Artificial Pancreas Research Articles

“I spend more time on the ecosystem than on the disease”: caring for the communicative loop with everyday ADM technology through maintenance and modification work

AbstractAutomated decision-making (ADM) systems can be worn in and on the body for various purposes, such as for tracking and managing chronic conditions. One case in point is do-it-yourself open-source artificial pancreas systems, through which users engage in what is referred to as “looping”; combining continuous glucose monitors and insulin pumps placed on the body with digital communication technologies to develop an ADM system for personal diabetes management. The idea behind these personalized systems is to delegate decision-making regarding insulin to an algorithm that can make autonomous decisions. Based on interviews and photo diaries with Danish “loopers”, this paper highlights two interrelated narratives of how users have to care for the loop by maintaining a stable communication circuit between body and ADM system, and by modifying the loop through analysis and reflection. It shows how the human takes turns with the ADM system through practical doings and anticipation to safeguard continuous management of chronic disease.

A novel pulse-modulated closed-loop artificial pancreas based on intravenous administration of insulin and glucagon

Current attempts to implement and apply automated control systems for the management of glucose homeostasis in individuals with diabetes are partly successful. In most semi and closed loop control systems to mimic the action of a normal pancreas in diabetes patients, insulin is administered subcutaneously. However, the parameters for insulin diffusion and transport time constants are relatively large and have wide individual variations. Therefore, deviation from a normal meal can result in suboptimal euglycemic control. Stable and reliable closed loop feedback control using continuous glucose monitoring under these conditions is difficult and needs regular interventions from the user. This article describes the translation of the endocrine physiology of a normal pancreas to an electronic equivalent. With this translation, the complex effects of a direct intravenous pulsatile method of insulin and glucagon administration can be simulated in accordance with physiological observations in a healthy subject and has been built with standard electronic components. This device is applicable for any individual and under any condition to automatically maintain the desired optimal euglycemic condition. The insulin and glucagon response control is presented in the same physiological pulsatile manner as is observed in healthy individuals.

Comprehensive Review of Pharmacological Therapies and Clinical Guidelines for Diabetes Mellitus: A Depth Study

Diabetes mellitus (DM) is a colossal burden in global health, its prevalence increases steadily throughout the world. This systematic review explores the pathophysiology and epidemiology of diabetes, which describes the interactions that implicate that is the result of the play between genetic, environmental, and lifestyle factors in the development and progression of the disease. The review also describes a plethora of pharmacological treatments available to manage diabetes, with the oral agent, insulin regimens, and new approaches to therapy. Integrating the wisdom of the best clinical guidelines, those are from American diabetes association (ADA), European association for the study of diabetes (EASD), and International Diabetes Federation (IDF). Since these reviews are consistent with normative standards for evidence-based recommendations of optimal care of diabetes. The review goes further to cover technological advances in the management of diabetes. They include Continuous Glucose Monitoring, advanced insulin delivery systems, and Artificial Pancreas Systems. Such technologies hold more promising routes to better glucose control and patient quality of life. All this comes along with challenges that their deployment and maintenance that run through economic, adherence issues, and unequal access to healthcare. The future research and innovation in the battle against DM will include the application of personalized medicine and the development of advanced digital health solutions. This article will become a treasure for health professionals, researchers, and policymakers who have dedicated themselves to battling the adversities of diabetes mellitus.

A model predictive control algorithm for double-hormone artificial pancreas based on optimal three-branch decision switching rules

At present, double-hormone artificial pancreas is a hotspot in the research of regulating blood glucose level; however, there are few studies on switching mechanism of the subsystems for double-hormone artificial pancreas, and most of the existing studies rely on the prior knowledge of application fields. A model predictive control algorithm for double-hormone artificial pancreas based on optimal three-branch decision switching rules is proposed to solve this problem. The blood glucose controllers of insulin subsystems, zero infusion subsystems, and glucagon subsystems are constructed using model predictive control algorithms. Then, using the three-branch decision theory, switching rules between the three subsystems are designed, and the optimal switching threshold value can be solved by the mixed fruit fly algorithm. The algorithm can realize automatic acquisition of the threshold in the three-branch decision switching rules and gets rid of the dependence on the prior knowledge in the field of diabetes research. By the end, the UVa simulation platform is used to conduct experiments on 33 virtual patients and the simulation results verify that the proposed algorithm can control blood glucose effectively within the normal range and can achieve satisfactory performabce in error of tracking set point, safety, operation efficiency, group control quality, and system stability.

Eighteen-Month Hybrid Closed-Loop Use in Very Young Children With Type 1 Diabetes: A Single-Arm Multicenter Trial.

We aimed to evaluate the longer-term safety and efficacy of hybrid closed-loop (CL) therapy in very young children with type 1 diabetes (T1D). Following a 16-week multinational, randomized crossover trial comparing hybrid CL with sensor-augmented pump (SAP) therapy in 74 very young children aged 1-7 years with T1D, participants were invited to an extension phase using CL for a further 18 months. Outcomes were compared with the primary-phase SAP period and primary-phase CL period. After the primary study phase, 60 participants were eligible to enroll in the extension. Of these, 49 consented (mean ± SD age 6.6 ± 1.5 years) to continue use of CL for 18 months. Percentage time in range (TIR) 3.9-10.0 mmol/L was 8.4 percentage points (95% CI 6.7 to 10.1; P < 0.001) higher, while HbA1c was 0.4% ([5.0 mmol/mol], 95% CI 0.3 to 0.6 [3.7 to 6.2]; P < 0.001) lower during the CL extension phase compared with primary-phase SAP period. At 18 months, mean HbA1c was 6.7 ± 0.5% and TIR was 70 ± 7%, compared with 6.7 ± 0.5% and 71 ± 6% in the primary-phase CL period. Time in hypoglycemia (<3.9 mmol/L) was similar between CL extension phase and both primary-phase SAP (P = 0.31) and CL periods (P = 0.70). There were two severe hypoglycemia events and one other serious adverse event during the extension phase. One unexpected serious adverse device effect occurred. Use of the Cambridge hybrid CL system led to sustained improvements in glycemic control lasting more than 18 months in very young children with T1D.

Periodontal ligament stem cell tissue engineering scaffolds can guide and promote canine periodontal tissue regeneration.

The immunogenicity of allogeneic mesenchymal stem cells (MSCs) is significantly enhanced after transplantation or differentiation, and these cells can be recognized and cleared by recipient immune cells. Graft rejection has become a major obstacle to improving the therapeutic effect of allogeneic MSCs or, after their differentiation, transplantation in the treatment of diabetes and other diseases. Solving this problem is helpful for prolonging the time that cells play a role in the recipient body and for significantly improving the clinical therapeutic effect. In this study, canine adipose-derived mesenchymal stem cells (ADSCs) were used as seed cells, and gene editing technology was used to knock out the B2M gene in these cells and cooperate with the overexpression of the PD-L1 gene. Gene-edited ADSCs (GeADSCs), whose biological characteristics and safety are not different from those of normal canine ADSCs, have been obtained. The immunogenicity of GeADSCs is reduced, the immune escape ability of GeADSCs is enhanced, and GeADSCs can remain in the body for a longer time. Using the optimized induction program, the efficiency of the differentiation of GeADSCs into new islet β-cells was increased, and the maturity of the new islet β-cells was increased. The immunogenicity of new islet β-cells decreased, and their immune escape ability was enhanced after the cells were transplanted into diabetic dogs (the graft site was prevascularized by the implantation of a scaffold to form a vascularized pouch). The number of infiltrating immune cells and the content of immune factors were decreased at the graft site. New islet β-cell transplantation, which has low immunogenicity, can reverse diabetes in dogs, and the therapeutic effect of cell transplantation is significantly enhanced. This study provides a new method for prolonging the survival and functional time of cells in transplant recipients and significantly improving the clinical therapeutic effect.

7437 Harnessing Diabetes Technology for Management of Immune Checkpoint Inhibitor Associated Diabetes

Abstract Disclosure: A. Knape: None. A. Kotwal: None. D. Rohlfsen: None. A. Patel: None. W. Goldner: None. Background: Immune checkpoint inhibitors (ICIs) are a key treatment modality for many cancers. One percent of patients who receive ICIs can develop insulin deficient ICI associated diabetes (ICI-DM). These patients often have complex oncologic therapy plans making treatment of newly diagnosed insulin deficient diabetes challenging which often results in additional healthcare-related burden for the patient. Methods: This is a retrospective case series of five adults who developed ICI-DM after receiving either PD-1, CTLA-4 or PDL-1 inhibitors or a combination. Data on demographics, medical history and labs were collected in addition to mode of glucose monitoring and treatment after diagnosis of ICI-DM. Results: Patients were age 44-72 at diagnosis of ICI-DM, three were female. Two were admitted to the hospital in DKA, one was diagnosed in the ER with severe hyperglycemia without DKA, two had outpatient hyperglycemia without DKA. Glucose at diagnosis ranged from 215-510 mg/dL. C-peptide was low for all patients. Fingerstick blood sugar (FSBS) monitoring and basal/bolus insulin was immediately initiated for all patients. All were transitioned from FSBS to continuous glucose monitor (CGM) and 2/5 ultimately transitioned to sensor-augmented insulin pump therapy (AID). Time in range on CGM improved in four patients with percent time in range improving between 10-48%. In two patients, hypoglycemia frequency declined by 6% and 3%. Hyperglycemia frequency declined by 13% and 48% in patients on AID therapy and by 14% and 25% in two of those on basal/bolus insulin therapy. All patients preferred CGM over FSBS and remained on CGM therapy. Those who chose AID preferred it over basal/bolus insulin therapy stating it was less complicated than basal/bolus insulin with CGM. Conclusions: We report successful treatment of five cases of ICI-DM with CGM + basal/bolus insulin or AID. 80% demonstrated improved time in range and decreased overall frequency of hyperglycemia. 40% had decreased frequency of hypoglycemia. CGM allows for dynamic adjustment of insulin therapy in a population with complex medical regimens resulting in fluctuating glucose levels. The integrated pump and sensor technology in AID was less burdensome than basal/bolus in those who chose it. Managing ICI-DM is complicated. The burden of this entity can be lessened by harnessing the power of diabetes technology. Presentation: 6/2/2024

Platform for precise, personalised glucose forecasting through continuous glucose and physical activity monitoring and deep learning

Emerging research has demonstrated the advantage of continuous glucose monitoring for use in artificial pancreas and diabetes management in general. Recent studies demonstrate that glucose level forecasting using deep learning can help avoid postprandial hyperglycemia (≥ 180 mg/dL) or hypoglycemia (≤70 mg/dL) from delayed or increased insulin dosing in artificial pancreas. In this paper, a novel hybrid deep learning framework with integration of content-based attention learning is presented, to effectively predict the glucose measurements with prediction horizons (PH) = 15, 30 and, 60 minutes for T1D and T2D patients based on past data. We also present a complete cloud-based system and mobile app used for collecting CGM sensor, physical activity data, CHO values and insulin measurements to perform glucose forecasts using the proposed model running on Cloud. This model was validated using clinical data of individual with Type 1 diabetes (OhioT1DM) and individual with Type 2 diabetes. The mean absolute relative difference (MARD) was 12.33±3.15, 7.14±1.76% for PH=60 and, 30 min respectively on OhioT1DM clinical Dataset. The root mean squared error (RMSE) was 29.41±5.92 mg/dL and 17.19±3.22 mg/dL and the mean absolute error (MAE) was 21.96±4.67 mg/dL and 12.58±2.34 mg/dL for PH=60 and, 30 min respectively on the same clinical dataset. It was observed that inclusion of physical activity leads to improved glucose forecasting accuracy. Furthermore, all these results were obtained by training the model on only 8 days of clinical data of a single patient, followed by testing on clinical data on the following days. The results indicate that training on a single patient data may lead to better personalisation and better glucose forecasting results compared to existing works.

Real-world efficacy and safety of open-source automated insulin delivery for people with type 1 diabetes mellitus: Experience from mainland China

BackgroundOpen-source automated insulin delivery systems are increasingly adopted yet predominantly discussed outside of Asia. This study aimed to describe efficacy and safety of android artificial pancreas (AAPS) in people with type 1 diabetes mellitus (T1DM) from mainland China. MethodsThis real-world study recruited people who initiated AAPS for ≥ 3 months between 2019 and 2024. Key outcomes included glycated hemoglobin A1c (HbA1c) and metrics from continuous glucose monitoring, rates of diabetic ketoacidosis (DKA) and severe hypoglycemia. Findings292 (male, 46·9 %) participants aged 25·7 (14·7, 35·0) years were included, with 183 (62·7 %) and 68 (23·3 %) using AAPS for 6 and 12 months. Prior-AAPS HbA1c was 7·6 ± 1·7 % with 44·5 % achieving < 7·0 %. After 3 months, mean HbA1c improved by −1·5 ± 2·0 % to 6·3 ± 0·8 % (P < 0.01), with 82·9 % achieving < 7.0 %. Time in range 3·9-10·0 mmol/L (TIR) improved to 78·8 ± 12·9 %, with 80·5 % achieving > 70 %, followed by time below 3·9 mmol/L of 3·9 (2·1, 6·1) %. After 12 months, HbA1c and TIR remained similar at 6·4 ± 1·0 % and 77·9 ± 12·2 %. No DKA and severe hypoglycemia was observed. InterpretationReal-world data from mainland China highlights current uptake of open-source AAPS with potential glycemic benefits. No safety signals are seen. More support to enhance access and utilization of all AID systems in this region is warranted.

Interesting Life Journey of a Type 1 Diabetes Individual from the Era of Injectable Insulins to the Hybrid Closed-Loop Insulin Pump

Abstract Continuous subcutaneous insulin infusion (CSII) pump has shown good outcomes in individuals with diabetes. An individual with type 1 diabetes (T1D) was diagnosed in 1996 at the age of 14 years with GAD-positive and absent C Peptide reserves confirming the diagnosis. Initially, the individual was started with the conventional treatment for T1D consisting of multiple insulin injections per day, however, with this treatment, there was a wide fluctuation in the blood sugar levels with frequent hyperglycemic and hypoglycemic episodes, and could not achieve strict HbA1c control. In 2019, the treatment was switched to a 620G CSII pump, the fluctuations in blood sugar levels were reduced, but still, there were occasional hyperglycemic and hypoglycemic episodes failing to achieve the target HbA1c control. In 2022, again the treatment was changed to the latest 780G CSII pump termed the artificial pancreas which helped the person with T1D to achieve strict blood sugar level control and excellent ‘Time in Range’ with no hyperglycemic and hypoglycemic episodes and a better quality of life.

Personalized food consumption detection with deep learning and Inertial Measurement Unit sensor

For individuals diagnosed with diabetes mellitus, it is crucial to keep a record of the carbohydrates consumed during meals, as this should be done at least three times daily, amounting to an average of six meals. Unfortunately, many individuals tend to overlook this essential task. For those who use an artificial pancreas, carbohydrate intake proves to be a critical factor, as it can activate the insulin pump in the artificial pancreas to deliver insulin to the body. To address this need, we have developed personalized deep learning model that can accurately detect carbohydrate intake with a high degree of accuracy. Our study employed a publicly available dataset gathered by an Inertial Measurement Unit (IMU), which included accelerometer and gyroscope data. The data was sampled at a rate of 15 Hz, necessitating preprocessing. For our tailored to the patient model, we utilized a recurrent network comprising Long short-term memory (LSTM) layers. Our findings revealed a median F1 score of 0.99, indicating a high level of accuracy. Additionally, the confusion matrix displayed a difference of only 6 s, further validating the model’s accuracy. Therefore, we can confidently assert that our model architecture exhibits a high degree of accuracy. Our model performed well above 90% on the dataset, with most results between 98%–99%. The recurrent networks improved the problem-solving capabilities significantly, though some outliers remained. The model’s average prediction latency was 5.5 s, suggesting that later meal predictions result in extended meal progress predictions. The dataset’s limitation of mostly single-day data points raises questions about multi-day performance, which could be explored by collecting multi-day data, including night periods. Future enhancements might involve transformer networks and shorter time windows to improve model responsiveness and accuracy. Therefore, we can confidently assert that our model exhibits a high degree of accuracy.

EndoC-βH3 pseudoislets are suitable for intraportal transplantation in diabetic mice

ABSTRACT Background Islet or β-cell transplantation is a therapeutical approach to substitute the insulin-producing cells which are abolished in type 1 diabetes mellitus. The shortage of human islets as well as the complicated and costly isolation process limit the application of these techniques in daily clinical practice. EndoC-βH is a human β-cell line that readily forms aggregates termed pseudoislets, providing an alternative to primary human islets or β-cells. Methods EndoC-βH3 cells were seeded and incubated to form pseudoislets. Their insulin secretion was analyzed by ELISA and compared with cell monolayers. Pseudoislets were transplanted into streptozotocin-treated NMRi nu/nu mice. Blood glucose was monitored before and after transplantation and compared with wild types. Grafts were analyzed by immunohistology. Results This study shows that EndoC-βH cells are able to form pseudoislets by aggregation, leading to an enhanced glucose stimulated insulin secretion in vitro. These pseudoislets were then successfully transplanted into the livers of diabetic mice and produced insulin in vitro. Blood glucose levels of the streptozocin-treated recipient mice were significantly decreased when compared to pre-transplantation and matched the levels found in control mice. Conclusion We suggest pseudoislets aggregated from EndoC-βH cells as a valuable and promising model for islet transplantation research.

Nurses’ Perception regarding Artificial Pancreas among Type I Diabetes Mellitus at the National Diabetic Institute

Nurses’ Perception regarding Artificial Pancreas among Type I Diabetes Mellitus at the National Diabetic Institute

Computational techniques to monitoring fractional order type-1 diabetes mellitus model for feedback design of artificial pancreas

Background and objectives:In this paper, we developed a significant class of control issues regulated by nonlinear fractal order systems with input and output signals, our goal is to design a direct transcription method with impulsive instant order. Recent advances in the artificial pancreas system provide an emerging treatment option for type 1 diabetes. The performance of the blood glucose regulation directly relies on the accuracy of the glucose-insulin modeling. This work leads to the monitoring and assessment of comprehensive type-1 diabetes mellitus for controller design of artificial panaceas for the precision of the glucose-insulin glucagon in finite time with Caputo fractional approach for three primary subsystems. Methods:For the proposed model, we admire the qualitative analysis with equilibrium points lying in the feasible region. Model satisfied the biological feasibility with the Lipschitz criteria and linear growth is examined, considering positive solutions, boundedness and uniqueness at equilibrium points with Leray–Schauder results under time scale ideas. Within each subsystem, the virtual control input laws are derived by the application of input to state theorems and Ulam Hyers Rassias. Results:Chaotic Relation of Glucose insulin glucagon compartmental in the feasible region and stable in finite time interval monitoring is derived through simulations that are stable and bounded in the feasible regions. Additionally, as blood glucose is the only measurable state variable, the unscented power-law kernel estimator appropriately takes into account the significant problem of estimating inaccessible state variables that are bound to significant values for the glucose-insulin system. The comparative results on the simulated patients suggest that the suggested controller strategy performs remarkably better than the compared methods. Conclusion:In the model under investigation, parametric uncertainties are identified since the glucose, insulin, and glucagon system’s parameters are accurately measured numerically at different fractional order values. In terms of algorithm resilience and Caputo tracking in the presence of glucagon and insulin intake disturbance to maintain the glucose level. A comprehensive analysis of numerous difficult test issues is conducted in order to offer a thorough justification of the planned strategy to control the type 1 diabetes mellitus with designed the artificial pancreas.

Various Diabetes Management Activities: A Comprehensive Overview

Diabetes management is a multifaceted process crucial for preventing complications and improving the quality of life for individuals with diabetes. This comprehensive review explores various aspects of diabetes management, including education, self-monitoring, medication adherence, dietary management, physical activity, psychological support, and technological innovations. Diabetes self-management education (DSME) forms the foundation of effective care, equipping patients with the knowledge and skills necessary for optimal self-care. Self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) are essential for tracking and adjusting treatment plans. Medication management, including insulin therapy and oral medications, is vital for maintaining glycemic control, while personalized dietary plans and regular physical activity contribute to overall health. Psychological support and motivational strategies are crucial for addressing the emotional burden of diabetes and sustaining long-term self-care. Technological advancements, such as digital health tools and artificial pancreas systems, enhance diabetes management by providing real-time data and facilitating remote consultations. This review underscores the importance of an integrated approach to diabetes care, combining education, self-monitoring, medication management, lifestyle modifications, psychological support, and technological innovations to achieve better health outcomes and improve patient well-being. Keywords: Diabetes Management, Diabetes Self-Management Education, Insulin Therapy, Oral Medications.

Supported Open-source Artificial Pancreas Systems in Type 1 Diabetes With Suboptimal Glycemia

Supported Open-source Artificial Pancreas Systems in Type 1 Diabetes With Suboptimal Glycemia

Hybrid Control Policy for Artificial Pancreas Via Ensemble Deep Reinforcement Learning.

The artificial pancreas (AP) has shown promising potential in achieving closed-loop glucose control for individuals with type 1 diabetes mellitus (T1DM). However, designing an effective control policy for the AP remains challenging due to the complex physiological processes, delayed insulin response, and inaccurate glucose measurements. While model predictive control (MPC) offers safety and stability through the dynamic model and safety constraints, it lacks individualization and is adversely affected by unannounced meals. Conversely, deep reinforcement learning (DRL) provides personalized and adaptive strategies but faces challenges with distribution shifts and substantial data requirements. We propose a hybrid control policy for the artificial pancreas (HyCPAP) to address the above challenges. HyCPAP combines an MPC policy with an ensemble DRL policy, leveraging the strengths of both policies while compensating for their respective limitations. To facilitate faster deployment of AP systems in real-world settings, we further incorporate meta-learning techniques into HyCPAP, leveraging previous experience and patient-shared knowledge to enable fast adaptation to new patients with limited available data. We conduct extensive experiments using the FDA-accepted UVA/Padova T1DM simulator across three scenarios. Our approaches achieve the highest percentage of time spent in the desired euglycemic range and the lowest occurrences of hypoglycemia. The results clearly demonstrate the superiority of our methods for closed-loop glucose management in individuals with T1DM. The study presents novel control policies for AP systems, affirming the great potential of proposed methods for efficient closed-loop glucose control.

More than pacemakers and defibrillators: perioperative management of implantable devices for patient safety.

The use of implantable medical devices (IMDs) continues to increase with estimates that 10% of the American population will have an IMD in their lifetime. IMDs require special considerations for management in the perioperative period to ensure optimal patient care and patient safety. This review summarizes the current perioperative considerations for IMDs. This review summarizes perioperative recommendations for spinal cord stimulators, deep brain stimulators, peripheral nerve stimulators, vagus nerve stimulators, muscle stimulators, intrathecal drug delivery systems, implantable infusion pumps, artificial pancreas devices, continuous glucose monitors, and cochlear implants. There are multiple publications and guidelines regarding the perioperative considerations of cardiac implantable electronic devices; thus, this review excludes those devices. This review includes recommendations on management of the device perioperatively, the potential complications, and postoperative care of the device. There are very few guidelines regarding the perioperative management of IMDs. Given the significant impact that these devices have on patient care and safety, evidence-based guidelines should be established.

Health beyond biology: the extended health hypothesis and technology.

There are ethical dilemmas faced by clinicians when responding to using unregistered medical devices, such as innovative internet technologies for managing type 1 diabetes mellitus. This chronic disease significantly impacts patients' health, requiring intensive daily activities like blood glucose monitoring, insulin injections, and specific dietary recommendations. Recent technological advances, including continuous glucose monitors and insulin pumps, have been shown to improve glycemic control. Di-it Yourself Artificial Pancreas Systems are emerging open-source automated delivery methods initiated by the diabetes community, although they are not clinically evaluated and present a liability challenge for healthcare providers. To use them or not? Should parents and healthcare providers use such technology that helps, but is not proven?Having all of that in mind, we argue that the World Health Organization's (WHO) definition of health is outdated, advocating for the "Extended Health Hypothesis". This hypothesis claims that health extends beyond traditional biological boundaries to include essential functional structures like diabetes-related technology, making technology a part of a patient's health. This view aligns with the "Extended Mind Hypothesis," suggesting that health should include elements beyond organic material if they are vital to a patient's functions.In the commentary, we highlight that both naturalist and normative conceptions of health support the extended health hypothesis, emphasizing that human health is not confined to organic material. This perspective raises critical questions about whether devices like insulin pumps and continuous glucose monitors are integral to a patient's health and whether their malfunction constitutes a form of disease. Devices are considered integral to health, there is no ethical dilemma in using unregistered medical devices for managing type 1 diabetes. Finally, we call for reevaluating the definitions of health and patients, particularly for children with type 1 diabetes using advanced technologies. It asserts that the optimal use of such devices represents a new form of health, creating a health-device symbiosis that should be evaluated with the child's best interests in mind.

Future acceptance of automated insulin delivery systems in youths with type 1 diabetes: validation of the Italian artificial pancreas-acceptance measure.

The purpose of this study was to develop a questionnaire to examine the future acceptance of Automatic insulin delivery systems (AIDs), their perceived usefulness, ease of use, and trust in the device in subjects with type 1 diabetes (T1D). A questionnaire in Italian, based on the Technology Acceptance Model, was developed to examine intention to use AIDs, considered as a measure of future acceptance, and its determinants to use the system. A total of 43 questions for children and 46 for parents were included, and a 5-point Likert scale was used. 239 subjects with T1D using multiple daily injections (MDI) or sensor-augmented pump (SAP) and their parents completed the questionnaire. The completion rate was excellent, with almost 100% of items answered. The overall Cronbach's coefficient for children and adolescents was 0.92 and 0.93 for parents, indicating excellent internal consistency in both groups. Parent-youth agreement was 0.699 (95% confidence interval: 0.689-0.709), indicating a good agreement between the two evaluations. Factor analysis identified measurement factors for the "artificial pancreas (AP)-acceptance labeled benefits and hassles of AIDs, and the internal consistency of the total scale was alpha = 0.94 for subjects with T1D and 0.95 for parents. The level of AP acceptance was more than neutral: 3.91 ± 0.47 and 3.99 ± 0.43 (p = 0.07) for youths and parents, respectively (possible score range 1 to 5, neutral score is 3.0). Parents reported higher scores in the benefit items than children-adolescents (p = 0.04). We developed a new questionnaire based on the items available in the literature, and we demonstrated that the "AP-acceptance" reveals a meaningful factor structure, good internal reliability, and agreement between parent-young people evaluations. This measure could be a valuable resource for clinicians and researchers to assess AP acceptance in pediatric patients with T1D and their parents. This patient profiling approach could help to enroll candidates for AIDs with proper expectations and who most likely will benefit from the system.