Felix Aberer

Background: Older adults with diabetes are vulnerable, facing multimorbidity and challenges in reaching glycemic targets. When insulin therapy is required, limited knowledge of geriatric therapeutic goals often leads to regimens that increase the risk of hypoglycemia. Simplified approaches such as once-daily basal insulin are recommended, yet titration protocols and unclear regimens remain barriers in home care nursing. Digital decision support systems (DDSSs) such as GlucoTab address this gap by offering evidence-based titration guidance integrated into nursing workflows. DDSSs also strengthen nurse autonomy, which leads to improved care processes and better outcomes in home care settings. Objective: This study assessed the effects of a nurse-guided intervention including a DDSS for diabetes management in older adults with type 2 diabetes receiving home care nursing. Methods: The prospective quasi-experimental feasibility and implementation study was conducted using GlucoTab for diabetes management as part of a multicomponent intervention at 2 home care nursing sites. Nine adults with type 2 diabetes receiving insulin therapy were enrolled (n=5 women; mean age 77, SD 10 years; mean BMI 27.7, SD 4.7 kg/m ² ; mean baseline hemoglobin A _1c 59.5, SD 12.8 mmol/mol). After receiving training, nurses implemented the DDSS, which integrates evidence-based diabetes management recommendations for home care and nursing workflows. Diabetes-related hospital admissions and glycemic outcomes were assessed before, during, and after implementation of the DDSS intervention. Results: Before implementation, 5 diabetes-related emergency admissions led to 4 hospitalizations, compared with none during implementation and 4 after implementation, mostly due to hyperglycemia. The median hospital stay was 9 (IQR 8-15) days (before and after implementation). Diabetes management shifted from predominantly premixed insulin therapy at baseline to basal insulin with or without bolus insulin during implementation. Diabetes-related home care visits decreased from 1.81 to 1.01 per day (before vs during implementation), and general practitioner contacts for insulin adjustments declined (19 before implementation vs 16 after implementation). Mean morning blood glucose decreased from 212 (SD 72) mg/dL to 162 (SD 42) mg/dL, and the proportion of measurements within the target range (70‐180 mg/dL) increased from 39.0% to 70.8% before vs during implementation. No incidence of hypoglycemia requiring third-party help was observed during implementation. Adherence of nurses to DDSS recommendations exceeded 94%. Conclusions: This study suggests that integrating a DDSS in a multicomponent intervention into home care nursing yielded longitudinal changes in diabetes outcomes. Emergency visits and hospitalizations were lower during implementation, no severe hypoglycemia was observed, glycemic control improved throughout the observation period, and therapy was simplified in routine care. Nursing visits decreased, suggesting potential improvements in care processes and resource use. High adherence and acceptance by nurses indicate feasibility and usability in home care practice. These findings align with evidence from hospitalized patients and telehealth studies, suggesting the potential for clinical, safety, and efficiency benefits.

Background: Algorithm-based insulin dosing systems are increasingly used in hospitals and have shown the potential to efficiently and safely enable glycemic control. The goal of this study was to evaluate glycemic control using the ultralong-acting basal insulin degludec (IDeg) in combination with insulin aspart (IAsp) within an algorithm-driven electronic clinical decision support system (cDSS) in inpatients with type 2 diabetes (T2D). Methods: In this non-controlled single-arm pilot study, an electronic, algorithm-based cDSS was applied for the management of insulin treatment in an internal general ward. Thirty hospitalized patients with T2D (18 female, age 74.1 ± 10.9 years, HbA1c 72.4 ± 22.3 mmol/mol, BMI 28.6 ± 5.6 kg/m², diabetes duration 13.2 ± 11.6 years, creatinine 1.5 ± 1.2 mg/dL, length of hospital stay 9.1 ± 4.0 days) were included in the study. Capillary blood glucose (BG) was evaluated four times daily using a point-of-care device integrated into the hospital information system. In addition, all participants received a blinded continuous glucose monitoring (CGM; Abbott Freestyle Libre Pro) system. The primary endpoint was defined as the percentage of BG measurements within the target range of 3.9–7.8 mmol/L. Results: Overall, 722 BG values and 17,242 CGM data points were available. Of those, 52.2% and 55.0% were in the specified target area (3.9–7.8 mmol/L), respectively. Mean BG prior to study start was 11.9 ± 4.4 mmol/L and improved to 7.5 ± 1.9 mmol/L and 7.4 ± 1.4 mmol/L after 6 and 10 days of treatment. BG < 3.9, <3.0 and <2.2 mmol/L was 1.25%, 0.28% and 0%, respectively. Adherence to the total daily insulin dose suggested by the cDSS was 94.2%, and 99.5% of all basal and 85.3% of all bolus insulin suggestions were accepted by the nurses in charge. Basal-bolus therapy using the cDSS covered 85% of the participants’ total hospital stay. Conclusions: Glycemic control using IDeg within an algorithm-driven cDSS could effectively and safely be achieved in the hospital and was highly accepted.

Background: Long-term glycemic control in type 1 diabetes (T1D) varies substantially among affected individuals, but the role of baseline characteristics at diagnosis and their association with later glycemic control remain incompletely understood. Identifying early predictors of glycemic control may facilitate timely, individualized therapeutic interventions. Methods: We retrospectively analyzed electronic health records of individuals with newly diagnosed T1D between 2001 and 2022 to assess anthropometric and metabolic parameters at the first presentation of the condition across age groups and determine predictors of glycated hemoglobin (HbA_1c) trajectories over 24 months. The multicentric cohort, which comprised people who were diagnosed with T1D in the Austrian federal state of Styria, was classified as children (1c levels over 24 months. However, after adjustment for important covariates, only younger age remained a significant predictor of inferior glycemic control over 24 months, emphasizing the importance of structured, age-appropriate follow-up care. Conclusions: Younger age at T1D diagnosis independently predicts suboptimal glycemic trajectories over the first two years after T1D onset. Early identification may enable targeted, age-specific interventions to improve long-term outcomes.

Aims: Continuous glucose monitoring (CGM) systems have become important technologies to improve glycaemia in people with type 1 diabetes (T1D). However, it has been shown that during rapid glucose change, sensor performance can deteriorate. Comparative data on sensor performance during high rates of glucose change, such as during exercise, between a real‐time continuous glucose monitor (rtCGM) and an intermittently scanned continuous monitor (isCGM) remain limited. Methods: Twenty‐two people with T1D (8 women, age 42 ± 11 years, HbA _1c 59 ± 8 mmol/mol (7.6 ± 0.8%)) simultaneously used an rtCGM (Dexcom G6) and an isCGM (Freestyle Libre 1). Sixty‐minute exercise sessions were performed on a cycle ergometer at moderate intensity, and glucose values from both CGM systems were compared against capillary reference blood glucose measurements (EKF S‐Line; EKF Diagnostics, Germany). Data were assessed using the Median Absolute Relative Difference (MedARD) with interquartile range, as well as the Diabetes Technology Society Error Grid (DTS EG). Results: During exercise, the MedARD was 14.6% [7.0;23.8] for rtCGM (2304 comparison points) vs. 11.6% [5.6;19.6] for isCGM (2266 comparison points) ( p < 0.0001). When stratified by glycaemic range, the MedARD was 39.2% [31.8;46.8] vs. 27.0% [17.0;34.6] for time below range (p = 0.0001), 16.1% [8.1;24.8] vs. 12.8% [6.4;20.4] for time in range (70–180 mg/dL) ( p < 0.0001) and 9.5% [4.7;16.0] vs. 8.0% [3.8;13.7] for time above range (>180 mg/dL) ( p = 0.0064) for rtCGM vs. isCGM. Conclusion: In this head‐to‐head comparison of rtCGM and isCGM, isCGM demonstrated superior performance during exercise in adults with T1D.

Adolescence remains a crucial age associated with diabetes distress in individuals living with type 1 diabetes (T1D). The Austrian organization “Diabär” regularly hosts a one-week adventure camp for adolescents (12–18 years) living with T1D. The camp focuses on “fun activities” without a structured educational protocol in order to minimize diabetes distress and increase diabetes management skills. In contrast to educational camps, training is kept to a minimum. However, attendees analyze the glycemic data of the previous day with their medical supervisor once daily during the camp. All subjects used a standardized real-time continuous glucose monitoring (CGM) system (DexcomG7) throughout the whole study. Glycemic metrics were prospectively analyzed during three periods: week 1 = home phase, week 2 = adventure camp, and week 3 = after the camp. Safety (time below range 1 [TBR1], 69–54 mg/dL, and time below range 2 [TBR2], p = 0.001). The TBR1 significantly increased during camp compared to week 1 (2.5 ±1.7% vs. 1.3 ± 1.2%; p = 0.009), whereas the TBR2 did not differ. No serious adverse events occurred. This adventure camp without a main focus on education showed feasibility and safety in adolescents with T1D.

Objective: The aim of this analysis was to assess glycemic control before and during the coronavirus disease (COVID-19) pandemic. Methods: Data from 64 (main analysis) and 80 (sensitivity analysis) people with type 1 diabetes (T1D) using intermittently scanned continuous glucose monitoring (isCGM) were investigated retrospectively. The baseline characteristics were collected from electronic medical records. The data were examined over three periods of three months each: from 16th of March 2019 until 16th of June 2019 (pre-pandemic), from 1st of December 2019 until 29th of February 2020 (pre-lockdown) and from 16th of March 2020 until 16th of June 2020 (lockdown 2020), representing the very beginning of the COVID-19 pandemic and the first Austrian-wide lockdown. Results: For the main analysis, 64 individuals with T1D (22 female, 42 male), who had a mean glycated hemoglobin (HbA1c) of 58.5 mmol/mol (51.0 to 69.3 mmol/mol) and a mean diabetes duration 13.5 years (5.5 to 22.0 years) were included in the analysis. The time in range (TIR_{[70–180mg/dL]}) was the highest percentage of measures within all three studied phases, but the lockdown 2020 phase delivered the best data in all these cases. Concerning the time below range (TBR_{[) and the time above range (TAR[>180mg/dL]), the lockdown 2020 phase also had the best values. Regarding the sensitivity analysis, 80 individuals with T1D (26 female, 54 male), who had a mean HbA1c of 57.5 mmol/mol (51.0 to 69.3 mmol/mol) and a mean diabetes duration of 12.5 years (5.5 to 20.7 years), were included. The TIR[70–180mg/dL] was also the highest percentage of measures within all three studied phases, with the lockdown 2020 phase also delivering the best data in all these cases. The TBR[ and the TAR[>180mg/dL] underscored the data in the main analysis. Conclusion: Superior glycemic control, based on all parameters analyzed, was achieved during the first Austrian-wide lockdown compared to prior periods, which might be a result of reduced daily exertion or more time spent focusing on glycemic management.}