Reviewing U.S. Connected Diabetes Care: The Newest Member of the Team

Expert Panel Recommendations for a Standardized Ambulatory Glucose Profile Report for Connected Insulin Pens

Background: Connected insulin pens capture data on insulin dosing/timing and can integrate with continuous glucose monitoring (CGM) devices with essential insulin and glucose metrics combined into a single platform. Standardization of connected insulin pen reports is desirable to enhance clinical utility with a single report. Methods: An international expert panel was convened to develop a standardized connected insulin pen report incorporating insulin and glucose metrics into a single report containing clinically useful information. An extensive literature review and identification of examples of current connected insulin pen reports were performed serving as the basis for creation of a draft of a standardized connected insulin pen report. The expert panel participated in three virtual standardization meetings and online surveys. Results: The Ambulatory Glucose Profile (AGP) Report: Connected Insulin Pen brings all clinically relevant CGM-derived glucose and connected insulin pen metrics into a single simplified two-page report. The first page contains the time in ranges bar, summary of key insulin and glucose metrics, the AGP curve, and detailed basal (long-acting) insulin assessment. The second page contains the bolus (mealtime and correction) insulin assessment periods with information on meal timing, insulin-to-carbohydrate ratio, average bolus insulin dose, and number of days with bolus doses recorded. The report's second page contains daily glucose profiles with an overlay of the timing and amount of basal and bolus insulin administered. Conclusion: The AGP Report: Connected Insulin Pen is a standardized clinically useful report that should be considered by companies developing connected pen technology as part of their system reporting/output.

Effectiveness of a hybrid approach in integrating GLP-1 agonists and lifestyle guidance for obesity and pre-diabetes management: RWE retrospective study

Aim

Emerging anti-obesity pharmacotherapy provides an option to correct maladaptive physiological and hormonal changes associated with obesity. One of the widely used medications in this context is glucagon-like peptide 1 (GLP-1) agonists. However, the misuse of these medications without any guidance and monitoring of lifestyle modifications can lead to unfavorable outcomes. The study aims to evaluate the effectiveness of a hybrid care model, incorporating GLP-1 and GLP-1/GIP agonist therapies, in managing obese patients with/without pre-diabetes. This study showcases the midway results of a 6-month program, which includes a multidisciplinary care team and digital technology for continuous engagement and monitoring of patients, both in-clinic and remotely.

Methods

In a retrospective observational study, 115 participants were treated with GLP-1s (semaglutide, tirzepatide, and liraglutide). Physicians, dietitians, and coaches worked together to support behavioral changes using a dedicated app provided to patients. At the care team end, an integrated portal enabled continuous data flow allowing for the care team to provide personalized care via chat at regular intervals. Data collected included food logs, continuous glucose monitoring (CGM), and digital biomarkers such as sleep and activity.

Results

At the midpoint of the program, participants exhibited statistically significant improvements in various metabolic parameters. Mean weight reduction was 8 %, with significant reductions in BMI, fat mass, and cholesterol levels. 24 (20.9 %) of patients lost ≥5 % of body weight, 55 (47.8 %) patients lost ≥10 % weight, and 36 (31.3 %) patients lost ≥15 % weight. Sub-analysis of pre-diabetic patients (n=36) demonstrated substantial improvements, including control of pre-diabetes in 80.6 % of cases and reduced HbA1c levels back to normoglycemia (5.39 ± 0.27).

Conclusion

The Zone.Health's program, which combines pharmacotherapy with continuous engagement and monitoring to enable sustainable lifestyle modifications, demonstrated significant improvements in weight, body composition, and metabolic markers. Pre-diabetes was also effectively addressed. It is necessary to conduct further research to assess the long-term sustainability and optimal adoption of such care models into clinical practice.

Evaluating Therapeutic Inertia in Two Telehealth Interventions for Type 2 Diabetes: Secondary Analyses of a Randomized Trial

Introduction: Although therapeutic inertia is a known driver of suboptimal type 2 diabetes control, little is known about how to combat this phenomenon. We analyzed randomized trial data to determine whether a comprehensive telehealth intervention was more effective than a less structured telehealth approach (telemonitoring and care coordination) at promoting treatment intensification in poorly controlled diabetes. Methods: Patients with poorly controlled type 2 diabetes were randomized 1:1 to telemonitoring/care coordination or a comprehensive telehealth intervention, which included an active, study provider-guided medication management component. Prospectively collected medication lists were used to determine whether treatment intensification occurred for each patient during 3-month intervals throughout the study period. To examine between-arm differences in treatment intensification over time, we fit a generalized estimation equation model. In each arm, hemoglobin A1c levels at the beginning and end of each 3-month interval were used to distinguish between therapeutic inertia and potentially appropriate nonintensification of treatment. Results: The mean, model-estimated likelihood of treatment intensification during 3-month intervals was 61.3% in the comprehensive telehealth group versus 48.6% for telemonitoring/care coordination (odds ratio 1.7, 95% confidence interval 1.2-2.2; p = 0.0007), with no evidence that treatment effect varied over time (p = 0.54). Treatment intervals with observed therapeutic inertia were more common in the telemonitoring/care coordination arm than the comprehensive telehealth arm (116/300, 39% vs. 57/275, 21%). Conclusions: A comprehensive telehealth approach that integrated protocol-guided medication management increased treatment intensification and reduced therapeutic inertia compared with a less structured telehealth approach. The studied approaches may serve as examples of how systems might use telehealth to combat therapeutic inertia. Clinical Trial Registration: ClinicalTrials.gov NCT03520413.

Measuring the effectiveness of hybrid diabetes care over 90 days through continuous data monitoring in type 2 diabetic patients

Background

Diabetes Mellitus, a global health challenge, affects 537 million individuals. Traditional management relies on periodic clinic visits, but technological advancements, including remote monitoring, offer transformative changes. Telemedicine enhances access, convenience, adherence, and glycemic control. Challenges include trust-building and limitations in face-to-face interactions. Integrating remote monitoring with in-person healthcare creates a hybrid approach. This study evaluates the impact on Type 2 Diabetes patients over 3 months.

Methods

A retrospective case-control observational study. Inclusion criteria involved previous Type 2 Diabetes diagnosis and a minimum 3-month GluCare model period with two physical visits. Patients in the case group had in-clinic visits, bi-weekly app engagement, and monthly body weight readings. Control group had in-clinic visits only. Outcomes measured included HbA1c, lipid profile, CV risk, eGFR, urine Albumin/Creatinine Ratio, Uric Acid, and CRP.

Results

Case group showed significant HbA1c improvements (-2.19%), especially in higher baseline levels. Weight, BMI, LDL, total cholesterol, and CVD risk also improved. Controls showed smaller improvements. Higher digital interactions correlated with better outcomes. Patients with ≥11 interactions showed significant reductions in HbA1c (-2.38%) and weight (-6.00 kg).

Conclusion

The GluCare.Health hybrid model demonstrates promising outcomes in Type 2 diabetes management. The integration of in-clinic consultations with continuous remote monitoring leads to substantial improvements in glycemic control and clinical parameters. The study highlights the importance of patient engagement in achieving positive outcomes, with higher digital interactions associated with greater reductions in HbA1c and weight. The hybrid approach proves more effective than digital-only interventions, emphasizing the need for comprehensive, end-to-end solutions in diabetes care.

What is meant by 'integrated personalized diabetes management': A view into the future and what success should look like

Integrated personalized diabetes management (IPDM) has emerged as a promising approach to improving outcomes in patients with diabetes mellitus (DM). This care approach emphasizes the integration and coordination of different providers, including physicians, nurses, dietitians, social workers and pharmacists. The goal of IPDM is to provide patients with personalized care that is tailored to their needs. This review addresses the concept of integrated care and the use of technology (including data, software applications and artificial intelligence) as well as managerial, regulatory and financial aspects. The implementation and upscaling of digitally enabled IPDM are discussed, with elaboration of successful practices and related evidence. Finally, recommendations are made. It is concluded that the adoption of digitally enabled IPDM on a global level is inevitable, considering the challenges created by an increasing prevalence of patients with DM and the need for better outcomes and improvement of health system sustainability.

The Development and Potential Applications of an Automated Method for Detecting and Classifying Continuous Glucose Monitoring Patterns

INTRODUCTION

Continuous glucose monitoring (CGM) is emerging as a transformative tool for helping people with diabetes self-manage their glucose and supporting clinicians in effective treatment. Unfortunately, many CGM users, and clinicians, find interpreting the large volume of CGM data to be overwhelming and complex. To address this challenge, an efficient, intelligent method for detecting and classifying discernable patterns in CGM data was desired.

METHODS

We developed an automated artificial intelligence (AI)-driven method to detect and classify different discernable CGM patterns which called "CGM events." We trained different models using 60 days of CGM data from 27 individuals with diabetes from a publicly available data set and then evaluated model performance using separate test data from the same group. Each event is classified according to clinical significance based on three parameters: (1) glucose category at or near the beginning of the CGM event; (2) a calculated severity score that encompasses both signal shape and temporal characteristics (e.g., how high the CGM curve goes (measured in mg/dL) and how long it stays above target (as established by published consensus guidelines); and (3) the glucose category at or near the end of the event.

RESULTS

The system accurately detected and classified events from actual CGM data. This was also validated with expert diabetes clinicians.

CONCLUSIONS

Advanced pattern recognition methods can be used to detect and classify CGM events of interest and may be used to provide actionable insights and self-management support to CGM users and decision support to the clinicians caring for them.

7. Diabetes Technology: Standards of Care in Diabetes-2024

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Beyond the EHR: How Digital Health Tools Foster Participatory Health and Self-Care for Patients with Diabetes

Just as physicians managing patients with diabetes find that it is a data-driven process, for patients living with diabetes, it is even more so, as physicians see them every few months, but patients need to live with diabetes all the time. Fortunately, the advent of the web has allowed patients to connect with information, medical care, and other patients, while mobile and connected technologies such as smartphones have provided the flexibility to do this-and to manage and share their health information-from anywhere. Healthcare professionals who care for patients with diabetes should be aware of the digital health technologies that enable patients to better care for themselves, be more active participants in their healthcare, and improve the quality of their lives.

Effectiveness of a hybrid technology enabled care model as measured by ICHOM standard set on established and managed type 2 diabetes already using medications: A RWE retrospective study

Aims

Diabetes is a pressing global health issue, demanding innovative strategies for improved treatment. However, traditional care often falls short of patient goals. To address this, digital health solutions, including smartphone apps and remote monitoring, have emerged as crucial in diabetes management. This study aims to assess a comprehensive intervention, combining remote continuous data monitoring (RCDM) with in-clinic care, for enhancing diabetes-related outcomes. Additionally, it seeks to evaluate the effectiveness of the digital RCDM component by comparing adherent and non-adherent patients.

Methods

Conducted in the United Arab Emirates, a retrospective study involved 89 patients primarily on anti-diabetic medications. They were split into two groups based on adherence to RCDM. Over time, significant improvements were observed across various parameters.

Results

Notably, patients exhibited weight loss (-4.0 ± 5.3, p < 0.001), reduced waist circumference (-4.74 ± 7.8, p < 0.001), lowered HbA1c levels (-1.00 ± 1.3, p < 0.001), decreased systolic BP (-3.1 ± 13.1, p = 0.035), and diminished diastolic BP (-3.4 ± 9.9, p = 0.002) annually. Furthermore, patients adhering to the GluCare model demonstrated substantial HbA1c reductions (-1.53 ± 1.5, p < 0.001), improved lipid profiles, notably decreased total Cholesterol (-16.6 ± 50.3, p = 0.034), and lowered LDL levels (-18.65 ± 42.6, p = 0.006).

Conclusions

The intervention model effectively managed T2D patients through a comprehensive approach, yielding notable improvements in HbA1c levels and other outcomes within a year. The study underscores the limitations of traditional care and reliance simply on pharmacotherapy, and emphasizes the need for a hyper-personalized, and continuous approach for T2D management.

Impact of a Combined Continuous Glucose Monitoring-Digital Health Solution on Glucose Metrics and Self-Management Behavior for Adults With Type 2 Diabetes: Real-World, Observational Study

BACKGROUND

The BlueStar (Welldoc) digital health solution for people with diabetes incorporates data from multiple devices and generates coaching messages using artificial intelligence. The BlueStar app syncs glucose data from the G6 (Dexcom) real-time continuous glucose monitoring (RT-CGM) system, which provides a glucose measurement every 5 minutes.

OBJECTIVE

The objective of this real-world study of people with type 2 diabetes (T2D) using the digital health solution and RT-CGM was to evaluate change in glycemic control and engagement with the program over 3 months.

METHODS

Participants were current or former enrollees in an employer-sponsored health plan, were aged 18 years or older, had a T2D diagnosis, and were not using prandial insulin. Outcomes included CGM-based glycemic metrics and engagement with the BlueStar app, including logging medications taken, exercise, food details, blood pressure, weight, and hours of sleep.

RESULTS

Participants in the program that met our analysis criteria (n=52) were aged a mean of 53 (SD 9) years; 37% (19/52) were female and approximately 50% (25/52) were taking diabetes medications. The RT-CGM system was worn 90% (SD 8%) of the time over 3 months. Among individuals with suboptimal glycemic control at baseline, defined as mean glucose >180 mg/dL, clinically meaningful improvements in glycemic control were observed, including reductions in a glucose management indicator (-0.8 percentage points), time above range 181-250 mg/dL (-4.4 percentage points) and time above range >250 mg/dL (-14 percentage points; all P<.05). Time in range 70-180 mg/dL also increased by 15 percentage points (P=.016) in this population, which corresponds to an increase of approximately 3.5 hours per day in the target range. Over the 3-month study, 29% (15/52) of participants completed at least one engagement activity per week. Medication logging was completed most often by participants (23/52, 44%) at a rate of 12.1 (SD 0.8) events/week, and this was closely followed by exercise and food logging.

CONCLUSIONS

The combination of an artificial intelligence-powered digital health solution and RT-CGM helped people with T2D improve their glycemic outcomes and diabetes self-management behaviors.

Diabetes Specialists Value Continuous Glucose Monitoring Despite Challenges in Prescribing and Data Review Process

BACKGROUND

Diabetes clinicians are key facilitators of continuous glucose monitoring (CGM) provision, but data on provider behavior related to CGM use and CGM generated data are limited.

METHODS

We conducted a national survey of providers caring for people with diabetes on CGM-related opinions, facilitators and barriers to prescription, and data review practices.

RESULTS

Of 182 survey respondents, 73.2% worked at academic centers, 70.6% were endocrinologists, and 70.7% practiced in urban settings. Nearly 70% of providers reported CGM use in the majority of their patients with type 1 diabetes. Half of the providers reported CGM use in 10% to 50% of their patients with type 2 diabetes. All respondents believed CGM improved quality of life and could optimize diabetes control. We found no differences in reported rates of CGM use based on providers' years of experience, patient volume, practice setting, or clinic type. Most providers reviewed CGM data each visit (97.7%) and actively involved patients in the data interpretation (98.8%). Only 14.1% of clinicians reported reviewing CGM data without any prompting from patients or their family members outside of visits. Most providers (80.7%) reported their CGM data review was valued by patients although only half reported having adequate time (45.1%) or an efficient process (56.1%) to do so.

CONCLUSIONS

Despite uniform support for CGM by providers, ongoing challenges related to cost, insurance coverage, and difficulties with prescription were major barriers to CGM use. Increased use of CGM in appropriate populations will necessitate improvements in data access and integration, clearly defined workflows, and decreased administrative burden to obtain CGM.

eHealth profile of patients with diabetes

Background

Digital health technology can be useful to improve the health of patients with diabetes and to support patient-centered care and self-management. In this cross-sectional study, we described the eHealth profile of patients with diabetes, based on their use of digital health technology, and its association with sociodemographic characteristics.

Methods

We used data from the "Qualité Diabète Valais" cohort study, conducted in one region of Switzerland (Canton Valais) since 2019. Participants with type 1 or type 2 diabetes completed questionnaires on sociodemographic characteristics and on the use of digital health technology. We defined eHealth profiles based on three features, i.e., ownership or use of (1) internet-connected devices (smartphone, tablet, or computer), (2) mHealth applications, and (3) connected health tools (activity sensor, smart weight scale, or connected blood glucose meter). We assessed the association between sociodemographic characteristics and participants' eHealth profiles using stratified analyses and logistic regression models.

Results

Some 398 participants (38% women) with a mean age of 65 years (min: 25, max: 92) were included. The vast majority (94%) were Swiss citizens or bi-national and 68% were economically inactive; 14% had a primary level education, 51% a secondary level, and 32% a tertiary level. Some 75% of participants had type 2 diabetes. Some 90% of the participants owned internet-connected devices, 43% used mHealth applications, and 44% owned a connected health tool. Older age and a lower educational level were associated with lower odds of all features of the eHealth profile. To a lesser extent, having type 2 diabetes or not being a Swiss citizen were also associated with a lower use of digital health technology. There was no association with sex.

Conclusion

While most participants owned internet-connected devices, only about half of them used mHealth applications or owned connected health tools. Older participants and those with a lower educational level were less likely to use digital health technology. eHealth implementation strategies need to consider these sociodemographic patterns among patients with diabetes.

Connected Insulin Pens and Caps: An Expert's Recommendation from the Area of Diabetes of the Spanish Endocrinology and Nutrition Society (SEEN)

Undoubtedly, technological advances have revolutionised diabetes management in recent years. The development of advanced closed hybrid loop insulin pumps or continuous glucose monitoring (CGM) systems, among others, have increased the quality of life and improved glycaemic control of people with diabetes. However, only some patients have access to such technology, and only some want to use it. CGM has become much more widespread, but in terms of insulin delivery, most people with type 1 diabetes (T1D) and almost all people with type 2 diabetes (T2D) on insulin therapy are treated with multiple-dose insulin injections (MDI) rather than an insulin pump. For these patients, using connected insulin pens or caps has shown benefits in reducing missed insulin injections and promoting correct administration over time. In addition, using these devices improves the quality of life and user satisfaction. The integration of insulin injection and CGM data facilitates both users and the healthcare team to analyse glucose control and implement appropriate therapeutic changes, reducing therapeutic inertia. This expert's recommendation reviews the characteristics of the devices marketed or in the process of being marketed and their available scientific evidence. Finally, it suggests the profile of users and professionals who would benefit most, the barriers to its generalisation and the changes in the care model that implementing these devices can bring with it.

Characterizing Diabetes Empowerment and Motivation for Changing Health Behaviors Among People with Type 2 Diabetes: A Cross-Sectional Survey

INTRODUCTION

Effectively engaging people with type 2 diabetes (T2D) earlier in their health journeys is critical to prevent downstream complications. Digitally based diabetes programs are a growing component of care delivery that have the potential to engage individuals outside of traditional clinic-based settings and use personalized data to pair people to tailored diabetes self-management interventions. Knowing an individuals' diabetes empowerment and health-related motivation can help drive appropriate recommendations for personalized interventions. We aimed to characterize diabetes empowerment and motivation towards changing health behaviors among participants in Level2, a T2D specialty care organization in the USA that combines wearable technology with personalized clinical support.

METHODS

A cross-sectional online survey was conducted among people enrolled in Level2 (February-March 2021). Distributions of respondent-reported diabetes empowerment and health motivation were analyzed using Motivation and Attitudes Toward Changing Health (MATCH) and Diabetes Empowerment Scale Short Form (DES-SF) scales, respectively. Associations between MATCH and DES-SF scores with Level2 engagement measures and glycemic control were analyzed.

RESULTS

The final analysis included 1258 respondents with T2D (mean age 55.7 ± 8.4 years). Respondents had high average MATCH (4.19/5) and DES-SF (4.02/5) scores. The average MATCH subscores for willingness (4.43/5) and worthwhileness (4.39/5) were higher than the average ability subscore (3.73/5). Both MATCH and DES-SF scores showed very weak correlations with Level2 engagement measures and glycemic control (ρ = - 0.18-0.19).

CONCLUSIONS

Level2 survey respondents had high average motivation and diabetes empowerment scores. Further research is needed to validate sensitivity of these scales to detect changes in motivation and empowerment over time and to determine whether differences in scores can be used to pair people to personalized interventions.

Evolving regulatory perspectives on digital health technologies for medicinal product development

Digital health technology tools (DHTTs) present real opportunities for accelerating innovation, improving patient care, reducing clinical trial duration and minimising risk in medicines development. This review is comprised of four case studies of DHTTs used throughout the lifecycle of medicinal products, starting from their development. These cases illustrate how the regulatory requirements of DHTTs used in medicines development are based on two European regulatory frameworks (medical device and the medicinal product regulations) and highlight the need for increased collaboration between various stakeholders, including regulators (medicines regulators and device bodies), pharmaceutical sponsors, manufacturers of devices and software, and academia. As illustrated in the examples, the complexity of the interactions is further increased by unique challenges related to DHTTs. These case studies are the main examples of DHTTs with a regulatory assessment thus far, providing an insight into the applicable current regulatory approach; they were selected by a group of authors, including regulatory specialists from pharmaceutical sponsors, technology experts, academic researchers and employees of the European Medicines Agency. For each case study, the challenges faced by sponsors and proposed potential solutions are discussed, and the benefit of a structured interaction among the different stakeholders is also highlighted.

7. Diabetes Technology: Standards of Care in Diabetes-2023

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Integrating digital inhalers into clinical care of patients with asthma and chronic obstructive pulmonary disease

Modernizing inhaled medications through digital technology can help address persistent problems of non-adherence and poor inhaler technique in patients with obstructive lung diseases. With a growing body of supportive clinical studies, advances in digital inhaler sensors and platforms, greater support from payers and healthcare organizations, significant growth with these technologies is expected. While all digital (smart) inhalers record adherence, these are distinguished by their compatibility with commercial inhalers, capabilities to guide inhaler technique, use of patient-reported outcomes, and user-friendliness for both the healthcare professional (HCP) and patient. Due to the complexity and novelty of employing digital inhalers, collaboration with multiple entities within health systems is necessary and a well-planned integration is needed. For HCPs and patients, cybersecurity and privacy are critical, it will require review by each healthcare organization. In the US, some payers reimburse for remote monitoring using digital inhalers, but reimbursement is currently unavailable in other countries. There are several models for remote patient care, as employing an active, ongoing digital interface between the HCP and patient or they may choose to only review data at clinical encounters. Personalization of therapies and feedback are key to success. While digital inhaler malfunction uncommonly occurs, patient attrition over a year is significant. Some patients will be challenged to use digital platforms or have the necessary technology. Additional research is needed to address cost-effectiveness, in vivo accuracy of inspiratory measurement capable devices, ability to teach inhaler technique, their application for monitoring lung function, and lastly real-world adoption and implementation in routine clinical practice.

Weight loss in a digital app-based diabetes prevention program powered by artificial intelligence

Objective

The National Diabetes Prevention Program (DPP) reduces diabetes incidence and associated medical costs but is typically staffing-intensive, limiting scalability. We evaluated an alternative delivery method with 3933 members of a program powered by conversational Artificial Intelligence (AI) called Lark DPP that has full recognition from the Centers for Disease Control and Prevention (CDC).

Methods

We compared weight loss maintenance at 12 months between two groups: 1) CDC qualifiers who completed ≥4 educational lessons over 9 months (n  =  191) and 2) non-qualifiers who did not complete the required CDC lessons but provided weigh-ins at 12 months (n  =  223). For a secondary aim, we removed the requirement for a 12-month weight and used logistic regression to investigate predictors of weight nadir in 3148 members.

Results

CDC qualifiers maintained greater weight loss at 12 months than non-qualifiers (M  =  5.3%, SE  =  .8 vs. M  =  3.3%, SE  =  .8; p  =  .015), with 40% achieving ≥5%. The weight nadir of 3148 members was 4.2% (SE  =  .1), with 35% achieving ≥5%. Male sex (β = .11; P  =  .009), weeks with ≥2 weigh-ins (β = .68; P < .0001), and days with an AI-powered coaching exchange (β = .43; P < .0001) were associated with a greater likelihood of achieving ≥5% weight loss.

Conclusions

An AI-powered DPP facilitated weight loss and maintenance commensurate with outcomes of other digital and in-person programs not powered by AI. Beyond CDC lesson completion, engaging with AI coaching and frequent weighing increased the likelihood of achieving ≥5% weight loss. An AI-powered program is an effective method to deliver the DPP in a scalable, resource-efficient manner to keep pace with the prediabetes epidemic.

Continuous Glucose Monitoring: An Opportunity for Population-Based Diabetes Management

The increasing prevalence of prediabetes and diabetes in the United States poses a serious threat to the health of the population. Closely associated with obesity, hypertension, dyslipidemia, and cardiovascular disease, a diagnosis of diabetes or prediabetes carries with it life-altering demands. Critically important among these is the requirement for frequent glucose level checks-typically performed with blood obtained from a fingerstick. Innovative real-time continuous glucose monitoring (rtCGM) technology makes it possible for patients and their clinicians to receive continuous glucose level measurements around the clock without finger sticks. Accruing evidence shows that rtCGM is an effective tool for patients and their clinicians in managing diabetes (ie, maintaining glucose levels within the recommended target range) and in understanding how lifestyle behaviors directly influence glucose levels. In recent years, a number of large payer organizations (eg, private health insurers, employers) have incorporated rtCGM in their diabetes management programs as a means of addressing the dual challenge of achieving/maintaining control of glucose levels and preventing or delaying the onset of complications. Programmatic integrated approaches have been used successfully and cost-effectively for managing other chronic conditions. Incorporating rtCGM in similar integrated approaches in diabetes management may be of value in improving quality outcomes and reducing costs on a population level. The evidence supports broader adoption and continued study to evaluate models that incorporate the use of rtCGM.

An Economic Impact Model for Estimating the Value to Health Systems of a Digital Intervention for Diabetes Primary Care: Development and Usefulness Study

Background

Diabetes is associated with significant long-term costs for both patients and health systems. Regular primary care visits aligned with American Diabetes Association guidelines could help mitigate those costs while generating near-term revenue for health systems. Digital interventions prompting primary care visits among unengaged patients could provide significant economic value back to the health system as well as individual patients, but only few economic models have been put forth to understand this value.

Objective

Our objective is to establish a data-based method to estimate the economic impact to a health system of interventions promoting primary care visits for people with diabetes who have been historically unengaged with their care. The model was built with a focus on a specific digital health intervention, Precision Nudging, but can be used to quantify the value of other interventions driving primary care usage among patients with diabetes.

Methods

We developed an economic model to estimate the financial value of a primary care visit of a patient with diabetes to the health system. This model requires segmenting patients with diabetes according to their level of blood sugar control as measured by their most recent hemoglobin A_1c value to understand how frequently they should be visiting a primary care provider. The model also accounts for the payer mix among the population with diabetes, documenting the percentage of insurance coverage through a commercial plan, Medicare, or Medicaid, as these influence the reimbursement rates for the services. Then, the model takes into consideration the population base rates of comorbid conditions for patients with diabetes and the associated current procedural terminology codes to understand what a provider can bill as well as the expected inpatient revenue from a subset of patients likely to require hospitalization based on the national hospitalization rates for people with diabetes. Physician reimbursement is subtracted from the total. Finally, the model also accounts for the level of patient engagement with the intervention to ensure a realistic estimate of the impact.

Results

We present a model to prospectively estimate the economic impact of a digital health intervention to encourage patients with documented diabetes diagnoses to attend primary care visits. The model leverages both publicly available and health system data to calculate the per appointment value (revenue) to the health system. The model offers a method to understand and test the financial impact of Precision Nudging or other primary care–focused diabetes interventions inclusive of costs driven by comorbid conditions.

Conclusions

The proposed economic model can help health systems understand and evaluate the estimated economic benefits of interventions focused on primary care and prevention for patients with diabetes as well as help intervention developers determine pricing for their product.

A New Taxonomy for Technology-Enabled Diabetes Self-Management Interventions: Results of an Umbrella Review

BACKGROUND

A 2017 umbrella review defined the technology-enabled self-management (TES) feedback loop associated with a significant reduction in A1C. The purpose of this 2021 review was to develop a taxonomy of intervention attributes in technology-enabled interventions; review recent, high-quality systematic reviews and meta-analyses to determine if the TES framework was described and if elements contribute to improved diabetes outcomes; and to identify gaps in the literature.

METHODS

We identified key technology attributes needed to describe the active ingredients of TES interventions. We searched multiple databases for English language reviews published between April 2017 and April 2020, focused on PwD (population) receiving diabetes care and education (intervention) using technology-enabled self-management (comparator) in a randomized controlled trial, that impact glycemic, behavioral/psychosocial, and other diabetes self-management outcomes. AMSTAR-2 guidelines were used to assess 50 studies for methodological quality including risk of bias.

RESULTS

The TES Taxonomy was developed to standardize the description of technology-enabled interventions; and ensure research uses the taxonomy for replication and evaluation. Of the 26 included reviews, most evaluated smartphones, mobile applications, texting, internet, and telehealth. Twenty-one meta-analyses with the TES feedback loop significantly lowered A1C.

CONCLUSIONS

Technology-enabled diabetes self-management interventions continue to be associated with improved clinical outcomes. The ongoing rapid adoption and engagement of technology makes it important to focus on uniform measures for behavioral/psychosocial outcomes to highlight healthy coping. Using the TES Taxonomy as a standard approach to describe technology-enabled interventions will support understanding of the impact technology has on diabetes outcomes.

Discovering Engagement Personas in a Digital Diabetes Prevention Program

Digital health technologies are shaping the future of preventive health care. We present a quantitative approach for discovering and characterizing engagement personas: longitudinal engagement patterns in a fully digital diabetes prevention program. We used a two-step approach to discovering engagement personas among n = 1613 users: (1) A univariate clustering method using two unsupervised k-means clustering algorithms on app- and program-feature use separately and (2) A bivariate clustering method that involved comparing cluster labels for each member across app- and program-feature univariate clusters. The univariate analyses revealed five app-feature clusters and four program-feature clusters. The bivariate analysis revealed five unique combinations of these clusters, called engagement personas, which represented 76% of users. These engagement personas differed in both member demographics and weight loss. Exploring engagement personas is beneficial to inform strategies for personalizing the program experience and optimizing engagement in a variety of digital health interventions.

Continuous glucose monitoring in primary care - are we there?

PURPOSE OF REVIEW

In this review, we examine the expanding role of continuous glucose monitoring in glycaemic management in primary care.

RECENT FINDINGS

Improving technology and decreasing cost have increased the uptake of use of continuous glucose monitoring (CGM) for glycaemic management in primary care, wherein most diabetes is managed. Optimized use of this technology, however, will require a convergence of several factors. Availability of devices for people with diabetes, availability of data at the time of clinical interactions, and expertise in interpretation of CGM and ambulatory glucose profile (AGP) data, as well as optimization of therapies, will be required. Significant progress has been made in all three areas in recent years, yet creating systems of support for widespread use of CGM in primary care remains an area of active investigation.

SUMMARY

There has been significant uptake in the use of CGM in the management of diabetes in primary care. Optimized use, however, requires both access to CGM data and the expertise to use the data. Although promising strategies have emerged, the task of generalizing these strategies to the broad population of primary care in America is ongoing. CGM technology holds significant potential for improving glycaemic management in primary care, yet important work remains to leverage the full potential of this promising technology.

7. Diabetes Technology: Standards of Medical Care in Diabetes-2022

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc22-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc22-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Analysis of "Accuracy of a 14-Day Factory Calibrated Continuous Glucose Monitoring System With Advanced Algorithm in Pediatric and Adult Population With Diabetes"

In this study by Alva et al, accuracy of a second-generation factory calibrated continuous glucose monitoring system is evaluated. Compared to the first-generation FreeStyle Libre 14-day system (FSL), accuracy was improved throughout the 14-day wear period, including improved accuracy in hypoglycemia for adults and youth. The addition of optional real-time alerts for hypoglycemia and hyperglycemia as well as an integrated continuous glucose monitor (iCGM) designation by the FDA may further enable users to benefit from using CGM in real time, including in future automated insulin delivery systems. As CGM accuracy, affordability, and accessibility improve, we anticipate increased uptake of CGM by people on intensive insulin therapy, and also potential benefits and expansion into a broader patient population. There are growing opportunities to leverage cloud-connected CGM devices in the increasingly virtual, continuous telehealth-driven diabetes care model, which will require more focus on development and use of data interoperability standards.

Missed and Mistimed Insulin Doses in People with Diabetes: A Systematic Literature Review

Background: Development of coordinated management approaches is important to facilitate self-care in people with diabetes (PwD). Gaining a better understanding of suboptimal insulin use is key in this endeavor. This review aimed, for the first time, to systematically identify and narratively summarize real-world evidence on the extent of suboptimal insulin use (missed and mistimed insulin) in PwD. Methods: A systematic literature search of MEDLINE, EMBASE, and the Cochrane Database of Systematic Reviews identified studies reporting on missed and mistimed insulin dosing. Results: From 3305 studies, 37 publications reporting on 30 unique studies that involved 58,617 PwD were included. Studies were conducted across 12 different countries and most employed cross-sectional surveys. Observations regarding missed and mistimed insulin doses were reported in 25 and 10 studies, respectively. PwD reported missing insulin doses, but rates varied due to differences in reporting methods, participant populations, and insulin regimens. The association between missed dosing and glycemic control was evaluated in ten studies in which the authors reported lower glycated hemoglobin (HbA1c) levels in PwD who did not omit insulin. The proportion of PwD reporting mistiming of insulin was in the range of 20-45%, depending on the study; this was associated with higher rates of hypoglycemia and higher HbA1c as reported by study authors. Reasons for suboptimal insulin use were multifactorial, occurring due to disrupted daily routines, social situations, and hypoglycemia avoidance. Conclusions: This review suggests that suboptimal insulin use is widespread and that PwD using insulin may still be struggling with disease management. There is an unmet need for better integrated support in managing the complexities of insulin therapy and for the development of systems (e.g. digital solutions) that empower people to take control of insulin-treated diabetes.

Complementarity of Digital Health and Peer Support: “This Is What’s Coming”

Purpose

This study examined integration of peer support and a Food and Drug Administration-cleared, diabetes management app (DMA) in diabetes self-management support as a scalable model for those with type 2 diabetes mellitus (T2DM).

Methods

Two lay health Coaches delivered telephone-based self-management support to adults (N = 43) with T2DM recruited through a primary group practice. Those eligible were offered no-cost access to DMA for the entire 6-month study. Coaches introduced DMA and contacted individuals by phone and text with frequency dependent on participant needs/preferences. DMA supported monitoring of blood glucose, carbohydrate intake, and medication use, as well as messaging personalized to participants’ medication regimens. Clinical data were extracted from DMA, electronic medical records, and Coaches’ records. Structured interviews of 12 participants, 2 Coaches, and 5 project staff were analyzed using deductive pre-identified codes (regarding adoptability, patterns of use, value added, complementarity, and sustainability) utilizing standard procedures for qualitative analysis.

Results

Of the 43 participants, 38 (88.4%) enrolled in DMA. In general, participants used both DMA and lay health coaches, averaging 144.14 DMA entries (structured, e.g., medications, and free form, e.g., “ate at a restaurant” and “stressed”) and 5.86 coach contacts over the 6-month intervention. Correlation between DMA entries and coach contacts (r = .613, p &lt; 0.001) was consistent with complementarity as were participants’ and coaches’ observations that (a) DMA facilitated recognition of patterns and provided reminders and suggestions to achieve self-management plans, whereas (b) coaching provided motivation and addressed challenges that emerged. Mean hemoglobin A1c (A1c) declined from 9.93% to 8.86% (p &lt; 0.001), with no pattern of coaching or DMA use significantly related to reductions. Staff identified resources to coordinate coach/DMA interventions as a major sustainability challenge.

Conclusions

DMA and peer support for diabetes management are compatible and complementary. Additional practice integration research is needed for adoption and scale-up.

Healthcare Professional Perceptions of Blood Glucose Meter Features That Support Achievement of Self-Management Goals Recommended by Clinical Practice Guidelines

BACKGROUND

Blood glucose meters remain an effective tool for blood glucose monitoring (BGM) but not all meters provide the same level of insight beyond the numerical glucose result.

OBJECTIVE

To investigate healthcare professional (HCP) perceptions of four meters and how these meters support the achievement of self-management goals recommended by diabetes clinical practice guidelines.

METHODS

Three hundred and fifty-three HCPs from five countries reviewed the features and benefits of four meters using interactive webpages and then responded to statements about the utility of each meter and ranked each meter in terms of clinical value.

RESULTS

Meter D ranked significantly higher in terms of clinical utility for all 13 guideline questions (70%-84%, P < .05) compared to other meters. Endocrinologists (69%-85%), primary care physicians (PCP; 63%-80%), and diabetes nurses (DN; 80%-89%) consistently ranked meter D highest for all guideline questions. DNs ranked selected questions significantly higher compared to PCPs (8 of 13) or endocrinologists (3 of 13; P < .05). Meter D achieved strong endorsement from HCPs in France and Germany, followed by the United States and Canada, with comparatively lower responses from Italian HCPs (P < 0.05). With respect to self-management, 80% of HCPs selected meter D as their first choice for patients with type 1 diabetes to help patients improve diabetes management or understand their numbers to help them stay in range.

CONCLUSIONS

HCPs had strong preference for a meter providing additional insights, messages, and guidance direct to the patient to support achievement of self-management goals recommended by diabetes clinical practice guidelines.

Medication Optimization Among People With Type 2 Diabetes Participating in a CGM-Driven Virtual Care Program: Prospective Trial (Preprint)

Background

The Onduo virtual care program for people with type 2 diabetes (T2D) includes a mobile app, remote lifestyle coaching, connected devices, and telemedicine consultations with endocrinologists for medication management and prescription of real-time continuous glucose monitoring (RT-CGM) devices. In a previously described 4-month prospective study of this program, adults with T2D and baseline glycated hemoglobin (HbA_1c) ≥8.0% to ≤12.0% experienced a mean HbA_1c decrease of 1.6% with no significant increase in hypoglycemia.

Objective

The objective of this analysis was to evaluate medication optimization and management in the 4-month prospective T2D study.

Methods

Study participants received at least 1 telemedicine consultation with an Onduo endocrinologist for diabetes medication management and used RT-CGM intermittently to guide therapy and dosing. Medication changes were analyzed.

Results

Of 55 participants, 48 (87%) had a medication change consisting of a dose change, addition, or discontinuation. Of these, 15 (31%) participants had a net increase in number of diabetes medication classes from baseline. Mean time to first medication change for these participants was 36 days. The percentage of participants taking a glucagon-like peptide-1 receptor agonist increased from 25% (12/48) to 56% (n=27), while the percentages of participants taking a sulfonylurea or dipeptidyl peptidase 4 inhibitor decreased from 56% (n=27) to 33% (n=16) and 17% (n=8) to 6% (n=3), respectively. Prescriptions of other antidiabetic medication classes including insulin did not change significantly.

Conclusions

The Onduo virtual care program can play an important role in providing timely access to guideline-based diabetes management medications and technologies for people with T2D.

Trial Registration

ClinicalTrials.gov NCT03865381; https://clinicaltrials.gov/ct2/show/NCT03865381

Improved Glycemic Control With a Digital Health Intervention in Adults With Type 2 Diabetes: Retrospective Study

Background

Traditional lifestyle interventions have shown limited success in improving diabetes-related outcomes. Digital interventions with continuously available support and personalized educational content may offer unique advantages for self-management and glycemic control.

Objective

In this study, we evaluated changes in glycemic control among participants with type 2 diabetes who enrolled in a digital diabetes management program.

Methods

The study employed a single-arm, retrospective design. A total of 950 participants with a hemoglobin A_1c (HbA_1c) baseline value of at least 7.0% enrolled in the Vida Health Diabetes Management Program. The intervention included one-to-one remote sessions with a Vida provider and structured lessons and tools related to diabetes management. HbA_1c was the primary outcome measure. Of the 950 participants, 258 (27.2%) had a follow-up HbA_1c completed at least 90 days from program start. Paired t tests were used to evaluate changes in HbA_1c between baseline and follow-up. Additionally, a cluster-robust multiple regression analysis was employed to evaluate the relationship between high and low program usage and HbA_1c change. A repeated measures analysis of variance was used to evaluate the difference in HbA_1c as a function of the measurement period (ie, pre-Vida enrollment, baseline, and postenrollment follow-up).

Results

We observed a significant reduction in HbA_1c of –0.81 points between baseline (mean 8.68, SD 1.7) and follow-up (mean 7.88, SD 1.46; t₂₅₇=7.71; P<.001). Among participants considered high risk (baseline HbA_1c≥8), there was an average reduction of –1.44 points between baseline (mean 9.73, SD 1.68) and follow-up (mean 8.29, SD 1.64; t₁₃₉=9.14; P<.001). Additionally, average follow-up HbA_1c (mean 7.82, SD 1.41) was significantly lower than pre-enrollment HbA_1c (mean 8.12, SD 1.46; F_{2, 210}=22.90; P<.001) There was also significant effect of program usage on HbA_1c change (β=–.60; P<.001) such that high usage was associated with a greater decrease in HbA_1c (mean –1.02, SD 1.60) compared to low usage (mean –.61, SD 1.72).

Conclusions

The present study revealed clinically meaningful improvements in glycemic control among participants enrolled in a digital diabetes management intervention. Higher program usage was associated with greater improvements in HbA_1c. The findings of the present study suggest that a digital health intervention may represent an accessible, scalable, and effective solution to diabetes management and improved HbA_1c. The study was limited by a nonrandomized, observational design and limited postenrollment follow-up data.

Cytokine storm modulation in COVID-19: a proposed role for vitamin D and DPP-4 inhibitor combination therapy (VIDPP-4i)

A dysregulated immune response characterized by the hyperproduction of several pro-inflammatory cytokines (a.k.a. 'cytokine storm') plays a central role in the pathophysiology of severe coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this Perspective article we discuss the evidence for synergistic anti-inflammatory and immunomodulatory properties exerted by vitamin D and dipeptidyl peptidase-4 (DPP-4) inhibitors, the latter being a class of antihyperglycemic agents used for the treatment of Type 2 diabetes, which have also been reported as immunomodulators. Then, we provide the rationale for investigation of vitamin D and DPP-4 inhibitor combination therapy (VIDPP-4i) as an immunomodulation strategy to ratchet down the virulence of SARS-CoV-2, prevent disease progression and modulate the cytokine storm in COVID-19.

Use of Flash Continuous Glucose Monitoring Is Associated With A1C Reduction in People With Type 2 Diabetes Treated With Basal Insulin or Noninsulin Therapy

BACKGROUND

Glycemic control is suboptimal in many individuals with type 2 diabetes. Although use of flash continuous glucose monitoring (CGM) has demonstrated A1C reductions in patients with type 2 diabetes treated with a multiple daily injection or insulin pump therapy regimen, the glycemic benefit of this technology in patients with type 2 diabetes using nonintensive treatment regimens has not been well studied.

METHODS

This retrospective, observational study used the IBM Explorys database to assess changes in A1C after flash CGM prescription in a large population with suboptimally controlled type 2 diabetes treated with nonintensive therapy. Inclusion criteria were diagnosis of type 2 diabetes, age <65 years, treatment with basal insulin or noninsulin therapy, naive to any CGM, baseline A1C ≥8%, and a prescription for the FreeStyle Libre flash CGM system during the period between October 2017 and February 2020. Patients served as their own control subject.

RESULTS

A total of 1,034 adults with type 2 diabetes (mean age 51.6 ± 9.2 years, 50.9% male, baseline A1C 10.1 ± 1.7%) were assessed. More patients received noninsulin treatments (n = 728) than basal insulin therapy (n = 306). We observed a significant reduction in A1C within the full cohort: from 10.1 ± 1.7 to 8.6 ± 1.8%; Δ -1.5 ± 2.2% (P <0.001). The largest reductions were seen in patients with a baseline A1C ≥12.0% (n = 181, A1C reduction -3.7%, P <0.001). Significant reductions were seen in both treatment groups (basal insulin -1.1%, noninsulin -1.6%, both P <0.001).

CONCLUSION

Prescription of the flash CGM system was associated with significant reductions in A1C in patients with type 2 diabetes treated with basal insulin or noninsulin therapy. These findings provide evidence for expanding access to flash CGM within the broader population of people with type 2 diabetes.

Digital Therapeutics for Obesity and Eating-Related Problems

In recent years, digital technologies have rapidly advanced and are being applied to remedy medical problems. These technologies allow us to monitor and manage our physical and mental health in our daily lives. Since lifestyle modification is the cornerstone of the management of obesity and eating behavior problems, digital therapeutics (DTx) represent a powerful and easily accessible treatment modality. This review discusses the critical issues to consider for enhancing the efficacy of DTx in future development initiatives. To competently adapt and expand public access to DTx, it is important for various stakeholders, including health professionals, patients, and guardians, to collaborate with other industry partners and policy-makers in the ecosystem.

Diabetes clinic reinvented: will technology change the future of diabetes care?

Diabetes is a chronic disease that affects nearly 463 million people globally and involves multiple co-morbid conditions that require effective treatment and continuous management. These include lifestyle and behavioral modifications, compliance to diabetes medications and close patient monitoring, all of which can be efficiently conducted via telehealth. Integrating digital technology of telehealth and mobile health into diabetes care may improve diabetes management and increase its efficiency. In this review, we examine recent advances in healthcare technology of diabetes. Moreover, we present an example of a comprehensive virtual diabetes clinic, the "Joslin HOME," as an innovative digital ecosystem for future application in diabetes care. This model utilizes digital health technology and comprises frequent short visits with easy two-way scheduling, focused documentation and simple billing methods. In this new model, a multidisciplinary team is connected with their patients using telehealth and mobile health to overcome the barriers of distance and location. It may possibly extend quality diabetes care to remote, underserved or rural areas.

Impact of COVID-19 on Health Economics and Technology of Diabetes Care: Use Cases of Real-Time Continuous Glucose Monitoring to Transform Health Care During a Global Pandemic

Background: The coronavirus disease 2019 (COVID-19) pandemic has exposed vulnerabilities and placed tremendous financial pressure on nearly all aspects of the U.S. health care system. Diabetes care is an example of the confluence of the pandemic and heightened importance of technology in changing care delivery. It has been estimated the added total direct U.S. medical cost burden due to COVID-19 to range between $160B (20% of the population infected) and $650B (80% of the population infected) over the course of the pandemic. The corresponding range for the population with diabetes is between $16B and $65B, representing between 5% and 20% of overall diabetes expenditure in the United States. We examine the evidence to support allocating part of this added spend to infrastructure capabilities to accelerate remote monitoring and management of diabetes. Methods and Results: We reviewed recent topical literature and COVID-19-related analyses in the public health, health technology, and health economics fields in addition to databases and surveys from government sources and the private sector. We summarized findings on use cases for real-time continuous glucose monitoring in the community, for telehealth, and in the hospital setting to highlight the successes and challenges of accelerating the adoption of a digital technology out of necessity during the pandemic and beyond. Conclusions: One critical and lasting consequence of the pandemic will be the accelerated adoption of digital technology in health care delivery. We conclude by discussing ways in which the changes wrought by COVID-19 from a health care, policy, and economics perspective can add value and are likely to endure postpandemic.

Flash CGM Is Associated With Reduced Diabetes Events and Hospitalizations in Insulin-Treated Type 2 Diabetes

Purpose

Suboptimal glycemic control among individuals with diabetes is a leading cause of hospitalizations and emergency department utilization. Use of flash continuous glucose monitoring (flash CGM) improves glycemic control in type 1 and type 2 diabetes, which may result in lower risk for acute and chronic complications that require emergency services and/or hospitalizations.

Methods

In this retrospective, real-world study, we analyzed IBM MarketScan Commercial Claims and Medicare Supplemental databases to assess the impact of flash CGM on diabetes-related events and hospitalizations in a cohort of 2463 individuals with type 2 diabetes who were on short- or rapid-acting insulin therapy. Outcomes were changes in acute diabetes-related events (ADE) and all-cause inpatient hospitalizations (ACH), occurring during the first 6 months after acquiring the flash CGM system compared with event rates during the 6 months prior to system acquisition. ICD-10 codes were used to identify ADE for hypoglycemia, hypoglycemic coma, hyperglycemia, diabetic ketoacidosis, and hyperosmolarity.

Results

ADE rates decreased from 0.180 to 0.072 events/patient-year (hazard ratio [HR]: 0.39 [0.30, 0.51]; P < 0.001) and ACH rates decreased from 0.420 to 0.283 events/patient-year (HR: 0.68 [0.59 0.78]; P < 0.001). ADE reduction occurred regardless of age or gender.

Conclusions

Acquisition of the flash CGM system was associated with reductions in ADE and ACH. These findings provide support for the use of flash CGM in type 2 diabetes patients treated with short- or rapid-acting insulin therapy to improve clinical outcomes and potentially reduce costs.

7. Diabetes Technology: Standards of Medical Care in Diabetes-2021

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc21-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc21-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Impact of Participation in a Virtual Diabetes Clinic on Diabetes-Related Distress in Individuals With Type 2 Diabetes

The Onduo Virtual Diabetes Clinic is a telehealth program for people with type 2 diabetes that combines mobile app technology, remote personalized lifestyle coaching, connected blood glucose meters, real-time continuous glucose monitoring (rtCGM) devices, and clinical support from board-certified endocrinologists. This analysis evaluated change in diabetes distress among 228 program participants who reported moderate distress (score 2.0-2.9) or high distress (score ≥3.0) on the 17-item Diabetes Distress Scale (DDS17) at enrollment. Participants reported significant reductions in overall distress from 3.0 ± 0.8 at baseline to 2.5 ± 0.9 (P <0.001) at an average of 6 months of follow-up. Significant reductions in all DDS17 subscale scores were observed; most notable were reductions in the regimen-related and emotional distress subscales (-0.9 and -0.4, respectively; both P <0.001). Significantly greater reductions in overall distress (P = 0.012) and regimen-related distress (P <0.001) were reported by participants who were prescribed and used intermittent rtCGM (n = 77) versus nonusers (n = 151). Although the generalizability of these findings may be limited by the study's small sample size and potential for self-selection bias, these results do suggest that telemedicine programs such as the Onduo VDC could be a valuable tool for addressing the problem of diabetes-related distress.

Glycemic Outcomes in Adults With Type 2 Diabetes Participating in a Continuous Glucose Monitor-Driven Virtual Diabetes Clinic: Prospective Trial

BACKGROUND

The Onduo virtual diabetes clinic (VDC) for people with type 2 diabetes (T2D) combines a mobile app, remote personalized lifestyle coaching, connected devices, and live video consultations with board-certified endocrinologists for medication management and prescription of real-time continuous glucose monitoring (RT-CGM) devices for intermittent use.

OBJECTIVE

This prospective single-arm study evaluated glycemic outcomes associated with participation in the Onduo VDC for 4 months.

METHODS

Adults aged ≥18 years with T2D and a baseline glycated hemoglobin (HbA1c) of ≥8% to ≤12% were enrolled from 2 primary care centers from February 2019 to October 2019. Participants were asked to engage at ≥1 time per week with their care team and to participate in a telemedicine consultation with a clinic endocrinologist for diabetes medication review. Participants were asked to use a RT-CGM device and wear six 10-day sensors (total 60 days of sensor wear) intermittently over the course of 4 months. The primary outcome was change in HbA1c at 4 months from baseline. Other endpoints included change in weight and in RT-CGM glycemic metrics, including percent time <70, 70-180, 181-250, and >250 mg/dL. Changes in blood pressure and serum lipids at 4 months were also evaluated.

RESULTS

Participants (n=55) were 57.3 (SD 11.6) years of age, body mass index 33.7 (SD 7.2), and 40% (22/55) female. HbA1c decreased significantly by 1.6% (SD 1%; P<.001). When stratified by baseline HbA1c of 8.0% to 9.0% (n=36) and >9.0% (n=19), HbA1c decreased by 1.2% (SD 0.6%; P<.001) and 2.4% (SD 1.3%; P<.001), respectively. Continuous glucose monitoring-measured (n=43) percent time in range (TIR) 70-180 mg/dL increased by 10.2% (SD 20.5%; P=.002), from 65.4% (SD 23.2%) to 75.5% (SD 22.7%), which was equivalent to a mean increase of 2.4 hours TIR per day. Percent time 181-250 mg/dL and >250 mg/dL decreased by 7.2% (SD 15.4; P=.005) and 3.0% (SD 9.4; P=.01), respectively. There was no change in percent time <70 mg/dL. Mean weight decreased by 9.0 lb (SD 10.4; P<.001). Significant improvements were also observed in systolic blood pressure, total cholesterol, low-density lipoprotein cholesterol, and triglycerides (P=.04 to P=<.001).

CONCLUSIONS

Participants in the Onduo VDC experienced significant improvement in HbA1c, increased TIR, decreased time in hyperglycemia, and no increase in hypoglycemia at 4 months. Improvements in other metabolic health parameters including weight and blood pressure were also observed. In conclusion, the Onduo VDC has potential to support people with T2D and their clinicians between office visits by increasing access to specialty care and advanced diabetes technology including RT-CGM.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03865381; https://clinicaltrials.gov/ct2/show/NCT03865381.

The Reference Guide to Integrate Smart Insulin Pens Into Data-Driven Diabetes Care and Education Services

PURPOSE

More than 7 million Americans who have diabetes use insulin therapy. The majority continue to use syringes and vials or traditional insulin pens to deliver their insulin doses. Using these tools to deliver insulin presents numerous challenges for both the person with diabetes and their clinicians. This article provides an in-depth introduction to a new category of insulin delivery devices and integrated management systems, referred to as smart insulin pens. The article includes information about how these integrated insulin delivery systems can reduce many of the challenges of rapid-acting insulin dosing via injection by enabling easier and more accurate dose recording, dose calculations, and sharing of diabetes management data with clinicians. This article also discusses new roles for diabetes care and education specialists in diabetes data-driven care and practice and addresses how smart insulin pens represent one of many newer digital diabetes management tools that can assist people with diabetes and their clinicians to optimally achieve and deliver quality, data-driven diabetes care.

CONCLUSIONS

Newer and simplified insulin delivery devices with their integrated management systems, such as smart insulin pens, have the potential to minimize the challenges and complexities associated with insulin injection therapy while also providing people with diabetes and their clinicians more complete and integrated data in easily transmitted reports that support more efficient data analysis.

A Framework for Optimizing Technology-Enabled Diabetes and Cardiometabolic Care and Education: The Role of the Diabetes Care and Education Specialist

Purpose

The purpose of this article is to present a framework for optimizing technology-enabled diabetes and cardiometabolic care and education using a standardized approach. This approach leverages the expertise of the diabetes care and education specialist, the multiplicity of technologies, and integration with the care team. Technology can offer increased opportunity to improve health outcomes while also offering conveniences for people with diabetes and cardiometabolic conditions. The adoption and acceptance of technology is crucial to recognize the full potential for improving care. Understanding and incorporating the perceptions and behaviors associated with technology use can prevent a fragmented health care experience.

Conclusion

Diabetes care and education specialists (DCES) have a history of utilizing technology and data to deliver care and education when managing chronic conditions. With this unique skill set, DCES are strategically positioned to provide leadership to develop and deliver technology-enabled diabetes and cardiometabolic health services in the rapidly changing healthcare environment.

A care team-based classification and population management schema for connected diabetes care

It has been proposed that telehealth may help to combat the epidemic of diabetes and other chronic diseases in the US. As a result of rapid technological advancement over the past decade, there has been an explosion in virtual diabetes management program offerings rooted in smartphone technology, connected devices for blood glucose monitoring, and remote coaching or support. Such offerings take many forms with unique features. We provide a care team-based classification system for connected diabetes care programs and highlight their strengths and limitations. We also include a framework for how the different classes of connected diabetes care may be deployed in a health system to promote improved population health.

Digital Diabetes Care System Observations from a Pilot Evaluation Study in Vietnam

Digital technologies are gaining an important role in the management of patients with diabetes. We assessed clinical outcomes and user satisfaction of incorporating a digital diabetes care system in diabetes clinics of a developing country. The system integrated a wireless blood glucose monitor that communicates data to any smartphone utilizing a patented acoustic data transfer method, a mobile-app, and cloud-based software that stores, analyzes, and presents data. Five hospital endocrinology clinics in Vietnam sequentially recruited all patients willing to join the study, providing they had a smartphone and access to internet connectivity. Face-to-face visits were conducted at baseline and at 12 weeks, with monthly digital visits scheduled in the interim and additional digital visits performed as needed. HbA1c levels were measured at baseline and at 12 weeks (±20 days). The study included 300 patients of whom 279 completed the evaluation. Average glucose levels declined from 170.4 ± 64.6 mg/dL in the first 2 weeks to 150.8 ± 53.2 mg/dL in the last 2 weeks (n = 221; p < 0.001). HbA1c levels at baseline and 12 weeks declined from 8.3% ± 1.9% to 7.6% ± 1.3% (n = 126; p < 0.001). The digital solution was broadly accepted by both patients and healthcare professionals and improved glycemic outcomes. The durability, scalability, and cost-effectiveness of this approach merits further study.