BACKGROUND:Micro-randomization is a common method used to design and tailor AI-driven digital health interventions. However, applying it in real-world clinical settings can be challenging particularly when there are operational or resource constraints. We propose a novel design that integrates micro-randomization with treatment allocation policies to address such constraints, inspired by a pediatric type 1 diabetes (T1D) program. METHODS:We evaluated the design's properties through an extensive simulation study and developed a simulation-based power calculator, MRThreshold, to support such trial designs. RESULTS:Operational constraints that led to imbalance in treatment assignment affected efficiency. However, increasing resources had less impact relative to increases in study length (i.e., opportunities for micro-randomization). We observed a > 50% increase in power when lengthening a 16-week study to a 40-week study. Using our power calculator, we demonstrated that a 40-week study with 100 patients provides 84.0% power to detect a 2% change in time spent in glucose control, providing design considerations for our study. CONCLUSIONS:Careful consideration of study length, sample size, and operational capacity is essential for thoughtful design. Our novel design and tool balance micro-randomization and treatment allocation under operational constraints.

Continuous glucose monitoring (CGM) and automated insulin delivery (AID) systems have led to improved outcomes in type 1 diabetes (T1D). Diabetes technology use in minoritized populations is 50% lower than more privileged groups. Tailored, multi-factorial interventions are needed to address disparities and improve technology uptake in minoritized youth with T1D. The Building the Evidence to Address Disparities in Type 1 Diabetes (BEAD-T1D) Study assesses drivers of disparities in CGM and AID use in youth with T1D and public insurance to develop an intervention to increase uptake of diabetes technology. This manuscript describes the rationale, design, and protocols of the study. BEAD-T1D is a prospective, mixed-methods study grounded in the social-ecological model informed by sequential triangulation. Study Aim 1 constructs an evidence base of barriers and promoters to CGM and AID use in youth with T1D and public insurance to formulate and test a pilot intervention to increase device uptake in minoritized populations. Study Aim 2 constructs an evidence base of barriers and promoters to recommending devices to youth with T1D and public insurance to formulate and test a pilot intervention for healthcare providers to increase recommendations of devices. The primary outcome is diabetes technology acceptance analyzed via descriptive statistics and univariate analyses to inform the systematic building of a multivariable model. BEAD-T1D lays the groundwork for future efforts to reduce disparities in the uptake and continued use of diabetes technology in marginalized populations. Interventions effective in increasing the uptake and continued use of diabetes technology in youth with T1D and public insurance are necessary to mitigate disparities.

Objective To assess, in youth with type 1 diabetes (T1D), whether the initiation of early continuous glucose monitoring (CGM) with programmatic support to address sociodemographic barriers is associated with reduced disparities and improved glycemia. Study design CGM was initiated <1 month postdiagnosis with remote monitoring and <7% glycosylated hemoglobin A1c (HbA1c) target in youth with new-onset T1D in the Teamwork, Targets, Technology, and Tight Glycemia program. We evaluated HbA1c stratified by race and ethnicity, insurance, deprivation, and language across 3 cohorts: historical (June 2014 to December 2016), pilot (July 2018 to June 2020), and study 1 (June 2020 to March 2022). Results At 12 months in study 1, HbA1c was lowest among non-Hispanic White (6.5%; 95% CI 6.2%-6.9%), low deprivation (6.5%; 95% CI 6.2%-6.9%), private insurance (6.6%; 95% CI 6.3%-7%), and English preference (6.7%; 95% CI 6.4%-7%). HbA1c disparities were attenuated in study 1: ethnicity slopes changed from historical (0.09; 95% CI-0.02 to 0.20) to pilot (0.14; 95% CI-0.04 to 0.31) to study 1 (0.08; 95% CI-0.07 to 0.23). Insurance slopes improved from historical (0.20; 95% CI 0.09-0.31) to pilot (0.12; 95% CI-0.06to 0.29) to study 1 (0.01; 95%CI-0.14 to 0.16). Deprivation (41.3%) and race and ethnicity (32.2%) contributed most to HbA1c variability. Conclusions Study 1 was associated with improved glycemic outcomes and attenuation of some disparities, particularly by insurance and ethnicity, supporting the Teamwork, Targets, Technology, and Tight Glycemia program as an effective equity-oriented care model. Deprivation emerged as a key model-derived contributor to variability in HbA1c and may represent an important target to reduce disparities in pediatric T1D glycemia.

Diabetes Technology & TherapeuticsVol. 26, No. S1 Original ArticlesFree AccessDiabetes Technology and Therapy in the Pediatric Age GroupDavid M. Maahs, Priya Prahalad, Darja Smigoc Schweiger, and Shlomit ShalitinDavid M. MaahsDepartment of Pediatrics, Division of Endocrinology and Diabetes, Stanford University, Stanford, CA.Stanford Diabetes Research Center, Stanford University, Stanford, CA.Department of Health Research and Policy (Epidemiology), Stanford University, Stanford, CA.Search for more papers by this author, Priya PrahaladDepartment of Pediatrics, Division of Endocrinology and Diabetes, Stanford University, Stanford, CA.Stanford Diabetes Research Center, Stanford University, Stanford, CA.Search for more papers by this author, Darja Smigoc SchweigerDepartment of Pediatric Endocrinology, Diabetes and Metabolic Diseases, University Children's Hospital, University Medical Centre Ljubljana, Ljubljana, Slovenia.Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.Search for more papers by this author, and Shlomit ShalitinJesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel.Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.Search for more papers by this authorPublished Online:1 Mar 2024https://doi.org/10.1089/dia.2024.2508AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookXLinked InRedditEmail IntroductionIn this year's edition of the ATTD Yearbook, for the article focused on the pediatric age group we selected 20 articles from the numerous impactful publications in the past year. These articles have a common theme of advancement of diabetes technology in the pediatric age group in research with increasing translation to clinical practice. Diabetes technology has transformed pediatric diabetes care in a very positive way. However, challenges remain to further refine diabetes technology and very importantly to address barriers to increase access to the best possible care for all children, adolescents, and young adults with diabetes.Multiple studies were published on the development of automated insulin delivery systems in the pediatric population. These studies ranged from early safety studies performed as a necessary step before larger, pivotal trials for regulatory approval (which were also published this past year) with increasing "real-world" studies in which data describe approved closed-loop insulin delivery systems in use in pediatric diabetes clinics.The continued development of these systems and the transition from research to the clinic will continue to be highlighted in pediatric diabetes care in the years ahead. Common themes continue to be both the challenges and opportunities of diabetes technology in the pediatric population. Usability remains an important goal for translation from research to clinical implementation as does research in the youngest age groups. Broader access to diabetes technology will be an ongoing mission for all involved in pediatric diabetes care so that all children can benefit. In addition to closed-loop research, important articles in the pediatric age group were published on novel insulin formulations, national and individual clinical registry data to describe outcomes and best practices, and novel reports on the data generated from diabetes technology and their applications.To select these 20 articles focused on diabetes technology and therapeutics in the pediatric age group, we conducted a Medline search for articles dealing with the following topics: diabetes technology, insulin pump therapy (continuous subcutaneous insulin infusion, CSII), continuous glucose monitoring (CGM), closed-loop systems, and new therapies in type 1 diabetes (T1D) relating to the pediatric age group (0–18 years). We focused on key articles that offer some insight into these issues that were published between July 1, 2022, and June 30, 2023.Key Articles ReviewedCGM Metrics Identify Dysglycemic States in Participants from the TrialNet Pathway to Prevention StudyWilson DM, Pietropaolo SL, Acevedo-Calado M, Huang S, Anyaiwe D, Scheinker D, Steck AK, Vasudevan MM, McKay SV, Sherr JL, Herold KC, Dunne JL, Greenbaum CJ, Lord SM, Haller MJ, Schatz DA, Atkinson MA, Nelson PW, Pietropaolo M and the Type 1 Diabetes TrialNet Study GroupDiabetes Care2023; 46:526–534Glycemic Variability Patterns Strongly Correlate with Partial Remission Status in Children with Newly Diagnosed Type 1 DiabetesPollé OG, Delfosse A, Martin M, Louis J, Gies I, den Brinker M, Seret N, Lebrethon MC, Mouraux T, Gatto L, Lysy PA on behalf of the DIATAG Working GroupDiabetes Care2022; 45:2360–2368Disparities in Hemoglobin A1c Levels in the First Year after Diagnosis among Youths with Type 1 Diabetes Offered Continuous Glucose MonitoringAddala A, Ding V, Zaharieva DP, Bishop FK, Adams AS, King AC, Johari R, Scheinker D, Hood KK, Desai M, Maahs DM, Prahalad P for the Teamwork, Targets, Technology, and Tight Control (4T) Study GroupJAMA Netw Open2023; 6:e238881Continuous Glucose Monitoring versus Blood Glucose Monitoring for Risk of Severe Hypoglycaemia and Diabetic Ketoacidosis in Children, Adolescents, and Young Adults with Type 1 Diabetes: A Population-based StudyKarges B, Tittel SR, Bey A, Freiberg C, Klinkert C, Kordonouri O, Thiele-Schmitz S, Schröder C, Steigleder-Schweiger C, Holl RWLancet Diabetes Endocrinol2023; 11:314–323A Longitudinal View of Disparities in Insulin Pump Use among Youth with Type 1 Diabetes: The SEARCH for Diabetes in Youth StudyEverett EM, Wright D, Williams A, Divers J, Pihoker C, Liese AD, Bellatorre A, Kahkoska AR, Bell R, Mendoza J, Mayer-Davis E, Wisk LEDiabetes Technol Ther2023; 25:131–139Trial of Hybrid Closed-Loop Control in Young Children with Type 1 DiabetesWadwa RP, Reed ZW, Buckingham BA, DeBoer MD, Ekhlaspour L, Forlenza GP, Schoelwer M, Lum J, Kollman C, Beck RW, Breton MD for the PEDAP Trial Study GroupN Engl J Med2023; 388:991–1001MiniMed 780G Six-Month Use in Children and Adolescents with Type 1 Diabetes: Clinical Targets and Predictors of Optimal Glucose ControlLombardo F, Passanisi S, Alibrandi A, Bombaci B, Bonfanti R, Delvecchio M, Di Candia F, Mozzillo E, Piccinno E, Piona CA, Rigamonti A, Scialabba F, Maffeis C, Salzano GDiabetes Technol Ther2023; 25:404–413Simplified Meal Announcement versus Precise Carbohydrate Counting in Adolescents with Type 1 Diabetes Using the MiniMed 780G Advanced Hybrid Closed Loop System: A Randomized Controlled Trial Comparing Glucose ControlPetrovski G, Campbell J, Pasha M, Day E, Hussain K, Khalifa A, van den Heuvel TDiabetes Care2023; 46:544–550Increased Technology Use Associated with Lower A1C in a Large Pediatric Clinical PopulationAlonso GT, Triolo TM, Akturk HK, Pauley ME, Sobczak M, Forlenza GP, Sakamoto C, Pyle L, Frohnert BIDiabetes Care2023; 46:1218–1222A Meta-analysis of Randomized Trial Outcomes for the t:slim X2 Insulin Pump with Control-IQ Technology in Youth and Adults from Age 2 to 72Beck RW, Kanapka LG, Breton MD, Brown SA, Wadwa RP, Buckingham BA, Kollman C, Kovatchev BDiabetes Technol Ther2023; 25:329–342Open-Source Automated Insulin Delivery in Type 1 DiabetesBurnside MJ, Lewis DM, Crocket HR, Meier RA, Williman JA, Sanders OJ, Jefferies CA, Faherty AM, Paul RG, Lever CS, Price SKJ, Frewen CM, Jones SD, Gunn TC, Lampey C, Wheeler BJ, de Bock MIN Engl J Med2022; 387:869–881First Use of Open-Source Automated Insulin Delivery AndroidAPS in Full Closed-Loop Scenario: Pancreas4ALL Randomized Pilot StudyPetruzelkova L, Neuman V, Plachy L, Kozak M, Obermannova B, Kolouskova S, Pruhova S, Sumnik ZDiabetes Technol Ther2023; 25:315–323Extended Use of an Open-Source Automated Insulin Delivery System in Children and Adults with Type 1 Diabetes: The 24-Week Continuation Phase Following the CREATE Randomized Controlled TrialBurnside MJ, Lewis DM, Crocket HR, Meier RA, Williman JA, Sanders OJ, Jefferies CA, Faherty AM, Paul RG, Lever CS, Price SKJ, Frewen CM, Jones SD, Gunn TC, Lampey C, Wheeler BJ, de Bock MIDiabetes Technol Ther2023; 25:250–259Multicenter, Randomized Trial of a Bionic Pancreas in Type 1 DiabetesBionic Pancreas Research Group; Russell SJ, Beck RW, Damiano ER, El-Khatib FH, Ruedy KJ, Balliro CA, Li Z, Calhoun P, Wadwa RP, Buckingham B, Zhou K, Daniels M, Raskin P, White PC, Lynch J, Pettus J, Hirsch IB, Goland R, Buse JB, Kruger D, Mauras N, Muir A, McGill JB, Cogen F, Weissberg-Benchell J, Sherwood JS, Castellanos LE, Hillard MA, Tuffaha M, Putman MS, Sands MY, Forlenza G, Slover R, Messer LH, Cobry E, Shah VN, Polsky S, Lal R, Ekhlaspour L, Hughes MS, Basina M, Hatipoglu B, Olansky L, Bhangoo A, Forghani N, Kashmiri H, Sutton F, Choudhary A, Penn J, Jafri R, Rayas M, Escaname E, Kerr C, Favela-Prezas R, Boeder S, Trikudanathan S, Williams KM, Leibel N, Kirkman MS, Bergamo K, Klein KR, Dostou JM, Machineni S, Young LA, Diner JC, Bhan A, Jones JK, Benson M, Bird K, Englert K, Permuy J, Cossen K, Felner E, Salam M, Silverstein JM, Adamson S, Cedeno A, Meighan S, Dauber AN Engl J Med2022; 387:1161–1172The Insulin-Only Bionic Pancreas Improves Glycemic Control in non-Hispanic White and Minority Adults and Children with Type 1 DiabetesCastellanos LE, Russell SJ, Damiano ER, Beck RW, Shah VN, Bailey R, Calhoun P, Bird K, Mauras N; Bionic Pancreas Research GroupDiabetes Care2023; 46:1185–1190Association of Achieving Time in Range Clinical Targets with Treatment Modality among Youths with Type 1 DiabetesDovc K, Lanzinger S, Cardona-Hernandez R, Tauschmann M, Marigliano M, Cherubini V, Preikša R, Schierloh U, Clapin H, AlJaser F, Pelicand J, Shukla R, Biester TJAMA Netw Open2023; 6:e230077Temporal Changes in Hemoglobin A1c and Diabetes Technology Use in DPV, NPDA, and T1DX Pediatric Cohorts from 2010 to 2018Lal RA, Robinson H, Lanzinger S, Miller KM, Pons Perez S, Kovacic R, Calhoun P, Campbell F, Naeke A, Maahs DM, Holl RW, Warner JDiabetes Technol Ther2022; 24:628–634Transatlantic Comparison of Pediatric Continuous Glucose Monitoring Use in the Diabetes-Patienten-Verlaufsdokumentation Initiative and Type 1 Diabetes Exchange Quality Improvement CollaborativeDeSalvo DJ, Lanzinger S, Noor N, Steigleder-Schweiger C, Ebekozien O, Sengbusch SV, Yayah Jones NH, Laubner K, Maahs DM, Holl RWDiabetes Technol Ther2022; 24:920–924Effect of Tight Glycemic Control on Pancreatic Beta Cell Function in Newly Diagnosed Pediatric Type 1 Diabetes: A Randomized Clinical TrialMcVean J, Forlenza GP, Beck RW, Bauza C, Bailey R, Buckingham B, DiMeglio LA, Sherr JL, Clements M, Neyman A, Evans-Molina C, Sims EK, Messer LH, Ekhlaspour L, McDonough R, Van Name M, Rojas D, Beasley S, DuBose S, Kollman C, Moran A for the CLVer Study GroupJAMA2023; 329:980–989Effect of Verapamil on Pancreatic Beta Cell Function in Newly Diagnosed Pediatric Type 1 Diabetes: A Randomized Clinical TrialForlenza GP, McVean J, Beck RW, Bauza C, Bailey R, Buckingham B, DiMeglio LA, Sherr JL, Clements M, Neyman A, Evans-Molina C, Sims EK, Messer LH, Ekhlaspour L, McDonough R, Van Name M, Rojas D, Beasley S, DuBose S, Kollman C, Moran A; for the CLVer Study GroupJAMA2023; 329:990–999Continuous Glucose MonitoringCGM Metrics Identify Dysglycemic States in Participants from the TrialNet Pathway to Prevention StudyWilson DM1, Pietropaolo SL2, Acevedo-Calado M2, Huang S3, Anyaiwe D4, Scheinker D1, Steck AK5, Vasudevan MM2, McKay SV2,6, Sherr JL7, Herold KC8, Dunne JL9, Greenbaum CJ10, Lord SM10, Haller MJ11, Schatz DA11, Atkinson MA11, Nelson PW4, Pietropaolo M2, and the Type 1 Diabetes TrialNet Study Group1Division of Pediatric Endocrinology, Stanford University School of Medicine, Palo Alto, CA; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 2Division of Endocrinology, Diabetes, and Metabolism, Diabetes Research Center, Department of Medicine, Baylor College of Medicine, Houston, TX; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 3Department of Industrial & Systems Engineering, University of Washington, Seattle, WA; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 4Department of Mathematics & Computer Science, Lawrence Technological University, Southfield, MI; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 5Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 6Department of Pediatrics, Baylor College of Medicine, Houston, TX; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 7Division of Pediatric Endocrinology, Yale University School of Medicine, New Haven, CT; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 8Departments of Immunobiology and Internal Medicine, Yale University, New Haven, CT; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 9JDRF, New York, NY; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 10Center for Interventional Immunology and Diabetes Program, Benaroya Research Institute, Seattle, WA; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 11Department of Pediatrics, University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FLDiabetes Care 2023;46:526–534This study is also discussed in DIA-2024-2502, page S-14.Because continuous glucose monitoring (CGM) parameters may identify individuals at risk for progression to overt type 1 diabetes (T1D), this study examined whether CGM metrics could provide additional insight into the progression to clinical stage 3 T1D.MethodsFrom the TrialNet Pathway to Prevention study, 105 relatives of individuals in T1D probands (median age 16.8 years; 89% non-Hispanic White; 43.8% female) underwent 7-day CGM assessments and oral glucose tolerance tests (OGTTs) at 6-month intervals. The three groups evaluated were individuals with (1) stage 2 T1D (n = 42) with two or more diabetes-related autoantibodies and abnormal OGTT; (2) stage 1 T1D (n = 53) with two or more diabetes-related autoantibodies and normal OGTT; and (3) negative test for all diabetes-related autoantibodies and normal OGTT (n = 10).ResultsAfter the baseline data assessment, multiple CGM metrics were found to be associated with progression to stage 3 T1D: spending ≥ 5% time with glucose levels ≥ 140 mg/dL (P = 0.01), ≥ 8% time with glucose levels ≥ 140 mg/dL (P = 0.02), ≥ 5% time with glucose levels ≥ 160 mg/dL (P = 0.0001), and ≥ 8% time with glucose levels ≥ 160 mg/dL (P = 0.02). The stage 2 participants and those who progressed to stage 3 also exhibited higher mean daytime glucose values; they spent more time with glucose values over 120, 140, and 160 mg/dL, and they had greater variability.ConclusionsCGM could aid in the identification of individuals, including those with a normal OGTT, who are likely to rapidly progress to stage 3 T1D.CommentsRecent approval by the U.S. Food and Drug Association (FDA) of teplizumab for stage 2 T1D finally provides diabetes clinicians with an immunomodulatory treatment option to delay the development of stage 3 T1D. Now that such a treatment exists—and other immunotherapies are certain to follow—the case for increasing efforts for screening for T1D goes beyond prevention of diabetic ketoacidosis (DKA) to include therapies to delay initiation of insulin therapy. With the likelihood of increased screening efforts in the future, clinicians will be faced with questions of how to monitor progression of T1D and when to recommend immunotherapy. The data from CGM wearers provided by Wilson and TrialNet colleagues indicate clear thresholds at which rapid progression to stage 3 T1D occurs.As CGM has become more user friendly and more accepted by clinicians, it may well replace burdensome oral glucose tolerance tests as a more effective method to monitor for progression of T1D. This study and others will strengthen the evidence for the use of CGM in early stages of T1D. As diabetes technology becomes more effective in supporting care for T1D—and, we hope, more accessible to all people with T1D—we will see the role of CGM in ascertaining preclinical stages of T1D and timing of immunotherapies.Glycemic Variability Patterns Strongly Correlate with Partial Remission Status in Children with Newly Diagnosed Type 1 DiabetesPollé OG1,2, Delfosse A1,2, Martin M3, Louis J4, Gies I5,6, den Brinker M7,8, Seret N9, Lebrethon MC10, Mouraux T11, Gatto L3, Lysy PA1,2, on behalf of the DIATAG Working Group1Pôle de PEDI, Institut de Recherche Experimentale et Clinique, UCLouvain, Brussels, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 2Specialized Pediatrics Service, Cliniques Universitaires Saint-Luc, Brussels, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 3Computational Biology and Bioinformatics Unit, de Duve Institute, UCLouvain, Brussels, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 4Division of Pediatric Endocrinology, Department of Pediatrics, Grand Hôpital de Charleroi, Charleroi, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 5Division of Pediatric Endocrinology, Department of Pediatrics, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Brussels, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 6Research Group GRON, Vrije Universiteit Brussel, Brussels, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 7Laboratory of Experimental Medicine and Pediatrics and member of the Infla-Med Centre of Excellence, University of Antwerp, Faculty of Medicine and Health Sciences, Antwerp, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 8Division of Pediatric Endocrinology, Department of Pediatrics, Antwerp University Hospital, Antwerp, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 9Division of Pediatric Endocrinology, Department of Pediatrics, Centre Hospitalier Chrétien MontLégia, Liège, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 10Division of Pediatric Endocrinology, Department of Pediatrics, CHU Liège, Liège, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 11Division of Pediatric Endocrinology, Department of Pediatrics, CHU Namur, Namur, BelgiumDiabetes Care 2022;45:2360–2368Glucose variability parameters (also called CGM metrics) measured by continuous glucose monitoring (CGM) systems may strongly correlate with features of diabetes control related to β-cell function. CGM metrics improve the estimation of glucose control provided by HbA1c measurement and may help to better stratify existing phenotypes among patients with type 1 diabetes (T1D). This study evaluated whether indexes of glycemic variability may overcome residual β-cell secretion estimates in the longitudinal evaluation of partial remission (PR) in pediatric patients with new-onset T1D.MethodsIn this multicenter, prospective trial researchers tried to identify biomarkers of PR in children and adolescents (n = 78) with new-onset T1D. Values of residual β-cell secretion estimates, clinical parameters (e.g., HbA1c or insulin daily dose), and CGM data were longitudinally collected during 1 year and underwent cross-sectional comparison. Circadian patterns of CGM metrics were characterized and correlated to PR status using an adjusted mixed-effects model. Patients were clustered based on 46 CGM metrics and clinical parameters and were compared using nonparametric analysis of variance.ResultsThe mean age of the participants was 10.4 ± 3.6 years at diabetes onset; 65% of them underwent PR at 3 months. β-Cell residual secretion estimates demonstrated weak-to-moderate correlations with clinical parameters and CGM metrics. CGM metrics strongly correlated with clinical parameters (P < 0.05) and were satisfactory to distinguish those with PR from those without PR. Also, CGM metrics from those with PR showed specific early morning circadian patterns characterized by increased glycemic stability across days (within 63–140 mg/dL range) and decreased rate of grade II hypoglycemia (P < 0.0001) compared with those without PR. CGM analysis allowed the identification of four novel glucotypes (P < 0.001) that segregate patients into subgroups and reflect the evolution of PR after diabetes onset.ConclusionsCGM metrics (e.g., hyperglycemia and time in range) demonstrated a strong correlation with routine clinical parameters and demonstrated, for most of them, a specific circadian pattern that distinguished both remission groups.CommentsPartial remission (PR) is a state of low glycemic variability, low daily insulin needs, and lower HbA1c levels. Studies in young adults also have suggested that patients entering PR after diabetes onset were less at risk of vascular complications (1). Currently, little is known about the influence of PR and its duration on short-term glucose homeostasis outcomes, especially in children.PR can be evaluated by C-peptide levels and is commonly defined as the persistence of C-peptide secretion above a certain threshold (peak C-peptide > 200 pmol/L) (2). However, assays lack the power to discriminate residual β-cell mass from β-cell function. Therefore, new tools are needed that may both reflect the presence and predict the evolution of significant residual β-cell function, which qualifies PR.With the use of CGM systems, it seems that glucose variability parameters (CGM metrics) may strongly correlate with features of diabetes control related to β-cell function. Previous studies (3,4) showed that CGM metrics improve the estimation of glucose control provided by HbA1c measurement and may help to better stratify existing phenotypes among patients with T1D.In the study by Pollé and colleagues, patients without PR had a significantly higher prevalence of DKA at the onset of diabetes which may reflect a lower β-cell function or mass. Residual C-peptide secretion estimates, evaluated using either a single blood test or stimulation testing, were only weakly correlated with glucose homeostasis evaluated by CGM metrics and with clinical parameters (HbA1c, total daily dose of insulin, and insulin dose-adjusted A1c [IDAA1c]) without significant difference between the patients with and without PR. However, the CGM metrics showed strong correlations with the clinical parameters and allowed deeper characterization of glucose homeostasis (i.e., hypoglycemia episodes and glucose variability). As expected, the patients with PR spent more time in the target range and less time in hyperglycemia during the whole day.By using CGM data, the investigators identified specific circadian patterns among remission groups for most CGM metrics, which peaked in their discriminative features in the early morning period. By integrating CGM metrics and clinical parameters, they identified four clinically meaningful clusters that exhibit specific glucotypes and reflect the progressive loss of glucose homeostasis during the first year after T1D onset. Therefore, CGM metrics provided additional information to segregate patients.The study's limitations included its relatively small number of patients and the cross-sectional analysis of parameters (i.e., clinic, secretion, and CGM data) that were only available for a subset of patients. The study's strength is the novelty that integrates CGM, clinical parameters, and residual β-cell secretion data to find the characteristics of PR and identify new glucotypes during the first year of T1D.The implementation of various CGM metrics as end points in trials of residual β-cell function preservation may provide applicable and more noninvasive precise clues to select the subgroup of patients who are better candidates for intervention and to evaluate the patient response to treatment. Another implication of more comprehensive sampling as obtained with CGM metrics may improve diagnostic accuracy of the transition from health to prediabetes or stage 2 T1D. New insights may offer earlier therapeutic options for reversing dysglycemia more successfully.Disparities in Hemoglobin A1c Levels in the First Year after Diagnosis among Youths with Type 1 Diabetes Offered Continuous Glucose MonitoringAddala A1, Ding V2, Zaharieva DP1, Bishop FK1, Adams AS1,3,4,5, King AC3,6, Johari R7, Scheinker D1,5,7,8, Hood KK1,5, Desai M2, Maahs DM1,3,5, Prahalad P1,5 for the Teamwork, Targets, Technology, and Tight Control (4T) Study Group1Division of Pediatric Endocrinology, Department of Pediatrics, Stanford University, Stanford, CA; Stanford University, Stanford, CA; 2Division of Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA; Stanford University, Stanford, CA; 3Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA; Stanford University, Stanford, CA; 4Department of Health Policy, Stanford University School of Medicine, Stanford, CA; Stanford University, Stanford, CA; 5Stanford Diabetes Research Center, Stanford University, Stanford, CA; Stanford University, Stanford, CA; 6Stanford Prevention Research Center Division, Department of Medicine, Stanford University School of Medicine, Stanford, CA; Stanford University, Stanford, CA; 7Clinical Excellence Research Center, Stanford University, Stanford, CA; Stanford University, Stanford, CA; 8Department of Management Science and Engineering, Stanford University, Stanford, CAJAMA Netw Open 2023;6:e238881This study is also discussed in DIA-2024-2502, page S-14, and DIA-2024-2512, page S-187.Although continuous glucose monitoring (CGM) is associated with improvements in hemoglobin A1c (HbA1c) in youths with type 1 diabetes (T1D), youths from minoritized racial and ethnic groups and those with public insurance face greater challenges accessing this treatment. This study examined whether HbA1c decreases differed by ethnicity and insurance status among the youths in the Teamwork, Targets, Technology, and Tight Control (4T) study with newly diagnosed T1D on CGM.MethodsThe 4T study was a clinical research program that aimed to initiate CGM within 1 month of T1D diagnosis for all youths with new-onset T1D diagnosed between July 25, 2018, and June 15, 2020. For the Pilot-4T study cohort, youths at Stanford Children's Hospital were followed for 12 months and compared with a historical cohort of 272 youths diagnosed with T1D between June 1, 2014, and December 28, 2016. HbA1c change over the study period was assessed, and the analyses were stratified by ethnicity (Hispanic vs non-Hispanic) or insurance status (public vs private).ResultsThe Pilot-4T cohort comprised 135 youths, 71 male (52.6%), with a median age of 9.7 years (IQR, 6.8–12.7 years) at diagnosis. Participants' race was based on self-report and was categorized as 19 Asian or Pacific Islander (14.1%), 62 White (45.9%), and 39 other race (28.9%); 15 participants (11.1%) did not supply race information. The participants also self-reported their ethnicity: 29 Hispanic (21.5%) and 92 non-Hispanic (68.1%). A total of 104 participants (77.0%) had private insurance, and 31 (23.0%) had public insurance. Compared with the historical cohort, the Pilot-4T cohort had similar reductions in HbA1c at 6, 9, and 12 months after diagnosis observed for both the Hispanic individuals: estimated difference, −0.26% (95% CI, −1.05% to 0.43%), −0.60% (95% CI, −1.46% to 0.21%), and −0.15% (95% CI, −1.48% to 0.80%), respectively; and the non-Hispanic individuals: estimated difference, −0.27% (95% CI, −0.62% to 0.10%), −0.50% (95% CI, −0.81% to −0.11%), and −0.47% (95% CI, −0.91% to 0.06%), respectively. Similar reductions in HbA1c at 6, 9, and 12 months after diagnosis were also observed in the Pilot-4T cohort for both the publicly insured individuals: estimated difference, −0.52% (95% CI, −1.22% to 0.15%), −0.38% (95% CI, −1.26% to 0.33%), and −0.57% (95% CI, −2.08% to 0.74%, respectively; and the privately insured individuals: estimated difference, −0.34% (95% CI, −0.67% to 0.03%), −0.57% (95% CI, −0.85% to −0.26%), and −0.43% (−0.85% to 0.01%), respectively. Hispanic youths in the Pilot-4T cohort had higher HbA1c at 6, 9, and 12 months after diagnosis than the non-Hispanic youths (estimated difference, 0.28% [95% CI, −0.46% to 0.86%], 0.63% [95% CI, 0.02%–1.20%], and 1.39% [95% CI, 0.37%–1.96%]), as did the publicly insured youths compared with the privately insured youths (estimated difference, 0.39% [95% CI, −0.23% to 0.99%], 0.95%[95% CI, 0.28%–1.45%], and 1.16% [95% CI, −0.09% to 2.13%]).ConclusionsFor Hispanic and non-Hispanic youths as well as for publicly and privately insured youths, CGM initiation soon after diagnosis is associated with similar improvements in HbA1c. Equitable access to CGM soon after T1D diagnosis may be a first step to improve HbA1c for all youths but is unlikely to eliminate disparities entirely.CommentsCGM initiation early in the course of T1D is associated with improved clinical outcomes (5,6). The Pilot 4T study showed that a team-based approach to CGM initiation within the first month of diagnosis, supported by remote patient monitoring, can improve HbA1c at 1 year after diabetes diagnosis in a general diabetes clinic population (5).Addala and colleagues performed further analysis on the Pilot 4T population to determine whether disparities in clinical outcomes persisted among those from minoritized communities when receiving a standardized new onset protocol that removed provider-level bias. The results from their analysis demonstrated that the 4T intervention improved outcomes

The ability to reduce the risk of developing diabetic ketoacidosis (DKA) remains a major care gap for people with diabetes, particularly those on intensive insulin therapy. The anticipated availability of continuous ketone monitoring (CKM) has the potential to reduce the risk of developing DKA, one of the most life-threatening acute complications of type 1 and type 2 diabetes. International clinical guidelines have established ketone thresholds for suspected and confirmed diagnoses of DKA, based on use of point-of-care testing, as part of a triad of markers with allied thresholds for hyperglycaemia and acidosis. The increasing occurrence of euglycemic DKA, with glucose concentrations below established diagnostic thresholds, makes the availability and use of CKM technology an important addition to the diabetes management toolkit. CKM data could alert the user when the risk of acute DKA is high on sick days in addition to signalling that individuals might be predicted to be at greater overall risk of future DKA on the basis of the distribution and degree of ketone measures in daily life. If widespread use of CKM devices is to be safe and effective in reducing the occurrence of DKA, it is important to establish clear ketone thresholds which notify CKM users when action on their part is required. In defining these thresholds and actions, it was important to ensure that the CKM user is not exposed to avoidable anxiety or suffers alarm fatigue, thus adding to the burden of living with diabetes. In the absence of substantial evidence that can identify appropriate ketone thresholds for CKM use, a panel of international experts in the management of DKA was convened with the aim of developing a number of objective, practical recommendations on how this novel diabetes technology could improve outcomes for individuals at risk of DKA, the results of which we report in this Personal View. These recommendations have been endorsed by the International Society for Pediatric and Adolescent Diabetes (ISPAD).

Aims:To examine prescription of guideline-recommended therapies and achievement of treatment targets across the span of older adulthood in type 1 diabetes (T1D) in the United States and Germany/Austria. Materials and Methods:Cross-sectional data of adults aged ≥60 years with T1D for ≥1 year seen in 2022 in the T1D Exchange Quality Improvement Collaborative (T1DX-QI) and the Diabetes Prospective Follow-up (DPV) registry. Descriptive statistics and within-registry comparisons across age groups using analysis of variance and chi-squared tests were used to analyze the data. Results:Thirty-six hundred adults aged ≥60 years, median age 67.5 [interquartile range (IQR) 63.4, 72.8] in T1DX-QI (n = 1549) and 68.9 (IQR 63.6, 75.7) in DPV (n = 2051) were included. The prevalence of atherosclerotic cardiovascular disease (ASCVD) (34.6% vs 16.8%) and chronic kidney disease (28.5% vs 11.8%) was higher in the DPV than the T1DX-QI. Lipid-lowering therapy for secondary prevention (52.9% vs 38%) and angiotensin-converting enzyme inhibitor/angiotensin receptor blocker use (55.3% vs 44.8%) were higher in the DPV. Continuous glucose monitoring use was similar (50.3% vs 47.9%), insulin pump use was >2 × higher (40.7% vs 17%), and automated insulin delivery use was >3 × higher (20.4% vs 6.4%) in the T1DX-QI as compared to the DPV. Conclusion:Despite a high prevalence of ASCVD and risks of hypoglycemia, guideline-recommended treatments including lipid-lowering therapy for secondary prevention and diabetes technologies were used in approximately half or fewer of older adults with T1D. Additional attention to prescribing and practices to support clinicians and older adults in the use of diabetes technologies is urgently needed.

Continuous glucose monitoring (CGM) is now central to diabetes management, yet variation in how respective medical products are evaluated limits meaningful comparison between CGM systems. Three barriers currently constrain reliable interpretation of glucose-derived measures. The first is limited transparency: in several regulatory settings, particularly those using Conformité Européenne marking, clinical-study reports, reference-method information and analytical documentation required for market authorisation are not publicly accessible. The second barrier is heterogeneity in study procedures. Existing evaluations use different reference-glucose methods, sampling strategies, glucose-manipulation protocols and participant characteristics, leading to accuracy estimates that cannot be interpreted consistently across systems. The third barrier is calibration alignment. Even with full transparency and aligned procedures, CGM systems may differ because their calibration algorithms are trained on distinct reference-glucose datasets, influencing reported glucose ranges, automated insulin-delivery behaviour and interpretation during device transitions. A modified Delphi process involving clinicians, laboratory scientists, and researchers identified these issues as the principal determinants of comparability. During this process, the International Federation of Clinical Chemistry and Laboratory Medicine released a validated framework for performance evaluation of CGM systems, providing a unified approach to reference-method selection, dynamic in-clinic testing, and structured reporting. Adoption would reduce procedural variability but does not resolve calibration-alignment differences. This international clinical opinion proposes a pathway towards internationally interpretable CGM evaluation: immediate transparency of clinical evidence, routine declaration of calibration alignment, and progressive adoption of validated standardised procedures. These steps provide a foundation for reliable interpretation and globally comparable assessment of CGM technologies.

Sharing research code in an open access version-controlled repository offers significant benefits for both science as a whole and for individual researchers. In this article, we focus on this practice, which is fully aligned with the NIH's Gold Standard Science (GSS) program as well as FAIR (findable, accessible, interoperable, reusable) and TRUST (transparency, responsibility, user focus, sustainability, technology) principles. Gold Standard Science supports open science by emphasizing transparency, reproducibility, and the use of best practices that enable others to verify and extend research. Pairing a research article's cited data snapshot with a versioned, environment-specific code release, deposited in a companion code repository, ensures that, upon submission to a medical journal, readers and reviewers can directly verify results. An executable and updatable companion code repository complements, rather than replaces, established research data repositories. When code underlying medical research results is made openly available, then other scientists can inspect, run, and validate analyses. These activities enhance reproducibility, which is a core aim of GSS. Shared code also facilitates collaborative innovation by allowing researchers to extend the utility of the code to new datasets and applications. For researchers, code sharing can increase visibility, credibility, and citation impact. Demonstrating transparency through shared executable and updatable code builds trust with journal readers, peer reviewers, funders, and peers. Shared code in an open access repository signals adherence to high standards of scientific integrity and attracts opportunities for collaboration. A researcher who shares code receives recognition as a leader in reproducible, trustworthy research consistent with NIH's GSS principles.

Clinics continue to adopt remote patient monitoring for type 1 diabetes (T1D) and care models shaped by algorithmic CGM data analysis. No clinic-facing quantitative framework currently exists to track the impact of such algorithm-directed care on patient outcomes and clinical workload. The Teamwork, Targets, Technology, and Tight Control (4T) Study provides precision, whole-population care enabled by algorithms that use continuous glucose monitoring (CGM) data to direct clinician attention to patients with deteriorating glucose management. We used data from the 4T Pilot (n=133) and 4T Study 1 (n=135), in which algorithms use CGM data to identify youth with T1D meeting criteria for clinical review and potential clinician contact. Through iterative data analysis and interviews with diabetes educators and clinicians, we identified metrics for reviewing and revising clinical workloads, glucose management, and timeliness of care. For each metric, we developed an interactive dashboard to provide clinical and administrative leaders with an overview of the program. The metrics to track clinical workload were the total number of youths: (1) in the program, (2) in each study, and (3) cared for by each clinician. The metrics to track glucose management were the number of youths meeting each criterion for review: (4) in total, (5) for each clinician, and (6) for each study. The metric to track timeliness of care was (7) the number of days since meeting criteria for clinical review. When presented at weekly program leadership meetings, the metrics facilitated data-driven decision making about clinical and operational components of the program. We propose a novel quantitative framework for diabetes care teams to supervise and enhance algorithm-directed whole-population T1D care. As the role of algorithms grows in directing clinical effort and prioritizing patients for care, this framework may help clinics track clinical workload, patient outcomes, and the timeliness of care.

Objective: To evaluate inhaled technosphere insulin (TI) in children with diabetes. Research Design and Methods: Youth 4 to 17 years old with type 1 (98%) or type 2 (2%) diabetes treated with multiple daily injections of insulin were randomly assigned 1:1 to TI or rapid-acting analogue (RAA) insulin plus continuation of long-acting basal insulin and continuous glucose monitoring (CGM) for 26 weeks. The primary outcome was change in HbA1c, tested for non-inferiority with margin of 0.4%. Results: In intent-to-treat analysis, mean HbA1c was 8.22±0.87% at baseline and 8.41±1.38% at 26 weeks with TI and 8.21±0.96% and 8.21±1.10%, respectively, with RAA (adjusted difference = 0.18%, 95% CI -0.07 to 0.43, non-inferiority p-value = 0.091). CGM-measured time-in-range 70-180 mg/dL was not significantly different between groups (adjusted difference -2.2%, 95% CI -7.0% to 2.7%, p=0.38). Two severe hypoglycemic events occurred in the TI group and one in the RAA group. Change in forced expiration volume in one second (FEV1) from baseline to 26 weeks did not differ comparing TI and RAA (p=0.53). The TI group reported greater treatment satisfaction (p=0.004) and had less gain in weight and body mass index percentile (p=0.009) than the RAA group. Conclusions and Relevance: The primary analysis did not meet the pre-specified criteria for HbA1c non-inferiority. However, TI use was safe over 26 weeks without impacting pulmonary function and was associated with greater treatment satisfaction and less weight gain compared with RAA, supporting TI as a treatment option for some pediatric patients with type 1 diabetes.

A panel of experts in the use of continuous glucose monitoring (CGM) data in the treatment of diabetes met in Burlingame, California on October 27, 2025 to discuss the utility of the glycemia risk index (GRI) for clinical care research and population health management. The GRI composite metric is a single number (on a 0-100 percentile scale-lower is better) based on an expert-determined weighting of the seven individual components in the existing ambulatory glucose profile (AGP). The GRI describes the quality of glycemia based on glucose values collected in a 14-day CGM tracing, thus providing additional insights into CGM profiles beyond the AGP. During the meeting, the mathematical derivation of the GRI metric was presented along with its use for adult and pediatric individuals with diabetes and cancer who require medications that can adversely affect the glucose concentration. Examples where the GRI provided useful insights into the quality of CGM tracings were also discussed by the expert panel. In addition, a new smartphone application, the GRI Calculator, was presented. This app calculates the GRI of a CGM tracing and provides visualization of sequential CGM tracings for a specific individual. The GRI provides a reference measurement for the accuracy of artificial intelligence (AI) models assigning levels of glycemic quality to CGM tracings intended to match the assessments of clinicians. The GRI is now part of the data visualization panel for the Integration of Connected Diabetes Device Data into the Electronic Health Record (iCoDE-2) project, which standardizes both CGM and insulin dosing data. Further exploration of the potential value of the GRI for non-insulin users needs to be undertaken. The panel unanimously recommended that CGM manufacturers and developers of data visualization software for CGMs add the GRI to their data platforms for insulin users.

The Teamwork, Targets, and Technology for Tight Glycemia (4T) Program has demonstrated effectiveness in early continuous glucose monitor use with remote patient monitoring among three diverse cohorts of youth with new onset type 1 diabetes (T1D, n = 451). Based on promising single center experience, we examine the potential for program scaling to the T1D Exchange Quality Improvement (T1DX-QI) Collaborative using an implementation science approach. We used the Simplified Implementation Logic Model to identify contextual determinants of implementation success. Then, we described the connections between the implementation support strategies used in 4T to address the contextual determinants and barriers to program reach to youth with T1D and provider adoption. We hypothesized mechanisms linking barriers and strategies to effectiveness and implementation outcomes. Limited clinician time was identified as a major barrier to program success. Several targeted strategies were used to address this, including the creation of the Timely Interventions for Diabetes Excellence (TIDE) clinical decision support tool early in the program and ongoing refinement of the tool. After clinical effectiveness of the 4T Program was established, the next phase focused on further allocating clinician monitoring to participants who would benefit the most. Additionally, cost to the family participants, cost to the clinic, and interest-holder engagement were other determinants addressed by the program strategies. This implementation evaluation informed connections between determinants, support strategies, mechanisms, and outcomes of the 4T Program. This enabled a more precise design in preparation to scale the program across a national network of pediatric diabetes clinics in the T1DX-QI.

Context: Youth 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. Objective: To assess 3 years of glycemic outcomes in the Pilot 4T Study. Methods: The 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. Results: In 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. Conclusion: Youth 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.

Diabetes is one of the most prevalent chronic diseases worldwide, with rates that continue to increase. More than 30 years ago, the Diabetes Control and Complications Trial demonstrated that intensive glucose management decreased long-term vascular complications. Unfortunately, many people with diabetes still struggle to meet glycemic goals. In this mini-review, we highlight advances in diabetes technology that are associated with improvements in glycemic management. Continuous glucose monitoring (CGM) and automated insulin delivery systems are associated with significant improvements in glycated hemoglobin (HbA1c), time in range (70-180 mg/dL), and decreases in severe hypoglycemia and diabetic ketoacidosis in people with both type 1 and type 2 diabetes. Recent data show that these technologies improve outcomes early in the course of type 1 diabetes. CGM is also being explored as a tool to monitor progression through early-stage type 1 diabetes (2 antibodies positive) to identify individuals who may benefit from disease-modifying therapies as well as to prevent the onset of diabetic ketoacidosis at onset of stage 3 (insulin-requiring) type 1 diabetes. Adjunctive pharmacologic therapies and artificial intelligence may further expand and improve therapies, offering potential synergistic benefits. However, there continue to be significant disparities in access to diabetes technologies and access to insulin worldwide. This mini-review summarizes recently published data, highlights emerging applications, and underscores the need to pair technological innovation with strategies that promote equitable access and support for diabetes care to improve outcomes for all people with diabetes.

OBJECTIVE:The use of continuous glucose monitoring (CGM) with remote patient monitoring (RPM) continues to grow. We evaluated the cost-effectiveness of CGM with RPM compared with self-monitoring of blood glucose (SMBG) and CGM alone. RESEARCH DESIGN AND METHODS:We simulated type 1 diabetes progression with a Markov model in 5-year-old patients over a 20-year, 50-year, and lifetime horizon. We tracked diabetic ketoacidosis (DKA), severe hypoglycemia (SH), and seven chronic complications: retinopathy, neuropathy, nephropathy, cardiovascular disease, end-stage renal disease, lower-extremity amputation, and blindness. We compared three interventions: SMBG, CGM, and CGM with RPM. Efficacy estimates were derived from meta-analyses of pediatric CGM studies and the results of the Teamwork, Targets, Technology, and Tight Glycemia Study (4T Study 1). We evaluated quality-adjusted life years (QALYs) and health care costs (2022 U.S. dollars) discounted at 3% annually. We performed extensive sensitivity analyses. RESULTS:Compared with SMBG, CGM increased QALYs by 0.09 and costs by $8,900 over 20 years; CGM with RPM increased QALYs by 0.37, and costs by $10,300. CGM with RPM yielded more QALYs at a lower incremental cost-effectiveness ratio compared with CGM ($27,400/QALY vs. $103,700/QALY, respectively). Results were robust across sensitivity analyses and time horizons. CGM with RPM remained cost-effective when achieving at least 30% of 4T's clinical efficacy. CONCLUSIONS:CGM with RPM delivers superior health outcomes compared with SMBG and CGM and is likely cost-effective for patients with newly diagnosed type 1 diabetes. Despite higher intervention costs, CGM with RPM can reduce complications costs and generate net health care savings.