Utilizing CareEvolution’s MyDataHelps™ platform to study glycemic response in a diverse, remotely engaged cohort.
How Digital Trials Are Transforming Metabolic Health Research
Metabolic health plays a critical role in the maintenance of overall well-being, affecting numerous physiological pathways. Despite this, metabolic disorders such as type 2 diabetes (T2D) and obesity persist as significant public health challenges. In the United States, approximately 13% of adults are affected by T2D, while nearly 40% experience obesity. The PRediction Of Glycemic RESponse Study (PROGRESS), launched by the Scripps Research Digital Trials Center, was developed to explore the many different factors that influence glycemic response and how that differs at an individual level. Central to the success of this study was the use of CareEvolution’s MyDataHelps platform, which facilitated decentralized participant enrollment, multi-modal data collection, and engagement.
Decentralized Design: Expanding Reach and Reducing Costs
The PROGRESS study illustrates the growing shift towards decentralized, digital clinical trials (DCTs). By employing a fully remote design, the study overcame many of the limitations traditionally associated with in-person research. Recruitment and enrollment were conducted entirely through digital platforms, enabling the inclusion of a more geographically and demographically diverse cohort.
Using CareEvolution’s MyDataHelps platform, the study successfully consented 1,137 participants, exceeding its original target of 1,000, with half of the participants diagnosed with T2D and 49.8% of them coming from populations underrepresented in biomedical research (UBR). This decentralized approach not only broadened the diversity and geographical scope of the study but also demonstrated cost-efficiency, reducing per-participant expenses compared to traditional trials. The recruitment strategies employed in PROGRESS, which included broad-based social media ads on platforms like NextDoor as well as health system and community-based digital advertisements may serve as a model for future decentralized research, particularly in studies aiming to engage underrepresented populations.
At-home Data Collection: Biosamples and Continuous Real-World Data (RWD)
Deploying survey instruments are foundational to clinical trials. However, the ability to complement with other observational data allows for a richer dataset but often comes at the cost of in-person collection methods. By combining at-home biosampling with real-time data from continuous glucose monitors (CGM) and wearable devices like Fitbit, researchers gained insights into how daily lifestyle factors influence individual glycemic responses. Importantly, these biosamples—saliva, blood, and stool—were all self-collected at home, facilitated by easy-to-understand instructional videos provided through the MyDataHelps app, ensuring participants could comfortably and effectively participate in the study without the need for in-person visits.
Of the 699 participants who connected their EHRs, 76% returned saliva samples for polygenic risk scoring, 76% submitted blood samples for HbA1c analysis, and 71% returned stool samples, enabling gut microbiome profiling. These biosamples provided critical biological data that, when paired with real-time physiological data, created a rich dataset for analysis.
With 90% of participants who returned biosamples also sharing their Fitbit activity data and 87% logging nutritional intake, the study captured a detailed view of each participant’s metabolic responses in real-world conditions. The 73% of participants who provided sufficient CGM data during the 10-day active phase enabled a granular analysis of glucose levels in response to food intake, activity, and rest, building a comprehensive physiological profile for each individual.
This continuous data collection approach moves beyond the limitations of episodic, clinic-based assessments. It allows researchers to develop personalized models that account for dynamic changes throughout the day, offering a clearer understanding of the complex relationships between diet, physical activity, sleep, and metabolic health.
By the numbers
- 1,137
- 62%
- 415+
Patient-Mediated Data Exchange: Expanding EHR Data Access and Control
Electronic health record data is increasingly becoming a powerful way to capture clinical history as well as monitor potential clinical outcomes. For PROGRESS, participants sharing EHR data meant researchers could examine lab results, diagnoses, and treatments leading up to the study as well as any potential changes in their disease status in the longitudinal arm (e.g., progression from pre-diabetes to T2D). Of the 1,137 total participants who consented to the study, 699 (62%) connected their EHRs through the MyDataHelps platform, enabling the research team to collect longitudinal data from over 415 distinct healthcare providers. Notably, more than 150 participants connected records from multiple providers, adding further depth to the dataset.
The platform’s innovative EHR data collection model leveraged participant-mediated exchange to connect with hundreds of provider sites. This model not only enabled widespread geographical reach but also empowered participants to share data seamlessly through MyDataHelps. This approach ensured a smooth, scalable data collection process without the need for formal agreements with each health system.
By enabling patients to directly control and share their own health records, this methodology significantly expanded the study’s capacity to gather real-world, longitudinal data. It sets a precedent for how decentralized trials can efficiently integrate clinical data from a broad range of providers while maintaining patient autonomy and improving the diversity of data sources.
Participant Engagement: Gamification, Incentives, and Real-Time Feedback
Maintaining participant engagement is often one of the most challenging aspects of decentralized trials. The PROGRESS study effectively addressed this through a combination of gamification, financial incentives, real-time feedback, and dynamic nudges. Participants earned points by completing key tasks, such as logging meals, wearing their continuous glucose monitors (CGMs), and submitting biosamples. These points were redeemable for gift cards via the MyDataHelps™platform’s connection to Amazon’s Gift Card API.
Dynamic nudges and reminders—delivered via data-driven notifications and tips within the app—kept participants on track by prompting them to complete tasks such as food logging or refreshing their CGM data. These tailored reminders helped to maintain high engagement levels throughout the study, with participants responding positively to the prompts.
Additionally, real-time feedback allowed participants to visualize their glucose responses to dietary intake and activity. These personalized insights gave participants a clearer understanding of how their lifestyle choices (e.g.; the food they eat) influenced their metabolic health, and served as a strong motivator to adhere to study protocols. During the 10-day active data collection phase, 90% of participants shared their Fitbit activity data, 87% logged nutritional intake, and 73% provided sufficient CGM data.
The combination of gamification, real-time feedback, financial incentives, and dynamic nudges contributed to sustained participant engagement throughout the study, underscoring the power of these tools in decentralized trials.
Conclusion: The Future of Decentralized Clinical Trials
The PROGRESS study stands as a model for how decentralized digital clinical trials can be used to conduct complex, large-scale research in metabolic health. By utilizing CareEvolution’s MyDataHelps platform, the study effectively combined real-time data collection, patient-mediated EHR exchange, at-home biosampling, and innovative engagement strategies to overcome many of the traditional barriers in clinical research. This approach not only facilitated the inclusion of a diverse and geographically dispersed cohort but also provided deeper, individualized insights into glycemic response and metabolic health.
The combination of decentralized methods, real-world data (RWD), and innovative engagement tools demonstrates the potential for more inclusive, efficient, and scalable clinical research in the future. T2D is one of many common but complex disorders that are influenced by a wide range of factors requiring a multi-modal approach to understand what drives the disease. As the field of precision nutrition and metabolic health evolves, the methodologies employed in PROGRESS will likely serve as a blueprint for future studies, advancing the potential for digital and decentralized research to drive more efficient and scalable clinical trials.