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Clinics continue to adopt care models shaped by the algorithmic analysis of continuous glucose monitoring (CGM) data, such as remote patient monitoring for type 1 diabetes. No clinic-facing quantitative framework currently exists to track the impact of such algorithm-directed care on patient outcomes and clinical workload. We used CGM data from the Teamwork, Targets, Technology, and Tight Control (4T) Study (Pilot n=135 and Study 1 n=133), in which algorithms enable precision, whole-population care by directing clinician attention to patients with deteriorating glucose management. Youth meeting criteria for clinical review are then contacted by Certified Diabetes Care and Education Specialists. Through iterative data analysis and meetings with a variety of stakeholders, 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, including (4) 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 regular program leadership meetings, the metrics facilitated data-driven decision-making about clinical and operational components of the program. In this paper, we describe the process of developing and operationalizing this reproducible, clinician-facing key performance indicator tool to monitor an algorithm-enabled remote patient monitoring program. 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.

Digital decision support and remote patient monitoring may improve outcomes and efficiency, but rarely scale beyond a single institution. Over the last 5 years, the platform Timely Interventions for Diabetes Excellence (TIDE) has been associated with reduced care provider screen time and improved, equitable type 1 diabetes care and outcomes for 268 patients in a heterogeneous population as part of the Teamwork, Targets, Technology, and Tight Control (4T) Study (NCT03968055, NCT04336969). Previous efforts to deploy TIDE at other institutions continue to face delays. In partnership with the diabetes technology non-profit, Tidepool, we developed Tidepool-TIDE, a clinic-agnostic, turnkey solution available to any clinic in the United States. We present how we overcame common technical and operational barriers specific to scaling digital health technology from one site to many. The concepts described are broadly applicable for institutions interested in facilitating broader adoption of digital technology for population-level management of chronic health conditions.

Vitamin D deficiency is common and associated with increased cardiovascular disease (CVD) risk. Pulse wave velocity (PWV) is a marker of vascular stiffness associated with CVD. We hypothesized that Vitamin D (25 (OH) D) levels would be inversely associated with PWV in youth with and without type 1 diabetes (T1D). Comparisons were made between adolescents with T1D (n = 211; age = 17.5 ± 2.3 years; diabetes duration = 10.9 ± 3.2 years; A1c = 9.1 ± 1.7%) and non-DM controls (n = 67; age = 16.9 ± 1.9 years). PWV was measured in the carotid-femoral segment (Sphygmocor Vx, AtCor Medical, Lisle, IL). Vitamin D levels were similar in adolescents with T1D and controls (27.7 ± 0.7 v. 26.0 ± 1.3 ng/ml; p = 0.26). Vitamin D was significantly inversely associated with PWV after adjusting for age, sex, quarter of the year, and race-ethnicity in adolescents with T1D (beta = -0.01 ± 0.004, p = 0.02) but not in the non-DM adolescents (beta = -0.008 ± 0.008, p = 0.32). Vitamin D remained significantly associated with PWV after additionally adjusting for hs-CRP in adolescents with T1D (-0.01 ± 0.004, p = 0.01). After adjusting for BMI z-score, lipids, or blood pressure, the relationship of Vitamin D with PWV was not significant. Vitamin D levels were inversely associated with PWV in adolescents with T1D, but not independently of BMI, lipids, or blood pressure. Our data contrast with other reports and suggest further research is indicated to determine if Vitamin D supplementation would be beneficial to lower CVD risk in adolescents with T1D with vitamin D insufficiency or deficiency.

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 . In a prospective study, a team-based approach combining continuous glucose monitoring with a technology-assisted remote patient monitoring program improved glycemia in a diverse cohort of children, adolescents and young adults with newly diagnosed type 1 diabetes.

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. 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. 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. 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. •4T Exercise assesses structured exercise education in youth with new onset type 1 diabetes (T1D) and their parents•4T Exercise involved four tele-health exercise education sessions during the first year following T1D diagnosis•Exercise education increased comfort, confidence, and knowledge of how to optimize glycemia during physical activity•Physical activity tracking and exercise education in new onset T1D is feasible and acceptable

Purpose of Review This review aims to summarize the type 1 diabetes (T1D) and weight literature with an emphasis on barriers associated with weight management, the unique T1D-specific factors that impact weight loss success, maladaptive and adaptive strategies for weight loss, and interventions to promote weight loss. Recent Findings Weight gain is associated with intensive insulin therapy. Overweight and obese weight status in individuals with T1D is higher than the general population and prevalence is rising. A variety of demographic (e.g., female sex), clinical (e.g., greater insulin needs), environmental (e.g., skipping meals), and psychosocial (e.g., depression, stress) factors are associated with overweight/obese weight status in T1D. Fear of hypoglycemia is a significant barrier to engagement in physical activity. Studies evaluating adaptive weight loss strategies in people with T1D are limited. Summary There is a growing literature highlighting the prevalence and seriousness of overweight and obesity among both youth and adults with T1D. There is an urgent need to develop evidence-based weight management guidelines and interventions that address the unique concerns of individuals with T1D and that concurrently address glycemic control.

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).

Background Urine uric acid (UUA) has been implicated in the pathogenesis of diabetic nephropathy via its effect on tubular cells. We hypothesized that the UUA level would be higher in adolescents with type 1 diabetes (T1D) than in those without T1D. We also hypothesized that UUA and fractional uric acid excretion (FeUA) would be higher in adolescents with T1D and hyperfiltration [estimated glomerular filtration rate (eGFR) ≥141 mL/min/1.73 m 2 ] than in those without hyperfiltration. Methods The UUA concentration was determined and FeUA calculated in adolescents with ( n  = 239) and without T1D ( n  = 75). The eGFR was calculated using the Zappitelli equation based on serum creatinine and cystatin C concentrations. Results Compared to the non-diabetic adolescents enrolled in the study, those with T1D had a higher eGFR (mean ± standard deviation: 120 ± 22 vs. 112 ± 16 mL/min/1.73 m 2 ; p  = 0.0006), lower urine pH (6.2 ± 0.8 vs. 6.5 ± 1.0; p = 0.01), and higher UUA (37.7 ± 18.6 vs. 32.8 ± 18.1 mg/dL; p  = 0.049) and FeUA (median [interquartile range]: 6.2 [4.3–8.7] vs. 5.2 [3.6–7.0] %; p  = 0.02). Among adolescents with T1D, those with hyperfiltration had higher median FeUA (8.6 [5.2–9.9] vs. 6.0 [4.2–8.3] %; p  = 0.02) than those without hyperfiltration. Conclusions The adolescents with T1D enrolled in the study had higher eGFR, higher UUA and more acidic urine than the non-diabetic controls, which may have increased their risk of UUA crystallization. Adolescents with T1D and hyperfiltration had higher FeUA than those without hyperfiltration. These hypothesis-generating observations may suggest a potential pathophysiologic association between uricosuria and hyperfiltration.

Early onset and more advanced periodontal disease has been reported for children with diabetes. We surveyed oral health knowledge, attitudes, and behaviors among adolescents with diabetes in order to inform potential intervention strategies. Study subjects were youth (ages 12–19 years) with type 1 diabetes (N = 90) participating in a cohort study investigating determinants of periodontal disease at a regional pediatric diabetes specialty clinic. Over 90% of the youth had been instructed on how to brush and floss and had preventive dental care in the past year. However, 44% knew that periodontal disease is associated with diabetes and 32% knew that it can start in childhood with bleeding gums. Despite being at high risk for developing periodontal disease, the mean toothbrushing frequency was once per day and 42% did not floss. Significant opportunity exists for improving periodontal disease knowledge and adoption of preventive oral hygiene behaviors in adolescents with diabetes.

OBJECTIVE: To compare serum cystatin C levels, a novel biomarker of renal function, in adolescents with and without type 1 diabetes and to determine what factors affect cystatin C levels. RESEARCH DESIGN AND METHODS: Cystatin C was measured in youth 12–19 years of age with (n = 259, diabetes duration 9 ± 3 years, HbA1c 8.9 ± 1.6%) and without diabetes (n = 78). Data were compared by diabetes status, and linear regression was used to determine factors affecting cystatin C. RESULTS: Cystatin C (0.698 ± 0.083 vs. 0.688 ± 0.127 mg/L, P = 0.40) was similar by diabetes status. In multiple linear regression, cystatin C was associated with age and serum creatinine in nondiabetic subjects and sex, age, and serum creatinine in subjects with diabetes (P < 0.05). CONCLUSIONS: These data suggest sex differences and age-related changes in cystatin C in adolescents with type 1 diabetes. An understanding of these changes is needed to determine the potential role of cystatin C as a marker of renal function in this population.