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Abstract ContextYouth with type 1 diabetes (T1D) struggle to meet and sustain hemoglobin A1c (HbA1c) targets. Youth enrolled in the Pilot 4T Study improved HbA1c by 0.5% at 1 year, compared to historical controls. ObjectiveTo assess 3 years of glycemic outcomes in the Pilot 4T Study. MethodsThe Pilot 4T Extension cohort was prospectively followed to determine changes in HbA1c and continuous glucose monitoring (CGM) metrics over 3 years at the Stanford Medicine Children's Health Diabetes Clinic. Youth with T1D in the Pilot 4T Study enrolled in the extension phase started CGM in the first month of diabetes diagnosis, received intensified education and remote patient monitoring (RPM) weekly for the first year of diabetes diagnosis, and monthly RPM in the extension phase. HbA1c and CGM metrics were evaluated over the first 3 years of diagnosis. ResultsIn the Pilot 4T cohort, 78.5% (n = 102) of participants enrolled in the study extension phase and were followed through 3 years. The adjusted difference in HbA1c at 3 years was 1.2% (95% CI 0.7%-1.7%) lower in the Pilot 4T cohort than in the Historical cohort. In the Pilot 4T cohort, 68% and 37% met the <7.5% and <7% HbA1c targets at 3 years, respectively, compared to 37% and 20% in the Historical cohort. ConclusionYouth with T1D in the Pilot 4T extension phase sustained improvements in HbA1c over 3 years. Focusing resources on intensive management during the first year after T1D diagnosis may impact long-term glycemia. 

Background:Youth with type 1 diabetes (T1D) and public insurance have lower diabetes technology use. This pilot study assessed the feasibility of a program to support continuous glucose monitor (CGM) use with remote patient monitoring (RPM) to improve glycemia for youth with established T1D and public insurance. Methods:From August 2020 to June 2023, we provided CGM with RPM support via patient portal messaging for youth with established T1D on public insurance with challenges obtaining consistent CGM supplies. We prospectively collected hemoglobin A_1c(HbA_1c), standard CGM metrics, and diabetes technology use over 12 months. Results:The cohort included 91 youths with median age at enrollment 14.7 years, duration of diabetes 4.4 years, 33% non-English speakers, and 44% Hispanic. Continuous glucose monitor data were consistently available (≥70%) in 23% of the participants. For the 64% of participants with paired HbA_1cvalues at enrollment and study end, the median HbA_1cdecreased from 9.8% to 9.0% ( P < .001). Insulin pump users increased from 31 to 48 and automated insulin delivery users increased from 11 to 38. Conclusions:We established a program to support CGM use in youth with T1D and barriers to consistent CGM supplies, offering lessons for other clinics to address disparities with team-based, algorithm-enabled, remote T1D care. This real-world pilot and feasibility study noted challenges with low levels of protocol adherence and obtaining complete data in this cohort. Future iterations of the program should explore RPM communication methods that better align with this population’s preferences to increase participant engagement. 

Abstract AimsPsychosocial impacts of early continuous glucose monitoring (CGM) initiation in youth soon after type 1 diabetes diagnosis are underexplored. We report parent/guardian and youth patient‐reported outcomes (PROs) that measure psychosocial states for families in 4T Study 1. Materials and MethodsOf the 133 families in the 4T Study 1, 132 parent/guardian and 66 youth (≥11 years) were eligible to complete PROs. PROs evaluated included diabetes distress, global health, diabetes technology attitudes and CGM benefits/burden scales. Temporal trends of PROs were assessed via generalised linear mixed effects regression. Sociodemographic and clinical characteristics associated with PROs were evaluated. Psychosocial associations were evaluated by regressing parental distress on youth distress. ResultsPRO completion rates were 85.6% and varied between parent/guardian and youth. Throughout the study, parent/guardian and youth distress remained low and youth had increased technology acceptance (p = 0.046). Each additional month of CGM use was associated with a 14% decrease in the odds of experiencing diabetes distress (aOR = 0.86, 95% CI [0.76, 0.99],p = 0.029). Additionally, higher time‐in‐range was associated with decreased diabetes distress (p = 0.048). Age, diabetic ketoacidosis at diagnosis, gender, ethnicity, insurance status and language spoken were not associated with PROs. ConclusionsInitiation of CGM shortly after type 1 diabetes diagnosis does not have unintended negative psychological consequences. Longer duration of CGM use was associated with decreased youth distress and technology acceptance increased throughout the study. 

Abstract Introduction Algorithm‐enabled remote patient monitoring (RPM) programs pose novel operational challenges. For clinics developing and deploying such programs, no standardized model is available to ensure capacity sufficient for timely access to care. We developed a flexible model and interactive dashboard of capacity planning for whole‐population RPM‐based care for T1D. Methods Data were gathered from a weekly RPM program for 277 paediatric patients with T1D at a paediatric academic medical centre. Through the analysis of 2 years of observational operational data and iterative interviews with the care team, we identified the primary operational, population, and workforce metrics that drive demand for care providers. Based on these metrics, an interactive model was designed to facilitate capacity planning and deployed as a dashboard. Results The primary population‐level drivers of demand are the number of patients in the program, the rate at which patients enrol and graduate from the program, and the average frequency at which patients require a review of their data. The primary modifiable clinic‐level drivers of capacity are the number of care providers, the time required to review patient data and contact a patient, and the number of hours each provider allocates to the program each week. At the institution studied, the model identified a variety of practical operational approaches to better match the demand for patient care. Conclusion We designed a generalizable, systematic model for capacity planning for a paediatric endocrinology clinic providing RPM for T1D. We deployed this model as an interactive dashboard and used it to facilitate expansion of a novel care program (4 T Study) for newly diagnosed patients with T1D. This model may facilitate the systematic design of RPM‐based care programs. 

Use of diabetes technology (CGM, pump) is recommended for people with T1D, and early CGM initiation leads to improved glucose values. We compare %CGM and %pump use and time to initiation from T1D diagnosis in the Historical cohort, 4T Pilot, and 4T Study 1 and the associated workflow changes to increase early technology use. CGM initiation within 30 days of diagnosis increased from 2% in the historical cohort to 92% in Pilot 4T to 98% in 4T Study 1 (Table). Days to pump initiation from TID diagnosis decreased from 272 in the historical cohort to 144 days in Study 1. From 2014-2016 pumps and CGM were initiated when families expressed interest or if the provider discussed them. Families were required to attend a pre-pump class where the CDCES introduced pumps and CGMs prior to starting technology. During the 4T Pilot and 4T Study 1, CGMs were introduced and started during the first month of diagnosis. In Study 1, families were encouraged to attend pump class and initiate AID. The CDCES team does the CGM teach, CGM follow-up, pre-pump classes, and insulin pump starts for the families in preferred language. In 4T Study 2 (enrolling) standard of care is to complete a pre-pump class in the first 3 months after diagnosis. Changes in processes can lead to early implementation of diabetes technology. A structured, team-based process to introduce, reduce barriers, and encourage families to utilize diabetes technology increases early initiation. Disclosure B.P.Conrad: Advisory Panel; Edgepark medical supplies, Consultant; Abbott Diabetes. P.Prahalad: None. D.M.Maahs: Advisory Panel; Medtronic, LifeScan Diabetes Institute, MannKind Corporation, Consultant; Abbott, Research Support; Dexcom, Inc. F.K.Bishop: None. J.Leverenz: None. A.Chmielewski: None. P.Sagan: None. J.Senaldi: None. A.Martinez-singh: None. S.Lin: None. I.Chan: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (R18DK122422); The Leona M. and Harry B. Helmsley Charitable Trust (G-2002-04251-2); International Society for Pediatric and Adolescent Diabetes/JDRF (1P30DK, 11607401); Lucile Packard Child