Challenges and fears related to managing glucose levels around planned and spontaneous exercise affect outcomes and quality of life in people living with type 1 diabetes. Advances in technology, including continuous glucose monitoring, open-loop insulin pump therapy and hybrid closed-loop (HCL) systems for exercise management in type 1 diabetes, address some of these challenges. In this review, three research or clinical experts, each living with type 1 diabetes, leverage published literature and clinical and personal experiences to translate research findings into simplified, patient-centred strategies. With an understanding of limitations in insulin pharmacokinetics, variable intra-individual responses to aerobic and anaerobic exercise, and the features of the technologies, six steps are proposed to guide clinicians in efficiently communicating simplified actions more effectively to individuals with type 1 diabetes. Fundamentally, the six steps centre on two aspects. First, regardless of insulin therapy type, and especially needed for spontaneous exercise, we provide an estimate of glucose disposal into active muscle meant to be consumed as extra carbohydrates for exercise (\'ExCarbs\'; a common example is 0.5 g/kg body mass per hour for adults and 1.0 g/kg body mass per hour for youth). Second, for planned exercise using open-loop pump therapy or HCL systems, we additionally recommend pre-emptive basal insulin reduction or using HCL exercise modes initiated 90 min (1-2 h) before the start of exercise until the end of exercise. Modifications for aerobic- and anaerobic-type exercise are discussed. The burden of pre-emptive basal insulin reductions and consumption of ExCarbs are the limitations of HCL systems, which may be overcome by future innovations but are unquestionably required for currently available systems.

The achievement of glycemic management is challenging in patients with diabetes on enteral nutrition, limited literature exists on hybrid closed-loop systems\' efficacy in such a situation. We described the case of a patient with type 1 diabetes treated by advanced hybrid closed loop on enteral nutrition with satisfactory glycemic management.

BACKGROUND: The Medtronic MiniMed™ 780G (MM780G) system uses an algorithm that includes autocorrection bolus (AB) delivery. This study evaluates the impact of omitted meal boluses and the system settings, glucose target and active insulin time (AIT), on the AB.
METHODS: Retrospective observational study on data uploaded by all MiniMed 780G users in our healthcare area, obtained through the remote monitoring platform Care Connect, from April to August 2023. Downloads with a sensor usage time <95% were excluded.
RESULTS: 235 downloads belonging to 235 users were analysed. AB delivery was significantly higher at 2 h AIT (36.08 ± 13.17%) compared to the rest of settings (2.25-4 h) (26.43 ± 13.2%) (p < 0.001). AB differences based on the glucose target were not found. Patients with <3 meal boluses per day had higher AB delivery (46.91 ± 19.00% vs 27.53 ± 11.54%) (p < 0.001) and had more unfavourable glucometric parameters (GMI 7.12 ± 0.45%, TIR 67.46 ± 12.89% vs GMI 6.78 ± 0.3%, TIR 76.51 ± 8.37%) (p < 0.001). However, the 2-h AIT group presented similar TAR, TIR and GMI regardless of the number of meal boluses.
CONCLUSIONS: The fewer user-initiated boluses, the greater the autocorrection received. The active insulin time of 2 h entails a more active autocorrection pattern that makes it possible to more effectively compensate for the omission of meal boluses without increasing hypoglycaemias.

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This narrative review assesses the use of automated insulin delivery (AID) systems in managing persons with type 1 diabetes (PWD) in the pediatric population. It outlines current research, the differences between various AID systems currently on the market and the challenges faced, and discusses potential opportunities for further advancements within this field. Furthermore, the narrative review includes various expert opinions on how different AID systems can be used in the event of challenges with rapidly changing insulin requirements. These include examples, such as during illness with increased or decreased insulin requirements and during physical activity of different intensities or durations. Case descriptions give examples of scenarios with added user-initiated actions depending on the type of AID system used. The authors also discuss how another AID system could have been used in these situations.

In the last decade, technology developed by people with diabetes and their loved ones has added to the options for diabetes management. One such example is that of automated insulin delivery (AID) algorithms, which were created and shared as open source by people living with type 1 diabetes (T1D) years before commercial systems were first available. Now, numerous options for commercial systems exist in some countries, yet tens of thousands of people with diabetes are still choosing Open-Source AID (OS-AID), previously called \"do-it-yourself\" (DIY) systems, which are noncommercial versions of these open-source AID systems. In this article, we provide point and counterpoint perspectives regarding (1) safety and efficacy, (2) regulation and support, (3) user choice and flexibility, (4) access and affordability, and (5) patient and provider education, for open source and commercial AID systems. The perspectives reflected here include that of a person living with T1D who uses and has developed OS-AID systems, a physician-researcher based in the United States who conducts clinical trials to support development of commercial AID systems and supports people with diabetes using all types of AID, and an endocrinologist with T1D who uses both systems and treats people with diabetes using all types of AID.

UNASSIGNED: Clinical trials have demonstrated the efficacy and safety of hybrid closed-loop (HCL) systems, yet few studies have compared outcomes in the real-world setting.
UNASSIGNED: This retrospective study analyzed patients from an academic endocrinology practice between January 1, 2018, and November 18, 2022. The inclusion criteria were diagnosis code for type I diabetes (T1D), >18 years of age, new to any HCL system [Medtronic 670G/770G (MT), Tandem Control IQ (CIQ), or Omnipod 5 (OP5)], and availability of a pump download within three months. The outcomes included %time in range (TIR) of 70 to 180 mg/dL, %time below range (TBR) <70 mg/dL at 90 days, and HbA1c for 91 to 180 days.
UNASSIGNED: Of the 176 participants, 47 were MT, 74 CIQ, and 55 OP5. Median (25%, 75%) change in HbA1c was -0.1 (-0.8, 0.3), -0.6 (-1.1, -0.15), and -0.55 (-0.98, 0)% for MT, CIQ, and OP5, respectively, (P = .04). TIR was 70 (57, 76), 67 (59, 75), and 68 (60, 76)% (P = .95) at 90 days while TBR was 2 (1, 3), 1 (0, 2), and 1 (0, 1)%, respectively, (P = .002). The %time in automated delivery was associated with TIR and change in HbA1c. After controlling other factors including %time in automated delivery, HCL type was not an independent predictor of change in HbA1c nor TIR but remained a significant predictor of TBR.
UNASSIGNED: There were significant reductions in HbA1c in CIQ and OP5. TIR was similar across pumps, but TBR was highest with MT. The %time in automated delivery likely explains differences in change in HbA1c but not TBR between HCL systems.

OBJECTIVE: To investigate whether the positive effects on glycaemic outcomes of 3-month automated insulin delivery (AID) achieved in 2- to 6-year-old children endure over an extended duration and how AID treatment affects time in tight range (TITR), defined as 3.9-7.8 mmol/L.
METHODS: We analysed 18 months of follow-up data from a non-randomized, prospective, single-arm clinical trial (n = 35) conducted between 2021 and 2023. The main outcome measures were changes in time in range (TIR), glycated haemoglobin (HbA1c), time above range (TAR), TITR, and mean sensor glucose (SG) value during follow-up visits (at 0, 6, 12 and 18 months). The MiniMed 780G AID system in SmartGuard Mode was used for 18 months. Parental diabetes distress was evaluated at 3 and 18 months with the validated Problem Areas in Diabetes-Parent, revised (PAID-PR) survey.
RESULTS: Between 0 and 6 months, TIR and TITR increased, and HbA1c, mean SG value and TAR decreased significantly (p < 0.001); the favourable effect persisted through 18 months of follow-up. Between 3 and 18 months, PAID-PR score declined significantly (0 months: mean score 37.5; 3 months: mean score 28.6 [p = 0.06]; 18 months: mean score 24.6 [p < 0.001]).
CONCLUSIONS: Treatment with AID significantly increased TITR and TIR in young children. The positive effect of AID on glycaemic control observed after 6 months persisted throughout the 18 months of follow-up. Similarly, parental diabetes distress remained reduced during 18 months follow-up. These findings are reassuring and suggest that AID treatment improves glycaemic control and reduces parental diabetes distress in young children over an extended 18-month follow-up.

Aims: To compare glycemic control and maternal-fetal outcomes of women with type 1 diabetes (T1D) using hybrid closed loop (HCL) versus multiple daily insulin injections (MDI) plus continuous glucose monitoring. Methods: Multicenter prospective cohort study of pregnant women with T1D in Spain. We evaluated HbA1c and time spent within (TIR), below (TBR), and above (TAR) the pregnancy-specific glucose range of 3.5-7.8 mmol/L. Adjusted models were performed for adverse pregnancy outcomes, including baseline maternal characteristics and center. Results: One hundred twelve women were included (HCL n = 59). Women in the HCL group had a longer duration of diabetes and higher rates of prepregnancy care. There was no between-group difference in HbA1c in any trimester. However, in the second trimester, MDI users had a greater decrease in HbA1c (-6.12 ± 9.06 vs. -2.16 ± 7.42 mmol/mol, P = 0.031). No difference in TIR (3.5-7.8 mmol/L) and TAR was observed between HCL and MDI users, but with a higher total insulin dose in the second trimester [+0.13 IU/kg·day)]. HCL therapy was associated with increased maternal weight gain during pregnancy (βadjusted = 3.20 kg, 95% confidence interval [CI] 0.90-5.50). Regarding neonatal outcomes, newborns of HCL users were more likely to have higher birthweight (βadjusted = 279.0 g, 95% CI 39.5-518.5) and macrosomia (ORadjusted = 3.18, 95% CI 1.05-9.67) compared to MDI users. These associations disappeared when maternal weight gain or third trimester HbA1c was included in the models. Conclusions: In a real-world setting, HCL users gained more weight during pregnancy and had larger newborns than MDI users, while achieving similar glycemic control in terms of HbA1c and TIR.

The introduction of continuous glucose monitoring (CGM) systems in clinical practice has allowed a more detailed picture of the intra- and interdaily glycemic fluctuations of individuals with type 1 diabetes (T1D). However, CGM-measured glucose control indicators may be occasionally inaccurate. This study aims to assess the discrepancy between the glucose management indicator (GMI) and glycated hemoglobin (HbA1c) (ΔGMI-HbA1c) within a cohort of children and adolescents with T1D, exploring its correlation with other CGM metrics and blood count parameters. In this single-center, cross-sectional study, we gathered demographic and clinical data, including blood count parameters, HbA1c values, and CGM metrics, from 128 pediatric subjects with T1D (43% female; mean age, 13.4 ± 3.6 years). Our findings revealed higher levels of the coefficient of variation (CV) (p < 0.001) and time above range > 250 mg/dL (p = 0.033) among subjects with ΔGMI-HbA1c > 0.3%. No association was observed between blood count parameters and ΔGMI-HbA1c. In conclusion, despite the advancements and the widespread adoption of CGM systems, HbA1c remains an essential parameter for the assessment of glycemic control, especially in individuals with suboptimal metabolic control and extreme glycemic variability.