Clinical Research

Cardiometabolic digital health solutions, which address conditions like diabetes, are increasingly being integrated into clinical care. These solutions can provide personalized artificial intelligence (AI)-driven self-management support for individuals and treatment insights for clinicians. Understanding how to effectively integrate these technologies into an individual’s daily experience and the clinician’s workflow is essential. Successful implementation of these solutions can improve reach, access and outcomes for health and operational efficiencies at the individual and population levels. This article discusses these solutions and shares real-world examples of strategies for integrating them into clinical practice.

Thank you to Cutting Edge Nutrition and Diabetes Care for providing open access to our article. To purchase and read the full issue, please visit the journal’s website.

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.

Black individuals with cancer often face poorer health outcomes compared to other racial groups in the U.S., including a higher prevalence of cardiometabolic comorbidities, like diabetes and high blood pressure. A study published in Contemporary Clinical Trials Communications explores the potential of digital health tools to address these health disparities.

The study investigated the feasibility of incorporating the Welldoc cardiometabolic digital health app to improve blood pressure and/or blood glucose levels in Black individuals with breast or prostate cancer. Participants in this six month study used a home-monitoring device and the Welldoc app to track their health metrics weekly, with support from a community health worker.

While the study findings were modest, they suggest that digital health tools may be beneficial in helping individuals manage their overall health during cancer treatment. Further research is needed to optimize the integration of cardiometabolic health and digital health tools into cancer care, aiming to improve patient outcomes and reduce health disparities.

Proactive diabetes self-management requires accurate glucose value prediction and in-the-moment AI-driven coaching based on those predictions, all made possible by raw data from continuous glucose monitors (CGM).

Here, Welldoc builds upon our prior AI models that used CGM data only and expands to a new Large Glucose Model (LGM), which uses both CGM values and time series inputs to predict glucose trajectories at 30mins, 60mins and 2-hour time horizons. Results were analyzed across different Type 1 and Type 2 diabetes population subgroups (time of day, age group and total engagement levels) within a mobile diabetes management application.

This work will allow Welldoc to power new cardiometabolic focused capabilities and innovations in enhanced AI-driven personalization. Welldoc continues to drive this type of research to develop novel solutions leveraging data from real-world sensors, like CGM, and provide deep insights into subgroup level patterns and differences.

Welldoc continues to research how generative-AI models can be used in combination with data from real-time devices like continuous glucose monitors (CGM) to predict metrics like glucose risk indicator (GRI) and future health outcomes. Here, Welldoc developed a state-of-the-art GPT model to predict CGM trajectory at different time horizons and across two different prediction contexts. One particular context included model perplexity, which is an industry-standard metric of how well a model can predict a sample or next value in a sequence. The data show higher prediction uncertainty as the glucose profile complexity increases. This work supports Welldoc’s efforts to further optimize our diabetes solution, making the experience for individuals even more targeted and personalized.

Weight loss is a key factor in improving health outcomes for many cardiometabolic conditions, like diabetes. Building upon our previous AI-modeling research, Welldoc has now harnessed our AI to predict weight loss. Based on a large dataset of individuals with type 2 diabetes using our digital health solution’s food log, our AI models predicted at least 3% weight loss with high accuracy (93%). It also evaluated the effect of different nutrients on weight loss, as well as time-based correlations. Future research will dive deeper into nutrient- and time-of-day-based personalized digital nutrition coaching to better predict the likelihood and timing of achieving nutrition and weight loss goals for users. This work is integral in Welldoc’s ability to make our digital health solution for weight management even more targeted and accurate, as well as support weight loss-related outcomes across comorbid conditions in our platform.