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

Point-Counterpoint: The Need for Do-It-Yourself (DIY) Open Source (OS) AID Systems in Type 1 Diabetes Management

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.

Hybrid Closed Loop Using a Do-It-Yourself Artificial Pancreas System in Adults With Type 1 Diabetes

OBJECTIVE

There is increasing use of open-source artificial pancreas systems (APS) in the management of Type 1 diabetes. Our aim was to assess the safety and efficacy of the automated insulin delivery system AndroidAPS (AAPS), compared with stand-alone pump therapy in people with type 1 diabetes. The primary outcome was the difference in the percentage of time in range (TIR, 70-180 mg/dL). Secondary aims included mean sensor glucose value and percent continuous glucose monitor (CGM) time below range (TBR, <70 mg/dL).

RESEARCH DESIGN AND METHODS

This open-label single-center randomized crossover study (ANZCTR, Australian New Zealand clinical trial registry, ANZCTR-ACTRN12620001191987) comprised 20 participants with type 1 diabetes on established pump therapy, assigned to either stand-alone insulin pump therapy or the open-source AAPS hybrid closed-loop system for four weeks, with crossover to the alternate arm for the following four weeks. The CGM outcome parameters were measured by seven-day CGM at baseline and the final week of each four-week study arm.

RESULTS

Twenty participants were recruited (60% women), aged 45.8 ± 15.9 years, with mean diabetes duration of 23.9 ± 13.2 years, baseline glycated hemoglobin (HbA1c) 7.5% ± 0.5% (58 ± 6 mmol/mol) and mean TIR 62.3% ± 12.9%. The change in TIR from baseline for AAPS compared with stand-alone pump therapy was 18.6% (11.4-25.9), (P < .001), TIR 76.6% ± 11.7%, 58.0% ± 15.6%, for AAPS and stand-alone pump, respectively. Time glucose <54 mg/dL was not increased (mean = -2.0%, P = .191). No serious adverse events or episodes of severe hypoglycemia were recorded.

CONCLUSIONS

This clinical trial of the open-source AAPS hybrid closed-loop system performed in an at-home setting demonstrated comparable safety to stand-alone pump therapy. The glycemic outcomes of AAPS were superior with improved TIR, and there was no significant difference in TBR compared with stand-alone pump therapy.

Retrospective Comparison of Commercially Available Automated Insulin Delivery With Open-Source Automated Insulin Delivery Systems in Type 1 Diabetes

BACKGROUND

Automated insulin delivery (AID) systems have shown to improve glycemic control in a range of populations and settings. At the start of this study, only one commercial AID system had entered the Austrian market (MiniMed 670G, Medtronic). However, there is an ever-growing community of people living with type 1 diabetes (PWT1D) using open-source (OS) AID systems.

MATERIALS AND METHODS

A total of 144 PWT1D who used either the MiniMed 670G (670G) or OS-AID systems routinely for a period of at least three to a maximum of six months, between February 18, 2020 and January 15, 2023, were retrospectively analyzed (116 670G aged from 2.6 to 71.8 years and 28 OS-AID aged from 3.4 to 53.5 years). The goal is to evaluate and compare the quality of glycemic control of commercially available AID and OS-AID systems and to present all data by an in-depth descriptive analysis of the population. No statistical tests were performed.

RESULTS

The PWT1D using OS-AID systems spent more time in range (TIR)70-180 mg/dL (81.7% vs 73.9%), less time above range (TAR)181-250 mg/dL (11.1% vs 19.6%), less TAR>250 mg/dL (2.5% vs 4.3%), and more time below range (TBR)54-69 mg/dL (2.2% vs 1.7%) than PWT1D using the 670G system. The TBR<54 mg/dL was comparable in both groups (0.3% vs 0.4%). In the OS-AID group, median glucose level and glycated hemoglobin (HbA1c) were lower than in the 670G system group (130 vs 150 mg/dL; 6.2% vs 7.0%).

CONCLUSION

In conclusion, both groups were able to achieve satisfactory glycemic outcomes independent of age, gender, and diabetes duration. However, the PWT1D using OS-AID systems attained an even better glycemic control with no clinical safety concerns.

Open-source Artificial Pancreas Systems Are Safe and Effective When Supported In-clinic: Outcomes in 248 Consecutive Type 1 Diabetes Clients

OBJECTIVE

Our aim in this study was to determine the safety, glycemia, and quality of life (QoL) associated with in-clinic installation and management of supported open-source artificial pancreas systems (SOSAPS) in type 1 diabetes (T1D).

METHODS

This investigation is a retrospective cohort study of consecutive SOSAPS users at a Canadian diabetes centre. SOSAPS were offered to all moderately tech-savvy T1D clients on sensor-augmented multiple daily injection or pump, able to pay for hardware, and willing to sign a consent and waiver document. SOSAPS were installed and maintained by clinic staff at no cost to clients. iPhone users were assigned to either Loop (n=108) or iPhone artificial pancreas systems (iAPS; n=114) and Android users to Android-type APS (n=24). Outcomes included severe hypoglycemia and diabetic ketoacidosis (DKA), time in range (TIR) 4.0 to 10.0 mmol/L, time below range (TBR) <4 mmol/L, glucose management indicator (GMI), mean sensor glucose (MSG), change in glycated hemoglobin (A1C), and QoL.

RESULTS

Two hundred forty-eight subjects (131 males, 117 females), with a mean age of 36 years and diabetes duration of 21 years, experienced 3 episodes of severe hypoglycemia and no DKA over a follow-up of 17 months. TIR rose by 16%, from 64% to 80% (p<0.0001); TBR fell by 1.0%, from 3.5% to 2.5% (p=0.001); MSG fell from 9.0 to 8.1 mmol/L (p<0.001); GMI fell from 7.3% to 6.7% (p<0.001); and A1C fell from 7.2% to 6.7% (p<0.0001). QoL scores were healthy before and improved after SOSAPS.

CONCLUSIONS

Clients with T1D using SOSAPS and supported with no-cost care to the client (software, technology, and physician/physician assistant) safely achieved improved TIR, GMI, A1C, and QoL.

Are all HCL systems the same? long term outcomes of three HCL systems in children with type 1 diabetes: real-life registry-based study

Objective

To compare parameters of glycemic control among three types of hybrid closed loop (HCL) systems in children with T1D (CwD) using population-wide data from the national pediatric diabetes registry ČENDA.

Methods

CwD aged <19 years treated with Medtronic MiniMed 780G (780G), Tandem t:slim X2 (Control-IQ) or do-it-yourself AndroidAPS (AAPS) systems for >12 months and monitored by CGM >70% of the time were included. HbA1c, times in glycemic ranges, and Glycemia Risk Index (GRI) were used for cross-sectional comparison between the HCL systems.

Results

Data from 512 CwD were analyzed. 780G, Control-IQ and AAPS were used by 217 (42.4%), 211 (41.2%), and 84 (16.4%) CwD, respectively. The lowest HbA1c value was observed in the AAPS group (44 mmol/mol; IQR 8.0, p<0.0001 vs any other group), followed by Control-IQ and 780G groups (48 (IQR 11) and 52 (IQR 10) mmol/mol, respectively). All of the systems met the recommended criteria for time in range (78% in AAPS, 76% in 780G, and 75% in Control-IQ users). CwD using AAPS spent significantly more time in hypoglycemia (5% vs 2% in 780G and 3% in Control-IQ) and scored the highest GRI (32, IQR 17). The lowest GRI (27, IQR 15) was seen in 780G users.

Conclusion

Although all HCL systems proved effective in maintaining recommended long-term glycemic control, we observed differences that illustrate strengths and weaknesses of particular systems. Our findings could help in individualizing the choice of HCL systems.

Efficacy and safety of Android artificial pancreas system use at home among adults with type 1 diabetes mellitus in China: protocol of a 26-week, free-living, randomised, open-label, two-arm, two-phase, crossover trial

INTRODUCTION

Do-it-yourself artificial pancreas system (DIY APS) is built using commercially available insulin pump, continuous glucose monitoring (CGM) and an open-source algorithm. Compared with commercial products, DIY systems are affordable, allow personalised settings and provide updated algorithms, making them a more promising therapy for most patients with type 1 diabetes mellitus (T1DM). Many small and self-reported observational studies have found that their real-world use was associated with potential metabolic and psychological benefits. However, rigorous-designed studies are urgently needed to confirm its efficacy and safety.

METHODS AND ANALYSIS

In this 26-week randomised, open-label, two-arm, two-phase, crossover trial, participants aged 18-75 years, with T1DM and glycated haemoglobin (HbA1c) 7-11%, will use AndroidAPS during one 12-week period and sensor-augmented pump during another 12-week period. This study will recruit at least 24 randomised participants. AndroidAPS consists of three components: (1) real-time CGM; (2) insulin pump; (3) AndroidAPS algorithm implemented in Android smartphone. The primary endpoint is time in range (3.9-10.0 mmol/L) derived from CGM. The main secondary endpoints include percentage of sensor glucose values below, within and above target range; mean sensor glucose value; measures of glycaemic variability and centralised HbA1c. Safety endpoints mainly include the frequency of hypoglycaemia events, diabetic ketoacidosis and other serious adverse events.

ETHICS AND DISSEMINATION

This study has been approved by the Ethics Committee of the Third Affiliated Hospital of Sun Yat-sen University. There will be verbal and written information regarding the trial given to each participant. The study will be disseminated through peer-reviewed publications and conference presentations.

OVERALL STATUS

Recruiting.

STUDY START

11 February 2023.

PRIMARY COMPLETION

31 July 2024.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov Registry (NCT05726461).

Open-source automated insulin delivery systems (OS-AIDs) in a pediatric population with type 1 diabetes in a real-life setting: the AWeSoMe study group experience

PURPOSE

The use of open-source automated insulin delivery systems (OS-AIDs), for the management of type 1 diabetes (T1D), has increased over recent years in all age groups. Real-life data has demonstrated the safety and efficacy of these systems, however, studies in the pediatric population remain limited. In this study, we aimed to examine the effect of transition to an OS-AIDs on glycemic parameters, and on several aspects related to quality of life. In addition, we aimed to characterize the socioeconomic position of families who chose this treatment modality, assess their motivations to do so, and evaluate treatment satisfaction.

METHODS

In this multi-center observational real-life study from the AWeSoMe Group, we compared glycemic parameters of 52 individuals with T1D (56% males, mean diabetes duration 4.2 ± 3.9 years), from the last clinic visit prior to OS-AIDs initiation to the most recent clinic visit while using the system. Socioeconomic position (SEP) index was retrieved from the Israel Central Bureau of Statistics. Caregivers completed questionnaires assessing reasons for system initiation and treatment satisfaction.

RESULTS

Mean age at OS-AIDs initiation was 11.2 ± 4 years, range 3.3-20.7 years with a median usage duration of 11.1 months (range 3-45.7). Mean SEP Index was 1.033 ± 0.956 (value range: -2.797 to 2.590). Time in range (TIR) of 70 to 180 mg/dl increased from 69.0 ± 11.9 to 75.5 ± 11.7%, (P < 0.001), and HbA1c decreased from 6.9 ± 0.7 to 6.4 ± 0.6%, (P < 0.001). Time in tight range (TITR) of 70 to 140 mg/dl increased from 49.7 ± 12.9 to 58.8 ± 10.8% (P < 0.001). No episodes of severe hypoglycemia or DKA were reported. Reduction in diabetes burden and sleep quality improvement were the main reasons for OS-AID initiation.

CONCLUSIONS

In our cohort of youth with T1D, the transition to an OS-AID resulted in greater TIR and less severe hypoglycemia regardless of age, diabetes duration or SEP, which was found to be above average. The overall improvement in glycemic parameters in our study population with excellent baseline glycemic control, provides additional evidence of beneficence and efficacy of OS-AIDs in the pediatric population.

First Use of Open-Source Automated Insulin Delivery AndroidAPS in Full Closed-Loop Scenario; Pancreas4ALL Randomized Pilot Study

Objective: We evaluated the safety and feasibility of open-source automated insulin delivery AndroidAPS in adolescents and young adults with type 1 diabetes (T1D) and compared its efficacy in three different scenarios: hybrid closed loop (HCL) with meal boluses, meal announcement only (MA), and full closed loop (FCL). Research Design and Methods: In an open-label, prospective, randomized crossover trial (clinicaltrials.gov NCT04835350), 16 adolescents with T1D (10 females) with mean age of 17 years (range 15-20), glycated hemoglobin 56 mmol/mol (range 43-75), and mean duration of diabetes 5.9 years (9-15) underwent three distinct 3-day periods of camp living, comparing the above-mentioned scenarios of AndroidAPS. We used modified and locked version of AndroidAPS 3.1.03, which was called Pancreas4ALL for study purposes. The order of MA and FCL periods was assigned randomly. The primary endpoints were feasibility and safety of the system represented by percentage of time of glucose control by the system and time in hypoglycemia below 3 mmol/L. Results: The glycemia was controlled by the system 95% time of the study and the proportion of time below 3 mmol/L did not exceed 1% over the whole study period (0.72%). The HCL scenario reached significantly higher percentage of time below 3 mmol/L (HCL 1.05% vs. MA 0.0% vs. FCL 0.0%; P = 0.05) compared to other scenarios. No difference was observed among the scenarios in the percentage of time between 3.9 and 10 mmol/L (HCL 83.3% vs. MA 79.85% vs. FCL 81.03%, P = 0.58) corresponding to mean glycemia (HCL 6.65 mmol/L vs. MA 7.34 mmol/L vs. FCL 7.05 mmol/L, P = 0.28). No difference was observed in the mean daily dose of insulin or in the daily carbohydrate intake. No serious adverse event occurred during the study period. Conclusions: Our pilot study showed that FCL might be a realistic mode of treatment for people with T1D.

Continuous subcutaneous insulin infusions: Closing the loop

CONTEXT

Continuous subcutaneous insulin infusions (CSIIs) and continuous glucose monitors (CGMs) have revolutionized the management of diabetes mellitus (DM). Over the last two decades the development of advanced, small, and user-friendly technology has progressed substantially, essentially closing the loop in the fasting and post-absorptive state, nearing the promise of an artificial pancreas. The momentum was mostly driven by the diabetes community itself, to improve its health and quality of life.

EVIDENCE ACQUISITION

Literature regarding CSII and CGM was reviewed.

EVIDENCE SYNTHESIS

Management of DM aims to regulate blood glucose to prevent long term micro and macrovascular complications. CSIIs combined with CGMs provide an integrated system to maintain tight glycemic control in a safe and uninterrupted fashion, while minimizing hypoglycemic events. Recent advances have allowed to 'close the loop' by better mimicking endogenous insulin secretion and glucose level regulation. Evidence supports sustained improvement in glycemic control with reduced episodes of hypoglycemia using these systems, while improving quality of life. Ongoing work in delivery algorithms with or without counterregulatory hormones will allow for further layers of regulation of the artificial pancreas.

CONCLUSION

Ongoing efforts to develop an artificial pancreas have created effective tools to improve the management of DM. CSIIs and CGMs are useful in diverse populations ranging from children to the elderly, as well as in various clinical contexts. Individually and more so together, these have had a tremendous impact in the management of DM, while avoiding treatment fatigue. However, cost and accessibility are still a hindrance to its wider application.

Improved Glycemia and Quality of Life Among Loop Users: Analysis of Real-world Data From a Single Center

BACKGROUND

Despite do-it-yourself automated insulin delivery being an unapproved method of insulin delivery, an increasing number of people with type 1 diabetes (T1D) worldwide are choosing to use Loop, a do-it-yourself automated insulin delivery system.

OBJECTIVE

In this study, we aimed to assess glycemic outcomes, safety, and the perceived impact on quality of life (QOL) in a local Edmonton cohort of known Loop users.

METHODS

An observational study of adults with T1D who used Loop was performed. An assessment of glycemic and safety outcomes, HbA1c, time in range, hospital admissions, and time below range compared users most recent 6 months of Loop use, with their prior regulatory approved insulin delivery method. QOL outcomes were assessed using Insulin Dosing Systems: Perceptions, Ideas, Reflections, and Expectations, diabetes impact, and device satisfaction measures (with maximum scores of 100, 10, and 10, respectively) and semistructured interviews.

RESULTS

The 24 adults with T1D who took part in this study 16 (67%) were female, with a median age of 33 (IQR 28-45) years, median duration of diabetes of 22 (IQR 17-32) years, median pre-Loop HbA1c of 7.9% (IQR 7.6%-8.3%), and a median duration of Loop use of 18 (IQR 12-25) months. During Loop use, the participants had median (IQR) values of 7.1% (6.5%-7.5%), 54 mmol (48-58) for HbA1c and 76.5% (64.6%-81.9%) for time in range, which were a significant improvement from prior therapy (P=.001 and P=.005), with a nonsignificant reduction in time below range; 3.0 to 3.9 mmol/L (P=.17) and <3 mmol/L (P=.53). Overall, 2 episodes of diabetic ketoacidosis occurred in a total of 470 months of Loop use, and no severe hypoglycemia occurred. The positive impact of Loop use on QOL was explored in qualitative analysis and additionally demonstrated through a median Insulin Dosing Systems: Perceptions, Ideas, Reflections, and Expectations score of 86 (IQR 79-95), a median diabetes impact score of 2.8 (IQR 2.1-3.9), and a median device satisfaction score of 9 (IQR 8.2-9.4).

CONCLUSIONS

This local cohort of people with T1D demonstrated a beneficial effect of Loop use on both glycemic control and QOL, with no safety concerns being highlighted.

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.

A scoping review of Do-It-Yourself Automated Insulin Delivery system (DIY AID) use in people with type 1 diabetes

INTRODUCTION

User designed Automated Insulin Delivery systems (AID), termed Do-It-Yourself (DIY) AID include; AndroidAPS, OpenAPS and Loop. These unregulated systems provide challenges for healthcare providers worldwide, with potential legal and ethical barriers to supporting their use. We performed a scoping review of the currently available literature surrounding DIY AID systems, specifically to highlight the evidence available to facilitate healthcare providers to support persons with diabetes who may benefit from DIY AID.

METHODS

Studies relating to DIY AID systems were searched in Embase, Medline, Web of Science, Scopus, Proquest and Cochrane library until 31st December 2021. Publications were screened through title and abstract to identify study type and AID system type described. A thematic synthesis methodology was used for analysis of studies of DIY AID use due to the heterogeneity in study designs (case reports, qualitative, cross-sectional and cohort studies), with similarity in outcome themes.

RESULTS

Following implementation of the search strategy, 38 relevant full texts were identified; comprising 12 case reports, 9 qualitative studies and 17 cohort studies, and data was also available from 24 relevant conference abstracts. No randomized studies were identified. Common themes were identified in the outcomes across the studies; glycemic variability, safety, quality of life, healthcare provider attitudes and social media.

CONCLUSION

There is extensive real-world data, but a lack of randomized control trial evidence supporting DIY AID system use, due to the user-driven, unregulated nature of these systems. Healthcare providers report a lack of understanding surrounding, and confidence in supporting, DIY AID despite impressive observational and user self-reported improvements in glycemic variability, without any reported safety compromises.

Barriers to Uptake of Open-Source Automated Insulin Delivery Systems: Analysis of Socioeconomic Factors and Perceived Challenges of Caregivers of Children and Adolescents With Type 1 Diabetes From the OPEN Survey

Background

As a treatment option for people living with diabetes, automated insulin delivery (AID) systems are becoming increasingly popular. The #WeAreNotWaiting community plays a crucial role in the provision and distribution of open-source AID technology. However, while a large percentage of children were early adopters of open-source AID, there are regional differences in adoption, which has prompted an investigation into the barriers perceived by caregivers of children with diabetes to creating open-source systems.

Methods

This is a retrospective, cross-sectional and multinational study conducted with caregivers of children and adolescents with diabetes, distributed across the online #WeAreNotWaiting online peer-support groups. Participants—specifically caregivers of children not using AID—responded to a web-based questionnaire concerning their perceived barriers to building and maintaining an open-source AID system.

Results

56 caregivers of children with diabetes, who were not using open-source AID at the time of data collection responded to the questionnaire. Respondents indicated that their major perceived barriers to building an open-source AID system were their limited technical skills (50%), a lack of support by medical professionals (39%), and therefore the concern with not being able to maintain an AID system (43%). However, barriers relating to confidence in open-source technologies/unapproved products and fear of digital technology taking control of diabetes were not perceived as significant enough to prevent non-users from initiating the use of an open-source AID system.

Conclusions

The results of this study elucidate some of the perceived barriers to uptake of open-source AID experienced by caregivers of children with diabetes. Reducing these barriers may improve the uptake of open-source AID technology for children and adolescents with diabetes. With the continuous development and wider dissemination of educational resources and guidance—for both aspiring users and their healthcare professionals—the adoption of open-source AID systems could be improved.

Large-Scale Data Analysis for Glucose Variability Outcomes with Open-Source Automated Insulin Delivery Systems

Open-source automated insulin delivery (AID) technologies use the latest continuous glucose monitors (CGM), insulin pumps, and algorithms to automate insulin delivery for effective diabetes management. Early community-wide adoption of open-source AID, such as OpenAPS, has motivated clinical and research communities to understand and evaluate glucose-related outcomes of such user-driven innovation. Initial OpenAPS studies include retrospective studies assessing high-level outcomes of average glucose levels and HbA1c, without in-depth analysis of glucose variability (GV). The OpenAPS Data Commons dataset, donated to by open-source AID users with insulin-requiring diabetes, is the largest freely available diabetes-related dataset with over 46,070 days’ worth of data and over 10 million CGM data points, alongside insulin dosing and algorithmic decision data. This paper first reviews the development toward the latest open-source AID and the performance of clinically approved GV metrics. We evaluate the GV outcomes using large-scale data analytics for the n = 122 version of the OpenAPS Data Commons. We describe the data cleaning processes, methods for measuring GV, and the results of data analysis based on individual self-reported demographics. Furthermore, we highlight the lessons learned from the GV outcomes and the analysis of a rich and complex diabetes dataset and additional research questions that emerged from this work to guide future research. This paper affirms previous studies’ findings of the efficacy of open-source AID.

Real-world evidence on clinical outcomes of people with type 1 diabetes using open-source and commercial automated insulin dosing systems: A systematic review

AIMS

Several commercial and open-source automated insulin dosing (AID) systems have recently been developed and are now used by an increasing number of people with diabetes (PwD). This systematic review explored the current status of real-world evidence on the latest available AID systems in helping to understand their safety and effectiveness.

METHODS

A systematic review of real-world studies on the effect of commercial and open-source AID system use on clinical outcomes was conducted employing a devised protocol (PROSPERO ID 257354).

RESULTS

Of 441 initially identified studies, 21 published 2018-2021 were included: 12 for Medtronic 670G; one for Tandem Control-IQ; one for Diabeloop DBLG1; two for AndroidAPS; one for OpenAPS; one for Loop; three comparing various types of AID systems. These studies found that several types of AID systems improve Time-in-Range and haemoglobin A_1c (HbA_1c ) with minimal concerns around severe hypoglycaemia. These improvements were observed in open-source and commercially developed AID systems alike.

CONCLUSIONS

Commercially developed and open-source AID systems represent effective and safe treatment options for PwD of several age groups and genders. Alongside evidence from randomized clinical trials, real-world studies on AID systems and their effects on glycaemic outcomes are a helpful method for evaluating their safety and effectiveness.

A Brief Review on the Evolution of Technology in Exercise and Sport in Type 1 Diabetes: Past, Present, and Future

One hundred years ago, insulin was first used to successfully lower blood glucose levels in young people living with what was then called juvenile diabetes. While insulin was not a cure for diabetes, it allowed individuals to resume a near normal life and have some freedom to eat more liberally and gain the strength they needed to live a more active lifestyle. Since then, a number of therapeutic and technical advances have arisen to further improve the health and wellbeing of individuals living with type 1 diabetes, allowing many to participate in sport at the local, regional, national or international level of competition. This review and commentary highlights some of the key advances in diabetes management in sport over the last 100 years since the discovery of insulin.

Emotional and Physical Health Impact in Children and Adolescents and their Caregivers Using Open-Source Automated Insulin Delivery: Qualitative Analysis of Lived Experiences. (Preprint)

Background

Given the limitations in the access and license status of commercially developed automated insulin delivery (AID) systems, open-source AID systems are becoming increasingly popular among people with diabetes, including children and adolescents.

Objective

This study aimed to investigate the lived experiences and physical and emotional health implications of children and their caregivers following the initiation of open-source AID, their perceived challenges, and sources of support, which have not been explored in the existing literature.

Methods

Data were collected through 2 sets of open-ended questions from a web-based multinational survey of 60 families from 16 countries. The narratives were thematically analyzed, and a coding framework was identified through iterative alignment.

Results

A range of emotions and improvements in quality of life and physical health were reported, as open-source AID enabled families to shift their focus away from diabetes therapy. Caregivers were less worried about hypoglycemia at night and outside their family homes, leading to increased autonomy for the child. Simultaneously, the glycemic outcomes and sleep quality of both the children and caregivers improved. Nonetheless, the acquisition of suitable hardware and technical setup could be challenging. The #WeAreNotWaiting community was the primary source of practical and emotional support.

Conclusions

Our findings show the benefits and transformative impact of open-source AID and peer support on children with diabetes and their caregivers and families, where commercial AID systems are not available or suitable. Further efforts are required to improve the effectiveness and usability and facilitate access for children with diabetes, worldwide, to benefit from this innovative treatment.

International Registered Report Identifier (IRRID)

RR2-10.2196/15368

Open-source automated insulin delivery: international consensus statement and practical guidance for health-care professionals

Open-source automated insulin delivery systems, commonly referred to as do-it-yourself automated insulin delivery systems, are examples of user-driven innovations that were co-created and supported by an online community who were directly affected by diabetes. Their uptake continues to increase globally, with current estimates suggesting several thousand active users worldwide. Real-world user-driven evidence is growing and provides insights into safety and effectiveness of these systems. The aim of this consensus statement is two-fold. Firstly, it provides a review of the current evidence, description of the technologies, and discusses the ethics and legal considerations for these systems from an international perspective. Secondly, it provides a much-needed international health-care consensus supporting the implementation of open-source systems in clinical settings, with detailed clinical guidance. This consensus also provides important recommendations for key stakeholders that are involved in diabetes technologies, including developers, regulators, and industry, and provides medico-legal and ethical support for patient-driven, open-source innovations.

Empfehlungen zur Diabetes-Behandlung mit automatischen Insulin-Dosierungssystemen

Das Prinzip der automatischen Insulindosierung, kurz „AID“ genannt, zeigt in Zulassungsstudien und Real-World-Erfahrungen ausgezeichnete Behandlungsergebnisse. Beim AID wird eine Insulinpumpe mit einem System zur kontinuierlichen Glukosemessung zusammengeschaltet, während ein Rechenprogramm, der sogenannte Algorithmus, die Steuerung der Insulingabe nach Bedarf übernimmt. Idealerweise wäre das System ein geschlossener Kreis, bei dem die Menschen mit Diabetes keine Eingabe mehr machen müssten. Jedoch sind bei den heute verfügbaren Systemen verschiedene Grundeinstellungen und Eingaben erforderlich (insbesondere von Kohlenhydratmengen der Mahlzeiten oder körperlicher Aktivität), die sich von den bisherigen Empfehlungen der sensorunterstützten Pumpentherapie in einzelnen Aspekten unterscheiden. So werden die traditionellen Konzepte von „Basal“ und „Bolus“ mit AID weniger nützlich, da der Algorithmus beide Arten der Insulinabgabe verwendet, um die Glukosewerte dem eingestellten Zielwert zu nähern. Daher sollte bei diesen Systemen statt der Erfassung von „Basal“ und „Bolus“, zwischen einer „nutzerinitiierten“ und einer „automatischen“ Insulindosis unterschieden werden. Gemeinsame Therapieprinzipien der verschiedenen AID-Systeme umfassen die passgenaue Einstellung des Kohlenhydratverhältnisses, die Bedeutung des Timings der vom Anwender initiierten Insulinbolusgaben vor der Mahlzeit, den korrekten Umgang mit einem verzögerten oder versäumten Mahlzeitenbolus, neue Prinzipien im Umgang mit Sport oder Alkoholgenuss sowie den rechtzeitigen Umstieg von AID zu manuellem Modus bei Auftreten erhöhter Ketonwerte. Das Team vom Diabetes-Zentrum AUF DER BULT in Hannover hat aus eigenen Studienerfahrungen und der zugrunde liegenden internationalen Literatur praktische Empfehlungen zur Anwendung und Schulung der gegenwärtig und demnächst in Deutschland kommerziell erhältlichen Systeme zusammengestellt. Für den Erfolg der AID-Behandlung scheint das richtige Erwartungsmanagement sowohl beim Behandlungsteam und als auch beim Anwender von großer Bedeutung zu sein.

Automated Insulin Delivery Systems: Today, Tomorrow and User Requirements

Automated insulin delivery (AID) is the most recent advance in type 1 diabetes (T1D) management. It has the potential to achieve glycemic targets without disabling hypoglycemia, to improve quality of life and reduce diabetes distress and burden associated with self-management. Several AID systems are currently licensed for use by people with T1D in Europe, United States, and the rest of the world. Despite AID becoming a reality in routine clinical practice over the last few years, the commercially hybrid AID and other systems, are still far from a fully optimized automated diabetes management tool. Implementation of AID systems requires education and support of healthcare professionals taking care of people with T1D, as well as users and their families. There is much to do to increase usability, portability, convenience and to reduce the burden associated with the use of the systems. Co-design, involvement of people with lived experience of T1D and robust qualitative assessment is critical to improving the real-world use of AID systems, especially for those who may have greater need. In addition to this, information regarding the psychosocial impact of the use of AID systems in real life is needed. The first commercially available AID systems are not the end of the development journey but are the first step in learning how to optimally automate insulin delivery in a way that is equitably accessible and effective for people living with T1D.

Glucose Management Indicator for People with Type 1 Asian Diabetes Is Different from That of the Published Equation: Differences by Glycated Hemoglobin Distribution

Background: We aimed to determine whether there are racial differences in glucose management indicator (GMI) equation for Asians and propose an adjusted GMI equation specific to Asians. Methods: This was a 24-week, prospective, observational study. A total of 106 Korean subjects with type 1 diabetes was included in the analyses. Continuous glucose monitoring (CGM: Dexcom G5) data and glycated hemoglobin (HbA1c) were obtained at the end of 3 months (n = 106) and 6 months (n = 70) of use of a CGM device. Full 3-month CGM data were collected from 176 patients. Results: Linear regression analysis between HbA1c and CGM-derived mean glucose (GMI [%] = 2.814 + 0.026 × mean glucose [mg/dL], R² = 0.739, P < 0.001) showed significant correlation. An increase corresponding to each 25 mg/dL increase of mean glucose was higher with the Asian-Dexcom-specific GMI (0.7%) than with the published GMI (0.6%). The mean Asian-Dexcom-specific GMI was significantly lower than the published GMI (P = 0.022), especially in patients with HbA1c <7.0% (<6.0%: P = 0.003, 6.0%-6.9%: P = 0.001). Conclusions: The GMI equation specific for Asian Type 1 diabetes was different from the published GMI equation. For a given CGM-derived mean glucose, GMI calculated with the published equation could overestimate HbA1c in Asian subjects with HbA1c <7.0%. Although race partially explains the differences in GMI equation between published and Asian data, future research with larger databases is needed to develop a specific formula for Asian populations.

[Individualization of diabetes treatment by automated insulin delivery]

Insulinpumpen und Glucosesensoren haben sich in Registerdaten als effektiv in der Verbesserung der Diabetestherapie und Reduktion akuter Komplikationen gezeigt. In der pädiatrischen Diabetologie ist die Nutzung mindestens eines technischen Geräts Standard. Durch die Kombination beider Systeme ergibt sich Möglichkeit der automatischen Insulinabgabe („automated insulin delivery“, AID).

Viele AID-Systeme sind in klinischen Studien getestet und haben sich als sicher und effektiv erwiesen. Die Versorgungsituation in Deutschland erlaubt es derzeit nur, Mitgliedern der gesetzlichen Krankenversicherungen ein bestimmtes System zu verordnen; dieses ist für Kinder, die jünger als 7 Jahre sind, nicht geeignet. Gründe liegen in gesetzlichen Hürden und mangelnder Zertifizierung durch die Hersteller. Die CE-Zertifikate können Probleme bei der Insulinverordnung mit sich bringen. „Open-source“-Systeme sind Varianten, mit denen bestehende Regularien umgangen werden können. Daraus ergeben sich sowohl für Nutzer wie auch für Verordner Risiken.

Die dauerhafte Nutzung setzt sowohl auf Anwender- als auch auf Behandlerseite die fundierte Kenntnis der Eigenschaften der einzelnen AID-Systeme voraus. Eine vollständige Automatisierung funktioniert noch nicht. Zur Evaluation der AID-Therapie sind die metrischen Daten der Glucosesensoren, die „Zeit im Zielbereich“ und der „Glucose Management Indicator“ anerkannte und geeignete Parameter, da sie eine Beratung auf Basis der reellen Daten aus dem Alltag der Menschen mit Diabetes zulassen.

Da alle Glucosesensoren über Cloud-basierte Software ausgelesen werden oder die Daten automatisch aus einem telefonverbundenen Empfangsgerät beziehen, ist die ideale technische Grundlage für eine telemedizinische Betreuung geschaffen, die noch der Ausgestaltung bedarf.