Clinical Research
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:
AI
Our AI transforms complex health data into personalized insights and guidance when individuals need it most.
CGM
We convert continuous glucose data into personalized guidance that elevates traditional diabetes care standards.
Cardiometabolic
We address interconnected health conditions, turning digital engagement into measurable clinical improvements.
Diabetes complexities
We offer FDA-cleared solutions for T1 and T2 diabetes that simplify treatment while addressing daily health challenges.
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Personalized cardiometabolic care powered by artificial intelligence
Cluster-randomized trial of a mobile phone personalized behavioral intervention for blood glucose control
The Impact of a Mobile Diabetes Health Intervention on Diabetes Distress and Depression Among Adults: Secondary Analysis of a Cluster Randomized Controlled Trial
Educators: Go Mobile & Join the Digital Revolution!
Mobile diabetes intervention study: testing a personalized treatment/behavioral communication intervention for blood glucose control
WellDoc mobile diabetes management randomized controlled trial: change in clinical and behavioral outcomes and patient and physician satisfaction
Type 2 Diabetes Hypoglycemia Prediction: Using SMBG Data & Probabilistic Methods
Mobile Diabetes Intervention for Glycemic Control: Impact on Physician Prescribing
Mobile Diabetes Intervention for Glycemic Control in 45- to 64-Year-Old Persons With Type 2 Diabetes
Lessons From a Community-Based mHealth Diabetes Self-Management Program: “It’s Not Just About the Cell Phone”
Hypoglycemia prediction using machine learning models for patients with type 2 diabetes
Enhancing Diabetes and Hypertension Self-Management: A Randomized Trial of a Mobile Phone Strategy
Evidence-Based mHealth Chronic Disease Mobile App Intervention Design: Development of a Framework
Mobile Messaging: It’s More Than Texting. A Mobile Message Taxonomy that Utilizes the Capacity of Mobile Technology
Expanding Reach: AADE7® Moves into the Digital Space
Older Adult Self-Efficacy Study of Mobile Phone Diabetes Management
Diabetes Digital Health Learning Network (DDHLN): Educators Designing the Future
CONNECTING PATIENTS AND DIABETES EDUCATORS VIA A MOBILE PHONE AND WEB-BASED TECHNOLOGY SYSTEM: Content Analysis of Portal Messages
Case Study: The IoT and Big Data in Healthcare Unleashing the Next Generation of Value Creation
Integration of a mobile-integrated therapy with electronic health records: lessons learned.
Combining the High Tech with the Soft Touch: Population Health Management Using eHealth and Peer Support
Measures derived from patient-generated health data provide insights on glycemic control beyond A1C for people with type 2 diabetes
Population Health Diabetes Education: The Role of Digital Health & Patient Generated Health Data
Transform Your DSME/S Program: Leverage the Value of Mobile Health
A Novel Economic Analysis Applied to Innovative Diabetes Digital Health Intervention Demonstrates Significant Financial Benefits
Improving Diabetes Self-Management with a Mobile App: Preliminary Results of a Pilot Program at a Safety Net Hospital System
Mobile Prescription Therapy: The Potential for Patient Engagement to Enhance Outcomes
Meet the Newly Revised AADE7 Self-Care Behaviors® Up Close
An Effective Model of Diabetes Care and Education – Revising the AADE7 Self-Care Behaviors®
A Data Science Framework for Mobile Health–Engagement and Outcomes
eHealth-Assisted Lay Health Coaching for Diabetes Self-Management Support
Blood Pressure Improvement in People Using a Digital Health Solution for Comprehensive Diabetes Self-management
A Framework for Optimizing Technology-Enabled Diabetes and Cardiometabolic Care and Education
Are You Ready to Be an eEducator?
Development of Self-Management Behavior Scores and Profiles with Digital Health Data
Implementation Strategy for a Digital Health Tool Influences User Engagement
Integrating The 2017 National Standards For Diabetes Self-Management Education And Support Into A Technology-Enabled Population Health Diabetes Care And Education Framework
Technology-Enabled Diabetes Self-Management Education & Support
Engagement With an AI-enabled Digital Health Tool by Individuals With Overweight or Obesity Enrolled in a Weight Management Program Differs by Use of Anti-obesity Medications
A Probability State Transition Matrix can be Used to Estimate Weight Loss for Individuals with Overweight or Obesity Enrolled in an AI-enabled Digital Health Weight Management Program
Food Detection from Continuous Glucose Monitoring Sensors Using Pretrained Transformer-Based Models
The Critical Elements of Digital Health in Diabetes and Cardiometabolic Care
Technology to Overcome Therapeutic Inertia
Safety of a Novel CGM-Informed Insulin Bolus Calculator Mobile Application by People with Type 1 and Type 2 Diabetes
Public-Private-Industry Learning Network: Digital Health Expands the Reach and the Role of the Diabetes Care and Education Specialist
Interactive Digital Health: Engaging Healthy Behaviors & Clinical Outcomes
Integration of the Glucose Management Indicator (GMI) into the electronic health record through a diabetes-cardiometabolic digital health app
Individual Differences in Educational Engagement with a Digital Health Solution
Impact of Food on A Transformer Based Glucose Prediction Model to Predict Glucose Trajectories at Different Time Horizons
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
Identifying Digital Health Habits Correlated with Improved Blood Glucose Control
Human vs. Machine: A Comparative Study on CGM Event Detection and Classification
From Data to Outcomes: Digital Health & the Role of the Healthcare Provider
Examining The Ability of Different Machine Learning Approaches to Predict Health Outcomes with a Digital Health Platform
Evaluating the Impact of a Combined Real-Time CGM/Digital Health Solution on Glucose Control for People with Type 2 Diabetes
Evaluating a State-of-the-art Generative Pre-trained Transformer Model to Predict Continuous Glucose Monitoring Values at Different Time Intervals
Evaluating Perplexity and Glucose Level Impact on State-Of-The-Art Generative Pre-trained Transformer (GPT) Model to Predict Glucose Values at Different Time Intervals
Estimating the Economic Value of a Digital Therapeutic in Type 2 Diabetes
Enhancing Mobile Health to Achieve Both Hypertension and Diabetes Goals
Early Engagement Measures Can Accurately Identify Users at Risk of Abandoning Digital Therapeutics
Digital Health and AI: RDN Path to Success
Determinants of Engagement in Digital Health: What Makes it Stick?
Contextual Annotations Predict Digital Health Solution Persistence and Diabetes Outcomes
Consistent Engagement with a Digital Health Solution Enhances the Effect of Medication Changes on Blood Glucose Control
Complementarity of Digital Health and Peer Support: “This Is What’s Coming”
Comorbidities And Reducing InEquitieS (CARES): Feasibility of self-monitoring and community health worker support in management of comorbidities among Black breast and prostate cancer patients
CGM-GPT: A Transformer Based Glucose Prediction Model to Predict Glucose Trajectories at Different Time Horizons
Applying the Glycemia Risk Index (GRI) to User Data from a Digital Health Tool Reveals Patterns of Engagement That Differ by Type of Diabetes
Beyond Tracking: The Benefits of Contextual Annotation in a Diabetes Digital Therapeutic
An Approach for Evaluating and Visualizing CGM Data in People with Diabetes
A Systematic Review of Reviews Evaluating Technology-Enabled Diabetes Self-Management Education and Support
A Real-Time CGM-Enabled Digital Health Tool Highlights a Relationship Between Sentiment and Diabetes Distress in People Using Bolus Insulin
A Proposed Foundational Architecture for AI-powered Digital Health Platforms
A Payer Digital Health Study Shows Scalable Approach to Cost Savings and Outcomes
A Novel Approach to Estimating Cost Savings and Return on Investment (ROI) for Weight/BMI Changes with Digital Health
A Novel Automated AI Method for Detecting and Classifying CGM Patterns
A Novel Approach to Assess Patient Burden Using Data from a Digital Therapeutic for Type 2 Diabetes Predicts Glucose Outcomes
A Mobile App to Improve Self-Management of Individuals With Type 2 Diabetes: Qualitative Realist Evaluation
A Dynamic Duo: Virtual DSMES and a Digital App, a New Model for Self-Management Education
A Digital Health Solution with a CGM-informed Insulin Calculator Reduces Diabetes Distress in Individuals with Type 1 and Type 2 Diabetes
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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