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BACKGROUND: Loop is an open-source automated insulin dosing system that allows users unrivaled control over system settings that affect future glucose prediction. Thousands use Loop, but little is known about those who discontinue. METHODS: In a large observational study, 874 Loop participants completed surveys and provided glycemic data, 46 (5.3%) of those self-identified as discontinuing Loop use during the observation window, 45 completed a discontinued use survey, 22 provided system settings data, and 19 participated in semistructured interviews about their discontinuation. Qualitative data were transcribed, coded, and analyzed. RESULTS: Older age and not trusting Loop were associated with discontinued use, although no other demographic or clinical characteristics were significant correlates. The most endorsed reasons were “I decided to try something else” (27.8%) followed by “It just didn't help as much as I thought it would” (22.2%). Qualitative analyses revealed prominent themes centered upon mental and emotional burden and adjusting settings. Other reasons for discontinued use included fear of disapproval of Loop use from diabetes provider, barriers to acquiring component devices, a desire to try new/different technologies, concerns that Loop could not accommodate specific exercise or low insulin dose regimens, and worry about Loop use during pregnancy. It was noted that burdens might be alleviated by enhanced technical assistance and expert guidance. CONCLUSIONS: Although the majority of individuals in the Loop observational study continued use, those who discontinued reported similar challenges. Technical support and education specific to setting calculations could expand Loop benefits, alleviate burden, and support sustained use among new Loop users. Clinical Trial Registration: clinicaltrials.gov (NCT03838900).

Background Hybrid Closed Loop (HCL) systems aid individuals with type 1 diabetes in improving glycemic control, however, sustained use over time has not been consistent for all users. This study developed and validated prognostic models for successful 12-month use of the first commercial HCL system based on baseline and 1-month or 3-month data. Methods and materials Data from participants at the Barbara Davis Center (N=85) who began use of the MiniMed 670G HCL were used to develop prognostic models using logistic regression and Lasso model selection. Candidate factors included sex, age, duration of diabetes, baseline HbA1c, race, ethnicity, insurance status, history of insulin pump and continuous glucose monitor use, 1-month or 3-month Auto Mode use, boluses per day, and time in range (70-180 mg/dL; TIR), and scores on behavioral questionnaires. Successful use of HCL was predefined as Auto Mode use ≥60%. The 3-month model was then externally validated against a sample from Stanford University (N=55). Results Factors in the final model included baseline HbA1c, sex, ethnicity, 1-month or 3-month Auto Mode use, Boluses per Day, and TIR. The 1-month and 3-month prognostic models had very good predictive ability with area under the curve values of 0.894 and 0.900, respectively. External validity was acceptable with an area under the curve of 0.717. Conclusions Our prognostic models use clinically accessible baseline and early device-use factors to identify risk for failure to succeed with 670G HCL technology. These models may be useful to develop targeted interventions to promote success with new technologies.

ContextEarly initiation of continuous glucose monitoring (CGM) is advocated for youth with type 1 diabetes (T1D). Data to guide CGM use on time-in-range (TIR), hypoglycemia, and the role of partial clinical remission (PCR) are limited.ObjectiveOur aims were to assess whether 1) an association between increased TIR and hypoglycemia exists, and 2) how time in hypoglycemia varies by PCR status.MethodsWe analyzed 80 youth who were started on CGM shortly after T1D diagnosis and were followed for up to 1-year post diagnosis. TIR and hypoglycemia rates were determined by CGM data and retrospectively analyzed. PCR was defined as (visit glycated hemoglobin A1c) + (4*units/kg/day) less than 9.ResultsYouth were started on CGM 8.0 (interquartile range, 6.0-13.0) days post diagnosis. Time spent at less than 70 mg/dL remained low despite changes in TIR (highest TIR 74.6 ± 16.7%, 2.4 ± 2.4% hypoglycemia at 1 month post diagnosis; lowest TIR 61.3 ± 20.3%, 2.1 ± 2.7% hypoglycemia at 12 months post diagnosis). No events of severe hypoglycemia occurred. Hypoglycemia was rare and there was minimal difference for PCR vs non-PCR youth (54-70 mg/dL: 1.8% vs 1.2%, P = .04; ConclusionAs TIR gradually decreased over 12 months post diagnosis, hypoglycemia was limited with no episodes of severe hypoglycemia. Hypoglycemia rates did not vary in a clinically meaningful manner by PCR status. With CGM being started earlier, consideration needs to be given to modifying CGM hypoglycemia education, including alarm settings. These data support a trial in the year post diagnosis to determine alarm thresholds for youth who wear CGM.

Introduction: Hybrid closed-loop systems increase time-in-range (TIR) and reduce glycemic variability. Person-reported outcomes (PROs) are essential to assess the utility of new devices and their impact on quality of life. This article focuses on the PROs for pediatric participants (ages 6-13 years) with type 1 diabetes (T1D) and their parents during a trial using the Tandem Control-IQ system, which was shown to increase TIR and improve other glycemic metrics. Research Design and Methods: One hundred and one children 6 to 13 years old with T1D were randomly assigned to closed-loop control (CLC) or sensor-augmented pump (SAP) in a 16-week randomized clinical trial with extension to 28 weeks during which the SAP group crossed over to CLC. Health-related quality of life and treatment satisfaction measures were obtained from children and their parents at baseline, 16 weeks, and 28 weeks. Results: Neither the children in the CLC group nor their parents had statistically significant changes in PRO outcomes compared with the SAP group at the end of the 16-week randomized controlled trial and the 28-week extension. Parents in the CLC group reported nonsignificant improvements in some PRO scores when compared with the SAP group at 16 weeks, which were sustained at 28 weeks. Sleep scores for parents improved from "poor sleep quality" to "adequate sleep quality" between baseline and 16 weeks, however, the change in scores was not statistically different between groups. Conclusions: Children with T1D who used the Control-IQ system did not experience increased burden compared with those using SAP based on person-reported outcomes from the children and their parents. Clinical Trials Registration: NCT03844789.

Objective: To evaluate the safety and effectiveness of the Loop Do-It-Yourself automated insulin delivery system. Research Design and Methods: A prospective real-world observational study was conducted, which included 558 adults and children (age range 1–71 years, mean HbA1c 6.8% ± 1.0%) who initiated Loop either on their own or with community-developed resources and provided data for 6 months. Results: Mean time-in-range 70–180 mg/dL (TIR) increased from 67% ± 16% at baseline (before starting Loop) to 73% ± 13% during the 6 months (mean change from baseline 6.6%, 95% confidence interval [CI] 5.9%–7.4%; P

OBJECTIVE Disparities in type 1 diabetes related to use of technologies like continuous glucose monitors (CGMs) and utilization of diabetes care are pronounced based on socioeconomic status (SES), race, and ethnicity. However, systematic reports of perspectives from patients in vulnerable communities regarding barriers are limited. RESEARCH DESIGN AND METHODS To better understand barriers, focus groups were conducted in Florida and California with adults ≥18 years old with type 1 diabetes with selection criteria including hospitalization for diabetic ketoacidosis, HbA1c >9%, and/or receiving care at a Federally Qualified Health Center. Sixteen focus groups were conducted in English or Spanish with 86 adults (mean age 42 ± 16.2 years). Transcript themes and pre–focus group demographic survey data were analyzed. In order of frequency, barriers to diabetes technology and endocrinology care included 1) provider level (negative provider encounters), 2) system level (financial coverage), and 3) individual level (preferences). RESULTS Over 50% of participants had not seen an endocrinologist in the past year or were only seen once including during hospital visits. In Florida, there was less technology use overall (38% used CGMs in FL and 63% in CA; 43% used pumps in FL and 69% in CA) and significant differences in pump use by SES (P = 0.02 in FL; P = 0.08 in CA) and race/ethnicity (P = 0.01 in FL; P = 0.80 in CA). In California, there were significant differences in CGM use by race/ethnicity (P = 0.05 in CA; P = 0.56 in FL) and education level (P = 0.02 in CA; P = 0.90 in FL). CONCLUSIONS These findings provide novel insights into the experiences of vulnerable communities and demonstrate the need for multilevel interventions aimed at offsetting disparities in diabetes.

Summary Background Management of type 1 diabetes is challenging. We compared outcomes using a commercially available hybrid closed-loop system versus a new investigational system with features potentially useful for adolescents and young adults with type 1 diabetes. Methods In this multinational, randomised, crossover trial (Fuzzy Logic Automated Insulin Regulation [FLAIR]), individuals aged 14–29 years old, with a clinical diagnosis of type 1 diabetes with a duration of at least 1 year, using either an insulin pump or multiple daily insulin injections, and glycated haemoglobin (HbA1c) levels of 7·0–11·0% (53–97 mmol/mol) were recruited from seven academic-based endocrinology practices, four in the USA, and one each in Germany, Israel, and Slovenia. After a run-in period to teach participants how to use the study pump and continuous glucose monitor, participants were randomly assigned (1:1) using a computer-generated sequence, with a permuted block design (block sizes of two and four), stratified by baseline HbA1c and use of a personal MiniMed 670G system (Medtronic) at enrolment, to either use of a MiniMed 670G hybrid closed-loop system (670G) or the investigational advanced hybrid closed-loop system (Medtronic) for the first 12-week period, and then participants were crossed over with no washout period, to the other group for use for another 12 weeks. Masking was not possible due to the nature of the systems used. The coprimary outcomes, measured with continuous glucose monitoring, were proportion of time that glucose levels were above 180 mg/dL (>10·0 mmol/L) during 0600 h to 2359 h (ie, daytime), tested for superiority, and proportion of time that glucose levels were below 54 mg/dL ( Findings Between June 3 and Aug 22, 2019, 113 individuals were enrolled into the trial. Mean age was 19 years (SD 4) and 70 (62%) of 113 participants were female. Mean proportion of time with daytime glucose levels above 180 mg/dL (>10·0 mmol/L) was 42% (SD 13) at baseline, 37% (9) during use of the 670G system, and 34% (9) during use of the advanced hybrid closed-loop system (mean difference [advanced hybrid closed-loop system minus 670G system] −3·00% [95% CI −3·97 to −2·04]; p Interpretation Hyperglycaemia was reduced without increasing hypoglycaemia in adolescents and young adults with type 1 diabetes using the investigational advanced hybrid closed-loop system compared with the commercially available MiniMed 670G system. Testing an advanced hybrid closed-loop system in populations that are underserved due to socioeconomic factors and testing during pregnancy and in individuals with impaired awareness of hypoglycaemia would advance the effective use of this technology Funding National Institute of Diabetes and Digestive and Kidney Diseases.

Background: Despite the demonstrated benefits of diabetes device use, uptake of insulin pumps and continuous glucose monitors (CGMs) remains quite low. The current study aimed to identify profiles ...

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

Context Youth with type 1 diabetes (T1D) do not meet glycated hemoglobin A1c (HbA1c) targets. Objective This work aimed to assess HbA1c outcomes in children with new-onset T1D enrolled in the Teamwork, Targets, Technology and Tight Control (4T) Study. Methods HbA1c levels were compared between the 4T and historical cohorts. HbA1c differences between cohorts were estimated using locally estimated scatter plot smoothing (LOESS). The change from nadir HbA1c (month 4) to 12 months post diagnosis was estimated by cohort using a piecewise mixed-effects regression model accounting for age at diagnosis, sex, ethnicity, and insurance type. We recruited 135 youth with newly diagnosed T1D at Stanford Children’s Health. Starting July 2018, all youth within the first month of T1D diagnosis were offered continuous glucose monitoring (CGM) initiation and remote CGM data review was added in March 2019. The main outcomes measure was HbA1c. Results HbA1c at 6, 9, and 12 months post diagnosis was lower in the 4T cohort than in the historic cohort (–0.54% to –0.52%, and –0.58%, respectively). Within the 4T cohort, HbA1c at 6, 9, and 12 months post diagnosis was lower in those patients with remote monitoring than those without (–0.14%, –0.18% to –0.14%, respectively). Multivariable regression analysis showed that the 4T cohort experienced a significantly lower increase in HbA1c between months 4 and 12 (P Conclusion A technology-enabled, team-based approach to intensified new-onset education involving target setting, CGM initiation, and remote data review statistically significantly decreased HbA1c in youth with T1D 12 months post diagnosis.

Objectives Our aim was to evaluate self-reported depressive symptoms and clinical outcomes during routine screening for adolescents and young adults with type 2 diabetes (T2D), and examine associations among depressive symptoms and demographic and clinical characteristics. Methods The Patient Health Questionnaire (PHQ) was administered to 197 adolescents and young adults with T2D using the PHQ-2 or PHQ-9 in routine pediatric diabetes care at 4 academic medical centres. Data from electronic health records were extracted from the screening date and 12 months earlier. Results Adolescents and young adults with T2D (mean age, 16.85 years; 57% male; 77.2% non-Caucasian) completed the PHQ as part of routine diabetes care. On the PHQ, 19.3% of adolescents and young adults endorsed elevated depressive symptoms (PHQ score ≥10) and, among a subsample with item-level data (n=53), 18.9% endorsed thoughts of self-harm. Subsequently, 50.0% of those with depressive symptoms had a documented referral for mental health treatment in the electronic health record after the positive screening outcome. Older age, shorter diabetes duration, higher glycated hemoglobin level, being non-Hispanic white, more blood glucose checks per day and being prescribed oral medications were significantly associated with more depressive symptoms. Conclusions Screening for depressive symptoms identifies individuals in need of referral for mental health treatment. A focus on self-harm assessment, standardized methods for documentation of symptoms and mental health referrals and increased referral resources are needed.

Purpose Youth with type 1 diabetes (T1D) often use Continuous Glucose Monitoring (CGM) devices; however, many do not wear them consistently enough to obtain optimal glycemic benefit. This study aimed to identify demographic and psychosocial predictors of optimal CGM use in adolescents with T1D to inform nurse-led interventions to improve adherence. Design and methods Cross-sectional survey data from youth (12–19 years) using CGM were analyzed to determine whether perceived benefits/burdens of CGM, self-efficacy, and coping predicted being a “CGM Optimizer” (wearing CGM 6–7 days/week) or “CGM Sub-user.” Results Of 282 adolescents (54% female), 161 were CGM Optimizers and 121 were CGM Sub-Users. Optimizers were younger (15.91 ± 2.17 years vs. 16.79 ± 2.17, p = 0.001), more likely non-Hispanic White (91.9% vs 83.5%, p = 0.029), and more likely to have private insurance (82.0% vs. 69.4%, p = 0.009). Every 1-point increase on Benefits of CGM scale was associated with 2.8 times greater odds of being an Optimizer (OR = 2.82, 95% CI 1.548–5.132, p = 0.001), and every 1-point increase on the Burdens of CGM scale was associated with a 52% decrease in odds (OR = 0.48, 95% CI = 0.283–0.800, p = 0.005), with final logistic regression model (including only these two predictors) explaining 22.3% of variance. Conclusion CGM Optimizing adolescents were more likely to perceive higher benefit and lower burden of CGM. Practical implications Nurse-led interventions to promote benefits of CGM and mitigate burden may help youth increase adherence with CGM to achieve glycemic benefit.

This article reports on the lived experience of Medtronic advanced hybrid closed-loop (AHCL) in comparison to first generation hybrid closed-loop (HCL) in a randomized, open-label, two-period crossover trial. Patient-reported outcome (PROs) measures were administered before randomization and at the end of each study period in 113 adolescents and young adults with type 1 diabetes. Glucose monitoring satisfaction subscales for emotional burden and behavioral burden improved significantly (P

Background: Continuous glucose monitoring (CGM) can improve glycemic control for adults with type 1 diabetes (T1D) but certain barriers interfere with consistent use including cost, data overload, alarm fatigue, physical discomfort, and unwanted social attention. This pilot study aimed to examine feasibility and acceptability of a behavioral intervention, ONBOARD (Overcoming Barriers and Obstacles to Adopting Diabetes Devices) to support adults with T1D in optimizing CGM use. Methods: Adults (18–50 years) with T1D in their first year of CGM use were invited to participate in a tailored, multicomponent telehealth-based intervention delivered over four 60-min sessions every 2–3 weeks. Participants completed surveys (demographics; diabetes distress, Diabetes Distress Scale for adults with type 1 diabetes; satisfaction with program) and provided CGM data at baseline and postintervention (3 months). Data were analyzed using paired t-tests and Wilcoxon signed-rank tests. Results: Twenty-two participants (age = 30.95 ± 8.32 years; 59% women; 91% non-Hispanic; 86% White, 5% Black, 9% other; 73% pump users) completed the study. ONBOARD demonstrated acceptability and a high rate of retention. Moderate effect sizes were found for reductions in diabetes distress (P = 0.01, r = −0.37) and increases in daytime spent in target range (70–180 mg/dL: P = 0.03, r = −0.35). There were no significant increases in hypoglycemia. Conclusions: Findings show preliminary evidence of feasibility, acceptability, and efficacy of ONBOARD for supporting adults with T1D in optimizing CGM use while alleviating diabetes distress. Further research is needed to examine ONBOARD in a larger sample over a longer period.

Objective Resilience processes include modifiable individual and family-based skills and behaviors and are associated with better health and emotional outcomes for youth with Type I diabetes (T1D). There is likely heterogeneity among adolescents with T1D based on differing profiles of resilience processes. At-risk adolescents with lower levels of modifiable skills and assets may benefit more from psychosocial skill-building interventions, compared to adolescents who already have strong resilience processes. This article identified whether there are subgroups of adolescents with T1D based on resilience process profiles and assessed differences in glycemic control, diabetes management behaviors, and distress at baseline. It also evaluated subgroups as moderators of the efficacy of a psychosocial skill-building program. Method Two hundred sixty-four adolescents with T1D (14 to 18 years) were randomly assigned to a resilience-promoting program (N = 133) or diabetes education control (N = 131). Data were collected at seven time points over 3 years and analyzed with latent profile analysis and latent growth curve modeling. Results There were two subgroups with high- versus low-resilience processes. The low-resilience subgroup exhibited more distress, higher HbA1c, less glucose monitoring, and fewer diabetes management behaviors at baseline. Differences persisted over 3 years. Subgroup membership did not moderate the efficacy of a resilience-promoting program compared to control. The resilience program resulted in lower distress regardless of subgroup. Conclusions There is heterogeneity in resilience process profiles, which are associated with clinically meaningful differences in distress, diabetes management, and glycemic control. Findings can be used to identify at-risk teenagers and inform a targeted approach to care. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Background: Loop is an open-source do-it-yourself (DIY) automated insulin delivery system that allows users unrivaled control over physiologic settings that effect future glucose prediction. Over 9,000 individuals have adopted Loop, but little is known about those who discontinue. Methods: Twenty Loop users (14 adults and 6 caregivers; 60% female, 85% white, 75% household income >$100k/year, and HbA1c 6.7±1.0%) participated in semi-structured interviews about their experiences with Loop following discontinuation. Qualitative data were transcribed, coded, and analyzed. Results: Prominent themes reflected the perception that the stress, effort and/or time required to set up Loop and achieve management goals exceeded potential benefits. Two quotes: “I couldn’t spend any more time trying to work on my settings and trying to make it better,” and “I know I could get an improvement with it, but the problem was the cost of it⋯I couldn’t concentrate anymore at work⋯it felt like fog in my brain.” Technical issues with component devices, connectivity issues, lack of technical support, cost (time, energy, supplies), fear of provider disapproval, concerns related to pregnancy, exercise, hypoglycemia and managing low insulin doses, and desire to try other new technologies were among other reasons for ending use. Participants indicated that improved technical assistance and expert guidance might lead to them to use Loop again in the future. Conclusions: For those who manage to set up Loop there are additional challenges, even among individuals with adequate glycemic control and access to various financial, social, and personal resources. Technical support and education specific to setting calculations could expand Loop benefits, alleviate burden, and support sustained use among new Loop users. Disclosure J. J. Wong: None. S. Suttiratana: Employee; Spouse/Partner; CVS Health. M. S. Lanning: None. A. Dunlap: None. S. Hanes: None. K. K. Hood: Consultant; Self; Cecelia Health, Cercacor, LifeScan Diabetes Institute. R. Lal: Consultant; Self; Abbott Diabetes, Biolinq, Capillary Biomedical, Inc., Morgan Stanley, Tidepool. D. Naranjo: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust

Aims: We assessed safety and efficacy of Cambridge hybrid closed-loop (HCL) compared with usual care over 6 months in children and young people with T1D. Methods: In an open-label multicentre multinational parallel randomised controlled trial, we randomly assigned participants on insulin pump therapy aged 6 to 18 years to receive either HCL therapy (CL) or continue usual care (control) for 6 months. We used the same Cambridge model predictive control algorithm in two consecutive hardware iterations, FlorenceM then CamAPS FX. Primary endpoint was central laboratory HbA1c at 6 months. Results: We randomised 133 participants: 65 to CL and 68 to control (baseline HbA1c 8.2±0.7% vs. 8.3±0.8%). At 6 months mean HbA1c was 0.32% lower in CL compared to control (95% CI -0.59 to -0.04; p=0.02). Closed-loop usage was low (40% [26, 53]; median [IQR]) with FlorenceM due to hardware issues, and high (93% [88, 96]) with CamAPS FX. In the CamAPS FX CL group (n=21) HbA1c was 1.05% lower (95% CI -1.43 to -0.67; p Conclusion: Cambridge HCL is safe and significantly improves glycaemic control in children and young people with T1D. Efficacy relies on consistent usage of closed-loop, as demonstrated by CamAPS FX. Disclosure J. Fuchs: None. B. A. Buckingham: Advisory Panel; Self; Medtronic, Tolerion, Inc., Research Support; Self; Beta Bionics, Inc., Insulet Corporation, Medtronic, Tandem Diabetes Care. N. Davis: None. L. Dimeglio: Advisory Panel; Self; MannKind Corporation. N. Mauras: Advisory Panel; Self; Novo Nordisk, Research Support; Self; LifeScan, Medtronic, Novo Nordisk. R. Besser: None. A. Ghatak: None. S. A. Weinzimer: Advisory Panel; Self; Dompe, Zealand Pharma A/S, Speaker’s Bureau; Self; Medtronic. K. K. Hood: Consultant; Self; Cecelia Health, Cercacor, LifeScan Diabetes Institute. D. Fox: None. L. Kanapka: None. C. K. Boughton: None. C. Kollman: Consultant; Self; Diasome Pharmaceuticals, Inc., Research Support; Self; Dexcom, Inc., Tandem Diabetes Care. J. Sibayan: None. R. Beck: Consultant; Self; Bigfoot Biomedical, Inc., Diasome Pharmaceuticals, Inc., Insulet Corporation, Lilly Diabetes, vTv Therapeutics, Research Support; Self; Beta Bionics, Inc., Dexcom, Inc., Medtronic, Novo Nordisk, Tandem Diabetes Care. R. Hovorka: Research Support; Self; Abbott Laboratories, Dexcom, Inc., Medtronic, Speaker’s Bureau; Self; Dexcom, Inc., Eli Lilly and Company, Stock/Shareholder; Self; CamDiab Ltd. Dan05 consortium: n/a. J. M. Allen: None. M. E. Wilinska: Consultant; Self; CamDiab Ltd. M. Tauschmann: None. L. Denvir: None. A. Thankamony: None. F. Campbell: n/a. R. Wadwa: Consultant; Self; Tandem Diabetes Care, Research Support; Self; Dexcom, Inc., Eli Lilly and Company, Tandem Diabetes Care. Funding National Institute of Diabetes and Digestive and Kidney Diseases (UC4DK108520)

CGM use is associated with improvements in A1c. We hypothesized that initiation of CGM in the 1st month after T1D diagnosis would improve A1c. Youth with newly diagnosed T1D from July 2018 to May 2020 (pilot cohort, n=122) were offered CGM initiation in the 1st month after T1D diagnosis (119 started CGM). We compared A1c outcomes in the pilot cohort with those diagnosed from 2014-2016 (controls, n=272) who were not offered early CGM. We visualized A1c trajectories using locally estimated scatter plot smoothing (Fig). We and assessed for differences in A1c trajectory by cohort via interaction terms in a linear mixed model adjusted for baseline characteristics (age, sex, race and insurance type). The mean A1c at diagnosis was higher in the pilot cohort (12.2 vs. 10.7%). The median age of diagnosis was 9.5 years [6.8, 13.3], 64% male, 38% non-Hispanic White, and 76% with private insurance in the pilot cohort. In this pilot, 89% initiated CGM in the first 30 days after diagnosis compared to 2% in the control cohort. After adjusting for baseline characteristics, the mean A1c of the pilot cohort was lower at 6 months (-0.81, p = 0.019), 9 months (-1.43, p = 0.013), and 12 months (-2.05, p = 0.012) post-diagnosis compared to the historic cohort. Early initiation of CGM was associated with a lower A1c compared to those in a historic cohort. These data support early initiation of CGM as standard of care in youth with T1D. Disclosure P. Prahalad: None. J. Senaldi: None. A. Freeman: None. A. Addala: None. D. P. Zaharieva: None. K. K. Hood: Consultant; Self; Cecelia Health, Cercacor, LifeScan Diabetes Institute. D. Scheinker: Advisory Panel; Self; Carta Healthcare. M. Desai: None. D. M. Maahs: Advisory Panel; Self; Abbott Diabetes, Dompe, Eli Lilly and Company, Medtronic, Novo Nordisk, Consultant; Self; aditxt. V. Ding: None. B. Leverenz: None. J. Hooper: None. A. Chmielewski: None. B. P. Conrad: Advisory Panel; Self; Abbott Diabetes. J. Leverenz: None. A. Martinez-singh: None. P. Sagan: None. Funding National Institutes of Health (R18DK122422, P30DK116074)

Previous studies have shown that CGM use and frequent contact with the care team is associated with improved A1c. We hypothesized that youth with new onset T1D started on CGM with weekly CGM data review by a CDE would have improved outcomes compared to those on CGM alone. Youth with newly diagnosed T1D from 7/2018 to 3/2019 were started on CGM alone (control, n=46) while those from 3/2019 to 5/2020 were started on CGM with weekly data review by a CDE (GluVue, n=76). Dose adjustments were made via secure messaging. We visualized A1c trajectories using locally estimated scatter plot smoothing (Fig) and assessed for differences in A1c trajectory by GluVue status via cohort*time and cohort*time2 interaction terms in a linear mixed model regression adjusted for baseline characteristics (age, sex, race and insurance type). The mean (SD) A1c at diagnosis was 12.0% (1.8) in the control group and 12.3% (2.3) in the GluVue group. Baseline characteristics of control vs. GluVue: male 25% vs. 39%, Non-Hispanic White 41.3% vs. 35.5%, private insurance 80.4% vs. 73.7%. Mean A1c of the GluVue cohort was lower at 6 months (-0.45%), 9 months (-0.07%), and 12 months (-0.52%) post-diagnosis. A1c trajectories were significantly different between groups via regression analysis (p=0.004). Early initiation of CGM with remote monitoring was associated with lower A1c compared to CGM alone. These data support early CGM initiation plus care team contact in youth with T1D. Disclosure P. Prahalad: None. K. K. Hood: Consultant; Self; Cecelia Health, Cercacor, LifeScan Diabetes Institute. D. Scheinker: Advisory Panel; Self; Carta Healthcare. M. Desai: None. D. M. Maahs: Advisory Panel; Self; Abbott Diabetes, Dompe, Eli Lilly and Company, Medtronic, Novo Nordisk, Consultant; Self; aditxt. V. Ding: None. B. Leverenz: None. J. Hooper: None. J. Leverenz: None. P. Sagan: None. A. Martinez-singh: None. A. Freeman: None. D. P. Zaharieva: None. Funding National Institutes of Health (P30DK116074, R18DK122422)

Type 1 diabetes (T1D) is a lifelong disease that requires constant monitoring. New technologies seeking to relieve this burden must be user-friendly and provide treatment satisfaction to enable widespread adoption, glycemic benefit, and long-term success. We assessed these parameters for the Omnipod 5 Automated Insulin Delivery System during a 3-month outpatient study of the system. Participants (N=240) with T1D aged 6-70y completed validated assessment tools at study start and end, including the Insulin Device Satisfaction Survey (IDSS), Diabetes Treatment Satisfaction Questionnaire (DTSQ), and System Usability Scale (SUS). Parents/guardians of participants aged 6-11.9y (children, N=83) and 12-17.9y (teens, N=42) were assessed as appropriate. Participants were aged (mean±SD) 25±17y, with T1D duration 12±11y and baseline A1C 7.4±0.9%. Perceived usability and satisfaction with the treatment regimen and device were significantly improved with the study system compared to prior therapy (Table). Improvements were also seen in the IDSS subscales for burden and inconvenience (both p Disclosure S. A. Macleish: Advisory Panel; Self; Insulet Corporation. W. H. Polonsky: Advisory Panel; Self; Intarcia Therapeutics, Inc., Consultant; Self; Abbott Diabetes, ADOCIA, Boehringer Ingelheim Pharmaceuticals, Inc., Dexcom, Inc., Insulet Corporation, Lilly Diabetes, Novo Nordisk, Onduo LLC., Sanofi US. K. K. Hood: Consultant; Self; Cecelia Health, Cercacor, LifeScan Diabetes Institute. C. J. Levy: Advisory Panel; Self; Dexcom, Inc., Eli Lilly and Company, Employee; Spouse/Partner; AbbVie Inc., Other Relationship; Self; Dexcom, Inc., Research Support; Self; Abbott Diabetes, Dexcom, Inc., Insulet Corporation, Tandem Diabetes Care. L. M. Huyett: Employee; Self; Insulet Corporation, Stock/Shareholder; Self; Insulet Corporation. T. Vienneau: Employee; Self; Insulet Corporation, Stock/Shareholder; Self; Insulet Corporation. T. T. Ly: Employee; Self; Insulet Corporation. Funding Insulet Corporation

There are pervasive disparities in health outcomes for people living with diabetes based on socioeconomic status, race, and ethnicity. These longstanding disparities are magnified by the devastating impact of COVID-19 for underserved communities with diabetes. As part of a Project Extension for Community Healthcare Outcomes (ECHO™) Diabetes outreach program in Florida and California, over 15 participating Federally Qualified Health Centers (FQHC) were provided Diabetes Support Coaches (DSC) to facilitate patient engagement. DSCs hold invaluable expert knowledge as they live with diabetes, themselves, and reside in the geographic catchment areas they serve - combining a traditional Community Health Worker (CHW) role with peer support models. DSCs complete standardized training in health coaching and receive Diabetes Paraprofessional Level 1 certification through the American Association of Diabetes Care and Education Specialists (ADCES). DSCs offer one-on-one peer support for interested patients, create local resource guides for diabetes management, host community events, disseminate information about technologies like continuous glucose monitors (CGM), and assist with appointment reminders and other engagement activities. Patient encounters with DSCs are documented in REDCap® and pre/post-test evaluation for patients includes HbA1c, the Diabetes Distress Scale, use of diabetes technology, and social support density. Preliminary data from n=19 adults with T1D and T2D (9 Hispanic, 10 Non-Hispanic Black) participating with DSCs at two FQHCs in Miami show statistically significant improvement in HbA1c mean ± SD: -1.21 ± 1.49, 95% CI (-1.93, -0.49) p Disclosure A. F. Walker: None. S. L. Filipp: None. N. Cuttriss: Consultant; Self; Cecelia Health. L. G. Baer: None. L. Figg: None. K. K. Hood: Consultant; Self; Cecelia Health, Cercacor, LifeScan Diabetes Institute. R. Lal: Consultant; Self; Abbott Diabetes, Biolinq, Capillary Biomedical, Inc., Morgan Stanley, Tidepool. M. Basina: None. J. J. Wong: None. K. Yabut: None. D. M. Maahs: Advisory Panel; Self; Abbott Diabetes, Dompe, Eli Lilly and Company, Medtronic, Novo Nordisk, Consultant; Self; aditxt. M. J. Haller: Board Member; Self; SAB Biotherapeutics, Inc., Consultant; Self; MannKind Corporation, Other Relationship; Self; Janssen Pharmaceuticals, Inc., Sanofi. E. Sheehan: None. S. C. Westen: None. C. Anez-zabala: None. X. Roque: None. A. Bernier: None. L. Bain: None. M. J. Gurka: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust

Background: Access to psychosocial services is limited, especially during a pandemic. A Zoom-delivered family-based intervention for adolescents with type 1 diabetes has the potential to enhance access to psychosocial services. Methods: Nine families enrolled in a pilot of a 6-session online (Zoom) intervention for adolescents with type 1 diabetes and their parents during the COVID-19 pandemic. Qualitative and quantitative data were collected via online focus groups and surveys. Results: Teens’ ages ranged from 12-17 years (M=14.9, SD=1.76) and most self-identified as male (78%). All parents self-identified as female and most families self-identified as non-Hispanic white (78%). Qualitative results revealed that participants found the program to be generally helpful and would recommend the program to others. Perceived benefits included increased parental empathy toward teens, enhanced parental involvement in teens’ diabetes management, and decreased parent-teen conflict. Participants conveyed that these benefits were particularly impactful given increased family contact and tensions related to the COVID-19 pandemic. Identified areas for improvement included adding follow-up group sessions with parents to maintain program impact on parenting over time and to target teens in early adolescence, when family dynamics are more malleable. Quantitative results from paired t-tests of pre-post comparisons revealed no significant differences in teen or parental diabetes distress or depression (all p>0.05), but should be interpreted with caution due to small sample size. Conclusions: Adolescents with type 1 diabetes and their parents responded positively to this program and found it particularly relevant during the current pandemic. Zoom delivery of these programs may be instrumental in their feasibility, acceptability, and ultimately dissemination in the future. Disclosure J. J. Wong: None. M. S. Lanning: None. J. Ngo: None. C. A. Wu: None. S. Hanes: None. H. M. Linzmeyer: None. D. Naranjo: None. K. K. Hood: Consultant; Self; Cecelia Health, Cercacor, LifeScan Diabetes Institute. Funding National Institute of Diabetes and Digestive and Kidney Diseases (1K23DK121771-01A1)

The Project Extension for Community Healthcare Outcomes (ECHO™) model addresses disparities by empowering primary care provider (PCPs), through tele-education, to meet the needs of underserved communities lacking access to subspecialty care. Given the rapid replication of this model, it is vital to understand the perspectives of participating PCPs to refine program implementation. Project ECHO™ Type 1 Diabetes (T1D) program was piloted in Florida and California with >15 Federally Qualified Health Centers (FQHC). PCPs completed pre/post-test surveys assessing knowledge and confidence in T1D care. Exit surveys and focus groups were conducted to understand their experiences (N=92 providers, 41 from FL and 51 from CA were queried). Qualitative analysis of open-ended survey feedback and thematic analysis of focus group data used the Consolidated Framework for Implementation Research (CFIR). The CFIR identifies five domains that affect intervention implementation - including internal and external factors to the healthcare organization. Pre/post surveys demonstrated significant improvements in providers’ diabetes knowledge; mean ± SD change in score for an 11-item knowledge evaluation from pre/post: (1.0 ± 1.8, [Min: -2, Max: +5], p-value: 0.0003) and increase in overall confidence evaluated on a 4-point likert scale (0.6 ± 0.4 [Min: -0.1, Max: 1.6] p-value Disclosure A. F. Walker: None. S. L. Filipp: None. K. Turner: None. G. L. Zori: None. J. Maizel: None. N. Cuttriss: Consultant; Self; Cecelia Health. L. G. Baer: None. L. Figg: None. K. K. Hood: Consultant; Self; Cecelia Health, Cercacor, LifeScan Diabetes Institute. R. Lal: Consultant; Self; Abbott Diabetes, Biolinq, Capillary Biomedical, Inc., Morgan Stanley, Tidepool. M. Basina: None. M. J. Haller: Board Member; Self; SAB Biotherapeutics, Inc., Consultant; Self; MannKind Corporation, Other Relationship; Self; Janssen Pharmaceuticals, Inc., Sanofi. J. J. Wong: None. K. Yabut: None. D. Naranjo: None. E. Lewit: None. D. M. Maahs: Advisory Panel; Self; Abbott Diabetes, Dompe, Eli Lilly and Company, Medtronic, Novo Nordisk, Consultant; Self; aditxt. E. Sheehan: None. S. C. Westen: None. C. Anez-zabala: None. X. Roque: None. B. C. Fitzgerald: None. A. Bernier: None. M. J. Gurka: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust

Background: Despite observational evidence for the efficacy and safety of open-source automated insulin delivery (OS-AID) there is a lack of guidance for healthcare providers. The ethics of providing care for those using unregulated technology is a concern. Methods: Consensus guidelines are in development, through the OPEN project, by an experienced multi-disciplinary group of endocrinologists, diabetes care and education specialists, exercise physiologists and psychologists. Results: The principles of biomedical ethics - autonomy, justice, beneficence and non-maleficence - provide a framework for analysis. Autonomy requires that the user understands system risks and benefits. OS-AID provides complete transparency and customization, facilitating understanding and control. Algorithms used in commercial systems are proprietary, impeding the ability to discuss and personalize operation. Justice requires burdens and benefits be distributed fairly. Commercial systems are not uniformly accessible. OS-AID provides freely available software and allows the mixing and matching of compatible CGMs and pumps, supporting equity. Beneficence means doing good for the individual. In real-world observational trials both open-source (OpenAPS: n=34, mean TIR 71->80% at max 2.5 years; Loop: n=558, mean TIR 67->73% at 6 months) and commercial AID systems (670G: n=3141, mean TIR 66->73% at 3 months; Control-IQ: n=1659, median TIR: 68->78% at 1 month) provide a statistically significant and clinically meaningful increase in TIR at the population level. Non-maleficence requires treatment does not harm the individual. AID systems can reduce burden, hypoglycemia and hyperglycemia. Conclusions: The international consensus group concludes OS-AID is ethical, but unregulated. We recommend legal interpretations and frameworks be clarified and updated so that individuals are not deprived of beneficial therapy. Commercial AID systems may benefit from transparency and equity offered by OS-AID systems. Disclosure K. Braune: None. M. Riddell: Advisory Panel; Self; Zealand Pharma A/S, Consultant; Self; Lilly Diabetes, Research Support; Self; Dexcom, Inc., Insulet Corporation, Speaker’s Bureau; Self; Novo Nordisk Inc., Sanofi, Stock/Shareholder; Self; Zucara Therapeutics Inc. T. C. Skinner: Advisory Panel; Self; Liva Health Care, Novo Nordisk. K. Raile: Advisory Panel; Self; Abbott Diabetes, Lilly Diabetes, Research Support; Self; Dexcom, Inc. C. Johnston: None. R. Lal: Consultant; Self; Abbott Diabetes, Biolinq, Capillary Biomedical, Inc., Morgan Stanley, Tidepool. S. Hussain: Speaker’s Bureau; Self; Novo Nordisk. M. Quigley: None. L. Petruzelkova: None. G. Scheiner: Advisory Panel; Self; Capillary Biomedical, Inc., Companion Medical, Eli Lilly and Company, Consultant; Self; ADOCIA, Ascensia Diabetes Care, mySugr, Triple Jump, Ypsomed, Speaker’s Bureau; Self; Dexcom, Inc., Xeris Pharmaceuticals, Inc. P. Winterdijk: None. S. Schmidt: None. L. H. Raimond: None. K. K. Hood: Consultant; Self; Cecelia Health, Cercacor, LifeScan Diabetes Institute. Funding National Institute of Diabetes and Digestive and Kidney Diseases (T32DK007217, 1K12DK122550, 1K23DK122017, P30DK116074); European Commission’s Horizon 2020 Research and Innovation Programme/Marie Sklodowska-Curie Action Research and Innovation Staff Exchange (RISE) grant agreement number 823902

AIMS To explore the auxiliary psychosocial effects of a monetary reinforcement intervention targeting self-monitoring of blood glucose among young people with Type 1 diabetes. METHODS Sixty young people with Type 1 diabetes, HbA1c concentrations between 58 and 119 mmol/mol (7.5-13.0%), and average self-monitoring of blood glucose

OBJECTIVE In September 2016, the U.S. Food and Drug Administration approved the Medtronic 670G “hybrid” closed-loop system. In Auto Mode, this system automatically controls basal insulin delivery based on continuous glucose monitoring data but requires users to enter carbohydrates and blood glucose for boluses. To track real-world experience with this first commercial closed-loop device, we prospectively followed pediatric and adult patients starting the 670G system. RESEARCH DESIGN AND METHODS This was a 1-year prospective observational study of patients with type 1 diabetes starting the 670G system between May 2017 and May 2018 in clinic. RESULTS Of the total of 84 patients who received 670G and consented, 5 never returned for follow-up, with 79 (aged 9–61 years) providing data at 1 week and 3, 6, 9, and/or 12 months after Auto Mode initiation. For the 86% (68 out of 79) with 1-week data, 99% (67 out of 68) successfully started. By 3 months, at least 28% (22 out of 79) had stopped using Auto Mode; at 6 months, 34% (27 out of 79); at 9 months, 35% (28 out of 79); and by 12 months, 33% (26 out of 79). The primary reason for continuing Auto Mode was desire for increased time in range. Reasons for discontinuation included sensor issues in 62% (16 out of 26), problems obtaining supplies in 12% (3 out of 26), hypoglycemia fear in 12% (3 out of 26), multiple daily injection preference in 8% (2 out of 26), and sports in 8% (2 out of 26). At all visits, there was a significant correlation between hemoglobin A1c (HbA1c) and Auto Mode utilization. CONCLUSIONS While Auto Mode utilization correlates with improved glycemic control, a focus on usability and human factors is necessary to ensure use of Auto Mode. Alarms and sensor calibration are a major patient concern, which future technology should alleviate.

Recent, rapid changes in the treatment of type 1 diabetes have allowed for commercialization of an “artificial pancreas” that is better described as a closed-loop controller of insulin delivery. This review presents the current state of closed-loop control systems and expected future developments with a discussion of the human factor issues in allowing automation of glucose control. The goal of these systems is to minimize or prevent both short-term and long-term complications from diabetes and to decrease the daily burden of managing diabetes. The closed-loop systems are generally very effective and safe at night, have allowed for improved sleep, and have decreased the burden of diabetes management overnight. However, there are still significant barriers to achieving excellent daytime glucose control while simultaneously decreasing the burden of daytime diabetes management. These systems use a subcutaneous continuous glucose sensor, an algorithm that accounts for the current glucose and rate of change of the glucose, and the amount of insulin that has already been delivered to safely deliver insulin to control hyperglycemia, while minimizing the risk of hypoglycemia. The future challenge will be to allow for full closed-loop control with minimal burden on the patient during the day, alleviating meal announcements, carbohydrate counting, alerts, and maintenance. The human factors involved with interfacing with a closed-loop system and allowing the system to take control of diabetes management are significant. It is important to find a balance between enthusiasm and realistic expectations and experiences with the closed-loop system.

Parents of young children with type 1 diabetes (T1D) experience unique, developmental challenges in managing their child's T1D, resulting in psychosocial distress. Only a small portion of young children reach glucose goals and adherence to diabetes devices that help improve T1D management have historically been low in this population. The purpose of this study is to test four interventions that couple developmentally tailored behavioral supports with education to optimize use of diabetes devices, improve glucose control, and reduce psychosocial distress for parents of young children with T1D. The study team designed four behavioral interventions, two aimed at improving glucose control and two aimed at optimizing use of diabetes devices. The goal of this paper is to describe the behavioral interventions developed for this study, including the results of a pilot test, and describe the methods and analysis plan to test this intervention strategy with ninety participants in a large-scale, randomized trial using a sequential multiple assignment randomization trial (SMART) design. A SMART design will permit a clinically relevant evaluation of the intervention strategy, as it allows multiple randomizations based on individualized assessments throughout the study instead of a fixed intervention dose seen in most traditional randomized controlled trials.

Aims The current study sought to identify patient-level factors related to health care use among White and minority men and women with diabetes. Methods A sample of 447 of non-pregnant individuals with diabetes, ages 18–64, was drawn from the 2015–2016 National Health and Nutrition Examination Surveys dataset. Poisson regression models tested associations between health care use and self-rated health, depression, medical comorbidities, body mass index, marital status, number of children, income, insurance coverage, and age, stratified by gender and racial/ethnic minority status. Results Poorer self-rated health was the only significant correlate of increased health care use among White men with diabetes whereas income and insurance were significant correlates of increased use among minority men. Among White and minority women, higher levels of depression and being single were correlated with greater health care use. Comorbid medical conditions and insurance coverage were also related to use among minority women. Conclusions Among individuals with diabetes, health care use among White men appeared to be driven by subjective health whereas financial factors were critical among minority men. Family structure and mental health were instrumentally associated with health care use among all women. These factors can be targeted to promote equitable access to care.

Background:Continuous glucose monitors (CGM) are underutilized by individuals with type 1 diabetes (T1D), particularly during the adolescent years. Little is known about perceptions of CGM benefit and burdens, and few tools exist to quantify this information.Methods:Two questionnaires were developed and validated—Benefit of CGM (BenCGM) and Burdens of CGM (BurCGM)—in a sample of adolescents ages 12-19 years involved in the T1D Exchange Registry. We chose to start the validation process with adolescents given their low CGM uptake and high risk for suboptimal glycemic outcomes. Exploratory and confirmatory factor analyses were conducted to confirm factor structure and select items. The resultant scales were tested for internal reliability and convergent/divergent validity with critical diabetes and quality of life outcomes: age, depression, diabetes distress, self-efficacy, technology attitudes, and diabetes technology attitudes.Results:A total of 431 adolescents with T1D completed the questionnaires (51% female, mean age 16.3 ± 2.26, 83% white non-Hispanic, 70% having used CGM). Two single factor scales emerged, and scales were reduced to 8 items each. Those who perceived higher benefit of CGM exhibited lower diabetes distress, higher self-efficacy, and more positive attitudes toward technology. Those who perceived higher burden of CGM exhibited higher diabetes distress, lower self-efficacy, and less positive technology attitudes.Conclusion:The BenCGM and BurCGM questionnaires each comprise 8-items that demonstrate robust psychometric properties for use in adolescents with T1D, and can be used to develop targeted interventions to increase CGM wear to improve diabetes management.

The landmark Diabetes Control and Complications Trial (DCCT) identified the relationship between intensive diabetes management for those with type 1 diabetes (T1D) and a delay in the onset or progression of microvascular complications, shifting the paradigm of clinical care for this chronic medical condition (1). Importantly, the data from that trial continue to have a lasting legacy as they can now be analyzed to inform present-day questions. An example of this comes from the article by Beck et al. in this issue of Diabetes Care (2) that concisely states the primary implication of the article in its title, “Validation of Time in Range as an Outcome Measure for Diabetes Clinical Trials.” In recent years, and with increasing use of continuous glucose monitoring (CGM), the ability to classify sensor glucose levels into various ranges has been feasible. Yet, some have questioned the validity of using these measures as a surrogate outcome measure in ongoing and future clinical trials. Indeed, some have called for a “modern-day” DCCT assessing whether CGM and a greater time in range (TIR) would result in fewer microvascular end points compared with self-monitoring of blood glucose (with intermittent masked CGM). Unfortunately, the duration of time, funds required, and ethical concerns given clinical equipoise regarding the benefits of the technology would make completion of a rigorous randomized controlled trial a near-impossible feat. Recognizing the wealth of data that the DCCT affords, Beck et al. (2) found a creative solution to assess the hypothesis of whether TIR could serve as an outcome measure for future trials. Specifically, they capitalized on the glucose values captured seven times daily every 3 months during the course of the 6+ years of the DCCT. This allowed them to calculate the percent TIR of glucose values between 70 and 180 mg/dL and show a robust …

Aim This study aimed to capture the experience of parents of youth with recent onset Type 1 diabetes who initiated use of continuous glucose monitoring (CGM) technology soon after diagnosis, which is a new practice. Methods Focus groups and individual interviews were conducted with parents of youth with Type 1 diabetes who had early initiation of CGM as part of a new clinical protocol. Interviewers used a semi-structured interview guide to elicit feedback and experiences with starting CGM within 30 days of diagnosis, and the benefits and barriers they experienced when adjusting to this technology. Groups and interviews were audio recorded, transcribed and analysed using content analysis. Results Participants were 16 parents (age 44.13 ± 8.43 years; 75% female; 56.25% non-Hispanic White) of youth (age 12.38 ± 4.15 years; 50% female; 50% non-Hispanic White; diabetes duration 10.35 ± 3.89 months) who initiated CGM 11.31 ± 7.33 days after diabetes diagnosis. Overall, parents reported high levels of satisfaction with starting CGM within a month of diagnosis and described a high level of reliance on the technology to help manage their child's diabetes. All participants recommended early CGM initiation for future families and were committed to continue using the technology for the foreseeable future, provided that insurance covered it. Conclusion Parents experienced CGM initiation shortly after their child's Type 1 diabetes diagnosis as a highly beneficial and essential part of adjusting to living with diabetes.

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 sub-themes 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.

Each January, the editors of Diabetes Care look back at the last year and forward to the next. In January 2021 we have much to be thankful and happy about. The journal continues to publish outstanding scientific reports together with illuminating and provocative Commentary, Perspective, and Review articles. We are indebted to the authors who submit their manuscripts, the reviewers who evaluate them, and the editorial and production group that manages the process. They make it all possible. In 2020, Diabetes Care ’s impact factor increased once again, from 15.27 to 16.02. Accumulating scientific evidence presented by our journal and others continues to improve understanding of the pathophysiology of diabetes and add to the array of treatments. And yet . . . it’s been a hell of a year in other ways. The coronavirus disease 2019 (COVID-19) pandemic started a year ago and still has the world in its grip. It has tested all of us and brought many activities nearly to a halt. Countless people have fallen ill, sadly many have died, and the daily routines of most families are disturbed. The lockdown to prevent spread of the virus keeps people at home, limits travel, harms businesses, closes schools, and interferes with diagnosis and treatment of other ailments. Acute medical facilities have been overwhelmed in some regions. Fierce debates about controlling the spread of COVID-19 and mitigating its human and economic costs have ensued. Yet, in this crisis we see much heroism. Medical personnel and those who support …

Background: Closed-loop insulin delivery systems have the potential to address the issue of suboptimal glucose control in children and adolescents with type 1 diabetes. We compared safety and efficacy of Cambridge hybrid closed-loop algorithm with usual care over six months in this population. Methods: In a multicentre, multinational, one-period, parallel randomised controlled trial, participants aged 6-18 years using insulin pump therapy were recruited at seven UK and five US paediatric diabetes centres. We randomly assigned participants to closed-loop insulin delivery or to usual care (control) for six months. The Cambridge closed-loop algorithm running on a smartphone was used with either (i) modified Medtronic 640G pump, Medtronic Guardian 3 sensor, and Medtronic prototype phone enclosure (FlorenceM configuration) or (ii) Sooil Dana RS pump and Dexcom G6 sensor (CamAPS FX configuration). Primary endpoint was change in HbA1c at six months combing data from both configurations. Analysis was by intention-to-treat. Trial registration ClinicalTrials.gov, NCT02925299. Findings: We randomised 133 participants (mean±SD baseline HbA1c 8·2±0·7% vs 8·3±0·7%), 65 to closed-loop and 68 to control. At six months, HbA1c was 0·32% lower with closed-loop compared to control (95%CI 0·04 to 0·59; p=0·02). Closed-loop usage was low with FlorenceM due to failing phone enclosures (40% [26, 53]; median [IQR]), but consistently high with CamAPS FX (93% [88, 96]). In post-hoc analysis, HbA1c in the CamAPS FX closed-loop cohort (n=21) was 1·05% lower (95%CI 0·67 to 1·43; p

The purpose of this study was to determine clinician attitudes about the distinct barriers to uptake of continuous glucose monitoring (CGM) among people with diabetes. Survey data were collected measuring individual barriers, prerequisites to CGM, confidence in addressing barriers, and clinic staff resources. Results show that clinicians commonly report barriers to using CGM among people with diabetes in their clinic. Furthermore, clinicians who report a high number of barriers do not feel confident in overcoming the barriers to CGM. Interventions that attempt to empower clinicians to address concerns about CGM among people with diabetes may be warranted because low uptake does not appear to be directly related to available resources or prerequisites to starting CGM.

Objectives In this study, we examined the indirect effects of anxiety on glycated hemoglobin (A1C) via automatic negative thinking and diabetes distress among adolescents with type 1 diabetes (T1D) during the follow-up interval of a randomized controlled trial of an intervention targeting resilience promotion/depression prevention. Methods Adolescents (N=264) participating in the Supporting Teen Problem Solving clinical trial were included and assessed at 8, 12, 16 and 28 months postbaseline. A serial, double-mediation model was used to examine path effects from anxiety to A1C through automatic negative thinking, through diabetes distress and through both automatic negative thinking and diabetes distress. Relevant demographic and clinical covariates were included. Results Anxiety significantly predicted increases in both automatic negative thinking and diabetes distress. Automatic negative thinking was not found to mediate the association between anxiety and A1C, but diabetes distress did mediate the association. The double-mediation path through automatic negative thinking and diabetes distress together was significant. The indirect effect of anxiety on A1C through diabetes distress was significant and greater than the indirect effect of the double-mediator path. Anxiety did not predict A1C independent of its effects on automatic negative thinking and diabetes distress. Inclusion of demographic covariates did not substantively change the results. Conclusions Analyses suggest that automatic negative thinking and diabetes distress mediate the relationship between anxiety and A1C among adolescents with T1D. Diabetes distress appears to be a robust factor linking anxiety to A1C. Diabetes distress should be further examined as a mediator of glycemic variability in anxious youth with T1D.

Many youth with type 1 diabetes (T1D) do not achieve hemoglobin A1c (HbA1c) targets. The mean HbA1c of youth in the USA is higher than much of the developed world. Mean HbA1c in other nations has been successfully modified following benchmarking and quality improvement methods. In this review, we describe the novel 4T approach—teamwork, targets, technology, and tight control—to diabetes management in youth with new-onset T1D. In this program, the diabetes care team (physicians, nurse practitioners, certified diabetes educators, dieticians, social workers, psychologists, and exercise physiologists) work closely to deliver diabetes education from diagnosis. Part of the education curriculum involves early integration of technology, specifically continuous glucose monitoring (CGM), and developing a curriculum around using the CGM to maintain tight control and optimize quality of life.

Aims Psychosocial screenings are recommended and increasingly common in pediatric subspecialty clinics, though little is known about their acceptability. This study seeks to uncover profiles of acceptability and assess demographic and clinical correlates among adolescents with diabetes. Methods A sample of 124 adolescents (57.7% female) ages 12–21 years (M = 16.2 ± 2.3) completed screenings during routine diabetes appointments. K-means clustering of responses to acceptability items derived profiles; Analysis of Variance (ANOVA) and Chi-square tests assessed correlates. Results Adolescents with the most common profile (72.6%) placed high importance on medical providers’ awareness of their emotions and reported no difficulties/discomfort with the screener. These youth had moderate depressive symptoms, low diabetes distress, and low A1c. Those who fit a less common profile (18.5%) were uncomfortable with the screener and had the highest depressive symptoms and lowest A1c. Youth who fit a smaller profile (6.5%) endorsed technical difficulties and had high depressive symptoms and lowest diabetes distress. The smallest profile (2.4%, N = 3) had difficulty understanding and experienced discomfort with the screening and had the lowest depressive symptoms and the highest diabetes distress and A1c. These differences in depressive symptoms (F = 3.54, p = .017), A1c values (F = 4.03, p = .009), and diabetes distress (F = 3.27, p = .036) were significant though differences in age, gender, and diabetes duration were not. Conclusions Most youth responded favorably to in-clinic psychosocial screenings. Youth who were less satisfied were at increased risk for psychosocial and medical complications. Findings highlight areas of need, such as enhanced support with and an emphasized rationale for screenings, which may improve patient experience in subspecialty care.

Optimal glycemic control is associated with improved T1D outcomes. However, in the U.S. over the last decade, glycemic control has worsened. We aimed to monitor glucose time-in-range (TIR) in the first year after diagnosis in pediatric T1D to identify the time when glucose control worsens. Newly diagnosed patients (N=67) with T1D were commenced on continuous glucose monitoring (CGM). Data was uploaded to Dexcom Clarity and analyzed retrospectively. Sample size differences at each time period vary due to patient enrollment start dates. All data was analyzed by mixed model and Tukey’s multiple comparison test. In 67 children (mean age 10±5 yrs, A1c at onset 12.1±1.9%) glucose TIR decreased from 64±21% to 55±18% at 10±9 days to 361±23 days post-diagnosis (P=0.03, Table). Mean glucose increased from 146±28 mg/dL at nadir to 172±29 mg/dL at 361±23 days post-diagnosis (P=0.001). CGM wear time also increased from 83±20% to 95±9% at 10±9 days to 361±23 days post-diagnosis (P=0.04). Time spent Disclosure D. Zaharieva: Speaker’s Bureau; Self; Ascensia Diabetes Care, Insulet Corporation, Medtronic. P. Prahalad: None. A. Addala: None. D. Scheinker: None. M. Desai: None. K.K. Hood: Research Support; Self; Dexcom, Inc. Speaker’s Bureau; Self; LifeScan, Inc., MedIQ. B. Leverenz: None. D.M. Maahs: Advisory Panel; Self; Eli Lilly and Company, Insulet Corporation, Medtronic, Novo Nordisk A/S. Consultant; Self; Abbott, Sanofi. Research Support; Self; Bigfoot Biomedical, Dexcom, Inc., Roche Diabetes Care, Tandem Diabetes Care.

Disparities in the use of CGMs and insulin pumps in T1D based on socioeconomic status (SES) and race/ethnicity are pronounced. However, systematic reports of perspectives from patients in vulnerable communities are limited. To better understand barriers, focus groups were conducted in Florida and California with adults with T1D. Selection criteria included hospitalization for DKA, HbA1C >9%, or receiving care at a Federally Qualified Health Center. A total of 16 focus groups were conducted in English or Spanish with 86 adults (Table 1). Transcript themes and pre-focus group demographic survey data were analyzed. In order of frequency, barriers to diabetes technology included: (1) provider-level (overtly blocked/discouraged by provider) (2) system-level (financial barriers related to coverage) and (3) individual-level (difficulty with use or preference issues). In Florida there was less technology use overall (38% had used CGMs in FL, 63% in CA; 43% had used pumps in FL, 69% in CA) and significant differences in pump use by SES (P=0.02 in FL, P=0.08 in CA) and race/ethnicity (P=0.01 in FL, P=0.80 in CA). In California there were significant differences in CGM use by race/ethnicity (P=0.05 in CA, P=0.56 in FL) and education level (P=0.02 in CA, P=0.90 in FL). These findings provide novel insights and demonstrate the need for multi-level interventions and further research on variations in technology use based on geographic location. Disclosure A.F. Walker: None. N. Cuttriss: None. M.J. Haller: Advisory Panel; Self; SAB Biotherapeutics, Inc. S.L. Filipp: None. K. Yabut: None. C. Anez-Zabala: None. K.K. Hood: Research Support; Self; Dexcom, Inc. Speaker’s Bureau; Self; LifeScan, Inc., MedIQ. X. Roque: None. D. Naranjo: None. G. Aulisio: None. A. Addala: None. S. Look: None. M.J. Gurka: None. D.M. Maahs: Advisory Panel; Self; Eli Lilly and Company, Insulet Corporation, Medtronic, Novo Nordisk A/S. Consultant; Self; Abbott, Sanofi. Research Support; Self; Bigfoot Biomedical, Dexcom, Inc., Roche Diabetes Care, Tandem Diabetes Care. Funding The Leona M. and Harry B. Helmsley Charitable Trust

Project ECHOTM (Extension for Community Healthcare Outcomes) is a hub-spoke telehealth outreach model that democratizes specialty knowledge to reduce disparities and improve health outcomes. Limited access to endocrinologists forces many primary care providers (PCPs) to manage patients with type 1 diabetes (T1D) without specialty support. As such, Stanford and the University of Florida partnered to develop and pilot “ECHO T1D”, a teleECHOTM clinic focused on T1D in California (CA) and Florida (FL), respectively. Our goal was to demonstrate the feasibility of ECHO T1D and improve PCPs’ abilities to manage patients with T1D. Primary care spokes were recruited targeting Federally Qualified Health Centers (FQHCs) where vulnerable T1D patient populations more likely rely on PCPs for care. In CA, 11 spokes enrolled with 37 clinics serving ∼900 patients with T1D who do not receive routine specialty T1D care. In FL, 12 spokes enrolled with 67 clinics serving ∼1,000 patients with T1D. Multidisciplinary hub team specialists were recruited and a 6-month curriculum was developed. Weekly 1-hour teleECHO sessions consisting of didactic presentations and case reviews were offered for 27 sessions. Weekly engagement of the 70 participating PCPs (69% in CA) ranged from 0 to 27 (mean 14) sessions and 65 completed both baseline and exit surveys. Baseline data demonstrated low confidence in providing T1D care, especially related to diabetes technology. Pre/post surveys demonstrated statistically significant improvements in provider confidence in T1D management and knowledge (p=0.02). PCPs who attended ≥ 50% sessions had the largest improvements. Over 90% of PCPs reported changes in medical practice (e.g., insulin and pump adjustments) and 95% would recommend the program to colleagues. The ECHO model can be applied to T1D to improve PCPs’ ability to manage patients with T1D. Future directions include evaluating influence on patient level outcomes and applying the model to insulin-requiring T2D patients. Disclosure N. Cuttriss: None. C. Anez-Zabala: None. L.G. Baer: None. S.L. Filipp: None. M. Basina: None. M.J. Gurka: None. L. Figg: None. M.J. Haller: Advisory Panel; Self; SAB Biotherapeutics, Inc. K.K. Hood: Research Support; Self; Dexcom, Inc. Speaker’s Bureau; Self; LifeScan, Inc., MedIQ. X. Roque: None. R. Lal: Consultant; Self; Abbott, Biolinq. S.C. Westen: None. D.M. Maahs: Advisory Panel; Self; Eli Lilly and Company, Insulet Corporation, Medtronic, Novo Nordisk A/S. Consultant; Self; Abbott, Sanofi. Research Support; Self; Bigfoot Biomedical, Dexcom, Inc., Roche Diabetes Care, Tandem Diabetes Care. M. Town: Other Relationship; Self; Insulet Corporation. C. Wang: None. J.J. Wong: None. K. Yabut: None. A.F. Walker: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust

CGM technology allows care teams to remotely monitor glucose levels of people with diabetes. Provider availability to monitor and contact people is limited. Individuals respond differently to CGM review calls; some people improve glucose control while others disengage. No mathematical framework exists to optimize population-level care while accounting for provider constraints and differences in people’s responses. We hypothesize that a care team’s call schedule can be optimized using predictive modeling and personalized recommendations. We designed a recommendation tool that ranks people based on expected improvement in glucose management due to a call, incorporating people’s responsiveness. Individuals’ short- and long-term improvements after simulated previous calls were used to calculate the expected decrease in mean glucose due to a new call. The tool is designed with 724,672 hours of CGM data from 120 individuals with T1D (63% new diagnosis, 57% ≤ 18 years), through Tidepool. For a daily capacity of 5 calls, the recommendation has a 29.1 mg/dL expected decrease in mean glucose in contacted people, while current practice has a 4.7 mg/dL decrease. The tool allows for customizable care team constraints and adaptively learned responsiveness parameters. CGM and call data are currently being collected from pediatric patients with newly diagnosed T1D in our clinic, to tune and test the tool in a clinical setting. Disclosure J. Vallon: None. A.T. Ward: None. P. Prahalad: None. K.K. Hood: Research Support; Self; Dexcom, Inc. Speaker’s Bureau; Self; LifeScan, Inc., MedIQ. D.M. Maahs: Advisory Panel; Self; Eli Lilly and Company, Insulet Corporation, Medtronic, Novo Nordisk A/S. Consultant; Self; Abbott, Sanofi. Research Support; Self; Bigfoot Biomedical, Dexcom, Inc., Roche Diabetes Care, Tandem Diabetes Care. D. Scheinker: None.

Objective: To document rates of elevated depressive symptoms endorsed during routine screening for adolescents and young adults (AYA) with type 2 diabetes (T2D), review clinical actions taken in response to screening results, and evaluate associations of depressive symptoms with AYA characteristics. Research Design and Methods: The Patient Health Questionnaire (PHQ) was administered to 218 AYA with T2D using the PHQ-2 or PHQ-9 in routine pediatric diabetes care at 4 academic medical centers. Data from electronic health records were collected from the screening date and 12 months prior. Results: The mean age was 16.77 (SD 2.10 years); 56% male; 74% non-Hispanic White; Mean T2D duration 3.48 (SD 2.51 years). On the PHQ, 21.6% of AYA with T2D endorsed elevated depressive symptoms (PHQ score≥10) and 15.2% endorsed thoughts of self-harm. Older age, shorter T2D duration, not on insulin, and more blood glucose checks per day were significantly associated with more depressive symptoms. Of those AYAs endorsing the harm-to-self item, 8.3% were in mental health treatment. For AYAs expressing harm-to-self who were not in treatment (91.7%),18.2% did not have a documented referral for mental health treatment in the medical record. Conclusions: Screening for depressive symptoms identifies individuals in need of referral for mental health treatment; however, a focus on self-harm assessment, standardized methods for documentation of symptoms, mental health referrals and increased referral resources are needed. Disclosure J.Y. Stone: None. S.A. Mulvaney: None. C.A. Mara: None. J.C. Kichler: None. S. Majidi: Advisory Panel; Self; Companion Medical. K.A. Driscoll: None. S.C. Westen: None. A. Rawlinson: None. L.M. Jacobsen: None. K.K. Hood: Research Support; Self; Dexcom, Inc. Speaker’s Bureau; Self; LifeScan, Inc., MedIQ. M. Monaghan: Research Support; Self; American Diabetes Association. R.N. Adams: Employee; Self; Virta Health Corp. Funding National Center for Advancing Translational Sciences (UL1TR002243, UL1TR001427)

Background: Psychosocial screening is recommended and increasingly common, but little is known about youths’ perspectives on screenings. Methods: One hundred and twenty-four adolescents (57.7% female) ages 12-21 years (M=16.2±2.3) completed routine, clinic-integrated screenings. K-means clustering derived profiles and ANOVA assessed correlates profiles. Results: Four distinct profiles emerged (Table 1). Profiles varied by depression (F=3.54, p=0.017), diabetes distress (F=3.27, p=0.036), and A1c values (F=4.03, p=0.009). Majority (72.6%) placed high importance on diabetes teams awareness about their emotions and reported no difficulties/discomfort (Embracers). Those uncomfortable with screening (Avoiders) were most depressed and had lowest A1C. Youth who endorsed difficulties with tablet and placed low importance about team awareness about their emotions (Skeptics) had relatively high depression but least diabetes distress. A small fourth group (Strugglers; n=3) must be interpreted with caution due to size. No differences were found for age, gender, diabetes duration/type, race/ethnicity, self-rated global health, or anxiety. Conclusions: Most youth responded favorably to in-clinic psychosocial screenings. Those less satisfied may have increased mental health risk and may benefit from technical support and an emphasized rationale for screenings. Disclosure J.J. Wong: None. S. Hanes: None. M.S. Lanning: None. M.L. Tanenbaum: None. D. Naranjo: None. K.K. Hood: Research Support; Self; Dexcom, Inc. Speaker’s Bureau; Self; LifeScan, Inc., MedIQ.

CGM use is associated with lower HbA1c in people with T1D. We hypothesize early CGM initiation will improve longer term HbA1c. Youth with newly diagnosed T1D from July 2018 to May 2019 (pilot cohort, n=65), were offered CGM initiation in the 1st month of T1D diagnosis with 62 initiating CGM. We compared HbA1c outcomes in this pilot cohort with those diagnosed in 2014-2016 (control cohort, n=272) with follow-up duration restricted to 15 months. HbA1c trajectories were visualized using locally estimated scatter plot smoothing (Figure 1) and compared using a generalized mixed effects regression model adjusted for baseline characteristics (HbA1c at onset, age, sex, race and insurance type). Mean HbA1c at diagnosis was higher in the pilot cohort (12.0% vs. 10.7%). Over the first 15 months of T1D, CGM was initiated in 95% of the pilot cohort (78.5% within 30 days), whereas 56% initiated in the control cohort (1.8% within 30 days). Adjusting for baseline characteristics, mean HbA1c levels were 0.5% lower (95%CI: -1.0, -0.1) in the pilot cohort at 3 months and 1.7% lower (95% CI: -2.6, -0.8) at 6 months compared to the control cohort. Differences beyond 6 months were also in the expected direction and statistically significant at the 0.05 level. Early initiation of CGM in the new onset period was associated with lower HbA1c compared to historic controls. These data suggest that early CGM initiation should be considered a part of standard new onset T1D care. Disclosure P. Prahalad: None. V. Ding: None. A. Addala: None. C. New: None. B.P. Conrad: None. A. Chmielewski: None. E. Geels: None. J. Leverenz: None. A. Martinez-Singh: None. P. Sagan: None. J. Senaldi: None. A. Freeman: None. D. Scheinker: None. K.K. Hood: Research Support; Self; Dexcom, Inc. Speaker’s Bureau; Self; LifeScan, Inc., MedIQ. M. Desai: None. D.M. Maahs: Advisory Panel; Self; Eli Lilly and Company, Insulet Corporation, Medtronic, Novo Nordisk A/S. Consultant; Self; Abbott, Sanofi. Research Support; Self; Bigfoot Biomedical, Dexcom, Inc., Roche Diabetes Care, Tandem Diabetes Care.

Primary care providers (PCPs) manage type 1 diabetes (T1D) and complex diabetes for people who lack access to multispecialty care. Project ECHOTM (Extension for Community Healthcare Outcomes) is a low-dose, high frequency workforce development hub-spoke model that leverages telehealth to connect multidisciplinary specialists (“hub”) with PCPs across regional clinic sites (“spokes”). Applying the ECHO model, we piloted the model for T1D (Project ECHO T1D) in the states of California (CA) and Florida (FL) targeting PCPs managing adult and pediatric patients with T1D. Weekly hour-long teleECHOTM sessions were led by each hub in CA and FL over a 6 month pilot period. Each session included 1 to 2 case presentation. Prior to each teleECHO session, PCPs uploaded de-identified case presentations for discussion via REDCap®. Recommendations were provided by other participants and the multidisciplinary hub teams. Characteristics of case presentations were tracked in CA and FL. During the 6 month intervention, 47 new and follow-up cases were presented in CA and 45 in FL. Middle age adults (33.9±17.4 years-old, 20% pediatric) with poorly controlled diabetes (HbA1c 9.6±2.2% in CA and 10.4±2.8% in FL) were frequent cases. Common thematic questions submitted by PCPs in CA and FL, respectively, included: insulin dose adjustments (49%, 47%), social concerns (37%, 47%), behavioral health (29%, 34%), diabetes devices (44%, 13%), and diabetes education (15%, 38%). Patients commonly had depression (29.3%, 28.1%), recent hospitalizations (27%, 17%) and ER visits (33%, 26%) in the past year. Greatest barriers to care were social support (34%, 28%), financial (29%, 22%), diabetes knowledge (22%, 25%), attitudes and beliefs (27%, 16%) and psychiatric issues (20%, 19%). Findings illustrate the complexity of diabetes being managed by PCPs and the need for healthcare delivery innovation in diabetes education to support PCPs and primary care clinics who manage patients with complex diabetes who lack access to diabetes specialty care. Disclosure N. Cuttriss: None. C. Anez-Zabala: None. L.G. Baer: None. S.L. Filipp: None. M. Basina: None. M.J. Gurka: None. L. Figg: None. M.J. Haller: Advisory Panel; Self; SAB Biotherapeutics, Inc. K.K. Hood: Research Support; Self; Dexcom, Inc. Speaker’s Bureau; Self; LifeScan, Inc., MedIQ. X. Roque: None. R. Lal: Consultant; Self; Abbott, Biolinq. S.C. Westen: None. D.M. Maahs: Advisory Panel; Self; Eli Lilly and Company, Insulet Corporation, Medtronic, Novo Nordisk A/S. Consultant; Self; Abbott, Sanofi. Research Support; Self; Bigfoot Biomedical, Dexcom, Inc., Roche Diabetes Care, Tandem Diabetes Care. M. Town: Other Relationship; Self; Insulet Corporation. C. Wang: None. J.J. Wong: None. K. Yabut: None. A.F. Walker: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust

The purpose of this study was to evaluate feasibility of initiating continuous glucose monitoring (CGM) through telehealth as a means of expanding access. Adults with type 1 diabetes (N = 27) or type 2 diabetes using insulin (N = 7) and interest in starting CGM selected a CGM system (Dexcom G6 or Abbott FreeStyle Libre), which they received by mail. CGM was initiated with a certified diabetes care and education specialist providing instruction via videoconference or phone. The primary outcome was days per week of CGM use during the last 4 weeks. Hemoglobin A1c (HbA1c) was measured at baseline and 12 weeks. Participant self-reported outcome measures were also evaluated. All 34 participants (mean age, 46 ± 18 years; 53% female, 85% white) were using CGM at 12 weeks, with 94% using CGM at least 6 days per week during weeks 9 to 12. Mean HbA1c decreased from 8.3 ± 1.6 at baseline to 7.2 ± 1.3 at 12 weeks (P