Diabetes Management According to Health Status in Older Adults with Type 2 Diabetes Staying in Geriatric Care Facilities

Background:About 25% of adults >70 years suffer from type 2 diabetes. Due to the heterogeneity of the geriatric population, guidelines emphasize the need to individualize glycemic goals and simplify treatment strategies with the main focus of avoiding hypoglycemia. The aim of this study was to assess glycemic control in patients with type 2 diabetes in geriatric care facilities based on their individual health status.Methods:170 medical records of older adults with type 2 diabetes in geriatric care facilities were retrospectively assessed (64.7% female, age 80 ± 9 years; glycated hemoglobin 6.8% ± 3.6% [51 ± 16 mmol/mol]; body mass index 27.9 ± 5.8 kg/m²). Based on the individual health status, patients were allocated to three groups (healthy n = 27, complex n = 86, and poor n = 57).Results:The overall blood glucose (BG) value was highest in the poor health group with 188 ± 47 mg/dL (poor) vs 167 ± 42 mg/dL (complex) vs 150 ± 34 mg/dL (healthy). BG values of 1.6% (poor) vs 2.8% (complex) vs 1.5% (healthy) of patients were below 90 mg/dL. 36.8% (poor) vs 23.4% (complex) vs 18.5% (healthy) of patients received insulin as the main diabetes therapy, but of these only 14.3% (poor) vs 20% (complex) vs 40% (healthy) were treated with basal insulin.Conclusions:Overall, BG values were higher in the poor and complex health group. There were a few low BG values in all groups. Although recommended by international guidelines, basal insulin therapy with its low complexity and low hypoglycemic risk is still underused, especially in the poor health group. Therefore, simplification of diabetes therapy should be considered further.     

Clinical Decision Support for Diabetes Care in the Hospital: A Time for Change Toward Improvement of Management and Outcomes

The increasing prevalence of diabetes permeates hospitals and dysglycemia is associated with poor clinical and economic outcomes. Despite endorsed guidelines, barriers to optimal management and gaps in care prevail. Providers’ limitations on knowledge, attitudes, and decision-making about hospital diabetes management are common. This adds to the complexity of dispersed glucose and insulin dosing data within medical records. This creates a dichotomy as safe and effective care are key objectives of healthcare organizations. This perspective highlights evidence of the benefits of clinical decision support (CDS) in hospital glycemic management. It elaborates on barriers CDS can help resolve, and factors driving its success. CDS represents a resource to individualize care and improve outcomes. It can help overcome a multifactorial problem impacting patients’ lives on a daily basis.     

Patients’ Experience of New Technologies and Digitalization in Diabetes Care in Germany

Background:Little is little known about how people with diabetes experience advancing digitization and new technologies in diabetes.Research question:What are the attitudes of people with diabetes (or, in the case of children with diabetes, their parents) toward digitization and advancing technology in diabetology? What significant advantages and disadvantages do they see, and how do they assess current developments of digitization in diabetology (eg, hybrid closed-loop systems, do-It-Yourself (DIY) closed-loop systems, data protection, and data donation)?Material and method:3,427 people with diabetes (47.7% female, 65.6% type 1 diabetes (T1D), 25.5% type 2 diabetes (T2D), 8.1% parents of children with diabetes; 0.8% other type of diabetes, age 49.2 ± 19.3 years) were interviewed with an online survey.Results:Overall, survey participants had a very positive attitude toward digitization (82.6%) and considered the potential for optimizing diabetology through digitization to be very high (78.8%). The highest rated advantages of digitization were “greater personal responsibility and self-determination in therapy” (80.0%), “better quality of treatment” (80.0%), and “better communication with the doctor/diabetes team” (77.3%), while the highest rated disadvantages were the “error-proneness of digital applications” (35.9%), the “risk of misuse of patient data” (32.3%) and the “fear that digitization will replace the doctor in many cases” (31.1%). The possibility of evaluating and analyzing glucose data by means of software, and AID systems, and the possibility of improving the interoperability of the various applications are currently rated as the most significant topics.Conclusions:The vast majority of people with diabetes are very positive about new technologies in diabetology and expect that it will improve and simplify their diabetes therapy and reduce the burdens associated with diabetes. In particular, people with type 1 diabetes have high expectations for AID systems, viewing them as a kind of “technical cure” for their diabetes.     

Adjustment of Insulin Pump Settings in Type 1 Diabetes Management: Advisor Pro Device Compared to Physicians’ Recommendations

Aims:To compare insulin dose adjustments made by physicians to those made by an artificial intelligence-based decision support system, the Advisor Pro, in people with type 1 diabetes (T1D) using an insulin pump and self-monitoring blood glucose (SMBG).Methods:This was a multinational, non-interventional study surveying 17 physicians from 11 countries. Each physician was asked to provide insulin dose adjustments for the settings of the pump including basal rate, carbohydrate-to-insulin ratios (CRs), and correction factors (CFs) for 15 data sets of pumps and SMBG of people with T1D (mean age 18.4 ± 4.8 years; eight females; mean glycated hemoglobin 8.2% ± 1.4% [66 ± 11mmol/mol]). The recommendations were compared among the physicians and between the physicians and the Advisor Pro. The study endpoint was the percentage of comparison points for which there was an agreement on the direction of insulin dose adjustments.Results:The percentage (mean ± SD) of agreement among the physicians on the direction of insulin pump dose adjustments was 51.8% ± 9.2%, 54.2% ± 6.4%, and 49.8% ± 11.6% for the basal, CR, and CF, respectively. The automated recommendations of the Advisor Pro on the direction of insulin dose adjustments were comparable )49.5% ± 6.4%, 55.3% ± 8.7%, and 47.6% ± 14.4% for the basal rate, CR, and CF, respectively( and noninferior to those provided by physicians. The mean absolute difference in magnitude of change between physicians was 17.1% ± 13.1%, 14.6% ± 8.4%, and 23.9% ± 18.6% for the basal, CR, and CF, respectively, and comparable to the Advisor Pro 11.7% ± 9.7%, 10.1% ± 4.5%, and 25.5% ± 19.5%, respectively, significant for basal and CR.Conclusions:Considerable differences in the recommendations for changes in insulin dosing were observed among physicians. Since automated recommendations by the Advisor Pro were similar to those given by physicians, it could be considered a useful tool to manage T1D.     

Safe and Sufficient Glycemic Control by Using a Digital Clinical Decision Support System for Patients With Type 2 Diabetes in a Routine Setting on General Hospital Wards

The aim was to investigate the applicability of a clinical decision support system in a real-world inpatient setting for patients with type 2 diabetes on general hospital wards.A total of 150 patients with type 2 diabetes requiring subcutaneous insulin therapy were treated with basal-bolus insulin therapy guided by a decision support system (GlucoTab) providing automated workflow tasks and suggestions for insulin dosing to health care professionals.By using the system, a mean daily blood glucose (BG) of 159 ± 32 mg/dL was achieved. 68.8% of measurements were in the target range (70 to <180 mg/dL). The percentage of BG values <40, <70, and ≥300 mg/dL was 0.02%, 2.2%, and 2.3%, respectively. Health care professionals’ adherence to suggested insulin doses and workflow tasks was high (>93% and 91%, respectively).The decision support system facilitates safe and efficacious inpatient diabetes care by standardizing treatment workflow and providing decision support for basal-bolus insulin dosing.     

Replay Simulations with Personalized Metabolic Model for Treatment Design and Evaluation in Type 1 Diabetes

Background:The capacity to replay data collected in real life by people with type 1 diabetes mellitus (T1DM) would lead to individualized (vs population) assessment of treatment strategies to control blood glucose and possibly true personalization. Patek et al introduced such a technique, relying on regularized deconvolution of a population glucose homeostasis model to estimate a residual additive signal and reproduce the experimental data; therefore, allowing the subject-specific replay of what-if scenarios by altering the model inputs (eg, insulin). This early method was shown to have a limited domain of validity. We propose and test in silico a similar approach and extend the method applicability.Methods:A subject-specific model personalization of insulin sensitivity and meal-absorption parameters is performed. The University of Virginia (UVa)/Padova T1DM simulator is used to generate experimental scenarios and test the ability of the methodology to accurately reproduce changes in glucose concentration to alteration in meal and insulin inputs. Method performance is assessed by comparing true (UVa/Padova simulator) and replayed glucose traces, using the mean absolute relative difference (MARD) and the Clarke error grid analysis (CEGA).Results:Model personalization led to a 9.08 and 6.07 decrease in MARD over a prior published method of replaying altered insulin scenarios for basal and bolus changes, respectively. Replay simulations achieved high accuracy, with MARD <10% and more than 95% of readings falling in the CEGA A-B zones for a wide range of interventions.Conclusions:In silico studies demonstrate that the proposed method for replay simulation is numerically and clinically valid over broad changes in scenario inputs, indicating possible use in treatment optimization.     

Effect of Professional CGM (pCGM) on Glucose Management in Type 2 Diabetes Patients in Primary Care

Background:Little data exists regarding the impact of continuous glucose monitoring (CGM) in the primary care management of type 2 diabetes (T2D). We initiated a quality improvement (QI) project in a large healthcare system to determine the effect of professional CGM (pCGM) on glucose management. We evaluated both an MD and RN/Certified Diabetes Care and Education Specialist (CDCES) Care Model.Methods:Participants with T2D for >1 yr., A1C ≥7.0% to <11.0%, managed with any T2D regimen and willing to use pCGM were included. Baseline A1C was collected and participants wore a pCGM (Libre Pro) for up to 2 weeks, followed by a visit with an MD or RN/CDCES to review CGM data including Ambulatory Glucose Profile (AGP) Report. Shared-decision making was used to modify lifestyle and medications. Clinic follow-up in 3 to 6 months included an A1C and, in a subset, a repeat pCGM.Results:Sixty-eight participants average age 61.6 years, average duration of T2D 15 years, mean A1C 8.8%, were identified. Pre to post pCGM lowered A1C from 8.8% ± 1.2% to 8.2% ± 1.3% (n=68, P=0.006). The time in range (TIR) and time in hyperglycemia improved along with more hypoglycemia in the subset of 37 participants who wore a second pCGM. Glycemic improvement was due to lifestyle counseling (68% of participants) and intensification of therapy (65% of participants), rather than addition of medications.Conclusions:Using pCGM in primary care, with an MD or RN/CDCES Care Model, is effective at lowering A1C, increasing TIR and reducing time in hyperglycemia without necessarily requiring additional medications.     

Adherence and Persistence to Insulin Therapy in People with Diabetes: Impact of Connected Insulin Pen Delivery Ecosystem

Diabetes is an increasing public health problem, and insulin is the mainstay for treatment of type 1 diabetes. In type 2 diabetes treatment, insulin therapy is used after oral or other injectable agents become inadequate to achieve glycemic control. Despite the advances in insulin therapy, management of diabetes remains challenging. Numerous studies have reported low adherence and persistence to insulin therapy, which acts as a barrier to successful glycemic control and diabetes management. The aim of this targeted review article is to provide an overview of adherence and persistence to insulin therapy in people with diabetes and to discuss the impact of the emergence of a new connected ecosystem of increasingly sophisticated insulin pens, glucose monitoring systems, telemedicine, and mHealth on diabetes management. With the emergence of a connected diabetes ecosystem, we have entered an era of advanced personalized insulin delivery, which will have the potential to enhance diabetes self-management and clinical management. Early systems promise to unlock the potential to address missed or late bolus insulin delivery, which should help to address non-adherence and non-persistence. Over time, improvements in this ecosystem have the potential to combine insulin data with previously missing contextualized patient data, including meal, glucose, and activity data to support personalized clinical decisions and ultimately revolutionize insulin therapy.     

Ethnicity and Quality of Diabetes Care in a Health System with Universal Coverage: Population-Based Cross-sectional Survey in Primary Care

BACKGROUND The UK has a universal health care system that is free at the point of access. Over the past decade, the UK government has implemented an ambitious agenda of quality improvement initiatives in chronic disease management. OBJECTIVE To assess the quality of diabetes care and intermediate clinical outcomes within a multiethnic population after a sustained period of investment in quality improvement. DESIGN Population based cross-sectional survey, using electronic general practice records, carried out between November 2005 and January 2006. PATIENTS Seven thousand six hundred five adults (≥18 years) with diabetes registered with 32 primary care practices. MEASUREMENTS Percentage achievement by ethnic group (black, south Asian, or white) of the quality indicators for diabetes in a new pay-for performance contract. RESULTS There were only modest variations in recording of process measures of care between ethnic groups, with no significant differences in recent measurement of blood pressure, HbA1c, cholesterol, micro-albuminuria, creatinine, or retinopathy screening attendance. Blacks and south Asians were significantly less likely to meet all three national treatment targets for diabetes (HbA1c ≤ 7.4%, blood pressure ≤ 145/85 mmHg, total cholesterol ≤ 5 mmol/L [193 mg/dL]) than whites (25.3%, 24.8% , and 32.0%, respectively). CONCLUSIONS Our findings suggest that substantial investment in quality improvement initiatives in the UK may have led to more systematic and equitable processes of care for diabetes but have not addressed ethnic disparities in intermediate clinical outcomes.     

Missed Opportunities in Diabetes Management: A Longitudinal Assessment of Factors Associated with Sub-optimal Quality

Background  In diabetic adults, tight control of risk factors reduces complications. Objective  To determine whether failure to make visits, monitor risk factors, or intensify therapy affects control of blood pressure, glucose, and lipids. Design  A non-concurrent, prospective study of data from electronic files and standardized abstraction of hard-copy medical records for the period 1/1/1999–12/31/2001. Participants  Three hundred eighty-three adults with diabetes managed in an academically affiliated managed care program. Measurements  Main exposure variable: Intensification of therapy or failure to intensify, reckoned on a quarterly basis. Main outcome measure: Hemoglobin A1c (A1c), systolic blood pressure (SBP), and LDL-cholesterol at the end of the interval. Results  In this visit-adherent cohort, control of glycemia and lipids showed improvement over 24 months, but many patients did not achieve targets. Only those with the worst blood pressure control (SBP ≥160 mmHg) showed any improvement over 2 years. Failure to intensify treatment in patients who kept visits was the single strongest predictor of sub-optimal control. Compared to their counterparts with no failures of intensification, patients with failures in ≥3 quarters showed markedly worse control of blood glucose (A1c 1.4% higher: 95% CI: 0.7, 2.1); hypertension (SBP 22.2 mmHg higher: 95% CI: 16.6, 27.9) and LDL cholesterol (LDL 43.7 mg/dl higher: 95% CI: 24.1, 63.3). These relationships were strong, graded, and independent of socio-demographic factors, baseline risk factor values, and co-morbidities. Conclusions  Failure to intensify therapy leads to suboptimal control, even with adequate visits and monitoring. Interventions designed to promote appropriate intensification should enhance diabetes care in primary practice.     

Mobile Health and Diabetes: Integration of a Mobile-Integrated Therapy with Electronic Health Records: Lessons Learned

Background:

Responses to the chronic disease epidemic have predominantly been standardized in their approach to date. Barriers to better health outcomes remain, and effective management requires patient-specific data and disease state knowledge be presented in methods that foster clinical decision-making and patient self-management.Mobile technology provides a new platform for data collection and patient–provider communication. The mobile device represents a personalized platform that is available to the patient on a 24/7 basis. Mobile-integrated therapy (MIT) is the convergence of mobile technology, clinical and behavioral science, and scientifically validated clinical outcomes. In this article, we highlight the lessons learned from functional integration of a Food and Drug Administration-cleared type 2 diabetes MIT into the electronic health record (EHR) of a multiphysician practice within a large, urban, academic medical center.

Methods:

In-depth interviews were conducted with integration stakeholder groups: mobile and EHR software and information technology teams, clinical end users, project managers, and business analysts. Interviews were summarized and categorized into lessons learned using the Architecture for Integrated Mobility^® framework.

Results:

Findings from the diverse stakeholder group of a MIT–EHR integration project indicate that user workflow, software system persistence, environment configuration, device connectivity and security, organizational processes, and data exchange heuristics are key issues that must be addressed.

Conclusions:

Mobile-integrated therapy that integrates patient self-management data with medical record data provides the opportunity to understand the potential benefits of bidirectional data sharing and reporting that are most valuable in advancing better health and better care in a cost-effective way that is scalable for all chronic diseases.     

Efficacy of a Smart Insulin Pen Cap for the Management of Patients with Uncontrolled Type 2 Diabetes: A Randomized Cross-Over Trial

Background:We studied a smart insulin pen cap that can be plugged to several brand of insulin pens, to track insulin administration via smart-phone Bluetooth technology, with alarm/reminder system aiming.Methods:This pilot randomized, cross-over design study assessed the use of a smart insulin pen cap in improving adherence, glycemic control and patient satisfaction in insulin-treated patients with poorly controlled type 2 diabetes. Eighty patients on basal insulin ± oral agents with hemoglobin A1C (HbA1c) between 7.0% and 12.0% were randomized to a 12-week active phase receiving alarms/reminders and a 12-week control/masked phase without feedback. We assessed differences between groups on treatment adherence, insulin omission, and mistiming of insulin injections, HbA1c, treatment satisfaction (using Diabetes Treatment Satisfaction Questionnaire Status).Results:Compared to the control/masked phase, the active phase resulted in lower mean daily blood glucose (147.0 ± 34 vs 157.6 ± 42 mg/dL, P < .01); and greater reduction in HbA1c from baseline (−0.98% vs −0.72%, P = .006); however, no significant differences in treatment adherence, insulin omission or insulin mistiming were observed. High patient satisfaction scores were reported in both active and control phases, with DTSQc of 15.5 ± 3.7 and 14.9 ± 3.6, respectively. Statistical models showed no residual effect after cross-over between active and control phases.Conclusions:The results of this pilot study indicates that this smart insulin pen cap was effective in improving glycemic control with overall good satisfaction in insulin treated patients with type 2 diabetes. Future studies are needed to confirm its potential for improving care in insulin treated patients with diabetes.     

A Digital Lifestyle Program in Outpatient Treatment of Type 2 Diabetes: A Randomized Controlled Study

Background:Lifestyle is important in type 2 diabetes mellitus (T2DM). This study’s aim was to investigate whether a healthy-lifestyle-supporting smartphone application could affect treatment outcomes at an endocrinology outpatient clinic.Methods:Consecutively invited patients were randomly assigned to an intervention or control group after age and gender stratification. In addition to standard care, intervention group participants used a smartphone application to access a lifestyle program (SidekickHealth) through which they received personalized recommendations and education about healthy lifestyles. Tests at baseline and every other month for six months included body weight and blood tests for glycated hemoglobin (HbA1c) and blood lipids, as well as questionnaires about distress related to diabetes, health-related quality of life, depression, and anxiety. Statistics included comparisons both within and between groups.Results:A total of 37 patients (23 women) were included, whereof 30 finished, 15 in each group (19% dropout); the average age was 51.2 ± 10.6 (25-70) years. No significant differences emerged between groups, but within the intervention group, there was a significant decrease in HbA1c from 61 ± 21.4 to 52.7 ± 15.2 mmol/mol, in disease-specific distress from 19.5 ± 16.5 to 11.7 ± 13.4, and in anxiety symptoms from 5.4 ± 4.0 to 4.1 ± 3.8. No significant changes occurred within the control group. The application usage was most frequent during the first months and differed interpersonally.Conclusions:Our results indicate that the SidekickHealth digital lifestyle program could potentially enhance outpatient treatment in T2DM, in terms of both glycemic control and psychological well-being but larger confirmative studies are needed.     

Methods for Insulin Bolus Adjustment Based on the Continuous Glucose Monitoring Trend Arrows in Type 1 Diabetes: Performance and Safety Assessment in an In Silico Clinical Trial

Background:Providing real-time magnitude and direction of glucose rate-of-change (ROC) via trend arrows represents one of the major strengths of continuous glucose monitoring (CGM) sensors in managing type 1 diabetes (T1D). Several literature methods were proposed to adjust the standard formula (SF) used for insulin bolus calculation by accounting for glucose ROC, but each of them provides different suggestions, making it difficult to understand which should be applied in practice. This work aims at performing an extensive in-silico assessment of their performance and safety.Methods:The methods of Buckingham (BU), Scheiner (SC), Pettus/Edelman (PE), Klonoff/Kerr (KL), Aleppo/Laffel (AL), Ziegler (ZI), and Bruttomesso (BR) were evaluated using the UVa/Padova T1D simulator, in single-meal scenarios, where ROC and glucose at mealtime varied between [-2,+2] mg/dL/min and [80,200] mg/dL, respectively. Efficacy of postprandial glucose control was quantitatively assessed by time in, above and below range (TIR, TAR, and TBR, respectively).Results:For negative ROCs, all methods proved to increase TIR and decrease TAR and TBR vs SF, with KL, PE, and BR being the most effective. For positive ROCs, a general worsening of the performances is present, only BR improved the glycemic control when mealtime glucose was close to hypoglycemia, while SC resulted the safest in the other conditions.Conclusions:Insulin bolus adjustment methods are effective for negative ROCs, but they generally appear to overdose for positive ROCs, calling for safer strategies in such a scenario. These results can be useful in outlining guidelines to identify which adjustment to apply based on the mealtime condition.     

DUAL I China: Improved glycemic control with IDegLira versus its individual components in a randomized trial with Chinese participants with type 2 diabetes uncontrolled on oral antidiabetic drugs

BackgroundDUAL I China, one of the DUAL trials, assessed efficacy/safety of insulin degludec/liraglutide (IDegLira) in Chinese adults with type 2 diabetes (T2D) not controlled by oral antidiabetic drugs (OADs).MethodsThis phase 3a, treat‐to‐target multicenter trial randomized participants (glycated hemoglobin [HbA1c] 53.0‐85.8 mmol/mol; previous metformin ± another OAD) 2:1:1 to IDegLira (n = 361), degludec (n = 179), or liraglutide (n = 180). Primary endpoint was change in HbA1c after 26 weeks. Secondary endpoints included: HbA1c < 53.0 mmol/mol attainment, weight change, treatment‐emergent hypoglycemia, end‐of‐treatment insulin dose, and safety.ResultsAt 26 weeks, HbA1c had decreased by a mean 18.12 mmoL/moL (IDegLira), 12.37 mmoL/moL (degludec) (estimated treatment difference [ETD] −6.50 mmoL/moL; 95% confidence interval [CI] −7.96, −5.04; P < .0001), and 11.33 mmoL/moL (liraglutide) (ETD −6.87 mmoL/moL; 95% CI −8.33, −5.41; P < 0.0001), indicating noninferiority for IDegLira vs degludec and superiority vs liraglutide. HbA1c < 53.0 mmoL/moL attainment was 77.0% (IDegLira), 46.4% (degludec), and 48.3% (liraglutide). Mean weight change with IDegLira (0.1 kg) was superior to degludec (1.2 kg) (ETD −1.08 kg; 96% CI −1.55, −0.62; P < 0.0001). Severe or confirmed hypoglycemic event rates were 0.24 (IDegLira) and 0.17 (degludec) episodes/participant‐year (estimated rate ratio 1.46; 95% CI 0.71, 3.02; P = .3008, not significant). At the end of treatment, the IDegLira insulin dose was lower (24.5 U/d) vs degludec (30.3 U/d) (ETD −5.49 U; 95% CI −7.77, −3.21; P < 0.0001). No unexpected safety issues occurred.ConclusionsIDegLira is efficacious and well tolerated in Chinese adults with T2D not controlled by OADs.     

The Impact of a Recently Approved Automated Insulin Delivery System on Glycemic,Sleep, and Psychosocial Outcomes in Older Adults With Type 1 Diabetes: A Pilot Study

Background:Older adults with type 1 diabetes (≥65 years) are often under-represented in clinical trials of automated insulin delivery (AID) systems. We sought to test the efficacy of a recently FDA-approved AID system in this population.Methods:Participants with type 1 diabetes used sensor-augmented pump (SAP) therapy for four weeks and then used an AID system (Control-IQ) for four weeks. In addition to glucose control variables, patient-reported outcomes (PRO) were assessed with questionnaires and sleep parameters were assessed by actigraphy.Results:Fifteen older adults (mean age 68.7 ± 3.3, HbA1c of 7.0 ± 0.8) completed the pilot trial. Glycemic outcomes improved during AID compared to SAP. During AID use, mean glucose was 146.0 mg/dL; mean percent time in range (TIR, 70-180 mg/dL) was 79.6%; median time below 70 mg/dL was 1.1%. The AID system was in use 92.6% ± 7.0% of the time. Compared to SAP, while participants were on AID the TIR increased significantly (+10%, P = .002) accompanied by a reduction in both time above 180 mg/dL (−6.9%, P = .005) and below 70 mg/dl (−0.4%, P = .053). Diabetes-related distress decreased significantly while using AID (P = .028), but sleep parameters remained unchanged.Conclusions:Use of this AID system in older adults improved glycemic control with high scores in ease of use, trust, and usability. Participants reported an improvement in diabetes distress with AID use. There were no significant changes in sleep.