Efficacy, safety, and user experience of DIY or open-source artificial pancreas systems: a systematic review

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.

Accuracy of an Off-Label Transmitter and Data Manager Paired With an Intermittent Scanned Continuous Glucose Monitor in Adults With Type 1 Diabetes

BACKGROUND

This work evaluates the accuracy and agreement between the FreeStyle Libre sensor (FSL) and an off-label converted real-time continuous glucose monitor (c-rtCGM) device consisting of the MiaoMiao transmitter and the xDrip+ application which can be coupled to the FSL.

METHODS

Four weeks of glucose data were collected from 21 participants with type 1 diabetes using the c-rtCGM and FSL: two weeks with a single initial calibration (uncalibrated) and two weeks with a daily calibration (calibrated). Accuracy and agreement evaluation included mean absolute relative difference (MARD), the %20/20 rule, Bland-Altman plots, and the Consensus Error Grid analysis.

RESULTS

Values reported by the c-rtCGM system compared with the FSL resulted in an overall MARD of 12.06% and 84.71% of the results falling within Consensus Error Grid Zone A when the device is calibrated. For uncalibrated devices, an overall MARD of 17.49% was obtained. Decreased accuracy was shown in the hypoglycemic range and for rates of change greater than 2 mg/dL/min. The between-device bias also incremented with increasing glucose values.

CONCLUSION

Measurements recorded by the c-rtCGM were found to be accurate when compared with FSL data only when performing daily c-rtCGM device calibrations. High drops in accuracy and agreement between devices occurred when the c-rtCGM was not calibrated.

New advances in type 1 diabetes

Type 1 diabetes is an autoimmune condition resulting in insulin deficiency and eventual loss of pancreatic β cell function requiring lifelong insulin therapy. Since the discovery of insulin more than 100 years ago, vast advances in treatments have improved care for many people with type 1 diabetes. Ongoing research on the genetics and immunology of type 1 diabetes and on interventions to modify disease course and preserve β cell function have expanded our broad understanding of this condition. Biomarkers of type 1 diabetes are detectable months to years before development of overt disease, and three stages of diabetes are now recognized. The advent of continuous glucose monitoring and the newer automated insulin delivery systems have changed the landscape of type 1 diabetes management and are associated with improved glycated hemoglobin and decreased hypoglycemia. Adjunctive therapies such as sodium glucose cotransporter-1 inhibitors and glucagon-like peptide 1 receptor agonists may find use in management in the future. Despite these rapid advances in the field, people living in under-resourced parts of the world struggle to obtain necessities such as insulin, syringes, and blood glucose monitoring essential for managing this condition. This review covers recent developments in diagnosis and treatment and future directions in the broad field of type 1 diabetes.

Comparison of Control-IQ and open-source AndroidAPS automated insulin delivery systems in adults with type 1 diabetes: The CODIAC study

AIM

To compare open-source AndroidAPS (AAPS) and commercially available Control-IQ (CIQ) automated insulin delivery (AID) systems in a prospective, open-label, single-arm clinical trial.

METHODS

Adults with type 1 diabetes who had been using AAPS by their own decision entered the first 3-month AAPS phase then were switched to CIQ for 3 months. The results of this treatment were compared with those after the 3-month AAPS phase. The primary endpoint was the change in time in range (% TIR; 70-80 mg/dL).

RESULTS

Twenty-five people with diabetes (mean age 34.32 ± 11.07 years; HbA1c 6.4% ± 3%) participated in this study. CIQ was comparable with AAPS in achieving TIR (85.72% ± 7.64% vs. 84.24% ± 8.46%; P = .12). Similarly, there were no differences in percentage time above range (> 180 and > 250 mg/dL), mean sensor glucose (130.3 ± 13.9 vs. 128.3 ± 16.9 mg/dL; P = .21) or HbA1c (6.3% ± 2.1% vs. 6.4% ± 3.1%; P = .59). Percentage time below range (< 70 and < 54 mg/dL) was significantly lower using CIQ than AAPS. Even although participants were mostly satisfied with CIQ (63.6% mostly agreed, 9.1% strongly agreed), they did not plan to switch to CIQ.

CONCLUSIONS

The CODIAC study is the first prospective study investigating the switch between open-source and commercially available AID systems. CIQ and AAPS were comparable in achieving TIR. However, hypoglycaemia was significantly lower with CIQ.

Extended Use of an Open-Source Automated Insulin Delivery System in Children and Adults with Type 1 Diabetes: The 24-Week Continuation Phase Following the CREATE Randomized Controlled Trial

Aim: To assess long-term efficacy and safety of open-source automated insulin delivery (AID) in children and adults (7-70 years) with type 1 diabetes. Methods: Both arms of a 24-week randomized controlled trial comparing open-source AID (OpenAPS algorithm within a modified version of AndroidAPS, preproduction DANA-i™ insulin pump, Dexcom G6 continuous glucose monitor) with sensor-augmented pump therapy (SAPT), entered a 24-week continuation phase where the SAPT arm (termed SAPT-AID) crossed over to join the open-source AID arm (termed AID-AID). Most participants (69/94) used a preproduction YpsoPump^® insulin pump during the continuation phase. Analyses incorporated all 52 weeks of data, and combined between-group and within-subject differences to calculate an overall "treatment effect" of AID versus SAPT. Results: Mean time in range (TIR; 3.9-10 mmol/L [70-180 mg/dL]) was 12.2% higher with AID than SAPT (95% confidence interval [CI] 10.4 to 14.1; P < 0.001). TIR was 56.9% (95% CI 54.2 to 59.6) with SAPT and 69.1% (95% CI 67.1 to 71.1) with AID. The treatment effect did not differ by age (P = 0.39) or insulin pump type (P = 0.37). HbA1c was 5.1 mmol/mol lower [0.5%] with AID (95% CI -6.6 to -3.6; P < 0.001). There were no episodes of diabetic ketoacidosis or severe hypoglycemia with either treatment over the 48 weeks. Six participants (all in SAPT-AID) withdrew: three with hardware issues, two preferred SAPT, and one with infusion-site skin irritation. Conclusion: Further evaluation of the community derived automated insulin delivery (CREATE) trial to 48 weeks confirms that open-source AID is efficacious and safe with different insulin pumps, and demonstrates sustained glycemic improvements without additional safety concerns.

Interviews with Indigenous Māori with type 1 diabetes using open-source automated insulin delivery in the CREATE randomised trial

Purpose

Open-source automated insulin delivery (AID) is used by thousands of people with type 1 diabetes (T1D), but has unknown generalisability to marginalised ethnic groups. This study explored experiences of Indigenous Māori participants in the CREATE trial with use of an open-source AID system to identify enablers/barriers to health equity.

Methods

The CREATE randomised trial compared open-source AID (OpenAPS algorithm on an Android phone with a Bluetooth-connected pump) to sensor-augmented pump therapy. Kaupapa Māori Research methodology was used in this sub-study. Ten semi-structured interviews with Māori participants (5 children, 5 adults) and whānau (extended family) were completed. Interviews were recorded and transcribed, and data were analysed thematically. NVivo was used for descriptive and pattern coding.

Results

Enablers/barriers to equity aligned with four themes: access (to diabetes technologies), training/support, operation (of open-source AID), and outcomes. Participants described a sense of empowerment, and improved quality of life, wellbeing, and glycaemia. Parents felt reassured by the system’s ability to control glucose, and children were granted greater independence. Participants were able to use the open-source AID system with ease to suit whānau needs, and technical problems were manageable with healthcare professional support. All participants identified structures in the health system precluding equitable utilisation of diabetes technologies for Māori.

Conclusion

Māori experienced open-source AID positively, and aspired to use this therapy; however, structural and socio-economic barriers to equity were identified. This research proposes strength-based solutions which should be considered in the redesign of diabetes services to improve health outcomes for Māori with T1D.

Trial Registration: The CREATE trial, encompassing this qualitative sub-study, was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12620000034932p) on the 20^th January 2020.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-023-01215-3.

Mapping the Landscape of Do-it-Yourself Medicine

The practice of medicine is typically conceptualized as remaining within the boundaries of a hospital or clinic. However, in recent years, patients have been able to gain access to information about medical research as it is ongoing. As a result, there has been a rise in do-it-yourself (DIY) medicine, where individuals treat themselves for medical conditions outside of clinical settings, often mimicking experimental therapies that remain inaccessible to the wider public. For example, in DIY brain stimulation, individuals suffering from depression build at-home electrical headsets using nine-volt batteries, mimicking an experimental neuroscience technique used in scientific laboratories. In DIY fecal transplantation, those with intestinal disorders like C. Difficile and inflammatory bowel disease transplant stool from donors into themselves with the aid of blenders and enemas. In the open Artificial Pancreas System movement, diabetes patients hacked together an artificial pancreas system from their glucose monitors and insulin pumps, years before such a system was approved by the United States Food and Drug Administration (US FDA). To date, scholarship on DIY medicine has largely been relegated to specific medical domains (e.g., neurology, gastroenterology, infectious disease). In this paper, however, I recognize DIY medicine as a cross-cutting phenomenon that has emerged independently across medical domains but shares common features. I map the varieties of DIY medicine across these domains and suggest that four key factors lead to their creation, growth, and uptake. In doing so, this essay sheds light on an understudied area of biomedical citizen science that is likely to grow substantially in the coming decades.

Open-Source Automated Insulin Delivery in Type 1 Diabetes

BACKGROUND

Open-source automated insulin delivery (AID) systems are used by many patients with type 1 diabetes. Data are needed on the efficacy and safety of an open-source AID system.

METHODS

In this multicenter, open-label, randomized, controlled trial, we assigned patients with type 1 diabetes in a 1:1 ratio to use an open-source AID system or a sensor-augmented insulin pump (control). The patients included both children (defined as 7 to 15 years of age) and adults (defined as 16 to 70 years of age). The AID system was a modified version of AndroidAPS 2.8 (with a standard OpenAPS 0.7.0 algorithm) paired with a preproduction DANA-i insulin pump and Dexcom G6 CGM, which has an Android smartphone application as the user interface. The primary outcome was the percentage of time in the target glucose range of 70 to 180 mg per deciliter (3.9 to 10.0 mmol per liter) between days 155 and 168 (the final 2 weeks of the trial).

RESULTS

A total of 97 patients (48 children and 49 adults) underwent randomization (44 to open-source AID and 53 to the control group). At 24 weeks, the mean (±SD) time in the target range increased from 61.2±12.3% to 71.2±12.1% in the AID group and decreased from 57.7±14.3% to 54.5±16.0% in the control group (adjusted difference, 14 percentage points; 95% confidence interval, 9.2 to 18.8; P<0.001), with no treatment effect according to age (P = 0.56). Patients in the AID group spent 3 hours 21 minutes more in the target range per day than those in the control group. No severe hypoglycemia or diabetic ketoacidosis occurred in either group. Two patients in the AID group withdrew from the trial owing to connectivity issues.

CONCLUSIONS

In children and adults with type 1 diabetes, the use of an open-source AID system resulted in a significantly higher percentage of time in the target glucose range than the use of a sensor-augmented insulin pump at 24 weeks. (Supported by the Health Research Council of New Zealand; Australian New Zealand Clinical Trials Registry number, ACTRN12620000034932.).

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.

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.

Errors of commission or omission: The net risk safety analysis conversation we should be having around automated insulin delivery systems

The question of safety often arises when discussing automated insulin delivery systems, but discussion of safety is often anchored on a comparison to the risk to a person without diabetes, overlooking the risks of living with insulin-requiring diabetes. We should use a net risk safety perspective for evaluating diabetes technology that takes into account the ongoing risks of insulin management for people living with diabetes.

User-driven open-source artificial pancreas systems and patient-reported outcomes: A missed opportunity?

AIM

This paper aims to highlight the attributes of engagement and urgency to act to control diabetes demonstrated by open-source artificial pancreas system users with the view that increased user involvement in research and practice can capitalize on these self-management traits; and to outline the challenges of researching outcomes in the context of unlicensed therapies.

METHODS

A group of technically minded people with type 1 diabetes have reverse-engineered commercially available diabetes devices to help them achieve the diabetes outcomes they desire. Although studies have reported improved biomedical outcomes with these artificial pancreas systems, there are only a few studies examining patient-reported outcomes.

RESULTS

The investigation of patient-reported outcomes for open-source artificial pancreas system users has been hampered by the rapid advances in the technology, the lack of randomized controlled trials and the ethical challenges of researching unregulated technologies. There is an on-going debate about the most appropriate types of measures to evaluate patient-related outcomes.

CONCLUSIONS

The early adopters of open-source artificial pancreas systems exhibit many of the characteristics that predict optimal diabetes outcomes through engagement and urgency regarding self-management. These qualities should be harnessed to improve research in this and other areas of diabetes management.

Closed-Loop Insulin Delivery Systems: Past, Present, and Future Directions

Closed-loop (artificial pancreas) systems for automated insulin delivery have been likened to the holy grail of diabetes management. The first iterations of glucose-responsive insulin delivery were pioneered in the 1960s and 1970s, with the development of systems that used venous glucose measurements to dictate intravenous infusions of insulin and dextrose in order to maintain normoglycemia. Only recently have these bulky, bedside technologies progressed to miniaturized, wearable devices. These modern closed-loop systems use interstitial glucose sensing, subcutaneous insulin pumps, and increasingly sophisticated algorithms. As the number of commercially available hybrid closed-loop systems has grown, so too has the evidence supporting their efficacy. Future challenges in closed-loop technology include the development of fully closed-loop systems that do not require user input for meal announcements or carbohydrate counting. Another evolving avenue in research is the addition of glucagon to mitigate the risk of hypoglycemia and allow more aggressive insulin dosing.

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.

Review of Automated Insulin Delivery Systems for Type 1 Diabetes and Associated Time in Range Outcomes

Automated insulin delivery (AID) systems play an important role in the management of type 1 diabetes mellitus (T1DM). These systems include three components: a continuous glucose monitor (CGM), an insulin pump and an algorithm that adjusts the pump based on the CGM sensor glucose readings. They are not fully automated and still require the user to administer bolus insulin doses for food. Some AID systems have automatic correction boluses, while others only have automatic basal or background insulin adjustments. As CGM has become more accurate and the technology has evolved, AID systems have demonstrated improved glycaemic outcomes. The clinical evaluation of AID systems in randomized controlled trials and real-world studies have shown their utility in helping glycaemic management. In this review, we compare AID systems that are commercially available in the US and summarize the literature, with a special focus on time in range in T1DM. The review also discusses new AID systems on the horizon and explores considerations for personalized care.

The automated pancreas: A review of technologies and clinical practice

Insulin pumps and glucose sensors are effective in improving diabetes therapy and reducing acute complications. The combination of both devices using an algorithm-driven interoperable controller makes automated insulin delivery (AID) systems possible. Many AID systems have been tested in clinical trials and have proven safety and effectiveness. However, currently, none of these systems are available for routine use in children younger than 6 years in Europe. For continued use, both users and prescribers must have sound knowledge of the features of the individual AID systems. Presently, all systems require various user interactions (e.g. meal announcements) because fully automated systems are not yet developed. Open-source systems are non-regulated variants to circumvent existing regulatory conditions. There are risks here for both users and prescribers. To evaluate AID therapy, the metric data of the glucose sensors, 'time in target range' and 'glucose management index', are novel recognized and suitable parameters allowing a consultation based on real glucose and insulin pump download data from the daily life of people with diabetes. Read out via cloud-based software or automatic download of such individual treatment data provides the ideal technical basis for shared decision-making through telemedicine, which must be further evaluated for general use.

Use of automated insulin delivery systems in people with type 1 diabetes fasting during Ramadan: An observational study

Fasting among people with type 1 diabetes imposes the risk of metabolic decompensation. Automated insulin dosing systems can allow better glycemic control without safety concerns. The utility in prolonged and repetitive fasting has not been studied. In this observational study, validated glycemic data were reviewed and analyzed from people with type 1 diabetes who observed fasting during Ramadan in 2019 and 2020 using automated insulin dosing systems. Six profiles met the inclusion criteria. The average age was 33.7 ± 4.8 years, diabetes duration was 23.5 ± 7.9 years, body mass index 23.6 ± 1.9 kg/m² and glycated hemoglobin was 6.3 ± 0.2% (45 ± 5 mmol/mol). The average glucose during Ramadan was 7.0 ± 0.5 mmol/L (126 ± 9 mg/dL), coefficient of variation 28.5%, percentage of time in range 3.9–10 mmol/L (70–180 mg/dL) 88.8 ± 7.3% and percentage time <3.9 mmol/L (<70.0 mg/dL) 2.5 ± 1.3%. The number of fasting days was 27.3 ± 3.3, and the number of days where fasting was broken due diabetes was 1 ± 1.5/participant. No significant differences in glycemic outcomes were noted between Ramadan and non‐Ramadan periods. In this first clinically validated study, automated insulin dosing systems showed a safe and effective management strategy to support prolonged and consecutive fasting in people with type 1 diabetes.

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.

Fault Tolerant Strategies for Automated Insulin Delivery Considering the Human Component: Current and Future Perspectives

Automated Insulin Delivery (AID) are systems developed for daily use by people with type 1 diabetes (T1D). To ensure the safety of users, it is essential to consider how the human factor affects the performance and safety of these devices. While there are numerous publications on hardware-related failures of AID systems, there are few studies on the human component of the system. From a control point of view, people with T1D using AID systems are at the same time the plant to be controlled and the plant operator. Therefore, users may induce faults in the controller, sensors, actuators, and the plant itself. Strategies to cope with the human interaction in AID systems are needed for further development of the technology. In this paper, we present an analysis of potential faults introduced by AID users when the system is under normal operation. This is followed by a review of current fault tolerant control (FTC) approaches to identify missing areas of research. The paper concludes with a discussion on future directions for the new generation of FTC AID systems.

Standardized Hybrid Closed-Loop System Reporting

The hybrid closed-loop (HCL) system has been shown to improve glycemic control and reduce hypoglycemia. Optimization of HCL settings requires interpretation of the glucose, insulin, and factors affecting glucose such as food intake and exercise. To the best of our knowledge, there is no published guidance on the standardized reporting of HCL systems. Standardization of HCL reporting would make interpretation of data easy across different systems. We reviewed the literature on patient and provider perspectives on downloading and reporting glucose metric preferences. We also incorporated international consensus on standardized reporting for glucose metrics. We describe a single-page HCL data reporting, referred to here as "artificial pancreas (AP) Dashboard." We propose seven components in the AP Dashboard that can provide detailed information and visualization of glucose, insulin, and HCL-specific metrics. The seven components include (A) glucose metrics, (B) hypoglycemia, (C) insulin, (D) user experience, (E) hyperglycemia, (F) glucose modal-day profile, and (G) insight. A single-page report similar to an electrocardiogram can help providers and patients interpret HCL data easily and take the necessary steps to improve glycemic outcomes. We also describe the optimal sampling duration for HCL data download and color coding for visualization ease. We believe that this is a first step in creating a standardized HCL reporting, which may result in better uptake of the systems. For increased adoption, standardized reporting will require input from providers, patients, diabetes device manufacturers, and regulators.