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.

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.

Glycemic outcomes of children 2-6 years of age with type 1 diabetes during the pediatric MiniMed™ 670G system trial

BACKGROUND

Highly variable insulin sensitivity, susceptibility to hypoglycemia and inability to effectively communicate hypoglycemic symptoms pose significant challenges for young children with type 1 diabetes (T1D). Herein, outcomes during clinical MiniMed™ 670G system use were evaluated in children aged 2-6 years with T1D.

METHODS

Participants (N = 46, aged 4.6 ± 1.4 years) at seven investigational centers used the MiniMed™ 670G system in Manual Mode during a two-week run-in period followed by Auto Mode during a three-month study phase. Safety events, mean A1C, sensor glucose (SG), and percentage of time spent in (TIR, 70-180 mg/dl), below (TBR, <70 mg/dl) and above (TAR, >180 mg/dl) range were assessed for the run-in and study phase and compared using a paired t-test or Wilcoxon signed-rank test.

RESULTS

From run-in to end of study (median 87.1% time in auto mode), mean A1C and SG changed from 8.0 ± 0.9% to 7.5 ± 0.6% (p < 0.001) and from 173 ± 24 to 161 ± 16 mg/dl (p < 0.001), respectively. Overall TIR increased from 55.7 ± 13.4% to 63.8 ± 9.4% (p < 0.001), while TBR and TAR decreased from 3.3 ± 2.5% to 3.2 ± 1.6% (p = 0.996) and 41.0 ± 14.7% to 33.0 ± 9.9% (p < 0.001), respectively. Overnight TBR remained unchanged and TAR was further improved 12:00 am-6:00 am. Throughout the study phase, there were no episodes of severe hypoglycemia or diabetic ketoacidosis (DKA) and no serious adverse device-related events.

CONCLUSIONS

At-home MiniMed™ 670G Auto Mode use by young children safely improved glycemic outcomes compared to two-week open-loop Manual Mode use. The improvements are similar to those observed in older children, adolescents and adults with T1D using the same system for the same duration of time.

Glycemic Outcomes During Real-World Hybrid Closed-Loop System Use by Individuals With Type 1 Diabetes in the United States

BACKGROUND

Glycemic outcomes during real-world hybrid closed-loop (HCL) system use by individuals with type 1 diabetes, in the United States, were retrospectively analyzed.

METHODS

Hybrid closed-loop system data voluntarily uploaded to Carelink™ personal software from March 2017 to November 2020 by individuals (aged ≥7 years) using the MiniMed™ 670G system and having ≥10 days of continuous glucose monitoring data after initiating Auto Mode were assessed. Glycemic outcomes including the mean glucose management indicator (GMI), sensor glucose (SG), percentage of time spent in (TIR), below (TBR), and above (TAR) target range (70-180 mg/dL) were analyzed. Outcomes were also analyzed in a subgroup of users per baseline GMI of <7% versus >8%.

RESULTS

The overall cohort (N = 123 355 users, with a mean of 87.9% of time in Auto Mode) had a GMI of 7.0% ± 0.4%, TIR of 70.4% ± 11.2%, TBR <70 mg/dL of 2.2% ± 2.1% and TAR>180 mg/dL of 27.5% ± 11.6%, post-Auto Mode initiation. Compared with pre-Auto Mode initiation, users (N = 52 941, 88.6% of time in Auto Mode) had a GMI that decreased from 7.3% ± 0.6% to 7.1% ± 0.5% (P < .001), TIR that increased from 61.5% ± 15.1% to 68.1% ± 11.9% (P < .001), TAR>180 mg/dL that decreased from 36.3% ± 15.7% to 29.8% ± 12.2% (P < .001) and TBR<70 mg/dL that decreased from 2.11 ± 2.4 to 2.07% ± 2.25% (P = .002). While all metrics statistically improved for the baseline GMI >8.0% group, the baseline GMI <7.0% group had unchanged TIR (77.4% ± 7.4% to 77.5% ± 8.0%, P = .456) and TAR>180 mg/dL that increased (19.2 ± 6.7 to 19.6 ± 7.9%, p < 0.001).

CONCLUSION

Real-world HCL system use in the U.S. demonstrated overall glycemic control that trended similarly with the system pivotal trial outcomes and previous real-world system use analyses.

Zone-MPC Automated Insulin Delivery Algorithm Tuned for Pregnancy Complicated by Type 1 Diabetes

Type 1 diabetes (T1D) increases the risk for pregnancy complications. Increased time in the pregnancy glucose target range (63-140 mg/dL as suggested by clinical guidelines) is associated with improved pregnancy outcomes that underscores the need for tight glycemic control. While closed-loop control is highly effective in regulating blood glucose levels in individuals with T1D, its use during pregnancy requires adjustments to meet the tight glycemic control and changing insulin requirements with advancing gestation. In this paper, we tailor a zone model predictive controller (zone-MPC), an optimization-based control strategy that uses model predictions, for use during pregnancy and verify its robustness in-silico through a broad range of scenarios. We customize the existing zone-MPC to satisfy pregnancy-specific glucose control objectives by having (i) lower target glycemic zones (i.e., 80-110 mg/dL daytime and 80-100 mg/dL overnight), (ii) more assertive correction bolus for hyperglycemia, and (iii) a control strategy that results in more aggressive postprandial insulin delivery to keep glucose within the target zone. The emphasis is on leveraging the flexible design of zone-MPC to obtain a controller that satisfies glycemic outcomes recommended for pregnancy based on clinical insight. To verify this pregnancy-specific zone-MPC design, we use the UVA/Padova simulator and conduct in-silico experiments on 10 subjects over 13 scenarios ranging from scenarios with ideal metabolic and treatment parameters for pregnancy to extreme scenarios with such parameters that are highly deviant from the ideal. All scenarios had three meals per day and each meal had 40 grams of carbohydrates. Across 13 scenarios, pregnancy-specific zone-MPC led to a 10.3 ± 5.3% increase in the time in pregnancy target range (baseline zone-MPC: 70.6 ± 15.0%, pregnancy-specific zone-MPC: 80.8 ± 11.3%, p < 0.001) and a 10.7 ± 4.8% reduction in the time above the target range (baseline zone-MPC: 29.0 ± 15.4%, pregnancy-specific zone-MPC: 18.3 ± 12.0, p < 0.001). There was no significant difference in the time below range between the controllers (baseline zone-MPC: 0.5 ± 1.2%, pregnancy-specific zone-MPC: 3.5 ± 1.9%, p = 0.1). The extensive simulation results show improved performance in the pregnancy target range with pregnancy-specific zone MPC, suggest robustness of the zone-MPC in tight glucose control scenarios, and emphasize the need for customized glucose control systems for pregnancy.

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

Commercial automated insulin delivery (AID) systems are usually assessed based on clinical outcomes, ignoring uptake. A qualitative study evaluated user experiences when switching to currently available commercial AID. Interview feedback was coded on key themes including the adoption experience with regards to quality of life, clinical outcomes, and users' expectations. Most felt their learning curve was easy. Most saw reduced hypoglycemia and increased time in range, although there were outliers. Many mentioned post-meal hyperglycemia as an improvement area for commercial AID. Users with one particular continuous glucose monitor (CGM) type reported sleep disruption. Companies should consider real-world user feedback with regards to improving training materials for new users with less CGM experience and by improving target flexibility and postprandial algorithm performance, plus reducing manual interventions required by users.

The Need for Diabetes Advance Directives to Support the Care of People With Diabetes

It is time to adopt an advance directive specific to diabetes management. Research shows that people with diabetes in the hospital are often removed from existing diabetes self-management, resulting in poorer outcomes. Diabetes advance directives, which outline preferred diabetes self-management in scenarios such as hospitalization or outpatient procedures, are key for enabling patients with diabetes to continue successful diabetes management including use of existing diabetes technology. A diabetes advance directive is a new concept for both patients and providers that can improve clinical outcomes and patient-reported outcomes. Given the risk of harm in the absence of such a document, diabetes advance directives can be a useful new tool for patients and providers and to aid in the discussion, care planning, and self-management with diabetes technology.

Using an Online Disturbance Rejection and Anticipation System to Reduce Hyperglycemia in a Fully Closed-Loop Artificial Pancreas System

INTRODUCTION

Hyperglycemia following meals is a recurring challenge for people with type 1 diabetes, and even the most advanced available automated systems currently require manual input of carbohydrate amounts. To progress toward fully automated systems, we present a novel control system that can automatically deliver priming boluses and/or anticipate eating behaviors to improve postprandial full closed-loop control.

METHODS

A model predictive control (MPC) system was enhanced by an automated bolus system reacting to early glucose rise and/or a multistage MPC (MS-MPC) framework to anticipate historical patterns. Priming was achieved by detecting large glycemic disturbances, such as meals, and delivering a fraction of the patient's total daily insulin (TDI) modulated by the disturbance's likelihood (bolus priming system [BPS]). In the anticipatory module, glycemic disturbance profiles were generated from historical data using clustering to group days with similar behaviors; the probability of each cluster is then evaluated at every controller step and informs the MS-MPC framework to anticipate each profile. We tested four configurations: MPC, MPC + BPS, MS-MPC, and MS-MPC + BPS in simulation to contrast the effect of each controller module.

RESULTS

Postprandial time in range was highest for MS-MPC + BPS: 60.73 ± 25.39%, but improved with each module: MPC + BPS: 56.95±25.83 and MS-MPC: 54.83 ± 26.00%, compared with MPC: 51.79 ± 26.12%. Exposure to hypoglycemia was maintained for all controllers (time below 70 mg/dL <0.5%), and improvement came primarily from a reduction in postprandial time above range (MS-MPC + BPS: 39.10 ± 25.32%, MPC + BPS: 42.99 ± 25.81%, MS-MPC: 45.09 ± 25.96%, MPC: 48.18 ± 26.09%).

CONCLUSIONS

The BPS and anticipatory disturbance profiles improved blood glucose control and were most efficient when combined.

A New Meal Absorption Model for Artificial Pancreas Systems

BACKGROUND

Artificial pancreas (AP) systems reduce the treatment burden of Type 1 Diabetes by automatically regulating blood glucose (BG) levels. While many disturbances stand in the way of fully closed-loop (automated) control, unannounced meals remain the greatest challenge. Furthermore, different types of meals can have significantly different glucose responses, further increasing the uncertainty surrounding the meal.

METHODS

Effective attenuation of a meal requires quick and accurate insulin delivery because of slow insulin action relative to meal effects on BG. The proposed Variable Hump (VH) model adapts to meals of varying compositions by inferring both meal size and shape. To appropriately address the uncertainty of meal size, the model divides meal absorption into two disjoint regions: a region with coarse meal size predictions followed by a fine-grain region where predictions are fine-tuned by adapting to the meal shape.

RESULTS

Using gold-standard triple tracer meal data, the proposed VH model is compared to three simpler second-order response models. The proposed VH model increased model fit capacity by 22% and prediction accuracy by 12% relative to the next best models. A 47% increase in the accuracy of uncertainty predictions was also found. In a simple control scenario, the controller governed by the proposed VH model provided insulin just as fast or faster than the controller governed by the other models in four out of the six meals. While the controllers governed by the other models all delivered at least a 25% excess of insulin at their worst, the VH model controller only delivered 9% excess at its worst.

CONCLUSIONS

The VH Model performed best in accuracy metrics and succeeded over the other models in providing insulin quickly and accurately in a simple implementation. Use in an AP system may improve prediction accuracy and lead to better control around mealtimes.

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.

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.

Youth and parent preferences for an ideal AP system: It is all about reducing burden

BACKGROUND

As new diabetes technologies improve to better manage glucose levels, users' priorities for future technologies may shift to prioritize burden reduction and ease of use. We used qualitative methods to explore youth and parent desired features of an "ideal" artificial pancreas (AP) system.

METHODS

We conducted semi-structured interviews with 39 youth, ages 10-25 years, and 44 parents. Interviews were audio-recorded, transcribed, and coded using thematic analysis.

RESULTS

Youth (79% female, 82% non-Hispanic white) were (M ± SD) ages 17.0 ± 4.7 years, with diabetes for 9.4 ± 4.9 years, and HbA1c of 8.4 ± 1.1%; 79% were pump-treated and 82% used CGM. Of parents, 91% were mothers and 86% were non-Hispanic white. Participants suggested various ways in which an ideal AP system could reduce physical and emotional burdens of diabetes. Physical burdens could be reduced by lessening user responsibilities to manage glucose for food and exercise, and wear or carry devices. Emotional burden could be reduced by mitigating negative emotional reactions to sound and frequency of alerts, while increasing feelings of normalcy. Youth and parents differed in their suggestions to reduce emotional burden. Participants suggested features that would improve glycemia, but nearly always in the context of how the feature would directly reduce their diabetes-specific burden.

CONCLUSIONS

Although participants expressed interest in improving glucose levels, the pervasive desire among suggested features of an ideal AP system was to minimize the burden of diabetes. Understanding and addressing users' priorities to reduce physical and emotional burden will be necessary to enhance uptake and maintain use of future AP systems.

Cost considerations for adoption of diabetes technology are pervasive: A qualitative study of persons living with type 1 diabetes and their families

BACKGROUND

Cost is a major consideration in the uptake and continued use of diabetes technology. With increasing use of automated insulin delivery systems, it is important to understand the specific cost-related barriers to technology adoption. In this qualitative analysis, we were interested in understanding and examining the decision-making process around cost and diabetes technology use.

MATERIALS AND METHODS

Four raters coded transcripts of four stakeholder groups using inductive coding for each stakeholder group to establish relevant themes/nodes. We applied the Social Ecological Model in the interpretation of five thematic levels of cost.

RESULTS

We identified five thematic levels of cost: policy, organizational, insurance, interpersonal and individual. Equitable diabetes technology access was an important policy-level theme. The insurance-level theme had multiple subthemes which predominantly carried a negative valence. Participants also emphasized the psychosocial burden of cost specifically identifying diabetes costs to their families, the guilt of diabetes related costs, and frustration in the time and involvement required to ensure insurance coverage.

CONCLUSION

We found broad consensus in how cost is experienced by stakeholder groups. Cost considerations for diabetes technology uptake extended beyond finances to include time, cost to society, morality and interpersonal relationships. Cost also reflected an important moral principle tied to the shared desire for equitable access to diabetes technology. Knowledge of these considerations can help clinicians and researchers promote equitable device uptake while anticipating barriers for all persons living with type 1 diabetes and their families.

Diabetes technologies in people with type 1 diabetes mellitus and disordered eating: A systematic review on continuous subcutaneous insulin infusion, continuous glucose monitoring and automated insulin delivery

AIMS

In this systematic review, we aimed (1) to identify and describe research investigating the use of advanced diabetes technologies (continuous subcutaneous insulin infusion, CSII; continuous glucose monitoring, CGM; automated insulin delivery, AID) in people with type 1 diabetes (T1DM) and disordered eating and (2) to discuss potential (dis)advantages of diabetes technology use in this population, derived from previous research.

METHODS

We conducted a systematic literature search in two electronic databases for English articles published between 2000 and 2020 addressing eating disorders and/or dysfunctional eating behaviours and diabetes technology use in children, adolescents and adults with T1DM (PROSPERO ID CRD42020160244).

RESULTS

Of 70 publications initially identified, 17 were included. Overall, evidence on the use of diabetes technologies in people with T1DM and disordered eating is scarce. The majority of the studies reports findings on CSII in people with T1DM and dysfunctional eating behaviours or eating disorders. Findings predominantly stem from observational data and are, in most cases, secondary findings of the respective studies. Providing the greatest flexibility in diabetes management, CSII may have benefits in disordered eating. CGM data may complement the diagnostic process of disordered eating with a physiological indicator of insulin restriction (i.e. time spent in hyperglycaemia).

CONCLUSIONS

Results on possible (dis)advantages of diabetes technology use in people with T1DM and disordered eating are based on observational data, small pilot trials and anecdotical evidence from case reports. Prospective data from larger samples are needed to reliably determine potential effects of diabetes technology on disordered eating in T1DM.

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

A review of biomarkers in the context of type 1 diabetes: Biological sensing for enhanced glucose control

As wearable healthcare monitoring systems advance, there is immense potential for biological sensing to enhance the management of type 1 diabetes (T1D). The aim of this work is to describe the ongoing development of biomarker analytes in the context of T1D. Technological advances in transdermal biosensing offer remarkable opportunities to move from research laboratories to clinical point-of-care applications. In this review, a range of analytes, including glucose, insulin, glucagon, cortisol, lactate, epinephrine, and alcohol, as well as ketones such as beta-hydroxybutyrate, will be evaluated to determine the current status and research direction of those analytes specifically relevant to T1D management, using both in-vitro and on-body detection. Understanding state-of-the-art developments in biosensing technologies will aid in bridging the gap from bench-to-clinic T1D analyte measurement advancement.

Full closed loop open-source algorithm performance comparison in pigs with diabetes

Understanding how automated insulin delivery (AID) algorithm features impact glucose control under full closed loop delivery represents a critical step toward reducing patient burden by eliminating the need for carbohydrate entries at mealtimes. Here, we use a pig model of diabetes to compare AndroidAPS and Loop open‐source AID systems without meal announcements. Overall time‐in‐range (70–180 mg/dl) for AndroidAPS was 58% ± 5%, while time‐in‐range for Loop was 35% ± 5%. The effect of the algorithms on time‐in‐range differed between meals and overnight. During the overnight monitoring period, pigs had an average time‐in‐range of 90% ± 7% when on AndroidAPS compared to 22% ± 8% on Loop. Time‐in‐hypoglycemia also differed significantly during the lunch meal, whereby pigs running AndroidAPS spent an average of 1.4% (+0.4/−0.8)% in hypoglycemia compared to 10% (+3/−6)% for those using Loop. As algorithm design for closed loop systems continues to develop, the strategies employed in the OpenAPS algorithm (known as oref1) as implemented in AndroidAPS for unannounced meals may result in a better overall control for full closed loop systems.

Review of the Long-Term Implantable Senseonics Continuous Glucose Monitoring System and Other Continuous Glucose Monitoring Systems

The article published by Kevin Cowart in this issue of the Journal of Diabetes Science and Technology (JDST) is a detailed overview of the clinical trial data and analysis used to demonstrate the safety and effectiveness of the Eversense continuous glucose monitoring (CGM) System for regulatory approval and clinical acceptance. The article describes the published study results for safety, accuracy, reliability, ease of insertion/removal, adverse events, and ease of diabetes patient-use for controlling their glucose levels short and long term. The author nicely compares Eversense CGM System safety and performance with the short-term subcutaneous tissue CGM systems being commercialized by Dexcom, Medtronic Diabetes, and Abbott Diabetes. This comparison may help the clinician define which type of patient with diabetes might benefit the most from the long-term implantable CGM system. The majority of studied patients describe a positive experience managing their diabetes with the Eversense CGM System and request implantation of a new sensor 90 or 180 days later.

"Do It Yourself" (DIY)-Automated Insulin Delivery (AID) Systems: Current Status From a German Point of View

A group of dedicated people with a high affinity for technology and good understanding of how to treat their type 1 diabetes have developed systems that enable automated insulin delivery (AID). These persons build these AID systems only for themselves (do it yourself [DIY]) and the quality of glucose control achieved with DIY AID systems is impressively good. This overview presents the current status of this development from a German point of view. A high degree of efforts is required to start and maintain this type of therapy and the user must always remain aware of what she/he is doing in everyday life. One main obstacle is liability, because the medicinal products used by persons with diabetes for DIY AID systems are not approved for this indication. They must be regarded as experimental systems. As long as persons with diabetes build and use these systems for themselves and not for other people, they act at their own risk. If a person with diabetes expresses interest in such a system or is already using it, the diabetologist should inform him about the improper use of the medical devices and about the associated risks. The physician should document this information accordingly.

Health Care Provider Knowledge and Perceptions of FDA-Approved and Do-It-Yourself Automated Insulin Delivery

BACKGROUND

Automated insulin delivery (AID) technology may reduce variability in blood glucose, resulting in lower risk for hypoglycemia and associated complications, and by extension improve quality of life. While clinical trials, research, and patient experience have consistently demonstrated the value of AID, this technology is still inaccessible to many patients. Patient-driven innovation has resulted in alternative do-it-yourself (DIY) solutions to available off-the-shelf AID devices.

METHOD

This two-phase cross-sectional observational study addressed health care provider (HCP) perceptions of AID as well as the perceived need for, development of, and evaluation of an AID fact sheet comparing the most commonly used Federal Drug Administration approved AID and DIY AID devices.

RESULTS

Negative attitudes toward the use of DIY AID were low. The majority of HCPs saw their lack of knowledge about how DIY AID work to be the greatest barrier to answering patient questions about what is available (74.4%). Additionally, the majority of HCPs (64.5%) indicated they were either "likely" or "very likely" to use the fact sheet when answering patient questions about AID options.

CONCLUSION

Increased awareness and utilization of AID technology offer hope to further reduce the burden of diabetes, but there is a need to bridge the knowledge gap about DIY AID. A fact sheet provides a way to facilitate discussions of this emerging technology between HCPs and patients. Next steps could investigate additional ways to put needed information in the hands of HCPs.

Determining Physical Activity Characteristics from Wristband Data for Use in Automated Insulin Delivery Systems

Algorithms that can determine the type of physical activity (PA) and quantify the intensity can allow precision medicine approaches, such as automated insulin delivery systems that modulate insulin administration in response to PA. In this work, data from a multi-sensor wristband is used to design classifiers to distinguish among five different physical states (PS) (resting, activities of daily living, running, biking, and resistance training), and to develop models to estimate the energy expenditure (EE) of the PA for diabetes therapy. The data collected are filtered, features are extracted from the reconciled signals, and the extracted features are used by machine learning algorithms, including deep-learning techniques, to obtain accurate PS classification and EE estimation. The various machine learning techniques have different success rates ranging from 75.7% to 94.8% in classifying the five different PS. The deep neural network model with long short-term memory has 94.8% classification accuracy. We achieved 0.5 MET (Metabolic Equivalent of Task) root-mean-square error for EE estimation accuracy, relative to indirect calorimetry with randomly selected testing data (10% of collected data). We also demonstrate a 5% improvement in PS classification accuracy and a 0.34 MET decrease in the mean absolute error when using multi-sensor approach relative to using only accelerometer data.

A clinical review of the t:slim X2 insulin pump

Insulin pumps are commonly used for intensive insulin therapy to treat type 1 diabetes in adults and youth. Insulin pump technologies have advanced dramatically in the last several years to integrate with continuous glucose monitors (CGM) and incorporate control algorithms. These control algorithms automate some insulin delivery in response to the glucose information received from the CGM to reduce the occurrence of hypoglycemia and hyperglycemia and improve overall glycemic control. The t:slim X2 insulin pump system became commercially available in 2016. It is an innovative insulin pump technology that can be updated remotely by the user to install new software onto the pump device as new technologies become available. Currently, the t:slim X2 pairs with the Dexcom G6 CGM and there are two advanced software options available: Basal-IQ, which is a predictive low glucose suspend (PLGS) technology, and Control-IQ, which is a Hybrid Closed Loop (HCL) technology. This paper will describe the different types of advanced insulin pump technologies, review how the t:slim X2 insulin pump works, and summarize the clinical studies leading to FDA approval and commercialization of the Basal-IQ and Control-IQ technologies.