressed more concerns with safety. CONCLUSIONS: Youth and parents expressed creative solutions for an ideal artificial pancreas system to increase ease of use, enhance normalcy, and reduce burden of management. Designers of artificial pancreas systems will likely benefit from incorporating the desired preferences by end users in order to optimize acceptance and usability by young persons with diabetes.

Objective: This study analysis was designed to examine the 24-h effects of exercise on glycemic control as measured by continuous glucose monitoring (CGM). Methods: Individuals with type 1 diabetes (ages: 15–68 years; hemoglobin A1c: 7.5% ± 1.5% [mean ± standard deviation (SD)]) were randomly assigned to complete twice-weekly aerobic, high-intensity interval, or resistance-based exercise sessions in addition to their personal exercise sessions for a period of 4 weeks. Exercise was tracked with wearables and glucose concentrations assessed using CGM. An exercise day was defined as a 24-h period after the end of exercise, while a sedentary day was defined as any 24-h period with no recorded exercise ≥10 min long. Sedentary days start at least 24 h after the end of exercise. Results: Mean glucose was lower (150 ± 45 vs. 166 ± 49 mg/dL, P = 0.01), % time in range [70–180 mg/dL] higher (62% ± 23% vs. 56% ± 25%, P = 0.03), % time >180 mg/dL lower (28% ± 23% vs. 37% ± 26%, P = 0.01), and % time

Aim: Insulin bolus doses are mainly based on ambient glucose level and planned carbohydrate (CHO) intake, although fat and protein content of food also impact glycemic excursions. We examined the impact of macronutrient intake on glycemic outcomes in youth with T1D. Methods: Youth (N=136, ages 8-17) with T1D provided diet (3-day food records) and glucose (3-day masked CGM [iPro™]) data every 3 months for 1 year (5 time points). Diet data were analyzed using Nutrition Data System for Research (NDSR). Macronutrient % intake was derived as the mean for each 3-day period. Glycemic outcomes were A1c and CGM metrics: glucose % time in range (TIR) 70-180 mg/dL, % time (T) 180, and glucose CV. Longitudinal mixed models assessed the effect of macronutrient intake on glycemic outcomes. Results: Youth (48% male) had a mean age of 12.8±2.5 years and T1D duration of 5.9±3.1 years; youth checked BGs 5.6±2.4 X/day and 73% used insulin pumps. At baseline, A1c=8.1±1.0%, %TIR=49±17% (11.8 hrs), %T180=44±20% (10.6 hrs), and CV=41±8%; macronutrient % intake was 48±5% CHO, 36±5% fat, and 16±2% protein. Over 1 year, differences in macronutrient intake affected glycemic outcomes (Table). Conclusions: Our data confirm the significant impact of fat and protein intake on glycemic outcomes. These findings offer an opportunity to tailor algorithms to assist with meal-time bolus dosing and automated insulin delivery systems. Disclosure R.O. La Banca: None. L.K. Volkening: None. E. Dassau: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker’s Bureau; Self; Roche Diabetes Care. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. S.N. Mehta: None. L.M. Laffel: Advisory Panel; Self; Roche Diabetes Care. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc., ConvaTec Inc., Dexcom, Inc., Insulet Corporation, Insulogic LLC, Janssen Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk Inc., Sanofi US. Funding National Institutes of Health (P30DK036836, K12DK094721)

Insulin monitoring is clinically relevant for diabetes management. We present a first evaluation of an insulin immunosensor as a step towards point-of-care devices to enhance automated insulin delivery for T1D. The sensor is a novel, wearable, minimally invasive microneedle/microfluidic device for real-time insulin monitoring. To validate the sensor, a mid-study analysis was performed on 4 adults with T1D (43±12 years, 75% male) using insulin pumps. Insulin lispro or aspart was injected via syringe just prior to breakfast. Insulin levels were collected prior to injection and over the next 4h. Insulin quantification was performed using the participants’ serum samples extracted from venous blood. The change in real-time immunosensor-measured insulin levels was compared with values obtained by ELISA in a central lab. At each time point, a median insulin level was derived from replicate immunosensor samples. Concordance of immunosensor and ELISA insulin levels was assessed as a comparison of their changes from baseline (Figure). The mean absolute relative difference between immunosensor and venous insulin levels was 16%, 8%, and 18% at 1, 2, and 4h after the baseline measurement, respectively. The results of the 1st phase real-time insulin immunosensor evaluation are promising and may provide data to improve current insulin decay curve assumptions used in insulin-on-board approximations. Disclosure K.L. Wolkowicz: None. E. Vargas: None. H. Teymourian: None. F. Tehrani: None. J.E. Pinsker: Advisory Panel; Self; Medtronic. Consultant; Self; Eli Lilly and Company, Tandem Diabetes Care. Research Support; Self; Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Medtronic, Tandem Diabetes Care. Speaker’s Bureau; Self; Tandem Diabetes Care. M. Church: None. M. Piper: None. F.J. Doyle: Research Support; Self; DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Stock/Shareholder; Self; Mode AGC. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. M. Patti: Consultant; Self; Fractyl Laboratories, Inc. Research Support; Self; Dexcom, Inc., Xeris Pharmaceuticals, Inc. Other Relationship; Self; Academy of Nutrition and Dietetics, American Diabetes Association, American Society of Metabolic and Bariatric Surgery, Endocrine Society, Insulet Corporation, King Abdullah International Medical Research Center, SUNY Downstate. L.M. Laffel: Advisory Panel; Self; Roche Diabetes Care. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc., ConvaTec Inc., Dexcom, Inc., Insulet Corporation, Insulogic LLC, Janssen Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk Inc., Sanofi US. J. Wang: None. E. Dassau: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker’s Bureau; Self; Roche Diabetes Care. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. Funding The Leona M. and Harry B. Helmsley Charitable Trust (2018PG-TID061); Dexcom, Inc. (IIS-2019-052)

Pregnancies in type 1 diabetes (T1D) are high-risk, and data in the U.S. are limited regarding clinical and CGM based hypoglycemia throughout pregnancy while on sensor augmented insulin pump (SAP). Pregnant women with T1D on insulin pump ≤ 16 wks gestation were enrolled at 3 U.S. centers and used study Dexcom G6. We analyzed episodes of severe hypoglycemia (SH) and days with > 20 hrs of CGM for biochemical hypoglycemia (BH) based on international consensus guidelines ( Disclosure R. Kaur: None. B.H. Smith: None. J.E. Pinsker: Advisory Panel; Self; Medtronic. Consultant; Self; Eli Lilly and Company, Tandem Diabetes Care. Research Support; Self; Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Medtronic, Tandem Diabetes Care. Speaker’s Bureau; Self; Tandem Diabetes Care. B. Ozaslan: None. G. O’Malley: Research Support; Self; Abbott, Dexcom, Inc. M. Trinidad: None. D. Desjardins: None. K.N. Castorino: Research Support; Self; Abbott, Dexcom, Inc., Medtronic, Mylan, Novo Nordisk Inc. C. Levister: None. C. Reid: None. S.K. McCrady-Spitzer: None. S.J. Ogyaadu: None. M. Church: None. M. Piper: None. W.K. Kremers: Research Support; Self; AstraZeneca. B. Rosenn: None. C.J. Levy: Consultant; Self; Dexcom, Inc. Employee; Spouse/Partner; Allergan plc. Research Support; Self; Abbott, Dexcom, Inc., Insulet Corporation. E. Dassau: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker’s Bureau; Self; Roche Diabetes Care. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. Y.C. Kudva: Research Support; Self; Dexcom, Inc., Roche Diabetes Care. Other Relationship; Self; Abbott. Funding National Institutes of Health (122358); Dexcom, Inc. (AP-2018-016)

People with type 1 diabetes (T1D) have difficulty controlling glucose during exercise. Different types, intensities, and durations of exercise impact glucose differently. There is currently no algorithm that can accurately predict glucose changes during aerobic, resistance, interval, and free-living exercise. A support vector regression (SVR) model was designed to predict the maximum change in glucose during exercise. The model was trained and validated on 30-minute exercise sessions collected twice weekly over 4 weeks from 33 people with T1D (age 33±13 years, BMI 26.3±2.9 kg/m2, 18±12 years since diagnosis). Participants completed study assigned aerobic (n = 40), resistance (n= 33), or interval (n=20) exercise sessions in addition to their typical exercise regimen (n = 254). Participants wore a Dexcom (G5 or G6) or Medtronic CGM and used their own insulin pen (n = 10) or pump to administer insulin. The exercise information and food consumed was reported using a custom app. Heart rate, accelerometry, and sleep metrics were acquired using a Garmin vivosmart watch. Training was done on 90% of the observations while 10% of data were used for testing using 10-fold cross validation. The mean absolute error (MAE) of the model-predicted change in glucose during exercise was 23.59 mg/dL. The SVR algorithm may be useful within a decision support tool to help people with T1D better manage their glucose levels during exercise. Disclosure G. Young: None. Z. Li: None. P. Calhoun: Stock/Shareholder; Self; Dexcom, Inc. R.L. Gal: None. R. Beck: None. J.R. Castle: Advisory Panel; Self; Novo Nordisk Inc. Consultant; Self; ADOCIA, Dexcom, Inc., Zealand Pharma A/S. Other Relationship; Self; Pacific Diabetes Technologies, Roche Diabetes Care. M.A. Clements: Consultant; Self; Glooko, Inc. Other Relationship; Self; Glooko, Inc. E. Dassau: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker’s Bureau; Self; Roche Diabetes Care. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. F.J. Doyle: Research Support; Self; DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Stock/Shareholder; Self; Mode AGC. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. M.B. Gillingham: None. C.K. Martin: Advisory Panel; Self; EHE Health, NaturallySlim. Consultant; Self; Florida Hospital, Gila Therapeutics, OpenFit, Zafgen, Inc. Research Support; Self; Egg Board, Helmsley Trust, IDEA Public Schools, National Institutes of Health, NIHR, Patient-Centered Outcomes Research Institute, USDA, Weight Watchers International, Inc. Other Relationship; Self; Academy of Nutrition and Dietetics, Louisiana State Univ./Pennington Biomedical Research Center. M.R. Rickels: Consultant; Self; Semma Therapeutics, Inc. Research Support; Self; Xeris Pharmaceuticals, Inc. S.R. Patton: None. M. Riddell: Advisory Panel; Self; Zucara Therapeutics Inc. Consultant; Self; Lilly Diabetes. Research Support; Self; Dexcom, Inc., Insulet Corporation. Speaker’s Bureau; Self; Medtronic, Novo Nordisk A/S. P.G. Jacobs: Consultant; Self; SFC Fluidics. Research Support; Self; Dexcom, Inc. Stock/Shareholder; Self; Pacific Diabetes Technologies. Other Relationship; Self; AgaMatrix. Funding Exercise in Diabetes Initiative (T1-DEXI Main Study)

Aim: Current automated insulin delivery systems utilize insulin pumps and CGMs, both of which impose multiple alerts/alarms upon users. Patients and parents must respond to such notifications in order to effectively manage glucose levels. We interviewed children, teens, and young adults with T1D and parents of youth with T1D to explore preferences for alerting features of an ideal AP system. Methods: Semi-structured interviews were conducted with 39 youth, ages 10-25 years with T1D duration ≥1 year, and 28 parents at 2 diabetes centers. Interview transcripts were coded and underwent content analysis. Youth (72% female, 82% white) were (M±SD) age 17.0±4.7 years, with T1D duration 9.4±4.9 years, and A1c 8.4±1.1%; 79% used an insulin pump and 82% used a CGM. Of parents, 96% parents were mothers and 89% were white. Results: Youth and parents both endorsed a desire for the following 5 alerting features: 1) personalized sounds and volumes for different treatment situations (e.g., low vs. high glucose levels, insulin delivery issues), 2) custom schedules and glucose thresholds to account for different activities or times of day/night, 3) pleasant sounds to avoid negative emotional reactions, 4) flexible share settings (e.g., which alerts followers receive), and 5) consistent and personalized overnight alerts. Some youth wanted the option to turn off alerts completely. Parents wanted certainty that they would receive share alerts and suggested adding more share options (e.g., confirmation of insulin coverage for food or correction dose for high glucose), stating that such notifications may lessen their emotional distress and worry for their child’s safety. Conclusions: Youth and parents emphasized need for customizable alerts and control over alert settings to reduce physical and emotional burdens of diabetes care. AP designs that provide comprehensive, customizable alert options are essential to reduce care burden for youth with T1D and their parents. Disclosure L. Roethke: None. P.V. Commissariat: None. J.L. Finnegan: None. L.K. Volkening: None. D.A. Butler: None. E. Dassau: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker’s Bureau; Self; Roche Diabetes Care. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. S.A. Weinzimer: Consultant; Spouse/Partner; Tandem Diabetes Care. Consultant; Self; Zealand Pharma A/S. Speaker’s Bureau; Self; Insulet Corporation. L.M. Laffel: Advisory Panel; Self; Roche Diabetes Care. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc., ConvaTec Inc., Dexcom, Inc., Insulet Corporation, Insulogic LLC, Janssen Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk Inc., Sanofi US. Funding National Institutes of Health (P30DK036836, DP3DK113511, DP3DK104057, K12DK094721, T32DK007260)

Background: There are limited contemporaneous data about outcomes in pregnancies with pre-existing type 1 diabetes (T1D) in the U.S. Objective: To determine maternal and neonatal outcomes in pregnancies with preexisting T1D and pregnancies in age, parity and BMI matched healthy subjects. Methods: We retrospectively studied 32 pregnancies in patients with preexisting T1D matched for age (mean 25.83 years), gravidity/parity {G1P1 (66), G2P1 (19), G3P1 (9) and G4P1 (6) % respectively} and BMI with controls (n=32) during a 5 year period from 01/01/2015 at Mayo Clinic, Rochester. Results: In 32 T1D pregnancies, 4 (12.5%) congenital defects (CD) were observed in neonates, 1-dysplastic ribs with thumb hypoplasia and 3-congentinal heart defects leading to death in one in comparison to no congenital defect or death in controls. CD pregnancies were complicated by severe pre-eclampsia in all 4 and poor glycemic control (HbA1c >8) in 3. There was a significant difference in Apgar score at 1 min between T1D pregnancies and controls (p= Conclusion: Pregnancies in T1D continue to be high risk pregnancies and need tight glycemic control with a more advanced system such as closed loop insulin delivery during pregnancy to improve maternal and neonatal outcomes. Disclosure R. Kaur: None. S. Rizvi: None. M. Trinidad: None. B.H. Smith: None. S. Batthula: None. S.K. McCrady-Spitzer: None. C. Reid: None. D. Desjardins: None. G. O’Malley: Research Support; Self; Abbott, Dexcom, Inc. K.N. Castorino: Research Support; Self; Abbott, Dexcom, Inc., Medtronic, Mylan, Novo Nordisk Inc. J.E. Pinsker: Advisory Panel; Self; Medtronic. Consultant; Self; Eli Lilly and Company, Tandem Diabetes Care. Research Support; Self; Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Medtronic, Tandem Diabetes Care. Speaker’s Bureau; Self; Tandem Diabetes Care. W.K. Kremers: Research Support; Self; AstraZeneca. E. Dassau: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker’s Bureau; Self; Roche Diabetes Care. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. C.J. Levy: Consultant; Self; Dexcom, Inc. Employee; Spouse/Partner; Allergan plc. Research Support; Self; Abbott, Dexcom, Inc., Insulet Corporation. Y.C. Kudva: Research Support; Self; Dexcom, Inc., Roche Diabetes Care. Other Relationship; Self; Abbott.

PLGS systems have been demonstrated to limit hypoglycemia. Reduced insulin during suspensions may avoid the need for rescue CHO or lessen the amount of CHO needed. The approximately 20-hour inpatient evaluation of the Lilly investigational AID system’s PLGS feature, including a basal up-titration period to activate the PLGS and investigator-directed administration of rescue CHO, allowed assessment of hypoglycemia prevention and treatment requirements. Ten subjects with type 1 diabetes (40% male, mean age 39.0±13.0 years, A1C 7.2±0.6%, and insulin usage 0.6±0.2 U/kg/day) were studied. There were 59 suspensions, with all subjects experiencing suspensions during which CHO were not administered. Only six suspensions were associated with rescue CHO and five suspensions were associated with hypoglycemia (CGM glucose Disclosure J.E. Pinsker: Advisory Panel; Self; Medtronic. Consultant; Self; Eli Lilly and Company, Tandem Diabetes Care. Research Support; Self; Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Medtronic, Tandem Diabetes Care. Speaker’s Bureau; Self; Tandem Diabetes Care. A. Bartee: None. M. Katz: Employee; Self; Eli Lilly and Company. Stock/Shareholder; Self; Eli Lilly and Company. A. LaLonde: Employee; Self; Eli Lilly and Company. Stock/Shareholder; Self; Eli Lilly and Company. R. Jones: Employee; Self; Eli Lilly and Company. Stock/Shareholder; Self; Eli Lilly and Company. E. Dassau: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker’s Bureau; Self; Roche Diabetes Care. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. H. Wolpert: Employee; Self; Eli Lilly and Company.

Aim: Most youth with T1D meet neither nutrition guidelines nor glycemic goals. We assessed macronutrient content of meals/snacks and impact of daily number of meals/snacks on glycemic outcomes in youth with T1D over 1 year. Methods: Youth completed 3-day food records (2 week days/1 weekend day) and wore 3-day masked CGM (iPro™) every 3 months for 1 year. Diet data were reviewed by 2 RDs and analyzed using Nutrition Data System for Research (NDSR). Glycemic outcomes were A1c and CGM metrics (glucose % time in range [TIR] 70-180 mg/dL, % time [T] 180, and glucose CV [SD/mean]). Longitudinal mixed models assessed effect of number of daily meals/snacks on glycemic outcomes. Results: Youth (N=136, ages 8-17, 48% male) were ages 12.8±2.5 years with T1D duration 5.9±3.1 years and daily insulin dose 0.9±0.3 U/kg; 73% used insulin pumps. Number of meals/snacks ranged from 1-9 with most youth (69%) reporting 3-4 meals/snacks daily. Macronutrient intake varied by meal/snack (Figure). An increase of 1 meal/snack per day led to a 0.1% decrease in A1c (p=.001), 2.4% (35 minutes/day) more TIR (p=.0006), and 3.1% (45 minutes/day) less T>180 (p=.0001); T Conclusions: In youth with T1D, eating frequent meals/snacks may lead to lower A1c, more TIR, and less hyperglycemia. These findings may be due to dividing energy intake across the day with more frequent bolus dosing, given predominance of carbohydrates in meals/snacks. Disclosure R.O. La Banca: None. L.K. Volkening: None. E. Dassau: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker’s Bureau; Self; Roche Diabetes Care. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. S.N. Mehta: None. L.M. Laffel: Advisory Panel; Self; Roche Diabetes Care. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc., ConvaTec Inc., Dexcom, Inc., Insulet Corporation, Insulogic LLC, Janssen Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk Inc., Sanofi US. Funding National Institutes of Health (P30DK036836, K12DK094721)

Aim: Although hybrid closed loop insulin delivery systems simplify self-care with automated insulin delivery, it remains necessary for patients to enter planned carbohydrate intake for meals and snacks. We interviewed children, teens, and young adults with T1D and parents of youth with T1D about their willingness to trade off limiting carb intake to 50g if this would remove need to manually bolus for each meal/snack. Methods: Semi-structured interviews were conducted with 39 youth, ages 10-25 years, and 44 parents of youth at 2 U.S. diabetes centers. Interviews were audio-recorded, transcribed, and coded using thematic analysis. Youth (72% female, 82% white) were (M±SD) age 17.0±4.7 years, with T1D duration 9.4±4.9 years and A1c 8.4±1.1%; 79% were pump users and 82% were CGM users. Of parents, 86% were white and 91% were mothers. Results: Most youth and parents strongly preferred to manually bolus for meals/snacks rather than use a fully automated system that requires limiting carb intake at each meal/snack; many stated they did not want to feel restricted. However, both youth and parents said they would like automatic coverage for meals/snacks of Conclusions: Youth and parents agreed that a fully automated system that did not require manual bolusing would reduce physical and mental burdens of care, but not if it limited carb intake in order for the system to work effectively. AP designers should address patient and parent aversions to dietary restrictions in future AP devices. Disclosure P.V. Commissariat: None. L. Roethke: None. J.L. Finnegan: None. L.K. Volkening: None. D.E. McGill: None. E. Dassau: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker’s Bureau; Self; Roche Diabetes Care. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. S.A. Weinzimer: Consultant; Spouse/Partner; Tandem Diabetes Care. Consultant; Self; Zealand Pharma A/S. Speaker’s Bureau; Self; Insulet Corporation. L.M. Laffel: Advisory Panel; Self; Roche Diabetes Care. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc., ConvaTec Inc., Dexcom, Inc., Insulet Corporation, Insulogic LLC, Janssen Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk Inc., Sanofi US. Funding National Institutes of Health (P30DK036836, DP3DK113511, DP3DK104057, K12DK094721, T32DK007260)

The target time in range (TIR) for pregnant women with type 1 diabetes (T1D) by the International Consensus on TIR is 70% between 63-140 mg/dL. This is difficult to achieve and is rarely met in the literature. Insulin use increases during pregnancy, but prospective data and guidance on pump setting adjustments are limited. Insulin pump delivery and CGM data for 17 T1D women from 3 U.S. sites were prospectively collected every 2 weeks as part of the LOIS-P trial. Subjects enrolled before 17 weeks gestational age (GA) and wore personal pumps and study Dexcom G6 CGM. Changes in mean daily total, basal and bolus doses per kilogram, and TIR for every 2 weeks GA are reported, and linear mixed effects regression models are used for evaluation across trimesters. Enrollment HbA1C was 6.4±0.8%. Total daily dose increased from 0.68 to 0.73 to 0.98 U/kg during the first, second and third trimesters, respectively (p70% TIR during pregnancy. Time below target was 5%, 4% and 2%. Doses trended upwards around 24 weeks GA. Postpartum doses decreased significantly. While insulin doses were increased significantly across pregnancy, most subjects did not achieve > 70% TIR. Systems that are customized to this population’s targets with changing insulin sensitivity are needed. Disclosure G. O’Malley: Research Support; Self; Abbott, Dexcom, Inc. B. Ozaslan: None. C. Levister: None. M. Trinidad: None. K.N. Castorino: Research Support; Self; Abbott, Dexcom, Inc., Medtronic, Mylan, Novo Nordisk Inc. D. Desjardins: None. M. Church: None. B.H. Smith: None. S.J. Ogyaadu: None. M. Piper: None. C. Reid: None. S.K. McCrady-Spitzer: None. R. Kaur: None. W.K. Kremers: Research Support; Self; AstraZeneca. F.J. Doyle: Research Support; Self; DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Stock/Shareholder; Self; Mode AGC. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. J.E. Pinsker: Advisory Panel; Self; Medtronic. Consultant; Self; Eli Lilly and Company, Tandem Diabetes Care. Research Support; Self; Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Medtronic, Tandem Diabetes Care. Speaker’s Bureau; Self; Tandem Diabetes Care. C.J. Levy: Consultant; Self; Dexcom, Inc. Employee; Spouse/Partner; Allergan plc. Research Support; Self; Abbott, Dexcom, Inc., Insulet Corporation. B. Rosenn: None. Y.C. Kudva: Research Support; Self; Dexcom, Inc., Roche Diabetes Care. Other Relationship; Self; Abbott. E. Dassau: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker’s Bureau; Self; Roche Diabetes Care. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. Funding National Institutes of Health (R01DK120358); Dexcom, Inc. (AP-2018-016)

Exercise increases nocturnal hypoglycemia risk which may impact sleep in T1D. Adults with T1D (n=25; 19 CSII; age 33±14yrs; HbA1c 7.6±1.3%; T1D duration 15yrs [IQR: 10, 22]) were divided into 3 exercise groups: aerobic, interval, or resistance, performed twice/week using instructional videos for 4 weeks. Participants used CGM, and wore an activity/sleep monitor (Garmin Vivosmart 3). Sleep duration was lower on nights following interval exercise compared to nights following sedentary days (p=0.04, Figure 1a). Participants experienced hypoglycemia earlier in the night following interval or aerobic exercise, compared to sedentary or resistance exercise (Figure 1b). Exercise types differentially impact sleep loss and nocturnal hypoglycemia. Disclosure P.G. Jacobs: Consultant; Self; SFC Fluidics. Research Support; Self; Dexcom, Inc. Stock/Shareholder; Self; Pacific Diabetes Technologies. Other Relationship; Self; AgaMatrix. Z. Li: None. P. Calhoun: Stock/Shareholder; Self; Dexcom, Inc. R.L. Gal: None. R. Beck: None. J.R. Castle: Advisory Panel; Self; Novo Nordisk Inc. Consultant; Self; ADOCIA, Dexcom, Inc., Zealand Pharma A/S. Other Relationship; Self; Pacific Diabetes Technologies, Roche Diabetes Care. M.A. Clements: Consultant; Self; Glooko, Inc. Other Relationship; Self; Glooko, Inc. E. Dassau: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker’s Bureau; Self; Roche Diabetes Care. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. F.J. Doyle: Research Support; Self; DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Stock/Shareholder; Self; Mode AGC. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. M.B. Gillingham: None. C.K. Martin: Advisory Panel; Self; EHE Health, NaturallySlim. Consultant; Self; Florida Hospital, Gila Therapeutics, OpenFit, Zafgen, Inc. Research Support; Self; Egg Board, Helmsley Trust, IDEA Public Schools, National Institutes of Health, NIHR, Patient-Centered Outcomes Research Institute, USDA, Weight Watchers International, Inc. Other Relationship; Self; Academy of Nutrition and Dietetics, Louisiana State Univ./Pennington Biomedical Research Center. M.R. Rickels: Consultant; Self; Semma Therapeutics, Inc. Research Support; Self; Xeris Pharmaceuticals, Inc. S.R. Patton: None. M. Riddell: Advisory Panel; Self; Zucara Therapeutics Inc. Consultant; Self; Lilly Diabetes. Research Support; Self; Dexcom, Inc., Insulet Corporation. Speaker’s Bureau; Self; Medtronic, Novo Nordisk A/S. Funding The Leona M. and Harry B. Helmsley Charitable Trust (T1-DEXI Main Study)

Automated Insulin Delivery (AID) hybrid closed-loop systems have not been well studied in the context of prescribed meals. We evaluated our interoperable artificial pancreas system (iAPS) with scheduled meal challenges in a randomized crossover trial in an at-home setting. Ten adults with type 1 diabetes completed two weeks of AID-based control and two weeks of conventional therapy (sensor-augmented pump/predictive low-glucose suspend) at home in random order. During each period, subjects consumed pasta or white rice as part of a complete dinner meal on six different occasions (each meal three times in random order). The AID system increased time in range 70-180 mg/dL from 70.6 to 74.0% (3.4, 95% CI -2.3 to 9.1, p=0.22), while sensor time The AID system improved postprandial glucose control over conventional therapy in the handling of challenging meals in the at-home setting. Disclosure J.E. Pinsker: Advisory Panel; Self; Medtronic. Consultant; Self; Eli Lilly and Company, Tandem Diabetes Care. Research Support; Self; Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Medtronic, Tandem Diabetes Care. Speaker’s Bureau; Self; Tandem Diabetes Care. S. Deshpande: None. M. Church: None. M. Piper: None. C.C. Andre: None. J.S. Massa: None. F.J. Doyle: Research Support; Self; DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Stock/Shareholder; Self; Mode AGC. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. D.M. Eisenberg: Consultant; Self; Barilla Center For Food and Nutrition, Italy. E. Dassau: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker’s Bureau; Self; Roche Diabetes Care. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. Funding Barilla Center for Food & Nutrition Foundation; National Institutes of Health (DP3DK104057, DP3DK113511); Harvard Accelerator; Dexcom, Inc. (IIS-2018-019)

Introduction: Patients and their families and medical providers have assumed that psychologic stress impacts glucose control in T1D (Type 1 Diabetes) though studies providing confirmatory evidence in real world settings are, to our knowledge, lacking. We hypothesized that self-reported psychologic stress worsens glucose control in T1D. Method: We studied 20 adults with T1D on continuous glucose monitor (CGM), sensor augmented insulin pump (SAP) prospectively at 2 clinical research centers. Patients reported psychological stress through stress diaries for 5 weeks on a severity scale of 1-7 using hard copy logs including time of onset and offset of stress and severity. For analytic purpose, grades 1-4 are classified as mild and grades 5-7 as severe. Results: Baseline characteristics were age 44.9±15.0 years, F/M 12/8, HbA1c 6.8 ± 0.7%, and diabetes duration of 22.9±15.9 years. We analyzed glucose variability during days of stress versus days without stress. During a 24 hour period, patients experienced less hypoglycemia during days with stress versus days without stress (p value 0.03). During the 5 week period, patients reported 23 ± 19.5 events. We analyzed the impact of self-reported stress on CGM data streams after excluding stress events associated with missing CGM data, nocturnal events (from 12 MN to 6 AM, too few events) and events for which subjects did not provide duration of stress. Thus, we analyzed 19.5 ± 17 events per patient from 6AM to 12MN. From 6 AM to 12 MN, the episodes lasted 179 ± 255 minutes with 83 % episodes being mild/moderate and 17% moderate/ severe. Number of CGM readings during daytime stress episodes were 717± 1120 compared to 8768± 1238 during non-stress periods. Impact of stress from 6 AM to 12 MN (Mid-Night) on CGM glucose was analyzed using matched paired t test. Mean glucose (160.6±41.9 vs 148.3± 28.6) and SD (53.2 ±17.7 vs 56.1±14.6) did not show a difference; however % of time spent below 70 mg/dl was less (4 ± 5) in patients during stressful periods compared to times without stress (6.3± 5.5, P value 0.02). Conclusions: To our knowledge, this is the first study attempting to analyze the impact of self-reported stress using daily stress diaries on CGM data streams in T1D patients on SAP. The study revealed significant challenges experienced by patients in reporting adequate data. Self-reported stress was not associated with hyperglycemia. However, days of self-reported stress and periods during patients reported stress were characterized by less hypoglycemia on CGM data streams.

The significant risks associated with pregnancies complicated by type 1 diabetes (T1D) were first recognized in the medical literature in the mid-twentieth century. Stringent glycemic control with hemoglobin A1c (HbA1c) values ideally less than 6% has been shown to improve maternal and fetal outcomes. The management options for pregnant women with T1D in the modern era include a variety of technologies to support self-care. Although self-monitoring of blood glucose (SMBG) and multiple daily injections (MDI) are often the recommended management options during pregnancy, many people with T1D utilize a variety of different technologies, including continuous glucose monitoring (CGM), continuous subcutaneous insulin infusion (CSII), and CSII including automated delivery or suspension algorithms. These systems have yielded invaluable diagnostic and therapeutic capabilities and have the potential to benefit this understudied higher-risk group. A recent prospective, multicenter study evaluating pregnant patients with T1D revealed that CGM significantly improves maternal glycemic parameters, is associated with fewer adverse neonatal outcomes, and minimizes burden. Outcome data for CSII, which is approved for use in pregnancy and has been utilized for several decades, remain mixed. Current evidence, although limited, for commercially available and emerging technologies for the management of T1D in pregnancy holds promise for improving patient and fetal outcomes.妊娠合并1型糖尿病(T1D)的重大风险在20世纪中叶的医学文献中被首次认识到。严格控制血糖，理想情况下使血红蛋白A1c(HbA1c)低于6%，已被证明可以改善母婴结局。在现代，对于患有T1D孕妇的管理选择包括各种支持自我护理的技术。虽然妊娠期自我血糖监测(self-monitoring of blood glucose，SMBG)和每日多次注射(multiple daily injections，MDI)通常是推荐的治疗方案，许多T1D患者使用各种不同的技术，包括连续血糖监测(continuous glucose monitoring，CGM)、连续皮下胰岛素输注(continuous subcutaneous insulin infusion，CSII)以及包括自动给药或暂停算法的CSII。这些系统已经产生了无价的诊断和治疗能力，并有可能使这一研究较少的高危群体受益。最近一项评估T1D孕妇的前瞻性多中心研究显示，CGM显著改善母亲的血糖参数，与较少的不良新生儿结局相关，并最大限度地减轻了负担。CSII被批准用于妊娠，并已使用了几十年，但其结果仍然喜忧参半。目前的证据虽然有限，但商业上可获得的和新兴的妊娠T1D管理技术对改善患者和胎儿的结局还是有希望的。.

OBJECTIVE Limited information is available about glycemic outcomes with a closed-loop control (CLC) system compared with a predictive low-glucose suspend (PLGS) system. RESEARCH DESIGN AND METHODS After 6 months of use of a CLC system in a randomized trial, 109 participants with type 1 diabetes (age range, 14–72 years; mean HbA1c, 7.1% [54 mmol/mol]) were randomly assigned to CLC (N = 54, Control-IQ) or PLGS (N = 55, Basal-IQ) groups for 3 months. The primary outcome was continuous glucose monitor (CGM)-measured time in range (TIR) for 70–180 mg/dL. Baseline CGM metrics were computed from the last 3 months of the preceding study. RESULTS All 109 participants completed the study. Mean ± SD TIR was 71.1 ± 11.2% at baseline and 67.6 ± 12.6% using intention-to-treat analysis (69.1 ± 12.2% using per-protocol analysis excluding periods of study-wide suspension of device use) over 13 weeks on CLC vs. 70.0 ± 13.6% and 60.4 ± 17.1% on PLGS (difference = 5.9%; 95% CI 3.6%, 8.3%; P < 0.001). Time >180 mg/dL was lower in the CLC group than PLGS group (difference = −6.0%; 95% CI −8.4%, −3.7%; P < 0.001) while time CONCLUSIONS Following 6 months of CLC, switching to PLGS reduced TIR and increased HbA1c toward their pre-CLC values, while hypoglycemia remained similarly reduced with both CLC and PLGS.

Background: This study assessed the safety and performance of the Omnipod® personalized model predictive control (MPC) algorithm using an investigational device in adults with type 1 diabetes in response to overestimated and missed meal boluses and extended boluses for high-fat meals. Materials and Methods: A supervised 54-h hybrid closed-loop (HCL) study was conducted in a hotel setting after a 7-day outpatient open-loop run-in phase. Adults aged 18–65 years with type 1 diabetes and HbA1c 6.0%–10.0% were eligible. Primary endpoints were percentage time in hypoglycemia

Diabetic ketoacidosis (DKA), a severe complication of diabetes mellitus with potentially fatal consequences, is characterized by hyperglycemia and metabolic acidosis due to the accumulation of ketone bodies, which requires people with diabetes to monitor both glucose and ketone bodies. However, despite major advances in diabetes management mainly since the emergence of new-generation continuous glucose monitoring (CGM) devices capable of in vivo monitoring of glucose directly in the interstitial fluid (ISF), the continuous monitoring of ketone bodies is yet to be addressed. Here, we present the first use of a real-time continuous ketone bodies monitoring (CKM) microneedle platform. The system is based on the electrochemical monitoring of β-hydroxybutyrate (HB) as the dominant biomarker of ketone formation. Such real-time HB detection has been realized using the β-hydroxybutyrate dehydrogenase (HBD) enzymatic reaction and by addressing the major challenges associated with the stable confinement of the enzyme/cofactor couple (HBD/NAD+) and with a stable and selective low-potential fouling-free anodic detection of NADH. The resulting CKM microneedle device displays an attractive analytical performance, with high sensitivity (with low detection limit, 50 μM), high selectivity in the presence of potential interferences, along with good stability during prolonged operation in artificial ISF. The potential applicability of this microneedle sensor toward minimally invasive monitoring of ketone bodies has been demonstrated in a phantom gel skin-mimicking model. The ability to detect HB along with glucose and lactate on a single microneedle array has been demonstrated. These findings pave the way for CKM and for the simultaneous microneedle-based monitoring of multiple diabetes-related biomarkers toward a tight glycemic control.

Background: We investigated the safety and efficacy of the addition of a trust index to enhanced Model Predictive Control (eMPC) Artificial Pancreas (AP) that works by adjusting the responsiveness of the controller's insulin delivery based on the confidence intervals around predictions of glucose trends. This constitutes a dynamic adaptation of the controller's parameters in contrast with the widespread AP implementation of individualized fixed controller tuning. Materials and Methods: After 1 week of sensor-augmented pump (SAP) use, subjects completed a 48-h AP admission that included three meals/day (carbohydrate range 29–57 g/meal), a 1-h unannounced brisk walk, and two overnight periods. Endpoints included sensor glucose percentage time 70–180, 180 mg/dL, number of hypoglycemic events, and assessment of the trust index versus standard eMPC glucose predictions. Results: Baseline characteristics for the 15 subjects who completed the study (mean ± SD) were age 46.1 ± 17.8 years, HbA1c 7.2% ± 1.0%, diabetes duration 26.8 ± 17.6 years, and total daily dose (TDD) 35.5 ± 16.4 U/day. Mean sensor glucose percent time 70–180 mg/dL (88.0% ± 8.0% vs. 74.6% ± 9.4%)

A novel Model Predictive Control (MPC) law for the closed-loop operation of an Artificial Pancreas (AP) to treat type 1 diabetes is proposed. The contribution of this paper is to simultaneously enhance both the safety and performance of an AP, by reducing the incidence of controller-induced hypoglycemia, and by promoting assertive hyperglycemia correction. This is achieved by integrating two MPC features separately introduced by the authors previously to independently improve the control performance with respect to these two coupled issues. Velocity-weighting MPC reduces the occurrence of controller-induced hypoglycemia. Velocity-penalty MPC yields more effective hyperglycemia correction. Benefits of the proposed MPC law over the MPC strategy deployed in the authors’ previous clinical trial campaign are demonstrated via a comprehensive in-silico analysis. The proposed MPC law was deployed in four distinct US Food & Drug Administration approved clinical trial campaigns, the most extensive of which involved 29 subjects each spending three months in closed-loop. The paper includes implementation details, an explanation of the state-dependent cost functions required for velocity-weighting and penalties, a discussion of the resulting nonlinear optimization problem, a description of the four clinical trial campaigns, and control-related trial highlights.

Background: The safety and feasibility of the OmniPod personalized model predictive control (MPC) algorithm in adult, adolescent, and pediatric patients with type 1 diabetes were investigated. Methods: This multicenter, observational trial included a 1-week outpatient sensor-augmented pump open-loop phase and a 36-h inpatient hybrid closed-loop (HCL) phase with announced meals ranging from 30 to 90 g of carbohydrates and limited physical activity. Patients aged 6–65 years with HbA1c between 6.0% and 10.0% were eligible. The investigational system included a modified version of OmniPod, the Dexcom G4 505 Share® AP System, and the personalized MPC algorithm running on a tablet computer. Primary endpoints included sensor glucose percentage of time in hypoglycemia 250 mg/dL. Additional glycemic targets were assessed. Results: The percentage of time 250 mg/dL was 8.0 (7.5), 3.6 (3.7), 4.9 (6.3), and 6.7 (5.6) in the study groups, respectively. Percentage of time in the target range of 70–180 mg/dL was 69.5 (14.4), 73.0 (15.0), 72.6 (15.5), and 70.1 (12.3), respectively. Conclusions: The OmniPod personalized MPC algorithm performed well and was safe during day and night use in adult, adolescent, and pediatric patients with type 1 diabetes. Longer term studies will assess the safety and performance of the algorithm under free living conditions with extended use.

Objective: The objective was to investigate the relationship of body mass index (BMI) to differing glycemic responses to psychological stress in patients with type 1 diabetes. Methods: Continuous blood glucose monitor (CGM) data were collected for 1 week from a total of 37 patients with BMI ranging from 21.5-39.4 kg/m2 (mean = 28.2 ± 4.9). Patients reported daily stress levels (5-point Likert-type scale, 0 = none, 4 = extreme), physical activity, carbohydrate intake, insulin boluses and basal rates. Daily reported carbohydrates, total insulin bolus, and average blood glucose (BG from CGM) were compared among patients based on their BMI levels on days with different stress levels. In addition, daily averages of a model-based “effectiveness index” (quantifying the combined impact of insulin and carbohydrate on glucose levels) were defined and compared across stress levels to capture meal and insulin independent glycemic changes. Results: Analyses showed that patient BMI likely moderated stress related glycemic changes. Linear mixed effect model results were significant for the stress-BMI interaction on both behavioral and behavior-independent glycemic changes. Across participants, under stress, an increase was observed in daily carbohydrate intake and effectiveness index at higher BMI. There was no significant interactive effect on daily insulin or average BG. Conclusion: Findings suggest that (1) stress has both behavioral and nonbehavioral glycemic effects on T1D patients and (2) the direction and magnitude of these effects are potentially influenced by level of stress and patient BMI. Possibly responsible for these observed effects are T1D/BMI related alterations in endocrine response.

Type 1 diabetes mellitus (T1DM) is a metabolic disorder characterized by the destruction of the pancreatic beta cells. With this disease, the body is no longer able to produce insulin, leading to chronically high concentrations of glucose in the blood (hyperglycemia) [1]. People with T1DM manage the disease by administering exogenous insulin doses as determined by multiple daily measurements of the blood glucose (BG) concentration; however, this approach requires significant effort from the patient and often achieves suboptimal results, leading to short- and long-term health complications [2]-[5].

Measurement of glycated hemoglobin (HbA1c) has been the traditional method for assessing glycemic control. However, it does not reflect intra- and interday glycemic excursions that may lead to acute events (such as hypoglycemia) or postprandial hyperglycemia, which have been linked to both microvascular and macrovascular complications. Continuous glucose monitoring (CGM), either from real-time use (rtCGM) or intermittently viewed (iCGM), addresses many of the limitations inherent in HbA1c testing and self-monitoring of blood glucose. Although both provide themeans to move beyond the HbA1c measurement as the sole marker of glycemic control, standardized metrics for analyzing CGM data are lacking. Moreover, clear criteria for matching people with diabetes to themost appropriate glucose monitoring methodologies, as well as standardized advice about howbest to use the new information they provide, have yet to be established. In February 2017, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress convened an international panel of physicians, researchers, and individuals with diabetes who are expert in CGM technologies to address these issues. This article summarizes the ATTD consensus recommendations and represents the current understanding of how CGM results can affect outcomes.

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: Continuous glucose monitoring (CGM) systems are increasingly becoming essential components in type 1 diabetes mellitus (T1DM) management. Current CGM technology requires frequent calibration to ensure accurate sensor performance. The accuracy of these systems is of great importance since medical decisions are made based on monitored glucose values and trends. Methods: In this work, we introduce a calibration strategy that is augmented with a weekly updating feature. During the life cycle of the sensor, the calibration mechanism periodically estimates the parameters of a calibration model to fit self-monitoring blood glucose (SMBG) measurements. At the end of each week of use, an optimization problem that minimizes the sum of squared residuals between past reference and predicted blood glucose values is solved remotely to identify personalized calibration parameters. The newly identified parameters are used to initialize the calibration mechanism of the following week. Results: The proposed method was evaluated using two sets of clinical data both consisting of 6 weeks of Dexcom G4 Platinum CGM data on 10 adults with T1DM (over 10 000 hours of CGM use), with seven SMBG data points per day measured by each subject in an unsupervised outpatient setting. Updating the calibration parameters using the history of calibration data indicated a positive trend of improving CGM performance. Conclusions: Although not statistically significant, the updating framework showed a relative improvement of CGM accuracy compared to the non-updating, static calibration method. The use of information collected for longer periods is expected to improve the performance of the sensor over time.

OBJECTIVE Artificial pancreas (AP) systems are best positioned for optimal treatment of type 1 diabetes (T1D) and are currently being tested in outpatient clinical trials. Our consortium developed and tested a novel adaptive AP in an outpatient, single-arm, uncontrolled multicenter clinical trial lasting 12 weeks. RESEARCH DESIGN AND METHODS Thirty adults with T1D completed a continuous glucose monitor (CGM)-augmented 1-week sensor-augmented pump (SAP) period. After the AP was started, basal insulin delivery settings used by the AP for initialization were adapted weekly, and carbohydrate ratios were adapted every 4 weeks by an algorithm running on a cloud-based server, with automatic data upload from devices. Adaptations were reviewed by expert study clinicians and patients. The primary end point was change in hemoglobin A1c (HbA1c). Outcomes are reported adhering to consensus recommendations on reporting of AP trials. RESULTS Twenty-nine patients completed the trial. HbA1c, 7.0 ± 0.8% at the start of AP use, improved to 6.7 ± 0.6% after 12 weeks (−0.3, 95% CI −0.5 to −0.2, P < 0.001). Compared with the SAP run-in, CGM time spent in the hypoglycemic range improved during the day from 5.0 to 1.9% (−3.1, 95% CI −4.1 to −2.1, P < 0.001) and overnight from 4.1 to 1.1% (−3.1, 95% CI −4.2 to −1.9, P < 0.001). Whereas carbohydrate ratios were adapted to a larger extent initially with minimal changes thereafter, basal insulin was adapted throughout. Approximately 10% of adaptation recommendations were manually overridden. There were no protocol-related serious adverse events. CONCLUSIONS Use of our novel adaptive AP yielded significant reductions in HbA1c and hypoglycemia.

OBJECTIVE As artificial pancreas (AP) becomes standard of care, consideration of extended use of insulin infusion sets (IIS) and continuous glucose monitors (CGMs) becomes vital. We conducted an outpatient randomized crossover study to test the safety and efficacy of a zone model predictive control (zone-MPC)–based AP system versus sensor augmented pump (SAP) therapy in which IIS and CGM failures were provoked via extended wear to 7 and 21 days, respectively. RESEARCH DESIGN AND METHODS A smartphone-based AP system was used by 19 adults (median age 23 years [IQR 10], mean 8.0 ± 1.7% HbA1c) over 2 weeks and compared with SAP therapy for 2 weeks in a crossover, unblinded outpatient study with remote monitoring in both study arms. RESULTS AP improved percent time 70–140 mg/dL (48.1 vs. 39.2%; P = 0.016) and time 70–180 mg/dL (71.6 vs. 65.2%; P = 0.008) and decreased median glucose (141 vs. 153 mg/dL; P = 0.036) and glycemic variability (SD 52 vs. 55 mg/dL; P = 0.044) while decreasing percent time CONCLUSIONS Zone-MPC significantly and safely improved glycemic control in a home-use environment despite prolonged CGM and IIS wear. This project represents the first home-use AP study attempting to provoke and detect component failure while successfully maintaining safety and effective glucose control.

The artificial pancreas (AP) has the potential to improve glycemic control in adolescents. This article presents the first evaluation in adolescents of the Zone Model Predictive Control and Health Monitoring System (ZMPC+HMS) AP algorithms, and their first evaluation in a supervised outpatient setting with frequent exercise.Adolescents with type 1 diabetes underwent 3 days of closed-loop control (CLC) in a hotel setting with the ZMPC+HMS algorithms on the Diabetes Assistant platform. Subjects engaged in twice-daily exercise, including soccer, tennis, and bicycling. Meal size (unrestricted) was estimated and entered into the system by subjects to trigger a bolus, but exercise was not announced.Ten adolescents (11.9-17.7 years) completed 72 h of CLC, with data on 95 ± 14 h of sensor-augmented pump (SAP) therapy before CLC as a comparison to usual therapy. The percentage of time with continuous glucose monitor (CGM) 70-180 mg/dL was 71% ± 10% during CLC, compared to 57% ± 16% during SAP (P = 0.012). Nocturnal control during CLC was safe, with 0% (0%, 0.6%) of time with CGM70 mg/dL compared to 1.1% (0.0%, 14%) during SAP. Despite large meals (estimated up to 120 g carbohydrate), only 8.0% ± 6.9% of time during CLC was spent with CGM250 mg/dL (16% ± 14% during SAP). The system remained connected in CLC for 97% ± 2% of the total study time. No adverse events or severe hypoglycemia occurred.The use of the ZMPC+HMS algorithms is feasible in the adolescent outpatient environment and achieved significantly more time in the desired glycemic range than SAP in the face of unannounced exercise and large announced meal challenges.

People with type 1 diabetes are at risk for early- and late-onset hypoglycemia following exercise. Reducing this risk may be possible with strategic modifications in carbohydrate intake and insulin use. We examined the exercise preparations and management techniques used by individuals with type 1 diabetes before and after physical activity and sought to determine whether use of differing diabetes technologies affects these health-related behaviours.We studied 502 adults from the Type 1 Diabetes Exchange's online patient community, Glu, who had completed an online survey focused on diabetes self-management and exercise.Many respondents reported increasing carbohydrate intake before (79%) and after (66%) exercise as well as decreasing their meal boluses before (53%) and after (46%) exercise. Most reported adhering to a target glucose level before starting exercise (77%). Despite these accommodations, the majority reported low blood glucose (BG) levels after exercise (70%). The majority of users of both insulin pump therapy (CSII) and continuous glucose monitoring (CGM) (Combined) reported reducing basal insulin around exercise (55%), with fewer participants adjusting basal insulin when using other devices (SMBG only = 20%; CGM = 34%; CSII = 42%; p0.001). However, CSII and Combined users reported that exercise makes their BG levels harder to control (p0.05) and makes them feel less able to predict their BG levels while exercising (p0.001); they show agreement that fear of low BG levels keeps them from exercising (p0.01).These findings highlight the need for exercise-management strategies tailored to individuals' overall diabetes management, for despite making exercise-specific adjustments for care, many people with type 1 diabetes still report significant difficulties with BG control when it comes to exercise.