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 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 2 of 2 results
Challenges of Incorporating Digital Health Technology Outcomes in a Clinical Trial: Experiences from PD STAT
The paper provides an overview of the challenges the authors encountered when incorporating digital health technologies (DHTs) as outcome measures in a clinical trial for Parkinson's disease, including issues with data completeness due to technical difficulties, software problems, and lack of staff training and engagement.
Challenges of Incorporating Digital Health Technology Outcomes in a Clinical Trial: Experiences from PD STAT
2022
J Parkinsons Dis
Example or case study
,
Lessons learned
Authors
Jacob O. Day, Stephen Smith, Alastair Noyce, Jane Alty, Alison Jeffery, Rebecca Chapman, Camille Carroll
The paper provides an overview of the challenges the authors encountered when incorporating digital health technologies (DHTs) as outcome measures in a clinical trial for Parkinson's disease, including issues with data completeness due to technical difficulties, software problems, and lack of staff training and engagement.
Findings
High rates of missing data in DHTs compared to traditional measures due to technical and software difficulties.
Practical issues such as unfamiliarity with platforms, connectivity difficulties, and lack of data visibility.
Specific technical issues like blocking of websites by firewalls and failed software updates leading to data loss.
Recommendations
Ensure appropriate workforce training for those involved in DHT deployment.
Conduct pilot evaluations in study sites to identify potential issues early.
Improve data visibility at both site and central coordinating team levels.
Implement robust feasibility testing before full-scale deployment.
Co-design DHTs with study staff and patients to enhance usability.
Regulatory Considerations
The FDA requires adequate training, education, and experience for those responsible for data capture using mobile technologies.
Ensure data integrity through oversight responsibilities as recommended by the Clinical Trials Transformation Initiative.
High rates of missing data in DHTs compared to traditional measures due to technical and software difficulties.
Practical issues such as unfamiliarity with platforms, connectivity difficulties, and lack of data visibility.
Specific technical issues like blocking of websites by firewalls and failed software updates leading to data loss.
Recommendations
Ensure appropriate workforce training for those involved in DHT deployment.
Conduct pilot evaluations in study sites to identify potential issues early.
Improve data visibility at both site and central coordinating team levels.
Implement robust feasibility testing before full-scale deployment.
Co-design DHTs with study staff and patients to enhance usability.
Regulatory Considerations
The FDA requires adequate training, education, and experience for those responsible for data capture using mobile technologies.
Ensure data integrity through oversight responsibilities as recommended by the Clinical Trials Transformation Initiative.
Keywords
Accelerometry
Clinical trial design
Clinical trials
Co-design
Data collection
Data management
Data quality
Decentralized clinical trials (DCTs)
Digital endpoints
Digital health technologies (DHTs)
Feasibility studies
Mobile applications
Neurodegenerative diseases
Patient burden
Patient centricity
Sensor-based digital health technologies (sDHTs)
Software validation
Technical validation
Training
Wearables
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.
Clinical Performance Assessment: Considerations for Computer-Assisted Detection Devices Applied to Radiology Images and Radiology Device Data – Premarket Approval (PMA) and Premarket Notification [510(k)] Submission
This guidance outlines recommendations for clinical performance studies of CADe devices applied to radiology images and data. It provides a framework for study design, including evaluation paradigms, endpoints, reference standards, and statistical methodologies. The document emphasizes the importance of robust testing methodologies (e.g., multiple reader multiple case (MRMC) designs) to evaluate CADe device performance relative to clinical practice.
Clinical Performance Assessment: Considerations for Computer-Assisted Detection Devices Applied to Radiology Images and Radiology Device Data – Premarket Approval (PMA) and Premarket Notification [510(k)] Submission
2022
FDA
FDA guidance - final
Authors
U.S. Department of Health and Human Services Food and Drug Administration, Center for Devices and Radiological Health
This guidance outlines recommendations for clinical performance studies of CADe devices applied to radiology images and data. It provides a framework for study design, including evaluation paradigms, endpoints, reference standards, and statistical methodologies. The document emphasizes the importance of robust testing methodologies (e.g., multiple reader multiple case (MRMC) designs) to evaluate CADe device performance relative to clinical practice.
Findings
CADe clinical performance studies must address key variables, including reader variability, disease prevalence, and device design differences.
Properly conducted MRMC studies are critical for assessing diagnostic effectiveness, incorporating both unaided and aided reading conditions.
Enriched datasets, while useful for stress testing, must be carefully designed to avoid bias and reflect intended use populations.
The truthing process (establishing reference standards) is essential to validate device performance claims and should be rigorously defined.
The FDA encourages pre-specification of hypotheses, statistical methods, and endpoints to ensure robust and interpretable results.
Recommendations
Design studies with representative patient populations and include diverse subgroups relevant to the device’s intended use.
Use validated statistical methods for MRMC analyses, reporting sensitivity, specificity, and receiver operating characteristic (ROC) curve metrics.
Develop and document a detailed truthing process for establishing reference standards, ensuring consistency and reliability.
Conduct stress testing with enriched datasets to evaluate device performance under challenging conditions but avoid overrepresenting certain subsets.
Submit a complete study protocol and statistical analysis plan, including sample size justification, randomization methods, and scoring techniques.
Regulatory Considerations
CADe devices classified under 21 CFR 892.2050 or 892.2070 must comply with premarket notification requirements, including performance testing and labeling.
Standalone performance assessments may suffice in certain scenarios, but clinical studies are often necessary for substantial equivalence determinations.
Use of foreign clinical data requires justification of its applicability to U.S. populations and medical practice.
FDA expects data integrity controls, such as firewalls and audit trails, to prevent tuning bias in test datasets reused across studies.
The FDA encourages early engagement (e.g., Pre-Submission requests) for feedback on study protocols and regulatory pathways.
CADe clinical performance studies must address key variables, including reader variability, disease prevalence, and device design differences.
Properly conducted MRMC studies are critical for assessing diagnostic effectiveness, incorporating both unaided and aided reading conditions.
Enriched datasets, while useful for stress testing, must be carefully designed to avoid bias and reflect intended use populations.
The truthing process (establishing reference standards) is essential to validate device performance claims and should be rigorously defined.
The FDA encourages pre-specification of hypotheses, statistical methods, and endpoints to ensure robust and interpretable results.
Recommendations
Design studies with representative patient populations and include diverse subgroups relevant to the device’s intended use.
Use validated statistical methods for MRMC analyses, reporting sensitivity, specificity, and receiver operating characteristic (ROC) curve metrics.
Develop and document a detailed truthing process for establishing reference standards, ensuring consistency and reliability.
Conduct stress testing with enriched datasets to evaluate device performance under challenging conditions but avoid overrepresenting certain subsets.
Submit a complete study protocol and statistical analysis plan, including sample size justification, randomization methods, and scoring techniques.
Regulatory Considerations
CADe devices classified under 21 CFR 892.2050 or 892.2070 must comply with premarket notification requirements, including performance testing and labeling.
Standalone performance assessments may suffice in certain scenarios, but clinical studies are often necessary for substantial equivalence determinations.
Use of foreign clinical data requires justification of its applicability to U.S. populations and medical practice.
FDA expects data integrity controls, such as firewalls and audit trails, to prevent tuning bias in test datasets reused across studies.
The FDA encourages early engagement (e.g., Pre-Submission requests) for feedback on study protocols and regulatory pathways.
Keywords
Algorithms
CADe devices
Clinical performance assessment
Clinical study design
Clinical trials
Clinical validation
Data collection
Data quality
Device software functions
Evaluation Paradigm and Study Endpoints
FDA
FDA guidance
Medical device development
Premarket notification (510(k))
Reference standard
Regulatory guidance
Software as a medical device (SaMD)
Statistical validation
Training
Validation
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.