Welcome to the sDHT Adoption Library, featuring NaVi
NaVi is a closed-environment AI research assistant that leverages a carefully curated library of more than 300+ vetted documents, including FDA guidance and industry best practices. NaVi helps you search and explore content across the sDHT Adoption Library and Roadmap using natural language questions.
The Library is intended to serve as a living resource. Content is added periodically as new guidance, standards, and peer-reviewed research are released.
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Library scope and selection
To ensure high-quality, relevant results, the Library follows a predefined scoping approach:
- Inclusions: FDA guidance, non-commercial standards, and peer-reviewed research (2018–Present) focused on sDHTs being used as measurement tools for medical products in U.S.-based clinical trials.
- Exclusions: Materials from single commercial entities, non-U.S. regulatory bodies (except select EMA guidances with direct U.S. cross-relevance), and conference proceedings, and conference proceedings.
Inclusion in the Library does not imply endorsement, completeness, or regulatory acceptability.
Library Resources
Library scope
Resources in the sDHT Adoption Library are identified using a predefined scoping approach and include publicly available FDA guidance, non-commercial standards and guidance, and peer-reviewed research relevant to sDHT use in U.S.-based clinical trials. Materials from single commercial entities, non-U.S. regulatory bodies, conference proceedings, and studies conducted exclusively outside the United States are excluded; inclusion does not imply endorsement or regulatory acceptability.
Last updated 2026: Library content is reviewed and updated on a periodic basis as new eligible materials become available.
Showing 3 of 3 results
Guide to Specific Actions to Enroll and Retain Diverse Participants
This resource outlines a strategic framework for increasing diversity, equity, and inclusion (DEI) in clinical trials through digital health technologies. It provides specific actions for integrating digital tools into clinical trial design, recruitment, and retention strategies, ensuring that underrepresented populations are included and engaged. The framework emphasizes patient-centered approaches, real-world data utilization, and accountability measures to promote sustainable progress in equitable clinical research.
Guide to Specific Actions to Enroll and Retain Diverse Participants
2023
Digital Medicine Society
Framework or roadmap
Authors
Improving and Increasing Diversity, Equity, and Inclusion in Digitized Clinical Trial Design and Implementation Project Team
This resource outlines a strategic framework for increasing diversity, equity, and inclusion (DEI) in clinical trials through digital health technologies. It provides specific actions for integrating digital tools into clinical trial design, recruitment, and retention strategies, ensuring that underrepresented populations are included and engaged. The framework emphasizes patient-centered approaches, real-world data utilization, and accountability measures to promote sustainable progress in equitable clinical research.
Findings
The clinical research ecosystem has longstanding diversity gaps, making targeted DEI strategies essential for equitable healthcare innovation.
Digital tools, including virtual visits, digital outreach campaigns, and AI-driven analytics, can increase access to trials for underrepresented populations.
Real-world data (RWD) and real-world evidence (RWE) help identify diverse participant pools and optimize recruitment strategies.
eConsent and educational resources improve patient engagement and retention by making clinical trials more transparent and accessible.
Trust-building measures, such as community partnerships and patient advocacy collaborations, are critical for long-term success in diversifying clinical trials.
Recommendations
Clinical trial sponsors should integrate digital tools at each stage of trial design to enhance participant diversity and reduce barriers to participation.
AI/ML and real-world data should be leveraged to identify, recruit, and retain diverse patient populations in a data-driven manner.
Digital engagement strategies, including social media outreach and mobile-friendly platforms, should be employed to improve awareness and accessibility.
Transparent communication, including clear eConsent processes and on-demand educational materials, should be prioritized to foster participant trust.
A comprehensive tracking system should be implemented to measure progress on diversity goals, ensuring accountability in clinical trial execution.
Regulatory Considerations
The FDA Diversity Plan requirement should be incorporated into clinical trial planning, with measurable targets for diverse participant inclusion.
Digital tools used for recruitment and engagement must comply with HIPAA, GDPR, and other privacy regulations to protect participant data.
The use of real-world evidence (RWE) in regulatory submissions should be expanded to demonstrate the efficacy of digital recruitment and retention strategies.
Standardized DEI reporting frameworks should be established to ensure regulatory bodies can assess the impact of diversity initiatives in clinical research.
Clinical trials utilizing digital tools should align with decentralized clinical trial (DCT) regulatory guidance to maximize accessibility and equity.
The clinical research ecosystem has longstanding diversity gaps, making targeted DEI strategies essential for equitable healthcare innovation.
Digital tools, including virtual visits, digital outreach campaigns, and AI-driven analytics, can increase access to trials for underrepresented populations.
Real-world data (RWD) and real-world evidence (RWE) help identify diverse participant pools and optimize recruitment strategies.
eConsent and educational resources improve patient engagement and retention by making clinical trials more transparent and accessible.
Trust-building measures, such as community partnerships and patient advocacy collaborations, are critical for long-term success in diversifying clinical trials.
Recommendations
Clinical trial sponsors should integrate digital tools at each stage of trial design to enhance participant diversity and reduce barriers to participation.
AI/ML and real-world data should be leveraged to identify, recruit, and retain diverse patient populations in a data-driven manner.
Digital engagement strategies, including social media outreach and mobile-friendly platforms, should be employed to improve awareness and accessibility.
Transparent communication, including clear eConsent processes and on-demand educational materials, should be prioritized to foster participant trust.
A comprehensive tracking system should be implemented to measure progress on diversity goals, ensuring accountability in clinical trial execution.
Regulatory Considerations
The FDA Diversity Plan requirement should be incorporated into clinical trial planning, with measurable targets for diverse participant inclusion.
Digital tools used for recruitment and engagement must comply with HIPAA, GDPR, and other privacy regulations to protect participant data.
The use of real-world evidence (RWE) in regulatory submissions should be expanded to demonstrate the efficacy of digital recruitment and retention strategies.
Standardized DEI reporting frameworks should be established to ensure regulatory bodies can assess the impact of diversity initiatives in clinical research.
Clinical trials utilizing digital tools should align with decentralized clinical trial (DCT) regulatory guidance to maximize accessibility and equity.
Keywords
Technologies
Open Resource
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
Lessons learned in the Apple Heart Study and implications for the data management of future digital clinical trials
An overview of the data management challenges faced in the Apple Heart Study, a large-scale digital clinical trial that leveraged wearable devices and virtual interactions to evaluate the ability of an algorithm on the Apple Watch to detect atrial fibrillation. The key challenges discussed include participant adherence, accounting for unique participants, and aligning timestamp data from multiple data sources. The authors build upon the CTTI guidelines for digital clinical trials and provide a comprehensive set of recommendations for data management in future digital trials.
Lessons learned in the Apple Heart Study and implications for the data management of future digital clinical trials
2022
J Biopharm Stat
Lessons learned
,
Recommendations / best practices
Authors
Ariadna Garcia, Vidhya Balasubramanian, Justin Lee, Rebecca Gardner, Santosh Gummidipundi, Grace Hung, Todd Ferris, Lauren Cheung, Christopher Granger, Peter Kowey, John Rumsfeld, Andrea Russo, Mellianie Ture Hills, Nisha Talati, Divya Nag, Jeffrey Stein, David Tsay, Sumbul Desai, Kenneth Mahaffey, Mintu Turakhia, Marco Perez, Haley Hedlin, Manisha Desai
An overview of the data management challenges faced in the Apple Heart Study, a large-scale digital clinical trial that leveraged wearable devices and virtual interactions to evaluate the ability of an algorithm on the Apple Watch to detect atrial fibrillation. The key challenges discussed include participant adherence, accounting for unique participants, and aligning timestamp data from multiple data sources. The authors build upon the CTTI guidelines for digital clinical trials and provide a comprehensive set of recommendations for data management in future digital trials.
Findings
Digital health technologies often produce noisier data with additional sources of variation compared to traditional clinical trial settings.
There is a significant challenge in maintaining participant engagement and adherence in digital trials.
The need for pilot studies to address data flow, integration, and integrity is crucial.
Recommendations
Enhance participant engagement through hybrid approaches combining digital and traditional methods.
Conduct pilot studies to test data flow and integration before full-scale trials.
Refine data management guidelines based on experiences from digital trials like AHS.
Include diverse expertise in trial leadership, such as software engineers and biostatisticians.
Plan for comprehensive data analysis, including handling missing data.
Regulatory Considerations
Ensure data security, privacy, and integrity throughout the trial.
Develop a comprehensive plan for data analysis and management.
Consider the composition of the Data & Safety Monitoring Board to include diverse expertise.
Digital health technologies often produce noisier data with additional sources of variation compared to traditional clinical trial settings.
There is a significant challenge in maintaining participant engagement and adherence in digital trials.
The need for pilot studies to address data flow, integration, and integrity is crucial.
Recommendations
Enhance participant engagement through hybrid approaches combining digital and traditional methods.
Conduct pilot studies to test data flow and integration before full-scale trials.
Refine data management guidelines based on experiences from digital trials like AHS.
Include diverse expertise in trial leadership, such as software engineers and biostatisticians.
Plan for comprehensive data analysis, including handling missing data.
Regulatory Considerations
Ensure data security, privacy, and integrity throughout the trial.
Develop a comprehensive plan for data analysis and management.
Consider the composition of the Data & Safety Monitoring Board to include diverse expertise.
Keywords
Open Resource
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation
The study assesses a smartwatch application to identify atrial fibrillation, finding that while the probability of receiving a notification was low, a significant proportion of those notified had atrial fibrillation confirmed, and the study design provides a foundation for future large-scale pragmatic studies using wearable devices.
Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation
2019
N Engl J Med
Clinical trial
,
Example or case study
Authors
Marco V. Perez, M.D., Kenneth W. Mahaffey, M.D., Haley Hedlin, Ph.D., John S. Rumsfeld, M.D., Ph.D., Ariadna Garcia, M.S., Todd Ferris, M.D., Vidhya Balasubramanian, M.S., et al for the Apple Heart Study Investigators
The study assesses a smartwatch application to identify atrial fibrillation, finding that while the probability of receiving a notification was low, a significant proportion of those notified had atrial fibrillation confirmed, and the study design provides a foundation for future large-scale pragmatic studies using wearable devices.
Findings
The probability of receiving an irregular pulse notification was low, indicating a gap in detection sensitivity.
The study was not designed to assess the algorithm as a screening tool, highlighting a need for further research in this area.
The paroxysmal nature of atrial fibrillation presents challenges in interpreting notifications, suggesting a gap in understanding the condition's episodic nature.
Recommendations
Conduct further research to understand the implications of irregular pulse notifications.
Explore the potential for digital health technologies to engage users with healthcare systems.
Investigate the use of smartwatches and similar devices as population screening tools.
Develop methods to improve the accuracy and reliability of health monitoring algorithms.
Enhance user engagement and follow-up after receiving health notifications.
Regulatory Considerations
Ensure data privacy and consent in large-scale digital health studies.
Address the accuracy and reliability of health monitoring algorithms.
Consider the implications of using consumer-owned devices for health monitoring.
The probability of receiving an irregular pulse notification was low, indicating a gap in detection sensitivity.
The study was not designed to assess the algorithm as a screening tool, highlighting a need for further research in this area.
The paroxysmal nature of atrial fibrillation presents challenges in interpreting notifications, suggesting a gap in understanding the condition's episodic nature.
Recommendations
Conduct further research to understand the implications of irregular pulse notifications.
Explore the potential for digital health technologies to engage users with healthcare systems.
Investigate the use of smartwatches and similar devices as population screening tools.
Develop methods to improve the accuracy and reliability of health monitoring algorithms.
Enhance user engagement and follow-up after receiving health notifications.
Regulatory Considerations
Ensure data privacy and consent in large-scale digital health studies.
Address the accuracy and reliability of health monitoring algorithms.
Consider the implications of using consumer-owned devices for health monitoring.
Keywords
Algorithms
Cardiovascular health / disease
Consumer-grade wearables
Data collection
Decentralized clinical trials (DCTs)
Digital health technologies (DHTs)
Digital recruitment
Mobile applications
Mobile clinical trials (MCTs)
Patient-generated data
Real-world data (RWD)
Real-world evidence (RWE)
Validation
Wearables
Technologies
Open Resource
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.