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.
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.
Mansur Shomali, MD, CM, Abhimanyu Kumbara, MS, MBA, and Anand Iyer, PhD, MBA
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.
Mansur Shomali, Colleen Kelly, Anand Iyer, Jean Park, Grazia Aleppo
The Impact of a Mobile Diabetes Health Intervention on Diabetes Distress and Depression Among Adults: Secondary Analysis of a Cluster Randomized Controlled Trial
Charlene C Quinn, Krystal K Swasey, J Christopher F Crabbe, Michelle D Shardell, Michael L Terrin, Erik A Barr, Ann L Gruber-Baldini
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.
Catherine Brown, Anand Iyer, Abhi Kumbara, Mansur Shomali, Malinda Peeples
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.
Abhimanyu Kumbara, Anand Iyer, Keri Leone, Mansur Shomali
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.
Mansur E. Shomali, Colleen Kelly, Anand K. Iyer, Grazia Aleppo
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.
Mansur Shomali, MD, CM, Colleen Kelly, PhD, Abhimanyu Kumbara, MS, Anand Iyer, PhD, Jean Park, MD, Grazia Aleppo, MD
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.
Malinda Peeples, MS, RN, CDCES, FADCES, Marci Butcher, RD, CDCES, FADCES, Jennifer Scarsi, RD, CDCES, Deb Bjorsness, MPH, BC-ADM, RD, CDCES, Melissa House, MBA, and Janice Macleod, MA, RD, CDCES, FADCES
Technology-Enabled Diabetes Self-Management Education & Support
Malinda Peeples, MS, RN, CDE, FAADE, Deborah Greenwood, PhD, RN, BC-ADM, CDE, FAADE, Perry M Gee, PhD, RN
Population Health Diabetes Education: The Role of Digital Health & Patient Generated Health Data
Malinda Peeples, Janice Macleod, Shelley Taylor, Mansur Shomali
Older Adult Self-Efficacy Study of Mobile Phone Diabetes Management
Charlene C Quinn, Bilal Khokhar, Kelly Weed, Erik Barr, Ann L Gruber-Baldini
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.
Maya Mudambi, Kenyon Crowley, Michelle Dugas, Weiguang Wang, Di Hu, Anand K. Iyer, Malinda Peeples, Mansur Shomali, Guodong (Gordon) Gao
Patient-Generated Health Data Enhance Clinical Care for People with T2D Using a Digital Health Tool
Optimal management of people with Type 2 diabetes often requires collaboration among healthcare providers and educators. This study, presented at the Association of Diabetes Care & Education Specialists annual event, demonstrates how patient-generated health data enhances clinicians' ability to modify treatment plans.
Malinda Peeples, RN, MS, CDE, Janice Macleod, MA, RDN, LDN, CDE, and Mansur Shomali, MD, CM
Mobile diabetes intervention study: testing a personalized treatment/behavioral communication intervention for blood glucose control
Charlene C Quinn, Ann L Gruber-Baldini, Michelle Shardell, Kelly Weed, Suzanne S Clough, Malinda Peeples, Michael Terrin, Lauren Bronich-Hall, Erik Barr, Dan Lender
Lessons From a Community-Based mHealth Diabetes Self-Management Program: “It’s Not Just About the Cell Phone”
Richard Katz, Tsega Mesfin, Karen Barr
Meet the Newly Revised AADE7 Self-Care Behaviors® Up Close
The AADE7 Self-Care Behaviors® (AADE7) provides a robust framework for self-management of diabetes and other related conditions. In this poster presented at ADCES 2020, the authors review AADE7 and make recommendations on behavior names.
Malinda Peeples, MS, RN, CDE, FAADE and Carole’ Mensing
Mobile Messaging: It’s More Than Texting. A Mobile Message Taxonomy that Utilizes the Capacity of Mobile Technology
Robin C. Anthony Kouyaté, PhD, Malinda Peeples, RN, MS, CDE, Calvin C. Wilhide, PhD
Mobile Prescription Therapy: The Potential for Patient Engagement to Enhance Outcomes
Joshua Cohen, Malinda Peeples, Anand Iyer, Mansur Shomali
Mobile Diabetes Intervention for Glycemic Control in 45- to 64-Year-Old Persons With Type 2 Diabetes
Charlene C Quinn, Michelle D Shardell, Michael L Terrin, Erik A Barr 2, DoHwan Park, Faraz Shaikh, Jack M Guralnik, Ann L Gruber-Baldini
Measures derived from patient-generated health data provide insights on glycemic control beyond A1C for people with type 2 diabetes
Mansur Shomali, MD, CM
Mobile Diabetes Intervention for Glycemic Control: Impact on Physician Prescribing
Charlene C Quinn, Patricia L Sareh, Michelle L Shardell, Michael L Terrin, Erik A Barr, Ann L Gruber-Baldini
Integration of a mobile-integrated therapy with electronic health records: lessons learned.
Malinda M Peeples, Anand K Iyer, Joshua L Cohen
Integrating The 2017 National Standards For Diabetes Self-Management Education And Support Into A Technology-Enabled Population Health Diabetes Care And Education Framework
Janice MacLeod, MA, RD, CDE, FAADE, Deborah A. Greenwood, PhD, RN, BC-ADM, CDE, FAADE
Impact of Food on A Transformer Based Glucose Prediction Model to Predict Glucose Trajectories at Different Time Horizons
In this research, Welldoc and Johns Hopkins Carey Business School leveraged dense, real-time glucose data from Continuous Glucose Monitoring (CGM), similar to how fitness trackers collect biometric signals. Our work shows that by combining this data with food intake, we can significantly improve the accuracy of future glucose predictions. This takes Welldoc one step closer to the real world application of reliable prediction and prevention of overnight hyperglycemia.Why It Matters:
- Smarter Predictions: We developed a "large health model" (LHM) that uses AI to analyze dense data from CGMs and food intake data. This model is more accurate in predicting future glucose levels over longer periods, with a greater improvement for the 2-hour interval when food's impact is highest. This improved accuracy is critical to establishing trust in the next generation of AI driven digital health coaching capabilities.
- Better Health Outcomes: This work establishes a clear pathway for integrating various types of health data into AI models to enhance their predictive power. The ultimate goal is to apply this improved accuracy to real-world clinical challenges, such as reliably predicting and preventing overnight hyperglycemia.
- A New Approach: Welldoc’s research goes beyond existing models by not only collecting data but also using it to predict future biometric values and offer automated coaching based on those predictions. This is a novel approach that leverages the power of AI to provide actionable insights for.
Junjie Luo, MS, Abhimanyu Kumbara, MS, Mansur Shomali, MD, CM, Anand Iyer, PhD, Gordon Gao, PhD
Individual Differences in Educational Engagement with a Digital Health Solution
This research, conducted by Welldoc in collaboration with the Robert H. Smith School of Business Center for Health Information and Decision Systems, evaluated the performance of a Welldoc automated method for detecting significantly adverse glucose events and, further, classifying those events by level of severity.
Shiping Liu, Mansur Shomali, Abhimanyu Kumbara, Kenyon Crowley, Michelle Dugas, Anand K. Iyer, Malinda Peeples, Guodong Gao
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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