Automated Insulin Delivery in Real Life (AID-IRL): Real-World User Perspectives on Commercial AID

Generating real-world evidence on diabetes technology using the CareLink Personal data management system

AIM

To establish trust in real-world evidence (RWE) derived from CareLink Personal (CP), Medtronic's data management system for MiniMed system users, we show that this database and its analyses strictly adhere to the principles of RWE.

METHODS

The methodology is applicable to all MiniMed iterations. We described every step from raw data to predefined outcomes. In addition, we showed CP's fitness-for-research by the below metrics (using last year's MiniMed 780G system data as a case study): representative population, relevant endpoints, appropriate granularity, high data completeness, high data representativity and consistency in results.

RESULTS

The process from raw data to outcomes has been validated, and metrics/logics adhere to established definitions. Over 95% of users have a CP account; with 96% providing consent, this allows the use of >91% of the census population. There is no rationale for an over-representation of a specific phenotype among users not included. CP includes >50 endpoints, including 'International Consensus on Time in Range' based metrics. Data are recorded at 5-min intervals (maximum 288 per day), and on average there were 263 data points per person per day. Ninety-nine per cent of uploads were automated. For the last year, only 1 in 6 users had a data gap >1 day, and 1 in 50 had a gap >1 week. The time in range from in-silico studies was similar to that of real-world studies from different geographies and with ever growing populations.

CONCLUSION

RWE from CP adheres to the principles of RWE and can serve as robust evidence on the performance and safety of MiniMed systems.

Refining Insulin on Board with netIOB for Automated Insulin Delivery

Automated insulin delivery (AID) systems enhance glucose management by lowering mean glucose level, reducing hyperglycemia, and minimizing hypoglycemia. One feature of most AID systems is that they allow the user to view "insulin on board" (IOB) to help confirm a recent bolus and limit insulin stacking. This metric, along with viewing glucose concentrations from a continuous glucose monitoring system, helps the user understand bolus insulin action and the future "threat" of hypoglycemia. However, the current presentation of IOB in AID systems can be misleading, as it does not reflect true insulin action or automatic, dynamic insulin adjustments. This commentary examines the evolution of IOB from a bolus-specific metric to its contemporary use in AID systems, highlighting its limitations in capturing real-time insulin modulation during varying physiological states.

Real-world lived experience of older adults with type 1 diabetes after an automated insulin delivery trial

AIMS

First-generation closed-loop automated insulin delivery improves glycaemia and psychosocial outcomes among older adults with type 1 diabetes in clinical trials. However, no study has previously assessed real-world lived experience of older adults using closed-loop therapy outside a trial environment.

METHODS

Semi-structured interviews were conducted with older adults who were pre-existing insulin pump users and previously completed the OldeR Adult Closed-Loop (ORACL) randomised trial. Interviews focused on perceptions of diabetes technology use, and factors influencing decisions regarding continuation.

RESULTS

Twenty-eight participants, mean age 70 years (SD 5), were interviewed at median 650 days (IQR 608-694) after their final ORACL trial visit. At interview, 23 participants (82%) were still using a commercial closed-loop system (requiring manual input for prandial insulin bolus doses). Themes discussed in interviews relating to closed-loop system use included sustained psychosocial benefits, cost and retirement considerations and usability frustrations relating to sensor accuracy and system alarms. Of the five participants who had discontinued, reasons included cost, continuous glucose monitoring-associated difficulties and usability frustrations. Cost was the largest consideration regarding continued use; most participants considered the increased ease of diabetes management to be worth the associated costs, though cost was prohibitive for some.

CONCLUSIONS

Almost 2 years after completing a closed-loop clinical trial, closed-loop automated insulin delivery remains the preferred type 1 diabetes therapy for the majority of older adult participants. Chronological age is not a barrier to real-world successful use of diabetes technology. Identifying age-related barriers, and solutions, to diabetes technology use among older adults is warranted.

Celebrating the Data from 100,000 Real-World Users of the MiniMed™ 780G System in Europe, Middle East, and Africa Collected Over 3 Years: From Data to Clinical Evidence

Introduction: The present report celebrates the benchmarking of 100,000 MiniMed™ 780G system users in Europe, Middle East, and Africa (EMEA) and summarizes the major insights into the usability and outcomes of this system. Methods: Carelink Personal data (August 2020-August 2023) of users living in EMEA were analyzed. Continuous glucose monitoring-based endpoints were aggregated for (1) the full cohort and (2) a 12-month longitudinal cohort. Subanalyses were done for users on optimal settings (those spending ≥95% of time with glucose target of 100 mg/dL, and ≥95% of time with active insulin time of 2 h), for self-reported age groups (≤15 and ≥56 years) and for various countries/regions. Results: Data from 101,629 users (34 countries) were analyzed. Mean time in range (TIR) was 72.3%, glucose management indicator (GMI) was 7%, time below 70 mg/dL (TBR70) was 2.0% and time below 54 mg/dL (TBR54) was 0.4%. In terms of international targets, 59.6% of users achieved a GMI <7%, 62.5% a TIR >70%, 88.4% a TBR70 < 4%, and 90.0% a TBR54 < 1%. Data improved impressively in optimal setting users (TIR = 78.8%, and users reaching TIR >70% = 86.3%) while safety remained (TBR70 = 2.2% and TBR54 = 0.4%). Data showed consistency across self-reported age groups and geographies. In the longitudinal cohort, TIR reached 75.5% in the first month and remained 73.3% or higher over the 12-month period. Conclusion: Over 100,000 users of the MiniMed™ 780G system have demonstrated consistency in achieving target control of glycemia.

Practical Guidance on Open Source and Commercial Automated Insulin Delivery Systems: A Guide for Healthcare Professionals Supporting People with Insulin-Requiring Diabetes

As increasing numbers of people with insulin-managed diabetes use automated insulin delivery (AID) systems or seek such technologies, healthcare providers are faced with a steep learning curve. Healthcare providers need to understand how to support these technologies to help inform shared decision making, discussing available options, implementing them in the clinical setting, and guiding users in special situations. At the same time, there is a growing diversity of commercial and open source automated insulin delivery systems that are evolving at a rapid pace. This practical guide seeks to provide a conversational framework for healthcare providers to first understand and then jointly assess AID system options with users and caregivers. Using this framework will help HCPs in learning how to evaluate potential new commercial or open source AID systems, while also providing a guide for conversations to help HCPs to assess the readiness and understanding of users for AID systems. The choice of an AID system is not as simple as whether the system is open source or commercially developed, and indeed there are multiple criteria to assess when choosing an AID system. Most importantly, the choices and preferences of the person living with diabetes should be at the center of any decision around the ideal automated insulin delivery system or any other diabetes technology. This framework highlights issues with AID use that may lead to burnout or perceived failures or may otherwise cause users to abandon the use of AID. It discusses the troubleshooting of basic AID system operation and discusses more advanced topics regarding how to maximize the time spent on AID systems, including how to optimize settings and behaviors for the best possible outcomes with AID technology for people with insulin-requiring diabetes. This practical approach article demonstrates how healthcare providers will benefit from assessing and better understanding all available AID system options to enable them to best support each individual.

Friend or Foe: a Narrative Review of the Impact of Diabetes Technology on Sleep

PURPOSE OF REVIEW

The purpose of this review is to present a review of sleep science, the relationship between sleep and type 1 diabetes, and highlight the current literature on sleep outcomes in adult and pediatric diabetes technology research.

RECENT FINDINGS

Sleep quality is associated with glycemic outcomes, diabetes self-management, and mental health in people with type 1 diabetes. Diabetes technologies, including insulin pumps, continuous glucose monitors, and hybrid closed-loop systems improve glycemic outcomes. However, many people find this technology challenging for a variety of reasons, including increased burden and frequent alarms, especially during the night. The impact of different devices on sleep quality and quantity has been mixed. The newest technology, the hybrid closed-loop systems, offers the best opportunity for nocturnal glycemic regulation and has improved patient and family perspectives on sleep quality. However, objective sleep assessment has not shown significant improvement on sleep duration. Sleep quality and quantity in people with type 1 diabetes are widely recognized as an important component of health care, and the literature regarding the impact of diabetes devices on sleep is increasing. However, sleep disruptions are common and a barrier to device use. Despite finding minimal changes to sleep duration with device use, subjective accounts of sleep quality are overall positive, especially in those using hybrid closed-loop systems. Sleep quantity and quality are important outcomes to consider as diabetes technology continues to evolve.

How It Started, How It Is Going: The Future of Artificial Pancreas Systems (Automated Insulin Delivery Systems)

Originally, the future of automated insulin delivery (AID) systems, or artificial pancreas systems (APS), was having them at all, in any form. We've learned in the last half dozen years that the future of all artificial pancreas systems holds higher time in range, less work required to manage automated insulin delivery systems to improve quality of life, and the ability to input critical information back into the system itself. The data and user experience stories make it clear: APS works. APS are an improvement over other diabetes therapy methods when they are made available, accessible, and affordable. Understanding the unmet expectations of current users of first generation APS technology may also aid in the development of improved technology and user experiences for the future of APS.

Why #WeAreNotWaiting-Motivations and Self-Reported Outcomes Among Users of Open-source Automated Insulin Delivery Systems: Multinational Survey

Background

Automated insulin delivery (AID) systems have been shown to be safe and effective in reducing hyperglycemia and hypoglycemia but are not universally available, accessible, or affordable. Therefore, user-driven open-source AID systems are becoming increasingly popular.

Objective

This study aims to investigate the motivations for which people with diabetes (types 1, 2, and other) or their caregivers decide to build and use a personalized open-source AID.

Methods

A cross-sectional web-based survey was conducted to assess personal motivations and associated self-reported clinical outcomes.

Results

Of 897 participants from 35 countries, 80.5% (722) were adults with diabetes and 19.5% (175) were caregivers of children with diabetes. Primary motivations to commence open-source AID included improving glycemic outcomes (476/509 adults, 93.5%, and 95/100 caregivers, 95%), reducing acute (443/508 adults, 87.2%, and 96/100 caregivers, 96%) and long-term (421/505 adults, 83.3%, and 91/100 caregivers, 91%) complication risk, interacting less frequently with diabetes technology (413/509 adults, 81.1%; 86/100 caregivers, 86%), improving their or child’s sleep quality (364/508 adults, 71.6%, and 80/100 caregivers, 80%), increasing their or child’s life expectancy (381/507 adults, 75.1%, and 84/100 caregivers, 84%), lack of commercially available AID systems (359/507 adults, 70.8%, and 79/99 caregivers, 80%), and unachieved therapy goals with available therapy options (348/509 adults, 68.4%, and 69/100 caregivers, 69%). Improving their own sleep quality was an almost universal motivator for caregivers (94/100, 94%). Significant improvements, independent of age and gender, were observed in self-reported glycated hemoglobin (HbA_1c), 7.14% (SD 1.13%; 54.5 mmol/mol, SD 12.4) to 6.24% (SD 0.64%; 44.7 mmol/mol, SD 7.0; P<.001), and time in range (62.96%, SD 16.18%, to 80.34%, SD 9.41%; P<.001).

Conclusions

These results highlight the unmet needs of people with diabetes, provide new insights into the evolving phenomenon of open-source AID technology, and indicate improved clinical outcomes. This study may inform health care professionals and policy makers about the opportunities provided by open-source AID systems.

International Registered Report Identifier (IRRID)

RR2-10.2196/15368

Safety and glycemic outcomes of do-it-yourself AndroidAPS hybrid closed-loop system in adults with type 1 diabetes

Background

The aim of the study was to assess the safety and glycemic outcomes with the use of a Do-It-Yourself (DIY) Hybrid Closed-Loop (HCL) system based on the AndroidAPS application in type 1 diabetes (T1D).

Methods

Single-center clinical trial, with 3-week run-in and 12-week study period. DIY HCL system consisted of the Dana Diabecare RS insulin pump, Dexcom G5 continuous glucose monitoring system and AndroidAPS application. Primary outcome was safety: incidences of severe hypoglycemia, diabetic ketoacidosis, time spent in glycemia <54 mg/dl. Secondary endpoints included percentage of time in range (TIR) 70–180 mg/dl, time below 70 mg/dl, HbA1c, insulin requirements, and body weight.

Results

In total 12 subjects (5 men, 7 women) were enrolled, mean age 31.3±6.7, 95%CI(27.7–34.9) years, mean diabetes duration 16.1±5.7, 95%CI(13.0–19.2) years. No episodes of severe hypoglycemia or ketoacidosis were observed. Percentage of time spent in glycemia below 54mg/dl was not increased. Average sensor glycemia was lower in the study period than baseline (141.1 ± 8.4, 95%CI(136.3–145.9) vs. 153.3 ± 17.9, 95%CI(143.2–163.4), mg/dl p<0.001). TIR 70–180 mg/dl was improved by 11.3%, 95%CI(2.8%-19.8%) (from 68.0 ± 12.7 to 79.3 ± 6.4%, p<0.001), without increasing hypoglycemia time. The HbA1c level decreased by -0.5%, 95%CI(-0.9%–-0.1%) (from 6.8 ± 0.5 to 6.3 ± 0.4%, p<0.001). Additionally, in the last 4 weeks of the study period participants significantly improved and showed TIR 70–180 mg/dl 82.1 ± 5.6%, 95%CI(78.9–85.3), time <54 mg/dl 0.30 (0.20–0.55)%, median 95%CI(0.1–0.7) and <70 mg/dl 1.90 (1.10–3.05)%, median 95%CI(0.7–3.2). The insulin requirement and body weight did not change in the study.

Conclusions

The study revealed safety of the Do-It-Yourself HCL system AndroidAPS in adults with T1D, limited to well-controlled, highly selected and closely monitored patients. The use of AndroidAPS significantly improved HbA1c, time in range and average sensor glycemia without increasing hypoglycemia. As both patients and their medical team are gaining experience using the system over time, they improve glycemic control.

Trial registration

German Clinical Trials Register: no. DRKS00015439; https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00015439.