The foundation of our clinical rigor
Proven science drives everything we do. Through rigorous research, we demonstrate how our health technology delivers real results: better patient outcomes, greater value for healthcare systems, and more advanced AI capabilities. Key areas include:
Our AI transforms complex health data into personalized insights and guidance when individuals need it most.
We convert continuous glucose data into personalized guidance that elevates traditional diabetes care standards.
We address interconnected health conditions, turning digital engagement into measurable clinical improvements.
We offer FDA-cleared solutions for T1 and T2 diabetes that simplify treatment while addressing daily health challenges.
eHealth-Assisted Lay Health Coaching for Diabetes Self-Management Support
This project, supported by a Gillings Innovation Laboratory award at the UNC Gillings School of Global Public Health, tested the feasibility and reach of integrating a telephone-based lay health coach with an eHealth intervention for diabetes self-management support in a Patient-Centered Medical Home practice in New Jersey.
WellDoc mobile diabetes management randomized controlled trial: change in clinical and behavioral outcomes and patient and physician satisfaction
Using a Digital Health Solution to Scale Diabetes Self Care across the State of Montana
Welldoc partnered with The Montana Diabetes Digital Health Learning Network (MDDHLN) to introduce the BlueStar app was into their Diabetes Care and Education Specialist (DCES) program. People with diabetes (PWD) who engaged with the app saw A1C improvement after 6 months of use and 87% of the population with an A1C of 8 or less demonstrated positive health outcomes. The average persistence in app use is 18 months with 20% of people still using the app at 2 years. In addition to this sustained engagement, over 53% of the participants sent the SMART Visit ReportTM to their DCES at least once per month showing ongoing support for DCES’ between office visits. This data shows that using a digital health solution can not only support diabetes education for PWD between office visits but that use also creates value in the information and feedback they receive, thus generating continued engagement.
Using an Automated, Real-time Data Enabled Feature Engineering Process to Predict Future Weight Outcomes
This research presents a novel artificial intelligence (AI) framework for building dynamic user profiles, based on interactions with digital health app features. This framework makes it possible to identify individual-level use-patterns and develop advanced AI models to accurately predict and effectively influence health outcomes.
In this research, Welldoc analyzed continuous glucose monitoring (CGM) + My E-Diary for Activities and Lifestyle (MEDAL) data, as collected within Welldoc’s cardiometabolic digital health platform to determine features and use patterns which influence future weight loss. This study proposes a novel AI framework to build dynamic user profiles based on how users interact with digital health apps. The researchers analyzed continuous glucose monitoring (CGM) and MEDAL data collected within Welldoc's digital health platform, to find patterns that could predict future weight loss. Continued research in this area will ultimately drive the development of next generation, personalized digital health solutions that further impact the user experience, individual behaviors and overall health outcomes.
Using Early Engagement Data from a Digital Health Solution to Predict Future Health Outcomes
Building on Welldoc's research focused on advancing digital health through connection with real-time connected devices, such as continuous glucose monitoring (CGM), this study demonstrates how utilization of early CGM and My E-Diary for Activities and Lifestyle (MEDAL) behavior patterns may support prediction of future health outcomes, such as Time in Range (TIR). Here, Welldoc establishes that analysis of the first 30 days of CGM + MEDAL engagement patterns can lead to accurately predicting future TIR patterns. This research reinforces the power of combining CGM with digital health in not only supporting individuals with more personal and precise insights, but also in developing highly accurate machine learning models into the next generation of AI and predictive digital health solutions.
Using Early Engagement Data from a Digital Health Solution to Predict Future Glycemia Risk Index (GRI)
In this real-world analysis, Welldoc builds upon prior research to show early engagement data from a digital health solution can predict the future Glycemic Risk Indicator (GRI) metric* in people with type 1 or type 2 diabetes using a continuous glucose monitoring (CGM) device. This research could enable care teams to use early digital health engagement data to build more personalized and optimized care plans for individuals, and supports Welldoc's efforts in developing next-generation digital health solutions that can improve efficiencies and health outcomes.
Using Early Engagement Data from a Digital Health Solution to Predict Future Engagement Patterns
The adoption and implementation of a digital health solution can provide early CGM and self-management behavior data, via engagement patterns and segmentation, to help predict engagement outcomes associated with later points in the patient journey.
Type 2 Diabetes Hypoglycemia Prediction: Using SMBG Data & Probabilistic Methods
Use of a Diabetes Digital Health Solution Leads to Improvements in Cardiometabolic Outcomes
In this research, Welldoc demonstrates the potential for digital health solutions focused on diabetes to also address a broader set of cardiometabolic outcomes. A multi-condition approach could help health plans, health systems and care teams to better manage the complexities associated with diabetes, while also addressing other cardiometabolic health comorbid conditions. This work can also support better understanding of digital health engagement patterns and the impact to specific health outcomes, ultimately contributing to the development of predictive models and advanced artificial intelligence capabilities.
Use of a Digital Health Tool to Support People with Diabetes Who Inject Bolus Insulin Improves the Glycemia Risk Index and Time in Tight Range
Continuous glucose monitoring (CGM) has emerged as an important tool to help people with diabetes manage food, activity, and insulin dosing. CGM measures Time in Range (TIR) and has become a key metric for clinical practice.
Building upon Welldoc’s research on the impact of CGM + digital health, this analysis sought to correlate the glycemic metrics, time in tight range (TITR) and glycemic risk index (GRI), to level of engagement with the digital health tool. Highlights from the study include further understanding of TIR improvement, particularly for those with type 2 diabetes, and initial findings specific to engagement with a digital health insulin calculator feature. Welldoc continues research in this area to drive further integration of GIR and TITR into AI-driven digital health solutions, to impact user engagement and cardiometabolic health.
Transform Your DSME/S Program: Leverage the Value of Mobile Health
The Use of a Novel CGM-Informed Insulin Bolus Calculator Mobile Application by People with Type 1 and Type 2 Diabetes Improves Time in Range
Building on Welldoc's previous work on the safety of using CGM data for insulin dose calculations, this study combines results from two clinical trial sites, MedStar Health Research Institute and Northwestern University, to demonstrate that people using the CGM insulin bolus calculator (IBC) embedded into Welldoc's FDA cleared BlueStar® mobile application improved their time in range without increasing hypoglycemia. Overall, there was a clinically meaningful increase in time in range of 4%. The improvement was greater in people who had type 2 diabetes (6.5%) and in those who used the IBC between 30 to 60 times per month (6.0%). This analysis reinforces the benefit of combining a digital health tool with CGM and other emerging real-time device innovations. This study adds to the continued research Welldoc is leading to demonstrate the value of digital health tools in supporting chronic condition self-management, positive health outcomes.
The Use of a Real-time CGM and Digital Health Solution Lowered A1C in People with Type 2 Diabetes
Welldoc® in collaboration with Dexcom developed a study to show how the utilization of a real-time CGM (rtCGM) system, Dexcom G6, in combination with Welldoc’s digital health solution, BlueStar®, can help support individuals with diabetes taking medications, lower their A1C. The study concluded that the combination of rtCGM and a digital health solution significantly improved an individual’s A1C after 12 and 24 weeks of use, regardless of baseline A1C or degree of CGM wear. Even though there was a decrease in A1C for all study participants, individuals who started with a high A1C, along with those who continuously used CGM, saw a greater decrease in their A1C.
Use of a Digital Health Tool to Amplify Patient Engagement with the ADCES7 Self-Care Behaviors®
Welldoc continues to research how AI-driven personalized digital health solutions can motivate and support better engagement, self-management and overall health. This Welldoc study analyzed digital health engagement patterns among individuals with type 1 and type 2 diabetes. Individuals who utilized the app to set goals and participate in simple health challenges, focused on building better habits, engaged with the app up to 8x more than those who did not. This indicates how breaking down health goals into manageable and how personalized steps can significantly increase digital health engagement and foster heathier routines.
The Use of a Digital Health Tool with AI-coaching for Patients Enrolled in a Virtual Diabetes Program is Associated with Improvements in Weight and Blood Pressure
In this real-world analysis, Welldoc shows how AI-driven digital coaching can provide personalized, whole-person support for individuals with multiple cardiometabolic conditions, like diabetes, hypertension, and obesity, and amplify the benefits of visits and communication with providers. These outcomes demonstrate how overall cardiovascular risk can be reduced in a high-risk population in a scalable manner, and support Welldoc’s efforts in developing next-generation digital health solutions that can improve health outcomes and increase access to care.
The Impact of a Mobile Diabetes Health Intervention on Diabetes Distress and Depression Among Adults: Secondary Analysis of a Cluster Randomized Controlled Trial
The Critical Elements of Digital Health in Diabetes and Cardiometabolic Care
In the latest issue of “Frontiers in Endocrinology,” Welldoc highlights key elements for designing and implementing successful diabetes digital health tools in clinical practice. We explore topics like the importance of regulatory oversight, looking beyond A1C, addressing technology literacy, and clinical integration to increase efficiency. Additionally, we outline the practical steps needed to become a digital health-ready practice. These insights are integral to Welldoc’s commitment to developing best-in-class digital health solutions and transforming the care continuum.
The Combined use of rtCGM and a Digital Health Tool Positively Impacts ADCES-7 Behaviors
Individuals living with type 2 diabetes were enrolled in a program that provided the Dexcom G6 system and BlueStar to understand how engagement with the combined solution influenced the Association of Diabetes Care and Education Specialists’ 7 self-care (ADCES7) behaviors. Data showed that those who continuously used real-time CGM (rtCGM) engaged with the app 13% more than those who used it intermittently. In addition, this data showed that engagement with a digital solution, coupled with continuous rtCGM use, can help individuals with T2D — even those who are not prescribed insulin — improve adherence to ADCES7 behaviors.
Technology to Overcome Therapeutic Inertia
In this issue of mHealth, the co-authors analyze the factors for why therapeutic inertia exists among those people battling Type 2 diabetes. The technology-enabled self-management feedback loop is presented as a more granular way to design studies to include technology interventions.
Real-world Digital Health Data Demonstrate the Utility of the Glycemia Risk Index (GRI) as a Composite CGM Metric
Welldoc® and Carey Business School of Johns Hopkins University (formerly CHIDS, University of Maryland) worked together to study how continuous glucose monitoring (CGM) and a digital health solution can help support better glycemic outcomes for individuals with type 1 and type 2 diabetes. A Glycemia Risk Index (GRI) metric was applied to this study to assist with the basic clinical interpretation of CGM data. GRI was calculated for each individual and classified into the five GRI zones from lowest (A: best) to highest (E: worst), with a breakdown by gender, age, and diabetes type. Our results showed that diabetes type was not a factor in GRI improvement, but, individuals whose baseline GRI started in higher zones improved GRI by at least 1 zone—supporting the use of CGM and a digital health solution in self-managing glycemia. In addition, the demographic data shows that GRI was significantly lower in females and individuals 65 years and older. The culmination of GRI and demographic data can help healthcare professionals support and manage individuals and populations with diabetes.
Safety of a CGM-Informed Insulin Bolus Calculator Mobile Application for People with Type 1 and Type 2 Diabetes
Welldoc® and investigators at Northwestern University in Chicago partnered to demonstrate the safety of an investigational insulin bolus calculator (IBC*), based on continuous glucose monitoring (CGM) data. This IBC was designed to help people with diabetes dose their insulin. The IBC, embedded into Welldoc's FDA cleared BlueStar® mobile phone app, was designed to translate CGM data into simple trend arrows, indicating bolus insulin dose recommendations. The study demonstrated the saftey of the IBC, with no reported increased hypoglycemia measurements.
Public-Private-Industry Learning Network: Digital Health Expands the Reach and the Role of the Diabetes Care and Education Specialist
Welldoc collaborated with the state of Montana and the Montana Diabetes Digital Health Learning Network (MDDHLN) to integrate digital health into their diabetes program. The intent was to scale services to better support Montana’s rural, frontier communities. This population typically has limited access to health resources and can benefit from innovative tools, like digital health platforms, to help better self-manage their diabetes. This article highlights the many learnings and best practices to efficiently and effectively implement novel cardiometabolic care models that integrate in-person, virtual and digital capabilities.
Predicting Success with a Diabetes Digital Health Application from Early Usage Data
Digital health applications have the potential to improve overall health status in people with diabetes. This research presents a predictive model that may determine which patients are likely to persist in using the application and which patients will experience improvement in blood glucose using Welldoc's BlueStar® solution.
Population Health Diabetes Education: The Role of Digital Health & Patient Generated Health Data
Safety of a Novel CGM-Informed Insulin Bolus Calculator Mobile Application by People with Type 1 and Type 2 Diabetes
Individuals with diabetes relying on basal-bolus insulin regimens often struggle with precise bolus dose adjustments. Current Continuous Glucose Monitoring (CGM) systems offer limited guidance, often relying on basic trend arrows. Building upon Welldoc’s extensive research on connecting digital health to CGM, Welldoc has developed a novel CGM-informed insulin bolus calculator. This advanced technology leverages sophisticated algorithms to analyze trend arrows and exercise factors, delivering real-time, personalized insulin dose recommendations.
This article outlines the results from a 30-day prospective clinical trial with participants with type 1 and type 2 diabetes. Participants experienced improved glycemic control and reduced diabetes distress, particularly for type 2 diabetes. Key findings include a notable Time in Range (TIR) improvement of approximately 3 points from 68.4 to 71.8% (N=54, P=0.013).
Welldoc’s CGM-informed insulin bolus calculator represents a significant advancement in diabetes management. By empowering individuals with diabetes to achieve better glycemic control and reduce the burden of the disease, our technology underscores the transformative potential of digital health when integrated with CGM.
Let’s partner to elevate cardiometabolic health.
Contact our sales team to talk about a solution for your enterprise or request a demo to see Welldoc’s predictive AI in healthcare in action.
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