Not all healthcare inequities in diabetes are equal: a comparison of two medically underserved cohorts.

INTRODUCTION: Diabetes disparities exist based on socioeconomic status, race, and ethnicity. The aim of this study is to compare two cohorts with diabetes from California and Florida to better elucidate how health outcomes are stratified within underserved communities according to state location, race, and ethnicity. RESEARCH DESIGN AND METHODS: Two cohorts were recruited for comparison from 20 Federally Qualified Health Centers as part of a larger ECHO Diabetes program. Participant-level data included surveys and HbA1c collection. Center-level data included Healthcare Effectiveness Data and Information Set metrics. Demographic characteristics were summarized overall and stratified by state (frequencies, percentages, means (95% CIs)). Generalized linear mixed models were used to compute and compare model-estimated rates and means. RESULTS: Participant-level cohort: 582 adults with diabetes were recruited (33.0% type 1 diabetes (T1D), 67.0% type 2 diabetes (T2D)). Mean age was 51.1 years (95% CI 49.5, 52.6); 80.7% publicly insured or uninsured; 43.7% non-Hispanic white (NHW), 31.6% Hispanic, 7.9% non-Hispanic black (NHB) and 16.8% other. Center-level cohort: 32 796 adults with diabetes were represented (3.4% with T1D, 96.6% with T2D; 72.7% publicly insured or uninsured). Florida had higher rates of uninsured (p<0.0001), lower continuous glucose monitor (CGM) use (18.3% Florida; 35.9% California, p<0.0001), and pump use (10.2% Florida; 26.5% California, p<0.0001), and higher proportions of people with T1D/T2D>9% HbA1c (p<0.001). Risk was stratified within states with NHB participants having higher HbA1c (mean 9.5 (95% CI 8.9, 10.0) compared with NHW with a mean of 8.4 (95% CI 7.8, 9.0), p=0.0058), lower pump use (p=0.0426) and CGM use (p=0.0192). People who prefer to speak English were more likely to use a CGM (p=0.0386). CONCLUSIONS: Characteristics of medically underserved communities with diabetes vary by state and by race and ethnicity. Florida's lack of Medicaid expansion could be a factor in worsened risks for vulnerable communities with diabetes.

Haptoglobin phenotype and levels in type 2 diabetes and effects of fenofibrate.

AIMS/HYPOTHESIS: In diabetes haptoglobin (Hp) 2 vs Hp 1 allelic product is associated with cardiac and renal complications. Few studies report both Hp phenotype and Hp levels. In a Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) trial substudy we evaluated the Hp phenotype, Hp levels, and fenofibrate effects. MATERIALS AND METHODS: In 480 adults with type 2 diabetes (T2D) the Hp phenotype was assessed and the Hp level quantified (both using ELISAs assays) in plasma from baseline, after 6 weeks of fenofibrate, and (in n = 200) at 2 years post-randomization to fenofibrate or placebo. RESULTS: The Hp phenotypes 1-1, 2-1, and 2-2 frequencies were 15%, 49%, and 36%, respectively. Baseline Hp levels differed by phenotype (P < 0.0001) and decreased (median 21%) after 6 weeks fenofibrate in all phenotypes (adjusted mean (95% CI): -0.27 (-0.32, -0.23) mg/mL in Hp 1-1, -0.29 (-0.31, -0.27) mg/mL in Hp 2-1 and -0.05 (-0.07, -0.02) mg/mL in Hp 2-2 (P = 0.005 and P = 0.055 vs Hp 1-1 and Hp 2-1, respectively)). At 2 years post-randomization the Hp levels in the placebo group had returned to baseline, whilst the fenofibrate-group levels remained similar to the 6 week levels. CONCLUSIONS: In type 2 diabetes, Hp levels differ by Hp phenotype and are decreased by fenofibrate in all phenotypes, but the effect is diminished in Hp 2-2.

'We're taught green is good': Perspectives on time in range and time in tight range from youth with type 1 diabetes, and parents of youth with type 1 diabetes.

AIMS: Continuous glucose monitoring (CGM) systems are standard of care for youth with type 1 diabetes with the goal of spending >70% time in range (TIR; 70-180 mg/dL, 3.9-10 mmol/L). We aimed to understand paediatric CGM user experiences with TIR metrics considering recent discussion of shifting to time in tight range (TITR; >50% time between 70 and 140 mg/dL, 3.9 and 7.8 mmol/L). METHODS: Semi-structured interviews and focus groups with adolescents with type 1 diabetes and parents of youth with type 1 diabetes focused on experiences with TIR goals and reactions to TITR. Groups and interviews were audio-recorded, transcribed and analysed using content analysis. RESULTS: Thirty participants (N = 19 parents: age 43.6 ± 5.3 years, 79% female, 47% non-Hispanic White, 20 ± 5 months since child's diagnosis; N = 11 adolescents: age 15.3 ± 2 years, 55% female, 55% non-Hispanic White, 16 ± 3 months since diagnosis) attended. Participants had varying levels of understanding of TIR. Some developed personally preferred glucose ranges. Parents often aimed to surpass 70% TIR. Many described feelings of stress and disappointment when they did not meet a TIR goal. Concerns about TITR included increased stress and burden; risk of hypoglycaemia; and family conflict. Some participants said TITR would not change their daily lives; others said it would improve their diabetes management. Families requested care team support and a clear scientific rationale for TITR. CONCLUSIONS: The wealth of CGM data creates frequent opportunities for assessing diabetes management and carries implications for management burden. Input from people with type 1 diabetes and their families will be critical in considering a shift in glycaemic goals and targets.

Refining Insulin on Board with netIOB for Automated Insulin Delivery.

Automated insulin delivery (AID) systems enhance glucose management by lowering mean glucose level, reducing hyperglycemia, and minimizing hypoglycemia. One feature of most AID systems is that they allow the user to view "insulin on board" (IOB) to help confirm a recent bolus and limit insulin stacking. This metric, along with viewing glucose concentrations from a continuous glucose monitoring system, helps the user understand bolus insulin action and the future "threat" of hypoglycemia. However, the current presentation of IOB in AID systems can be misleading, as it does not reflect true insulin action or automatic, dynamic insulin adjustments. This commentary examines the evolution of IOB from a bolus-specific metric to its contemporary use in AID systems, highlighting its limitations in capturing real-time insulin modulation during varying physiological states.

Changing Glucose Levels During the Menstrual Cycle as Observed in Adults in the Type 1 Diabetes Exercise Initiative Study.

OBJECTIVES: Evidence suggests that glucose levels in menstruating females with type 1 diabetes change throughout the menstrual cycle, reaching a peak during the luteal phase. The Type 1 Diabetes Exercise Initiative (T1DEXI) study provided the opportunity to assess glycemic metrics between early and late phases of the menstrual cycle, and whether differences could be explained by exercise, insulin, and carbohydrate intake. METHODS: One hundred seventy-nine women were included in our analysis. Glycemic metrics, carbohydrate intake, insulin requirements, and exercise habits during the early vs late phases of their menstrual cycles (i.e. 2 to 4 days after vs 2 to 4 days before reported menstruation start date) were compared. RESULTS: Mean glucose increased from 8.2±1.5 mmol/L (148±27 mg/dL) during the early follicular phase to 8.6±1.6 mmol/L (155±29 mg/dL) during the late luteal phase (p<0.001). Mean percent time-in-range (3.9 to 10.0 mmol/L [70 to 180 mg/dL]) decreased from 73±17% to 70±18% (p=0.002), and median percent time >10.0 mmol/L (>180 mg/dL) increased from 21% to 23% (p<0.001). Median total daily insulin requirements increased from 37.4 units during the early follicular phase to 38.5 units during the late luteal phase (p=0.02) and mean daily carbohydrate consumption increased slightly from 127±47 g to 133±47 g (p=0.05); however, the difference in mean glucose during early follicular vs late luteal phase was not explained by differences in exercise duration, total daily insulin units, or reported carbohydrate intake. CONCLUSIONS: Glucose levels during the late luteal phase were higher than those of the early follicular phase of the menstrual cycle. These glycemic changes suggest that glucose management for women with type 1 diabetes may need to be fine-tuned within the context of their menstrual cycles.

Equitable implementation of a precision digital health program for glucose management in individuals with newly diagnosed type 1 diabetes.

Few young people with type 1 diabetes (T1D) meet glucose targets. Continuous glucose monitoring improves glycemia, but access is not equitable. We prospectively assessed the impact of a systematic and equitable digital-health-team-based care program implementing tighter glucose targets (HbA1c < 7%), early technology use (continuous glucose monitoring starts <1 month after diagnosis) and remote patient monitoring on glycemia in young people with newly diagnosed T1D enrolled in the Teamwork, Targets, Technology, and Tight Control (4T Study 1). Primary outcome was HbA1c change from 4 to 12 months after diagnosis; the secondary outcome was achieving the HbA1c targets. The 4T Study 1 cohort (36.8% Hispanic and 35.3% publicly insured) had a mean HbA1c of 6.58%, 64% with HbA1c < 7% and mean time in the range (70-180 mg dl-1) of 68% at 1 year after diagnosis. Clinical implementation of the 4T Study 1 met the prespecified primary outcome and improved glycemia without unexpected serious adverse events. The strategies in the 4T Study 1 can be used to implement systematic and equitable care for individuals with T1D and translate to care for other chronic diseases. ClinicalTrials.gov registration: NCT04336969 .

Automated Insulin Delivery Systems in Pediatric Type 1 Diabetes: A Narrative Review.

This narrative review assesses the use of automated insulin delivery (AID) systems in managing persons with type 1 diabetes (PWD) in the pediatric population. It outlines current research, the differences between various AID systems currently on the market and the challenges faced, and discusses potential opportunities for further advancements within this field. Furthermore, the narrative review includes various expert opinions on how different AID systems can be used in the event of challenges with rapidly changing insulin requirements. These include examples, such as during illness with increased or decreased insulin requirements and during physical activity of different intensities or durations. Case descriptions give examples of scenarios with added user-initiated actions depending on the type of AID system used. The authors also discuss how another AID system could have been used in these situations.

Assessment of Glycemia Risk Index and Standard Continuous Glucose Monitoring Metrics in a Real-World Setting of Exercise in Adults With Type 1 Diabetes: A Post-Hoc Analysis of the Type 1 Diabetes and Exercise Initiative.

BACKGROUND: Standardized reporting of continuous glucose monitoring (CGM) metrics does not provide extra weighting for very high or very low glucose, despite their distinct clinical significance, and thus may underestimate glycemic risk in people with type 1 diabetes (T1D) during exercise. Glycemia Risk Index (GRI) is a novel composite metric incorporating clinician-validated extra weighting for glycemic extremes, which may provide a novel summary index of glycemia risk around exercise. METHODS: Adults (≥18 years) in the T1D EXercise Initiative study wore CGM and activity trackers for four weeks. For this analysis, exercise days were defined as 24 hours following ≥20 minutes of exercise, with no other exercise in the 24-hour period. Sedentary days were defined as any 24 hours with no recorded exercise within that period or the preceding 24 hours. Linear mixed-effects regression was used to evaluate exercise effects on GRI and CGM metrics within 24 hours postexercise. RESULTS: In 408 adults with T1D with >70% CGM and activity data, GRI on exercise (N = 3790) versus sedentary days (N = 1865) was significantly lower (mean [SD]: 29.9 [24.0] vs 34.0 [26.1], respectively, absolute mean difference -1.70 [-2.73, -0.67], P < .001), a ~5% reduction in glycemic risk. Percent time in range (TIR; 70-180 mg/dL) increased on exercise days (absolute mean difference 2.67 [1.83, 3.50], P < .001), as did time below range (TBR; relative mean difference 1.17 [1.12, 1.22], P < .001), while time above range (TAR) decreased (relative mean difference 0.84 [0.79, 0.88], P < .001). CONCLUSIONS: Glycemia Risk Index improved on exercise versus sedentary days, despite increased TBR, which is weighted most heavily in the GRI calculation, due to a robust reduction in TAR.

Smart Start - Designing Powerful Clinical Trials Using Pilot Study Data.

BACKGROUND: Digital health interventions may be optimized before evaluation in a randomized clinical trial. Although many digital health interventions are deployed in pilot studies, the data collected are rarely used to refine the intervention and the subsequent clinical trials. METHODS: We leverage natural variation in patients eligible for a digital health intervention in a remote patient-monitoring pilot study to design and compare interventions for a subsequent randomized clinical trial. RESULTS: Our approach leverages patient heterogeneity to identify an intervention with twice the estimated effect size of an unoptimized intervention. CONCLUSIONS: Optimizing an intervention and clinical trial based on pilot data may improve efficacy and increase the probability of success. (Funded by the National Institutes of Health and others; ClinicalTrials.gov number, NCT04336969.)

Exercising Safely with the MiniMed™ 780G Automated Insulin Delivery System.

The physical and psychological benefits of exercise are particularly pertinent to people with type 1 diabetes (T1D). The variability in subcutaneous insulin absorption and the delay in offset and onset in glucose lowering action impose limitations, given the rapidly varying insulin requirements with exercise. Simultaneously, there are challenges to glucose monitoring. Consequently, those with T1D are less likely to exercise because of concerns regarding glucose instability. While glucose control with exercise can be enhanced using automated insulin delivery (AID), all commercially available AID systems remain limited by the pharmacokinetics of subcutaneous insulin delivery. Although glycemic responses may vary with exercises of differing intensities and durations, the principles providing the foundation for guidelines include minimization of insulin on board before exercise commencement, judicious and timely carbohydrate supplementation, and when possible, a reduction in insulin delivered in anticipation of planned exercise. There is an increasing body of evidence in support of superior glucose control with AID over manual insulin dosing in people in T1D who wish to exercise. The MiniMed™ 780G AID system varies basal insulin delivery with superimposed automated correction boluses. It incorporates a temporary (elevated glucose) target of 8.3 mmol/L (150 mg/dL) and when it is functioning, the autocorrection boluses are stopped. As the device has recently become commercially available, there are limited data assessing glucose control with the MiniMed™ 780G under exercise conditions. Importantly, when exercise was planned and implemented within consensus guidelines, %time in range and %time below range targets were met. A practical approach to exercising with the device is provided with illustrative case studies. While there are limitations to spontaneity imposed on any AID device due to the pharmacokinetics associated with the subcutaneous delivery of current insulin formulations, the MiniMed™ 780G system provides people with T1D an excellent option for exercising safely if the appropriate strategies are implemented.

Diabetic Ketoacidosis at Diagnosis in Youth with Type 1 Diabetes Is Associated with a Higher Hemoglobin A1c Even with Intensive Insulin Management.

Introduction: Diabetic ketoacidosis (DKA) at diagnosis is associated with short- and long-term complications. We assessed the relationship between DKA status and hemoglobin A1c (A1c) levels in the first year following type 1 diabetes (T1D) diagnosis. Research Design and Methods: The Pilot Teamwork, Targets, Technology, and Tight Control (4T) study offered continuous glucose monitoring to youth with T1D within 1 month of diagnosis. A1c levels were compared between historical (n = 271) and Pilot 4T (n = 135) cohorts stratified by DKA status at diagnosis (DKA: historical = 94, 4T = 67 versus without DKA: historical = 177, 4T = 68). A1c was evaluated using locally estimated scatter plot smoothing. Change in A1c from 4 to 12 months postdiagnosis was evaluated using a linear mixed model. Results: Median age was 9.7 (interquartile range [IQR]: 6.6, 12.7) versus 9.7 (IQR: 6.8, 12.7) years, 49% versus 47% female, 44% versus 39% non-Hispanic White in historical versus Pilot 4T. In historical and 4T cohorts, DKA at diagnosis demonstrated higher A1c at 6 (0.5% [95% confidence interval (CI): 0.21-0.79; P < 0.01] and 0.38% [95% CI: 0.02-0.74; P = 0.04], respectively), and 12 months (0.62% [95% CI: -0.06 to 1.29; P = 0.07] and 0.39% [95% CI: -0.32 to 1.10; P = 0.29], respectively). The highest % time in range (TIR; 70-180 mg/dL) was seen between weeks 15-20 (69%) versus 25-30 (75%) postdiagnosis for youth with versus without DKA in Pilot 4T, respectively. Conclusions: Pilot 4T improved A1c outcomes versus the historical cohort, but those with DKA at diagnosis had persistently elevated A1c throughout the study and intensive diabetes management did not mitigate this difference. DKA prevention at diagnosis may translate into better glycemic outcomes in the first-year postdiagnosis. Clinical Trial Registration: clinicaltrials.gov: NCT04336969.

"It changed everything we do": A mixed methods study of youth and parent experiences with a pilot exercise education intervention following new diagnosis of type 1 diabetes.

AIMS: This pilot study delivered a comprehensive exercise education intervention to youth with new-onset type 1 diabetes (T1D) and their parents to increase knowledge and confidence with physical activity (PA) shortly after diagnosis. METHODS: Youth initiated continuous glucose monitoring (CGM) and PA trackers within 1 month of diagnosis. Youth and their parents received the 4-session intervention over 12 months. Participants completed self-report questionnaires at baseline, 6- and 12-months. Surveys were analyzed using linear mixed effects models. Semi-structured interviews and focus groups explored experiences with the exercise education intervention. Groups and interviews were audio-recorded, transcribed, and analyzed using content analysis. RESULTS: A total of 16 parents (aged 46 ± 7 years; 88 % female; 67 % non-Hispanic White) and 17 youth (aged 14 ± 2 years; 41 % female; 65 % non-Hispanic White) participated. Worry about hypoglycemia did not worsen throughout the study duration. Parents and youth reported increased knowledge and confidence in managing T1D safely and preventing hypoglycemia during PA following receiving the tailored exercise education intervention. CONCLUSION: This study assessed a novel structured exercise education program for youth and their parents shortly following T1D diagnosis. These results support the broad translation and acceptability of a structured exercise education program in new-onset T1D.

The Promising Success of Project Extension for Community Healthcare Outcomes (ECHO) Diabetes: Case Series.

BACKGROUND: In the United States, there are over 37 million people with diabetes but only 8000 endocrinologists. Therefore, many people with diabetes receive care exclusively from primary care providers (PCPs). To democratize knowledge regarding insulin-requiring diabetes through tele-education, Stanford University and the University of Florida developed Project Extension for Community Healthcare Outcomes (ECHO) Diabetes. OBJECTIVE: ECHO Diabetes uses a Hub and Spoke model connecting specialists (the "Hub") with PCPs (the "Spokes"). One-hour, weekly sessions include Hub diabetes didactic presentations and Spoke deidentified case presentations. Lessons learned during these sessions target provider knowledge and confidence surrounding diabetes management and patient care. METHODS: Spokes were asked to provide short descriptions of people with diabetes whose diabetes management improved directly or indirectly from their providers' participation or their involvement with a Diabetes Support Coach (DSC). We provide a case series to describe individuals and outcomes. Because this study was not a randomized controlled trial and was a prospective observation of patients with the intervention delivered to providers, the trial is not registered in a public trials registry. RESULTS: A case series of 11 people with diabetes was compiled from 10 PCPs and 1 DSC from California and Florida between 2021 and 2022. The principal impact of ECHO Diabetes is the education amplified from PCPs and DSCs to people with diabetes. In all cases, people with diabetes reported increased engagement and improved diabetes management. Several cases reflected increased access to diabetes technology, improvement in glycemic outcomes, and positive trends in mental health measures. CONCLUSIONS: This case series elucidates the potential value of the ECHO Diabetes program to people with diabetes who receive their diabetes care from PCPs. Those matched with a DSC saw clinically significant improvements in hemoglobin A1c and mental health outcomes.

"Empowering Us": A community-led survey of real-world perspectives of adults with type 1 diabetes using insulin pumps and continuous glucose monitoring to manage their glucose levels.

OBJECTIVE: To conduct an Australian community-led survey of adults with type 1 diabetes (T1D), identifying priorities for, and barriers to, optimal use of advanced glucose management technologies. RESEARCH DESIGN AND METHODS: A 30-question online survey of current or past users of insulin pump therapy (IPT), real-time continuous glucose monitoring (RT-CGM), or intermittently scanned CGM (isCGM) explored perceptions regarding device design, access, education, outcomes, and support. RESULTS: Between November 2021 and January 2022, surveys were completed by 3,380 participants (age [mean ± SD] 45 ± 16 years; 62% female; 20 ± 14 years diabetes), with 55%, 82%, and 55% reporting experience with IPT, RT-CGM, and isCGM, respectively. Overall, most considered diabetes technology '(extremely) important' for maintaining target glucose levels (98%) and reducing hypoglycaemia severity and frequency (93%). For most, technology contributed positively to emotional well-being (IPT 89%; RT-CGM 91%; isCGM 87%), which was associated with device effectiveness in maintaining glucose in range, comfort, and convenience. Barriers included affordability (IPT 68%; RT-CGM 81%; isCGM 69%) and insufficient information for informed choices about device suitability (IPT 39%; RT-CGM 41%; isCGM 36%). CONCLUSIONS: Technology is perceived by adults with T1D as important for managing glycaemia and emotional well-being. Modifiable barriers to use include affordability, and information regarding device suitability.

A Comparison of Faster Insulin Aspart with Standard Insulin Aspart Using Hybrid Automated Insulin Delivery System in Active Children and Adolescents with Type 1 Diabetes: A Randomized Double-Blind Crossover Trial.

Objective: To evaluate the use of faster acting (FIA) and standard insulin aspart (SIA) with hybrid automated insulin delivery (AID) in active youth with type 1 diabetes. Research Design and Methods: In this double-blind multinational randomized crossover trial, 30 children and adolescents with type 1 diabetes (16 females; aged 15.0 ± 1.7 years; baseline HbA1c 7.5% ± 0.9% [58 ± 9.8 mmol/mol]) underwent two unrestricted 4-week periods using hybrid AID with either FIA or SIA in random order. During both interventions, participants were using the hybrid AID (investigational version of MiniMed™ 780G; Medtronic). Participants were encouraged to exercise as frequently as possible, capturing physical activity with an activity monitor. The primary outcome was the percentage of sensor glucose time above range (180 mg/dL [10.0 mmol/L]) measured by continuous glucose monitoring. Results: In an intention-to-treat analysis, mean time above range was 31% ± 15% at baseline, 19% ± 6% during FIA use, and 20% ± 6% during SIA use with no difference between treatments: mean difference = -0.9%; 95% CI: -2.4% to 0.6%; P = 0.23. Similarly, there was no difference in mean time in range (TIR) (78% and 77%) or median time below range (2.5% and 2.8%). Glycemic outcomes during exercise or postprandial periods were comparable for the two treatment arms. No severe hypoglycemia or diabetic ketoacidosis events occurred. Conclusions: FIA was not superior to SIA with hybrid AID system use in physically active children and adolescents with type 1 diabetes. Nonetheless, both insulin formulations enabled high overall TIR and low time above and below ranges, even during and after documented exercise. Trial Registration Clinicaltrials.gov: NCT04853030.

An Overview of Diet and Physical Activity for Healthy Weight in Adolescents and Young Adults with Type 1 Diabetes: Lessons Learned from the ACT1ON Consortium.

The prevalence of overweight and obesity in young people with type 1 diabetes (T1D) now parallels that of the general population. Excess adiposity increases the risk of cardiovascular disease, which is already elevated up to 10-fold in T1D, underscoring a compelling need to address weight management as part of routine T1D care. Sustainable weight management requires both diet and physical activity (PA). Diet and PA approaches must be optimized towards the underlying metabolic and behavioral challenges unique to T1D to support glycemic control throughout the day. Diet strategies for people with T1D need to take into consideration glycemic management, metabolic status, clinical goals, personal preferences, and sociocultural considerations. A major barrier to weight management in this high-risk population is the challenge of integrating regular PA with day-to-day management of T1D. Specifically, exercise poses a substantial challenge due to the increased risk of hypoglycemia and/or hyperglycemia. Indeed, about two-thirds of individuals with T1D do not engage in the recommended amount of PA. Hypoglycemia presents a serious health risk, yet prevention and treatment often necessitates the consumption of additional calories, which may prohibit weight loss over time. Exercising safely is a concern and challenge with weight management and maintaining cardiometabolic health for individuals living with T1D and many healthcare professionals. Thus, a tremendous opportunity exists to improve exercise participation and cardiometabolic outcomes in this population. This article will review dietary strategies, the role of combined PA and diet for weight management, current resources for PA and glucose management, barriers to PA adherence in adults with T1D, as well as findings and lessons learned from the Advancing Care for Type 1 Diabetes and Obesity Network (ACT1ON).

A quantitative model to ensure capacity sufficient for timely access to care in a remote patient monitoring program.

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.