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.
Meet NaVi: Your AI-Powered Research Assistant
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 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.
Advancing the Use of Mobile Technologies in Clinical Trials: Recommendations from the Clinical Trials Transformation Initiative
Advancing the Use of Mobile Technologies in Clinical Trials: Recommendations from the Clinical Trials Transformation Initiative
Widespread use of mobile technologies in clinical trials is impeded by perceived challenges.
Scientific and technical challenges affect decision-making around using mobile technology for data capture.
Concerns include choosing appropriate technology, data collection and analysis, ensuring data authenticity, and designing protocols.
Recommendations
Use CTTI's framework for mobile technology selection to assist sponsors.
Conduct feasibility studies prior to launching trials.
Engage with regulatory authorities early to discuss endpoint appropriateness and validation processes.
Secure data generated by mobile technologies using recommended practical approaches.
Optimize data quality to minimize variability and ensure robust conclusions.
Regulatory Considerations
Engage with the FDA early in the trial design process.
Ensure data integrity and security throughout the data life cycle.
Maintain open dialogue with regulatory authorities regarding trial-specific strategies for data collection and sharing.
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.
Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research
Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research
Lack of regulatory guidance on validating precision and reliability of DHT data.
Challenges in managing large, complex datasets without appropriate processing.
Insufficient integration of DHTs into existing clinical trial standards.
Recommendations
Develop a metadata set to improve data interpretability and exchangeability.
Encourage standard development organizations to extend existing standards for DHTs.
Ensure that none of the proposed metadata is compulsory, allowing flexibility based on application and resources.
Regulatory Considerations
Meet additional regulatory standards for medical devices.
Utilize guides like the FDA's Clinical Trials Transformation Initiative for data capture.
Align metadata standards with regulatory requirements to facilitate approval.
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.