Clinical Research

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.
*Reference: Klonoff DC, et al. A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings. J Diabetes Sci Technol. 2023 Sep;17(5):1226-1242. doi: 10.1177/19322968221085273. Epub 2022 Mar 29. PMID: 35348391.

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.

In this journal article, Welldoc builds upon prior research focused on advancing chronic care through the use of digital health and real-time connected devices. This study examines the impact of combing real-time continuous glucose monitoring (RT-CGM) with an AI-driven digital health solution on helping individuals with type 2 diabetes to improve their glycemic metrics like time in range (TIR).

In this study, Welldoc builds upon our prior research on advancing digital health AI through machine learning (ML) and connection with real-time connected devices, such as continuous glucose monitoring (CGM). This study examines how CGM data combined with digital health My E-Diary for Activities and Lifestyle (MEDAL) data and behavior patterns can lead to better understanding of optimal digital health feature utilization and ML models, which can be used to predict future Time in Range (TIR). This research continues Welldoc's efforts in advancing our AI models and developing next generation digital health solutions geared towards personalized prevention and prediction.

Welldoc continues to focus our research on advancing digital health through connection with real-time connected devices, such as continuous glucose monitoring (CGM). Building on prior clinical research, which showed an improvement in glucose outcomes and reduction in diabetes distress with the use of an app-based CGM-informed insulin bolus calculator, this study demonstrates qualitatively how these same individuals interacted with the technology and how it made them feel. People with type 1 and type 2 diabetes who inject bolus insulin often find their diabetes to be overwhelming. Here, Welldoc found there was a significant proportional connection between those who felt less distress about their diabetes and felt positively about the technology, versus those who felt more distressed. This research reinforces the power of combining digital health with CGM in not only supporting individuals with a more personalized digital health solution that they enjoy using, but also providing clinicians with additional tools to support people with diabetes distress management.

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.