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Wrist-worn activity monitors are often used to monitor heart rate (HR) and energy expenditure (EE) in a variety of settings including more recently in medical applications. The use of real-time physiological signals to inform medical systems including drug delivery systems and decision support systems will depend on the accuracy of the signals being measured, including accuracy of HR and EE. Prior studies assessed accuracy of wearables only during steady-state aerobic exercise. The objective of this study was to validate the accuracy of both HR and EE for 2 common wrist-worn devices during a variety of dynamic activities that represent various physical activities associated with daily living including structured exercise. We assessed the accuracy of both HR and EE for two common wrist-worn devices (Fitbit Charge 2 and Garmin vívosmart HR+) during dynamic activities. Over a 2-day period, 20 healthy adults (age: mean 27.5 [SD 6.0] years; body mass index: mean 22.5 [SD 2.3] kg/m ; 11 females) performed a maximal oxygen uptake test, free-weight resistance circuit, interval training session, and activities of daily living. Validity was assessed using an HR chest strap (Polar) and portable indirect calorimetry (Cosmed). Accuracy of the commercial wearables versus research-grade standards was determined using Bland-Altman analysis, correlational analysis, and error bias. Fitbit and Garmin were reasonably accurate at measuring HR but with an overall negative bias. There was more error observed during high-intensity activities when there was a lack of repetitive wrist motion and when the exercise mode indicator was not used. The Garmin estimated HR with a mean relative error (RE, %) of -3.3% (SD 16.7), whereas Fitbit estimated HR with an RE of -4.7% (SD 19.6) across all activities. The highest error was observed during high-intensity intervals on bike (Fitbit: -11.4% [SD 35.7]; Garmin: -14.3% [SD 20.5]) and lowest error during high-intensity intervals on treadmill (Fitbit: -1.7% [SD 11.5]; Garmin: -0.5% [SD 9.4]). Fitbit and Garmin EE estimates differed significantly, with Garmin having less negative bias (Fitbit: -19.3% [SD 28.9], Garmin: -1.6% [SD 30.6], P<.001) across all activities, and with both correlating poorly with indirect calorimetry measures. Two common wrist-worn devices (Fitbit Charge 2 and Garmin vívosmart HR+) show good HR accuracy, with a small negative bias, and reasonable EE estimates during low to moderate-intensity exercise and during a variety of common daily activities and exercise. Accuracy was compromised markedly when the activity indicator was not used on the watch or when activities involving less wrist motion such as cycle ergometry were done.

Background. Managing glycemia around exercise is challenging for individuals with type 1 diabetes (T1D) and their healthcare professionals (HCP). We investigated HCP knowledge and confidence around exercise counseling for youth with T1D worldwide. Objective. To assess HCP familiarity with ISPAD Clinical Practice Consensus Guidelines and confidence to deliver recommendations about T1D and exercise. Methods. A new online survey was developed on strategies and competencies about exercise for youth with T1D, comprising of 64 questions, divided into eight different categories, assessing HCPs perceived exercise knowledge, confidence, training, and barriers to exercise counseling. Results. A total of 125 HCPs mean ± SD age 42 ± 8.2 years (74% female, 73% physicians) completed the survey. The ISPAD exercise guidelines were considered familiar to 68/125 (54%) of responders. Overall, 91/125 (73%) felt confident with giving recommendations about exercise with 47/125 (38%) recommending 45–60 mins/day of physical activity, while 16/125 (13%) recommended >60 mins/day. Several topics related to self-management around exercise were covered by most, but not all responders, and differences were observed in exercise content “confidence” and/or “competence” based on geographic location (p=0.048). No differences in exercise recommendation dose, confidence, or familiarity with ISPAD guidelines were observed for age, sex, type of HCP, years in practice, or healthcare type. Conclusions. Exercise counseling for youth with T1D remains a challenge in most healthcare settings, globally. In general, the number of physically active minutes per week is under-prescribed for youth with T1D and many HCPs in various settings around the world feel that more professional education is needed to boost confidence around the education of several exercise-related topics.

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. Haptoglobin (Hp) levels differed by Hp phenotype in adults with type 2 diabetes. Fenofibrate decreased Hp levels in all Hp phenotypes. A higher baseline Hp level and a smaller fenofibrate‐related decrease in Hp levels in Hp 2‐2 phenotype subjects might be indicative of a lower protective efficacy.

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.

Background: There are well-documented disparities in diabetes care outcomes and technology usage, stemming from differences in healthcare access, distrust in healthcare providers, and other factors. This study evaluated patient-level outcomes of a diabetes support coach (DSC) intervention aimed at improving underserved adults’ diabetes technology use, diabetes distress, and HbA1c levels.Methods: As part of a Project Extension for Community Healthcare Outcomes (ECHO) Diabetes program, a social support intervention involving 28 DSCs was piloted at 33 Federally Qualified Health Centers (FQHCs) in Florida and California from May 2021 to May 2022. DSCs, who were adults with diabetes, served in a capacity similar to peer mentors and community health workers and received uniform training/oversight by a clinical team. Intervention participants (n=74 adults with insulin-requiring diabetes at FQHCs) self-enrolled and engaged with DSCs via text messages, phone calls, and events. Participants’ outcomes were evaluated cross-sectionally via the Diabetes Distress Scale (DDS-17) and a diabetes technology usage survey and longitudinally via HbA1c tests upon enrollment and at 6-month follow-up. A group of adults with insulin-requiring diabetes from the same FQHCs who did not receive the DSC intervention (n=363) was used for comparison. Descriptive statistics were computed for all outcomes (n, percentage; mean, SD/95% CI). Between-group comparisons were evaluated via chi-squared and t-tests.Results: DSC intervention participants reported significantly lower diabetes distress than the comparison group (DDS-17 score mean=1.6 vs. 2.1, p<0.001), and significantly more participants in the DSC intervention regularly used continuous glucose monitors (CGMs) than the comparison group (69.9% vs. 38.8%, p<0.0001). There were no significant differences in insulin pump usage or HbA1c.Conclusions: Lower diabetes distress and greater CGM usage among intervention participants suggest that the DSCs’ shared lived experiences and healthcare navigation support positively influenced underserved adults’ outcomes. These findings show DSCs’ potential for improving diabetes care and technology equity.

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.

IntroductionDiabetes 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 methodsTwo 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.ResultsParticipant-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).ConclusionsCharacteristics 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.

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. 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. 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. 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.

Regular physical activity and exercise (PA) are cornerstones of diabetes care for individuals with type 1 diabetes. In recent years, the availability of automated insulin delivery (AID) systems has improved the ability of people with type 1 diabetes to achieve the recommended glucose target ranges. PA provide additional health benefits but can cause glucose fluctuations, which challenges current AID systems. While an increasing number of clinical trials and reviews are being published on different AID systems and PA, it seems prudent at this time to collate this information and develop a position statement on the topic. This joint European Association for the Study of Diabetes (EASD)/International Society for Pediatric and Adolescent Diabetes (ISPAD) position statement reviews current evidence on AID systems and provides detailed clinical practice points for managing PA in children, adolescents and adults with type 1 diabetes using AID technology. It discusses each commercially available AID system individually and provides guidance on their use in PA. Additionally, it addresses different glucose responses to PA and provides stratified therapy options to maintain glucose levels within the target ranges for these age groups. Graphical Abstract