Evaluation of a Diabetes Remote Monitoring Program Facilitated by Connected Glucose Meters for Patients With Poorly Controlled Type 2 Diabetes: Randomized Crossover Trial

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.

The Impact of Digital Self-Monitoring of Weight on Improving Diabetes Clinical Outcomes: Quasi-Randomized Study

BACKGROUND

The management of type 2 diabetes (T2D) and obesity, particularly in the context of self-monitoring, remains a critical challenge in health care. As nearly 80% to 90% of patients with T2D have overweight or obesity, there is a compelling need for interventions that can effectively manage both conditions simultaneously. One of the goals in managing chronic conditions is to increase awareness and generate behavioral change to improve outcomes in diabetes and related comorbidities, such as overweight or obesity. There is a lack of real-life evidence to test the impact of self-monitoring of weight on glycemic outcomes and its underlying mechanisms.

OBJECTIVE

This study aims to assess the efficacy of digital self-monitoring of weight on blood glucose (BG) levels during diabetes management, investigating whether the weight changes may drive glucose fluctuations.

METHODS

In this retrospective, real-world quasi-randomized study, 50% of the individuals who regularly used the weight monitoring (WM) feature were propensity score matched with 50% of the users who did not use the weight monitoring feature (NWM) based on demographic and clinical characteristics. All the patients were diagnosed with T2D and tracked their BG levels. We analyzed monthly aggregated data 6 months before and after starting their weight monitoring. A piecewise mixed model was used for analyzing the time trajectories of BG and weight as well as exploring the disaggregation effect of between- and within-patient lagged effects of weight on BG.

RESULTS

The WM group exhibited a significant reduction in BG levels post intervention (P<.001), whereas the nonmonitoring group showed no significant changes (P=.59), and both groups showed no differences in BG pattern before the intervention (P=.59). Furthermore, the WM group achieved a meaningful decrease in BMI (P<.001). Finally, both within-patient (P<.001) and between-patient (P=.008) weight variability was positively associated with BG levels. However, 1-month lagged back BMI was not associated with BG levels (P=.36).

CONCLUSIONS

This study highlights the substantial benefits of self-monitoring of weight in managing BG levels in patients with diabetes, facilitated by a digital health platform, and advocates for the integration of digital self-monitoring tools in chronic disease management. We also provide initial evidence of testing the underlying mechanisms associated with BG management, underscoring the potential role of patient empowerment.

Outcomes of an Asynchronous Care Model for Chronic Conditions in a Diverse Population: 12-Month Retrospective Chart Review Study

BACKGROUND

Diabetes and hypertension are some of the most prevalent and costly chronic conditions in the United States. However, outcomes continue to lag behind targets, creating further risk of long-term complications, morbidity, and mortality for people living with these conditions. Furthermore, racial and ethnic disparities in glycemic and hypertension control persist. Flexible telehealth programs leveraging asynchronous care allow for increased provider access and more convenient follow-up, ultimately improving critical health outcomes across demographic groups.

OBJECTIVE

We aim to evaluate the 12-month clinical outcomes of participants in the 9amHealth web-based clinic for diabetes and hypertension. We hypothesized that participation in the 9amHealth program would be associated with significant improvements in glycemic and blood pressure (BP) control across a diverse group of individuals.

METHODS

We enrolled 95 patients in a completely web-based care clinic for diabetes and hypertension who received nutrition counseling, health coaching, and asynchronous physician consultations for medication prescribing. Patients received standard or cellular-connected glucose meters and BP cuffs in order to share data. Laboratory tests were completed either with at-home phlebotomy draws or a self-administered test kit. Patients' first and last hemoglobin A1c (HbA1c) and BP results over the 12-month period were compared, and analyses were repeated across race and ethnicity groups.

RESULTS

Among all 95 patients, the average HbA1c decreased by -1.0 (from 8.2% to 7.2%; P<.001) over 12 months of program participation. In those with a baseline HbA1c >8%, the average HbA1c decreased by -2.1 (from 10.2% to 8.1%; P<.001), and in those with a baseline HbA1c >9%, the average HbA1c decreased by -2.8 (from 11% to 8.2%; P<.001). Among participants who identified as a race or ethnicity other than White, the HbA1c decreased by -1.2 (from 8.6% to 7.4%, P=.001). Further examination of subgroups confirmed HbA1c lowering within each race or ethnicity group. In the overall population, the average systolic BP decreased by 17.7 mm Hg (P=.006) and the average diastolic BP decreased by 14.3 mm Hg (P=.002). Among participants self-identifying as a race or ethnicity other than White, the results similarly showed a decrease in BP (average reduction in systolic BP of 10 mm Hg and in diastolic BP of 9 mm Hg).

CONCLUSIONS

A fully web-based model leveraging all-asynchronous physician review and prescribing, combined with synchronous and asynchronous coaching and nutrition support, was associated with clinically meaningful improvement in HbA1c and BP control over a 12-month period among a diverse group of individuals. Further studies should prospectively evaluate the effectiveness of such models among larger populations, assess the longer-term sustainability of these outcomes, and explore financial models to make these types of programs broadly accessible.

Patients' Expectations of Evidence-Based Service at the Pharmacy Regarding Information on Self-Care of the Feet for Persons with Diabetes at Risk of Developing Foot Ulcers - A Cross-Sectional Observational Study in Sweden

Purpose

Self-care of the feet is one of the cornerstones in the prevention of diabetic foot ulcers (DFU). Often, individuals with diabetes seek help at the pharmacy, but it is still unclear whether the service meets their expectations and needs. The aims were to explore patients' expectations of support from the pharmacy regarding self-care of their feet and explore how patients with diabetes felt that they managed the self-care of their feet.

Patients and Methods

The included participants (n = 17), aged 70 ± 9 years, answered surveys regarding their expectations of support from the pharmacy related to self-care of the feet and how they felt that they managed the self-care of their feet. By using software, MyFoot Diabetes, they assessed their risk of developing DFU (ranging from 1 = no risk to 4 = DFU). In addition, a healthcare professional assessed the risk grade.

Results

Sixteen patients had not received any information from the pharmacy regarding how to take care of their feet. Several suggestions for ways the pharmacy could help patients with diabetes to take care of their feet were registered. They included having the necessary skills and competence, giving advice regarding self-care, giving information regarding the products they market and have for sale and giving advice on ointments/creams. The participants gave several examples of how they self-managed their feet: by wearing shoes indoors and outdoors, wearing socks and compression stockings as often as possible, being physically active, inspecting their feet, being aware of the fact that their feet have no problems, washing, moisturising their feet, cutting their nails and finally seeking help to prevent DFU.

Conclusion

The participants thought that they should receive competent information from the personnel at the pharmacy to improve the self-care of their feet, eg, being given information about which ointments/creams to use.

Clinical Trial

NCT05692778.

Design of a Dyadic Digital Health Module for Chronic Disease Shared Care: Development Study

BACKGROUND

The COVID-19 pandemic forced the spread of digital health tools to address limited clinical resources for chronic health management. It also illuminated a population of older patients requiring an informal caregiver (IC) to access this care due to accessibility, technological literacy, or English proficiency concerns. For patients with heart failure (HF), this rapid transition exacerbated the demand on ICs and pushed Canadians toward a dyadic care model where patients and ICs comanage care. Our previous work identified an opportunity to improve this dyadic HF experience through a shared model of dyadic digital health. We call this alternative model of care "Caretown for Medly," which empowers ICs to concurrently expand patients' self-care abilities while acknowledging ICs' eagerness to provide greater support.

OBJECTIVE

We present the systematic design and development of the Caretown for Medly dyadic management module. While HF is the outlined use case, we outline our design methodology and report on 6 core disease-invariant features applied to dyadic shared care for HF management. This work lays the foundation for future usability assessments of Caretown for Medly.

METHODS

We conducted a qualitative, human-centered design study based on 25 semistructured interviews with self-identified ICs of loved ones living with HF. Interviews underwent thematic content analysis by 2 coders independently for themes derived deductively (eg, based on the interview guide) and inductively refined. To build the Caretown for Medly model, we (1) leveraged the Knowledge to Action (KTA) framework to translate knowledge into action and (2) borrowed Google Sprint's ability to quickly "solve big problems and test new ideas," which has been effective in the medical and digital health spaces. Specifically, we blended these 2 concepts into a new framework called the "KTA Sprint."

RESULTS

We identified 6 core disease-invariant features to support ICs in care dyads to provide more effective care while capitalizing on dyadic care's synergistic benefits. Features were designed for customizability to suit the patient's condition, informed by stakeholder analysis, corroborated with literature, and vetted through user needs assessments. These features include (1) live reports to enhance data sharing and facilitate appropriate IC support, (2) care cards to enhance guidance on the caregiving role, (3) direct messaging to dissolve the disconnect across the circle of care, (4) medication wallet to improve guidance on managing complex medication regimens, (5) medical events timeline to improve and consolidate management and organization, and (6) caregiver resources to provide disease-specific education and support their self-care.

CONCLUSIONS

These disease-invariant features were designed to address ICs' needs in supporting their care partner. We anticipate that the implementation of these features will empower a shared model of care for chronic disease management through digital health and will improve outcomes for care dyads.

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.

Effectiveness of telemonitoring intervention on glycaemic control in patients with type 2 diabetes mellitus: A systematic review and meta-analysis

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a rising global health concern that requires long-term treatment and close monitoring. Telemonitoring has been shown to be a promising tool to facilitate patient-physician interaction and improve glycaemic control.

METHOD

Randomised controlled trials (RCT) of telemonitoring in T2DM published between 1990 and 2021 were searched through multiple electronic databases. The primary outcome variables included HbA1c and fasting blood glucose (FBG), and BMI was a secondary outcome variable.

RESULTS

Thirty RCT with a total of 4,678 participants were included in this study. Twenty-six studies reported on HbA1c, which was shown to be significantly lower in participants on telemonitoring when compared to conventional care. Ten studies investigated FBG which collectively showed no statistically significant difference. Subgroup analysis demonstrated the effect of telemonitoring on glycaemic control is influenced by a range of factors concerning system practicality, user engagement, patient characteristics and disease education.

CONCLUSION

Telemonitoring exhibited a great potential to improve T2DM management. Several technical features and patient factors may influence the effectiveness of telemonitoring. Further studies are needed to verify the findings and address limitations before its implementation into routine practice.

Sustained Improvements in Readings in-Range Using an Advanced Bluetooth® Connected Blood Glucose Meter and a Mobile Diabetes App: Real-World Evidence from more than 55,000 People with Diabetes

INTRODUCTION

The OneTouch Verio Reflect^® (OTVR) Blood Glucose Meter features a color range indicator and provides on-meter guidance, insights, and encouragement. Diabetes management is enhanced by the OneTouch Reveal^® (OTR) Mobile App. We sought to provide real-world evidence (RWE) that combining devices improves glycemia.

METHODS

Anonymized glucose and app analytics from more than 55,000 people with diabetes (PWDs) were extracted from a server. Data from their first 14 days using OTVR Meter and OTR App was compared with 14 days prior to 90- and 180-day timepoints using paired within-subject differences.

RESULTS

In people with type 1 (PwT1D) or type 2 diabetes (PwT2D), readings in-range (RIR 70-180 mg/dL) improved by 7.8 percentage points (57.9-65.7%) and 12.0 percentage points (72.8-84.8%), respectively, over 180 days and hyperglycemia (> 180 mg/dL) was reduced by - 8.4 percentage points (37.9-29.5%) and - 12.2 percentage points (26.2-14.1%). RIR improved by > 10 percentage points in 38% of PwT1D and 39% of PwT2D. PwT1D spending > 2 to 4 sessions or > 10 to 20 min per week on the app improved RIR by 7.0 and 8.2 percentage points, respectively. PwT2D spending > 2 to 4 sessions or > 10 to 20 min per week on the app improved RIR by 12.6 and 12.1 percentage points, respectively. In PwT1D or T2D, mean blood glucose reduced by - 14.3 and - 19.8 mg/dL, respectively, from baseline to 180 days, with no clinically meaningful changes in percentage of hypoglycemic readings (< 70 mg/dL). PwT1D 65 years and older performed the most app sessions (10 per week) and improved RIR by 7.9 percentage points. PwT2D 65 years and older spent more time on the app (45 min per week) than PwT2D of any other age and improved RIR by 7.6 percentage points. All glycemic changes were statistically significant (p < 0.0005).

CONCLUSION

Real-world data from more than 55,000 PWDs demonstrates sustained improvements in readings in-range in PWDs using the OneTouch Verio Reflect Blood Glucose Meter and OneTouch Reveal App.

MATHEMATICAL MODEL FOR PREDICTING FASTING BLOOD GLUCOSE LEVEL IN DIABETES MELLITUS PATIENTS

OBJECTIVE

The aim: To substantiate the use of data on patients' lifestyle, parameters of blood glucose, heart rate, blood pressure and bread units to build a mathematical model for predicting fasting blood glucose level in diabetes mellitus patients to improve existing measures for diabetes prevention.

PATIENTS AND METHODS

Materials and methods: An open database consisting of the studied parameters of 359 people was used in the research. The linear regression method was used to predict fasting blood glucose level in diabetes mellitus patients. The statistical software IBM SPSS Statistics Version 23 was chosen for calculations.

RESULTS

Results: To calculate the coefficients of the linear regression equation, stepwise elimination of parameters was chosen. The analysis of the coefficients of influence of independent variables on dependent showed that the greatest effect on the change in glucose level had value of consumed bread units. The model for women diagnosed with type 2 diabetes showed the highest accuracy.

CONCLUSION

Conclusions: Mathematical modeling made it clear that any malnutrition or health disorders can lead to a significant change in glucose levels. The obtained models consist of a number of parameters, some of which might depend on the presence of concomitant diseases. Further studies should focus on the optimal combination of various parameters taking into account methods of treating comorbidities.

Exploration of the health needs of patients with poorly controlled type 2 diabetes using a user-centred co-production approach in the area of mHealth: an exploratory sequential mixed-method protocol

INTRODUCTION

Research on the needs and preferences of patients with poorly controlled type 2 diabetes mellitus (T2DM) with mobile health (mHealth) service is limited. With the principles of co-production, this study aims to address this research gap by exploring the health needs of Chinese patients with poorly controlled T2DM.

METHODS AND ANALYSIS

This study uses a three-phase, exploratory sequential mixed-method design. Phase 1 aims to assess the health needs of patients with poorly controlled T2DM by conducting semi-structured interviews with patients, doctors and nurses. Participants will be recruited by purposive sampling with maximum variation. Content analysis will be employed. Phase 2 will form item generation and develop the mHealth need scale. The scale will be subject to pilot testing and psychometric evaluation, including content validity, construct validity, discriminant validity, internal validity and test-retest reliability. Phase 3 will explore the priority of health needs perceived by patients with poorly controlled T2DM through a cross-sectional study. The measurement tools include an mHealth needs scale, the Summary of Diabetes Self-care Activities Questionnaire, the Diabetes Empowerment Scale-Short Form, the Diabetes Health Literacy Scale and the eHealth Literacy Scale. Multiple regression techniques with a hierarchical block design will be used for the model building to identify the factors contributing to the heterogeneity of the perceived mHealth needs. The findings of phase 1 and phase 3 will be integrated using data correlation, comparison and consolidation.

ETHICS AND DISSEMINATION

The Ethics Committee of the School of Nursing, Sun Yat-sen University, has approved this study (No. 2021ZSLYEC). The results of this study will be disseminated through conference presentations and peer-reviewed publications.

A Meta-Analysis of the Effectiveness of Telemedicine in Glycemic Management among Patients with Type 2 Diabetes in Primary Care

Introduction: Telemedicine interventions are gradually being used in primary health care to help patients with type 2 diabetes receive ongoing medical guidance. The purpose of this study was to analyze the effectiveness of using telemedicine in primary health care for the management of patients with type 2 diabetes. Methods: A systematic search was conducted from database inception to August 2021 in nine databases, including PubMed, Web of Science, Cochrane Library, EMBASE, EBSCO, CNKI, Wanfang Data, VIP, and CBM. Data extraction and quality assessment were performed for studies that met the inclusion criteria. The meta-analysis was performed using Review Manager 5.4 (Cochrane) and Stata v.16.0SE (College Station, TX, USA). Results: A total of 32 articles were included in this study. Analysis showed a reduction in glycated hemoglobin, fasting glucose, and postprandial glucose after the telemedicine intervention. Systolic blood pressure and self-efficacy improved significantly, but there was no significant improvement in weight, lipid metabolism, or diabetes awareness. Subgroup analysis based on the duration of intervention showed significant improvement in glycated hemoglobin at 6 months of intervention. Conclusions: Telemedicine interventions may help patients with type 2 diabetes to effectively control blood glucose and improve self-management in primary health care. There is only moderate benefit, and the benefit may not be sustained beyond 6 months. However, the evidence for the improvement in lipid metabolism is insufficient and further studies are needed.

Improving Outcomes Through Personalized Recommendations in a Remote Diabetes Monitoring Program: Observational Study

Background

Diabetes management is complex, and program personalization has been identified to enhance engagement and clinical outcomes in diabetes management programs. However, 50% of individuals living with diabetes are unable to achieve glycemic control, presenting a gap in the delivery of self-management education and behavior change. Machine learning and recommender systems, which have been used within the health care setting, could be a feasible application for diabetes management programs to provide a personalized user experience and improve user engagement and outcomes.

Objective

This study aims to evaluate machine learning models using member-level engagements to predict improvement in estimated A_1c and develop personalized action recommendations within a remote diabetes monitoring program to improve clinical outcomes.

Methods

A retrospective study of Livongo for Diabetes member engagement data was analyzed within five action categories (interacting with a coach, reading education content, self-monitoring blood glucose level, tracking physical activity, and monitoring nutrition) to build a member-level model to predict if a specific type and level of engagement could lead to improved estimated A_1c for members with type 2 diabetes. Engagement and improvement in estimated A_1c can be correlated; therefore, the doubly robust learning method was used to model the heterogeneous treatment effect of action engagement on improvements in estimated A_1c.

Results

The treatment effect was successfully computed within the five action categories on estimated A_1c reduction for each member. Results show interaction with coaches and self-monitoring blood glucose levels were the actions that resulted in the highest average decrease in estimated A_1c (1.7% and 1.4%, respectively) and were the most recommended actions for 54% of the population. However, these were found to not be the optimal interventions for all members; 46% of members were predicted to have better outcomes with one of the other three interventions. Members who engaged with their recommended actions had on average a 0.8% larger reduction in estimated A_1c than those who did not engage in recommended actions within the first 3 months of the program.

Conclusions

Personalized action recommendations using heterogeneous treatment effects to compute the impact of member actions can reduce estimated A_1c and be a valuable tool for diabetes management programs in encouraging members toward actions to improve clinical outcomes.

Blood Pressure Monitoring as a Digital Health Tool for Improving Diabetes Clinical Outcomes: Retrospective Real-world Study

Background

Remote data capture for blood glucose (BG) or blood pressure (BP) monitoring and the use of a supportive digital app are becoming the model in diabetes and hypertension chronic care. One of the goals in chronic condition management is to increase awareness and generate behavioral change in order to improve outcomes in diabetes and related comorbidities, such as hypertension. In addition, there is a lack of understanding of the association between BG and BP levels when using digital health tools.

Objective

By applying a rigorous study framework to digital health data, this study investigated the relationship between BP monitoring and BG and BP levels, as well as a lagged association between BP and BG. We hypothesized that during the first 6 months of BP monitoring, BG and BP levels would decrease. Finally, we suggested a positive association between BP levels and the following month’s BG levels.

Methods

In this retrospective, real-world case-control study, we extracted the data of 269 people with type 2 diabetes (T2D) who tracked their BG levels using the Dario digital platform for a chronic condition. We analyzed the digital data of the users who, in addition to BG, monitored their BP using the same app (BP-monitoring [BPM] group, n=137) 6 months before and after starting their BP monitoring. Propensity score matching established a control group, no blood pressure monitoring (NBPM, n=132), matched on demographic and baseline clinical measures to the BPM group. A piecewise mixed model was used for analyzing the time trajectories of BG, BP, and their lagged association.

Results

Analysis revealed a significant difference in BG time trajectories associated with BP monitoring in BPM and NBPM groups (t=–2.12, P=.03). The BPM group demonstrated BG reduction improvement in the monthly average BG levels during the first 6 months (t=–3.57, P<.001), while BG did not change for the NBPM group (t=0.39, P=.70). Both groups showed similarly stable BG time trajectories (B=0.98, t=1.16, P=.25) before starting the use of the BP-monitoring system. In addition, the BPM group showed a significant reduction in systolic (t=–6.42, P<.001) and diastolic (t=–4.80, P<.001) BP during the first 6 months of BP monitoring. Finally, BG levels were positively associated with systolic (B=0.24, t=2.77, P=.001) and diastolic (B=0.30, t=2.41, P=.02) BP.

Conclusions

The results of this study shed light on the association between BG and BP levels and on the role of BP self-monitoring in diabetes management. Our findings also underscore the need and provide a basis for a comprehensive approach to understanding the mechanism of BP regulation associated with BG.

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.

Feasibility and Impact of Remote Glucose Monitoring Among Patients With Newly Diagnosed Type 1 Diabetes: Single-Center Pilot Study

Background

Caregivers of children with newly diagnosed type 1 diabetes (T1D) maintain close contact with providers for several weeks to facilitate rapid adjustments in insulin dosing regimens. Traditionally, patient glucose values are relayed by telephone for provider feedback, but digital health technology can now enable the remote sharing of glucose data via mobile apps.

Objective

The aim of this study was to test the feasibility of remote glucose monitoring in a population of children and adolescents with newly diagnosed T1D and to explore whether remote monitoring alters habits for self-review of glucose data or perceived ease of provider contact in this population as compared to a nonrandomized control group.

Methods

Data were collected from families who chose to participate in remote monitoring (intervention group) as well as from patients receiving usual care (control group). The intervention group received Bluetooth-capable glucose meters and Apple iPod Touch devices. Patient-generated glucose data were passively relayed from the meter to the iPod Touch and then to both the electronic health record (EHR) and a third-party diabetes data platform, Tidepool. The principal investigator reviewed glucose data daily in the EHR and Tidepool and contacted the participants as needed for insulin dose adjustments during the time between hospital discharge and first clinic appointment. Families in the control group received usual care, which involved keeping written records of glucose values and contacting the diabetes team daily by telephone to relay data and receive treatment recommendations. A total of 40 families (20 for the intervention group and 20 for the control group) participated in the study. All families were surveyed at 1 month and 6 months regarding self-review of glucose data and ease of contacting the diabetes team.

Results

Patient-generated glucose data were remotely accessible for 100% of the participants via Tidepool and for 85% via the EHR. Survey data indicated that families in the intervention group were more likely than those in the control group to review their glucose data using mobile health apps after 1 month (P<.001), but by 6 months, this difference had disappeared. Perceived ease of contacting the clinical team for assistance was lower for the intervention group after 6 months (when receiving usual care) in comparison to during the intervention period (P=.48) and compared with a control group who did not have exposure to remote monitoring (P=.03).

Conclusions

Remote glucose monitoring is feasible among pediatric patients with newly diagnosed T1D and may be associated with the earlier adoption of mobile health apps for self-management. The use of broadscale remote monitoring for T1D in the future will depend on improved access to Bluetooth-enabled mobile devices for all patients, improved interoperability of mobile health apps to enable data transfer on Android as well as Apple devices, and new provider workflows to handle large-scale panel management based on patient-generated health data.

Trial Registration

ClinicalTrials.gov NCT04106440; https://clinicaltrials.gov/ct2/show/NCT04106440

Digital Behavior Change Interventions for the Prevention and Management of Type 2 Diabetes: Systematic Market Analysis

BACKGROUND

Advancements in technology offer new opportunities for the prevention and management of type 2 diabetes. Venture capital companies have been investing in digital diabetes companies that offer digital behavior change interventions (DBCIs). However, little is known about the scientific evidence underpinning such interventions or the degree to which these interventions leverage novel technology-driven automated developments such as conversational agents (CAs) or just-in-time adaptive intervention (JITAI) approaches.

OBJECTIVE

Our objectives were to identify the top-funded companies offering DBCIs for type 2 diabetes management and prevention, review the level of scientific evidence underpinning the DBCIs, identify which DBCIs are recognized as evidence-based programs by quality assurance authorities, and examine the degree to which these DBCIs include novel automated approaches such as CAs and JITAI mechanisms.

METHODS

A systematic search was conducted using 2 venture capital databases (Crunchbase Pro and Pitchbook) to identify the top-funded companies offering interventions for type 2 diabetes prevention and management. Scientific publications relating to the identified DBCIs were identified via PubMed, Google Scholar, and the DBCIs' websites, and data regarding intervention effectiveness were extracted. The Diabetes Prevention Recognition Program (DPRP) of the Center for Disease Control and Prevention in the United States was used to identify the recognition status. The DBCIs' publications, websites, and mobile apps were reviewed with regard to the intervention characteristics.

RESULTS

The 16 top-funded companies offering DBCIs for type 2 diabetes received a total funding of US $2.4 billion as of June 15, 2021. Only 4 out of the 50 identified publications associated with these DBCIs were fully powered randomized controlled trials (RCTs). Further, 1 of those 4 RCTs showed a significant difference in glycated hemoglobin A_1c (HbA_1c) outcomes between the intervention and control groups. However, all the studies reported HbA_1c improvements ranging from 0.2% to 1.9% over the course of 12 months. In addition, 6 interventions were fully recognized by the DPRP to deliver evidence-based programs, and 2 interventions had a pending recognition status. Health professionals were included in the majority of DBCIs (13/16, 81%,), whereas only 10% (1/10) of accessible apps involved a CA as part of the intervention delivery. Self-reports represented most of the data sources (74/119, 62%) that could be used to tailor JITAIs.

CONCLUSIONS

Our findings suggest that the level of funding received by companies offering DBCIs for type 2 diabetes prevention and management does not coincide with the level of evidence on the intervention effectiveness. There is considerable variation in the level of evidence underpinning the different DBCIs and an overall need for more rigorous effectiveness trials and transparent reporting by quality assurance authorities. Currently, very few DBCIs use automated approaches such as CAs and JITAIs, limiting the scalability and reach of these solutions.