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 204 of 249 results
Multiple Function Device Products: Policy and Considerations
This guidance explains FDA’s approach to evaluating multiple function device products, focusing on how non-device or exempt functions can impact the safety and effectiveness of device functions under review. It provides a framework for manufacturers to assess the impact of "other functions," mitigate risks, and document these evaluations in compliance with quality system regulations. The guidance emphasizes design separation, risk management, and the appropriate inclusion of non-device functions in premarket submissions and labeling.
Multiple Function Device Products: Policy and Considerations
2020
FDA
FDA guidance - final
Authors
Center for Devices and Radiological Health, Center for Biologics Evaluation and Research, Office of the Commissioner, Office of Clinical Policy and Programs, Office of Combination Products, Center for Drug Evaluation and Research
This guidance explains FDA’s approach to evaluating multiple function device products, focusing on how non-device or exempt functions can impact the safety and effectiveness of device functions under review. It provides a framework for manufacturers to assess the impact of "other functions," mitigate risks, and document these evaluations in compliance with quality system regulations. The guidance emphasizes design separation, risk management, and the appropriate inclusion of non-device functions in premarket submissions and labeling.
Findings
A “multiple function device product” contains at least one device function and one “other function” that may or may not be subject to FDA oversight.
FDA assesses "other functions" only to the extent they impact the safety or effectiveness of the device function under review or are claimed to provide a positive labeled impact.
Manufacturers must conduct and document risk assessments for all functions within the product to ensure safety and performance.
Functions not directly subject to FDA premarket review are still considered during inspections if they influence the device function under review.
Design separation between device and non-device functions can mitigate risks and simplify regulatory assessment.
Recommendations
Conduct thorough risk assessments for “other functions” and document the impacts, whether negative, positive, or neutral, on the device function under review.
Use design separation to minimize interdependencies between device and non-device functions where feasible.
Include only the relevant "other function" documentation in premarket submissions if it impacts the device function under review or is represented as a labeled positive impact.
For modifications to “other functions,” determine if they significantly affect the safety or effectiveness of the device function, and, if so, submit a new premarket notification as required.
Follow applicable labeling, quality system, and postmarket requirements for both device and non-device functions, ensuring clarity in what has been evaluated by the FDA.
Regulatory Considerations
Non-device functions are not regulated unless they impact the safety or effectiveness of a device function under review.
For device functions under review, manufacturers must comply with FDA's design validation and risk analysis requirements under 21 CFR 820.30(g).
Changes to non-device functions must be assessed for potential impacts on the device function under review to determine whether additional regulatory submissions are necessary.
FDA evaluates the premarket safety and effectiveness of device functions within the context of interactions with non-device functions but does not directly regulate the non-device functions themselves.
Postmarket requirements, such as adverse event reporting, apply to device functions, including when the event involves an interaction with a non-device function.
A “multiple function device product” contains at least one device function and one “other function” that may or may not be subject to FDA oversight.
FDA assesses "other functions" only to the extent they impact the safety or effectiveness of the device function under review or are claimed to provide a positive labeled impact.
Manufacturers must conduct and document risk assessments for all functions within the product to ensure safety and performance.
Functions not directly subject to FDA premarket review are still considered during inspections if they influence the device function under review.
Design separation between device and non-device functions can mitigate risks and simplify regulatory assessment.
Recommendations
Conduct thorough risk assessments for “other functions” and document the impacts, whether negative, positive, or neutral, on the device function under review.
Use design separation to minimize interdependencies between device and non-device functions where feasible.
Include only the relevant "other function" documentation in premarket submissions if it impacts the device function under review or is represented as a labeled positive impact.
For modifications to “other functions,” determine if they significantly affect the safety or effectiveness of the device function, and, if so, submit a new premarket notification as required.
Follow applicable labeling, quality system, and postmarket requirements for both device and non-device functions, ensuring clarity in what has been evaluated by the FDA.
Regulatory Considerations
Non-device functions are not regulated unless they impact the safety or effectiveness of a device function under review.
For device functions under review, manufacturers must comply with FDA's design validation and risk analysis requirements under 21 CFR 820.30(g).
Changes to non-device functions must be assessed for potential impacts on the device function under review to determine whether additional regulatory submissions are necessary.
FDA evaluates the premarket safety and effectiveness of device functions within the context of interactions with non-device functions but does not directly regulate the non-device functions themselves.
Postmarket requirements, such as adverse event reporting, apply to device functions, including when the event involves an interaction with a non-device function.
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.
National EvaluationSystem for healthTechnology CoordinatingCenter (NESTcc)Data Quality Framework
This framework, developed by the NEST Coordinating Center, outlines principles and processes to ensure high-quality data for generating real-world evidence (RWE) from real-world data (RWD) in the medical device ecosystem. It addresses governance, data characteristics, data capture and transformation, and curation practices. The framework also includes a Data Quality Maturity Model to assess organizational capabilities and guide improvements in data management for clinical and regulatory decision-making.
National EvaluationSystem for healthTechnology CoordinatingCenter (NESTcc)Data Quality Framework
2020
NEST Coordinating Center
Framework or roadmap
,
Recommendations / best practices
Authors
NEST Coordinating Center
This framework, developed by the NEST Coordinating Center, outlines principles and processes to ensure high-quality data for generating real-world evidence (RWE) from real-world data (RWD) in the medical device ecosystem. It addresses governance, data characteristics, data capture and transformation, and curation practices. The framework also includes a Data Quality Maturity Model to assess organizational capabilities and guide improvements in data management for clinical and regulatory decision-making.
Findings
High-quality data must be complete, accurate, timely, and fit for purpose, ensuring reliability for RWE generation.
Effective governance is critical to ensure transparency, ethical standards, and stakeholder engagement in managing RWD.
Data capture challenges include standardization, provenance tracking, and interoperability, particularly for EHR-based data.
Data curation is iterative and involves organizing, assessing, and preparing raw data to meet study-specific needs.
The maturity model identifies five stages of organizational data capabilities, emphasizing consistency, completeness, and automation.
Recommendations
Implement robust governance frameworks to address transparency, stakeholder engagement, and ethical considerations in RWD use.
Focus on improving data capture at the point of care through standardization and semantic interoperability.
Use common data models and validated extraction-transformation-loading (ETL) processes to enhance data consistency and reliability.
Prioritize iterative data curation practices, supported by metadata and provenance tracking, to improve fitness for use over time.
Leverage the NESTcc Data Quality Maturity Model to benchmark and enhance organizational capabilities in RWD management.
Regulatory Considerations
Ensure compliance with patient privacy laws such as HIPAA and GDPR, especially when linking data across sources.
Align data capture and curation practices with FDA guidance for RWE generation and medical device evaluation.
Establish clear data use agreements to protect patient data while enabling analysis for regulatory and research purposes.
Document data transformations, including metadata and provenance, to support reproducibility and transparency in regulatory submissions.
Embrace standard terminologies and data dictionaries to facilitate interoperability and regulatory acceptance.
High-quality data must be complete, accurate, timely, and fit for purpose, ensuring reliability for RWE generation.
Effective governance is critical to ensure transparency, ethical standards, and stakeholder engagement in managing RWD.
Data capture challenges include standardization, provenance tracking, and interoperability, particularly for EHR-based data.
Data curation is iterative and involves organizing, assessing, and preparing raw data to meet study-specific needs.
The maturity model identifies five stages of organizational data capabilities, emphasizing consistency, completeness, and automation.
Recommendations
Implement robust governance frameworks to address transparency, stakeholder engagement, and ethical considerations in RWD use.
Focus on improving data capture at the point of care through standardization and semantic interoperability.
Use common data models and validated extraction-transformation-loading (ETL) processes to enhance data consistency and reliability.
Prioritize iterative data curation practices, supported by metadata and provenance tracking, to improve fitness for use over time.
Leverage the NESTcc Data Quality Maturity Model to benchmark and enhance organizational capabilities in RWD management.
Regulatory Considerations
Ensure compliance with patient privacy laws such as HIPAA and GDPR, especially when linking data across sources.
Align data capture and curation practices with FDA guidance for RWE generation and medical device evaluation.
Establish clear data use agreements to protect patient data while enabling analysis for regulatory and research purposes.
Document data transformations, including metadata and provenance, to support reproducibility and transparency in regulatory submissions.
Embrace standard terminologies and data dictionaries to facilitate interoperability and regulatory acceptance.
Keywords
Clinical research
Data management
Data quality
Data standardization
Digital health data governance
Electronic health records (EHR)
Interoperability
Medical device development
Postmarket approval / monitoring
Real-world data (RWD)
Real-world evidence (RWE)
Stakeholder collaboration
Total Product Lifecycle (TPLC)
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.
Outcome measures based on digital health technology sensor data: data- and patient-centric approaches
An overview of two different approaches to developing digital outcome measures using data from digital health technology tools (DHTTs): a data-centric approach that maximizes sensitivity to detect disease progression, and a patient-centric approach that focuses on measuring symptoms and impacts that are most meaningful to patients in their daily lives.
Outcome measures based on digital health technology sensor data: data- and patient-centric approaches
2020
NPJ Digit Med
Framework or roadmap
,
Recommendations / best practices
Authors
Kirsten I Taylor, Hannah Staunton, Florian Lipsmeier, David Nobbs, Michael Lindemann
An overview of two different approaches to developing digital outcome measures using data from digital health technology tools (DHTTs): a data-centric approach that maximizes sensitivity to detect disease progression, and a patient-centric approach that focuses on measuring symptoms and impacts that are most meaningful to patients in their daily lives.
Findings
There is a gap in developing robust and meaningful outcome measures based on DHTT sensor data.
A major challenge is integrating vast amounts of sensor data into meaningful clinical outcome measures.
There is no clear pathway for developing these outcome measures, indicating a need for standardized methods.
Recommendations
Develop roadmaps for data-centric and patient-centric digital outcome measures.
Integrate patient insights into the development of outcome measures.
Ensure statistical robustness and validity in digital outcome measures.
Encourage scientific discourse to reach a consensus on digital outcome measure development.
Combine subjective and objective data for comprehensive patient assessment.
Regulatory Considerations
The field relies on guidance from regulatory authorities like the FDA and EMA.
Regulatory frameworks need to keep pace with the fast-moving nature of DHTTs.
There is a need for consensus on digital outcome measure development that respects established methods.
There is a gap in developing robust and meaningful outcome measures based on DHTT sensor data.
A major challenge is integrating vast amounts of sensor data into meaningful clinical outcome measures.
There is no clear pathway for developing these outcome measures, indicating a need for standardized methods.
Recommendations
Develop roadmaps for data-centric and patient-centric digital outcome measures.
Integrate patient insights into the development of outcome measures.
Ensure statistical robustness and validity in digital outcome measures.
Encourage scientific discourse to reach a consensus on digital outcome measure development.
Combine subjective and objective data for comprehensive patient assessment.
Regulatory Considerations
The field relies on guidance from regulatory authorities like the FDA and EMA.
Regulatory frameworks need to keep pace with the fast-moving nature of DHTTs.
There is a need for consensus on digital outcome measure development that respects established methods.
Keywords
Clinical trials
Concept of interest (COI)
Data analysis / analytics
Digital biomarkers
Digital health technologies (DHTs)
Digital outcome measures
Endpoint validation
Meaningful outcome measures
Patient-centered outcomes
Patient-centricity
Regulatory guidance
Sensor data integration
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.
Patient-Focused Drug Development: Collecting Comprehensive and Representative Input
This guidance outlines methods for collecting patient experience data that are robust, representative, and useful in regulatory decision-making. It describes systematic approaches to defining research objectives, selecting sampling methods, and determining data collection techniques. The document emphasizes the importance of representativeness in patient experience data to reflect diverse patient populations and outlines the benefits of combining quantitative, qualitative, and mixed-methods research approaches. It also addresses operational considerations for data collection, human subject protection, data management, and analysis.
Patient-Focused Drug Development: Collecting Comprehensive and Representative Input
2020
FDA
FDA guidance - final
Authors
U.S. Department of Health and Human Services Food and Drug Administration, Center for Drug Evaluation and Research (CDER), Center for Biologics Evaluation and Research (CBER)
This guidance outlines methods for collecting patient experience data that are robust, representative, and useful in regulatory decision-making. It describes systematic approaches to defining research objectives, selecting sampling methods, and determining data collection techniques. The document emphasizes the importance of representativeness in patient experience data to reflect diverse patient populations and outlines the benefits of combining quantitative, qualitative, and mixed-methods research approaches. It also addresses operational considerations for data collection, human subject protection, data management, and analysis.
Findings
Patient experience data encompass a range of inputs, including symptom burdens, treatment impacts, patient preferences, and views on unmet medical needs.
These data inform all stages of medical product development, from discovery to post-market use.
Quantitative methods (e.g., surveys) provide numerical insights, while qualitative methods (e.g., interviews) offer in-depth understanding. Mixed methods combine both for a fuller perspective.Social media and verified patient communities present novel data collection opportunities but require consideration of verification and representativeness challenges.
Probability sampling (e.g., stratified random sampling) is emphasized for generalizability, while non-probability methods (e.g., convenience sampling) are useful for exploratory research. Representativeness ensures that patient input reflects the diversity and heterogeneity of the target population.
Data collection should adhere to good clinical practices and regulatory standards.
Research protocols should address missing data, quality assurance, and confidentiality.
Early collaboration with the FDA is recommended to align on study designs and regulatory requirements.
Recommendations
Define clear research objectives and determine specific research questions before selecting data collection methods.
Use probability sampling methods whenever feasible to ensure representativeness of the target population.
Address data quality through rigorous planning, data management, and adherence to FDA-supported standards.
Incorporate diverse perspectives by including underrepresented patient populations, tailoring methods to specific subgroups as needed.
Leverage existing data sources, such as patient registries and literature, to complement primary data collection efforts.
Regulatory Considerations
Data submitted to FDA should include clear documentation of the study protocol, intended use, and data collection methodologies.
Researchers must comply with human subject protection regulations (e.g., 21 CFR Parts 50 and 56) and good clinical practice guidelines.
For data intended to support regulatory submissions, adherence to FDA-supported data standards (e.g., CDISC) is strongly encouraged.
Missing data should be addressed through pre-planned strategies and summarized in the study report.
Patient experience data must meet methodological rigor to ensure their reliability and relevance for regulatory decision-making.
Patient experience data encompass a range of inputs, including symptom burdens, treatment impacts, patient preferences, and views on unmet medical needs.
These data inform all stages of medical product development, from discovery to post-market use.
Quantitative methods (e.g., surveys) provide numerical insights, while qualitative methods (e.g., interviews) offer in-depth understanding. Mixed methods combine both for a fuller perspective.Social media and verified patient communities present novel data collection opportunities but require consideration of verification and representativeness challenges.
Probability sampling (e.g., stratified random sampling) is emphasized for generalizability, while non-probability methods (e.g., convenience sampling) are useful for exploratory research. Representativeness ensures that patient input reflects the diversity and heterogeneity of the target population.
Data collection should adhere to good clinical practices and regulatory standards.
Research protocols should address missing data, quality assurance, and confidentiality.
Early collaboration with the FDA is recommended to align on study designs and regulatory requirements.
Recommendations
Define clear research objectives and determine specific research questions before selecting data collection methods.
Use probability sampling methods whenever feasible to ensure representativeness of the target population.
Address data quality through rigorous planning, data management, and adherence to FDA-supported standards.
Incorporate diverse perspectives by including underrepresented patient populations, tailoring methods to specific subgroups as needed.
Leverage existing data sources, such as patient registries and literature, to complement primary data collection efforts.
Regulatory Considerations
Data submitted to FDA should include clear documentation of the study protocol, intended use, and data collection methodologies.
Researchers must comply with human subject protection regulations (e.g., 21 CFR Parts 50 and 56) and good clinical practice guidelines.
For data intended to support regulatory submissions, adherence to FDA-supported data standards (e.g., CDISC) is strongly encouraged.
Missing data should be addressed through pre-planned strategies and summarized in the study report.
Patient experience data must meet methodological rigor to ensure their reliability and relevance for regulatory decision-making.
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.
Playbook Digital Clinical Measures
This playbook serves as a comprehensive guide to developing and deploying digital clinical measures for remote monitoring across clinical research, healthcare, and public health. It provides a structured framework for defining, selecting, and validating digital endpoints, ensuring alignment with regulatory expectations and patient needs. Through case studies, technology evaluations, and operational guidelines, the playbook helps stakeholders implement digital measures that are meaningful, scalable, and clinically relevant. It includes resources tailored for various stakeholders, including pharma, healthcare executives, policymakers, and front-line providers. The Playbook is continuously updated and serves as a comprehensive toolkit for developing and deploying effective and trustworthy digital clinical measures
Playbook Digital Clinical Measures
2020
Digital Medicine Society
Recommendations / best practices
Authors
Digital Medicine Society
This playbook serves as a comprehensive guide to developing and deploying digital clinical measures for remote monitoring across clinical research, healthcare, and public health. It provides a structured framework for defining, selecting, and validating digital endpoints, ensuring alignment with regulatory expectations and patient needs. Through case studies, technology evaluations, and operational guidelines, the playbook helps stakeholders implement digital measures that are meaningful, scalable, and clinically relevant. It includes resources tailored for various stakeholders, including pharma, healthcare executives, policymakers, and front-line providers. The Playbook is continuously updated and serves as a comprehensive toolkit for developing and deploying effective and trustworthy digital clinical measures
Findings
Successful deployment of digital clinical measures requires a shared foundation of standardized methodologies, terminology, and best practices.
The selection of digital measures must prioritize patient-centered outcomes and align with meaningful aspects of health.
Technology validation processes, including the Verification, Analytical Validation, and Clinical Validation (V3) framework, are crucial to ensuring data accuracy and reliability.
Interoperability, data security, and governance remain key challenges for digital health technologies in both research and clinical applications.
Case studies demonstrate the real-world utility of digital clinical measures in clinical research, patient care, and public health initiatives.
Recommendations
Stakeholders should follow a structured, stepwise approach to selecting and validating digital clinical measures, starting with identifying meaningful health aspects.
Digital health tools must undergo rigorous verification and validation to ensure they are fit-for-purpose and meet clinical and regulatory standards.
Patient engagement should be integrated into every stage of digital measure development to ensure the relevance and usability of selected endpoints.
Regulatory and payer engagement should occur early in the process to streamline market access and reimbursement pathways.
Organizations should adopt a proactive approach to data privacy, security, and governance, ensuring compliance with regulations such as HIPAA and GDPR.
Regulatory Considerations
The FDA and other regulatory bodies emphasize the need for clinical validation of digital measures before they can be used as primary endpoints in trials.
Standardization of digital health technologies is critical to regulatory approval, requiring alignment with frameworks such as HL7 and ISO standards.
Data security and privacy regulations must be strictly adhered to, particularly in decentralized clinical trials where remote monitoring is used.
Digital endpoint validation must include real-world evidence (RWE) to support regulatory decision-making and post-market surveillance.
Organizations must consider the evolving regulatory landscape for AI-driven health technologies, ensuring compliance with best practices for algorithmic transparency and bias mitigation.
Successful deployment of digital clinical measures requires a shared foundation of standardized methodologies, terminology, and best practices.
The selection of digital measures must prioritize patient-centered outcomes and align with meaningful aspects of health.
Technology validation processes, including the Verification, Analytical Validation, and Clinical Validation (V3) framework, are crucial to ensuring data accuracy and reliability.
Interoperability, data security, and governance remain key challenges for digital health technologies in both research and clinical applications.
Case studies demonstrate the real-world utility of digital clinical measures in clinical research, patient care, and public health initiatives.
Recommendations
Stakeholders should follow a structured, stepwise approach to selecting and validating digital clinical measures, starting with identifying meaningful health aspects.
Digital health tools must undergo rigorous verification and validation to ensure they are fit-for-purpose and meet clinical and regulatory standards.
Patient engagement should be integrated into every stage of digital measure development to ensure the relevance and usability of selected endpoints.
Regulatory and payer engagement should occur early in the process to streamline market access and reimbursement pathways.
Organizations should adopt a proactive approach to data privacy, security, and governance, ensuring compliance with regulations such as HIPAA and GDPR.
Regulatory Considerations
The FDA and other regulatory bodies emphasize the need for clinical validation of digital measures before they can be used as primary endpoints in trials.
Standardization of digital health technologies is critical to regulatory approval, requiring alignment with frameworks such as HL7 and ISO standards.
Data security and privacy regulations must be strictly adhered to, particularly in decentralized clinical trials where remote monitoring is used.
Digital endpoint validation must include real-world evidence (RWE) to support regulatory decision-making and post-market surveillance.
Organizations must consider the evolving regulatory landscape for AI-driven health technologies, ensuring compliance with best practices for algorithmic transparency and bias mitigation.
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.
Precompetitive Consensus Building to Facilitate the Use of Digital Health Technologies to Support Parkinson Disease Drug Development through Regulatory Science
An overview of the challenges and opportunities in using digital health technologies (DHTs) to support Parkinson's disease (PD) drug development, and describes several precompetitive collaborations and public-private partnerships that are working to address these challenges.
Precompetitive Consensus Building to Facilitate the Use of Digital Health Technologies to Support Parkinson Disease Drug Development through Regulatory Science
2020
Digit Biomark
Framework or roadmap
,
Review
Authors
Diane Stephenson, Robert Alexander, Varun Aggarwal, Reham Badawy, Lisa Bain, Roopal Bhatnagar, Bastiaan R Bloem, Babak Boroojerdi, Jackson Burton, Jesse M Cedarbaum, Josh Cosman, David T Dexter, Marissa Dockendorf, E Ray Dorsey, Ariel V Dowling, Luc J W Evers, Katherine Fisher, Mark Frasier, Luis Garcia-Gancedo, Jennifer C Goldsack, Derek Hill, Janice Hitchcock, Michele T Hu, Michael P Lawton, Susan J Lee, Michael Lindemann, Ken Marek, Nitin Mehrotra, Marjan J Meinders, Michael Minchik, Lauren Oliva, Klaus Romero, George Roussos, Robert Rubens, Sakshi Sadar, Joseph Scheeren, Eiichi Sengoku, Tanya Simuni, Glenn Stebbins, Kirsten I Taylor, Beatrice Yang, Neta Zach
An overview of the challenges and opportunities in using digital health technologies (DHTs) to support Parkinson's disease (PD) drug development, and describes several precompetitive collaborations and public-private partnerships that are working to address these challenges.
Findings
Scarcity of reliable and frequent ground truth labels in real-world conditions.
Challenges in extracting clinically meaningful information from digital device data.
Lack of standardized methods for data collection, storage, organization, curation, and analysis.
Issues with participant diversity and digital literacy affecting patient engagement and adherence.
Need for alignment on methods to establish reliability and validity of DHT measures.
Recommendations
Focus on clinically meaningful outcomes for patients in PD drug development.
Build consensus on data and metadata standards for data exchangeability.
Develop open-source platforms for analysis across device types and studies.
Engage early and often with regulatory agencies via consortia.
Align with FDA review divisions and utilize EMA qualification methodologies.
Regulatory Considerations:
Align with regulatory science pathways to ensure scientific rigor and clinical validity.
Engage with regulatory agencies like FDA and EMA early in the process.
Adhere to standardized data collection and analytical approaches.
Scarcity of reliable and frequent ground truth labels in real-world conditions.
Challenges in extracting clinically meaningful information from digital device data.
Lack of standardized methods for data collection, storage, organization, curation, and analysis.
Issues with participant diversity and digital literacy affecting patient engagement and adherence.
Need for alignment on methods to establish reliability and validity of DHT measures.
Recommendations
Focus on clinically meaningful outcomes for patients in PD drug development.
Build consensus on data and metadata standards for data exchangeability.
Develop open-source platforms for analysis across device types and studies.
Engage early and often with regulatory agencies via consortia.
Align with FDA review divisions and utilize EMA qualification methodologies.
Regulatory Considerations:
Align with regulatory science pathways to ensure scientific rigor and clinical validity.
Engage with regulatory agencies like FDA and EMA early in the process.
Adhere to standardized data collection and analytical approaches.
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.
Qualification Process for Drug Development Tools Guidance for Industry and FDA Staff
This guidance outlines the qualification process for Drug Development Tools (DDTs) under Section 507 of the Federal Food, Drug, and Cosmetic Act (FD&C Act), focusing on biomarkers, clinical outcome assessments (COAs), and animal models. The document describes a three-stage qualification process (Letter of Intent, Qualification Plan, and Full Qualification Package), emphasizing the importance of defining a specific Context of Use (COU) for each DDT. It also discusses the collaborative role of biomedical consortia and promotes transparency through public disclosure of qualification-related information. Qualified DDTs are intended to facilitate regulatory decision-making and streamline drug development.
Qualification Process for Drug Development Tools Guidance for Industry and FDA Staff
2020
FDA
FDA guidance - final
Authors
Center for Drug Evaluation and Research, Center for Biologics Evaluation and Research
This guidance outlines the qualification process for Drug Development Tools (DDTs) under Section 507 of the Federal Food, Drug, and Cosmetic Act (FD&C Act), focusing on biomarkers, clinical outcome assessments (COAs), and animal models. The document describes a three-stage qualification process (Letter of Intent, Qualification Plan, and Full Qualification Package), emphasizing the importance of defining a specific Context of Use (COU) for each DDT. It also discusses the collaborative role of biomedical consortia and promotes transparency through public disclosure of qualification-related information. Qualified DDTs are intended to facilitate regulatory decision-making and streamline drug development.
Findings
Limited awareness and clarity regarding the qualification process and evidentiary expectations.
High resource and data demands for robust qualification submissions, particularly for novel DDTs.
Challenges in integrating multidisciplinary expertise to develop and validate DDTs.
Dependence on effective collaboration among stakeholders, including academia, industry, and regulatory bodies.
Potential delays in submission reviews if deficiencies are identified during initial assessments.
Recommendations
Define a clear and scientifically robust Context of Use (COU) for each DDT to streamline the qualification process.
Engage with FDA through early meetings to align on submission expectations and address potential gaps.
Leverage biomedical consortia and partnerships to pool resources and expertise for DDT development and validation.
Provide detailed study protocols, data analyses, and evidence in submissions to support DDT reliability and applicability.
Utilize FDA’s NextGen Portal for submission tracking and ensure compliance with recommended data standards.
Regulatory Considerations
Compliance with the three-stage qualification process: Letter of Intent (LOI), Qualification Plan (QP), and Full Qualification Package (FQP).
Adherence to transparency provisions under the 21st Century Cures Act, including public disclosure of qualification information and Determination Letters.
Rescission or modification of a qualified DDT or COU if new evidence challenges its validity.
Clear distinction between qualification for regulatory decision-making and approval for clinical use or marketing.
Encouragement to use study data standards (e.g., Clinical Data Interchange Standards Consortium) for submissions to ensure consistency and quality.
Limited awareness and clarity regarding the qualification process and evidentiary expectations.
High resource and data demands for robust qualification submissions, particularly for novel DDTs.
Challenges in integrating multidisciplinary expertise to develop and validate DDTs.
Dependence on effective collaboration among stakeholders, including academia, industry, and regulatory bodies.
Potential delays in submission reviews if deficiencies are identified during initial assessments.
Recommendations
Define a clear and scientifically robust Context of Use (COU) for each DDT to streamline the qualification process.
Engage with FDA through early meetings to align on submission expectations and address potential gaps.
Leverage biomedical consortia and partnerships to pool resources and expertise for DDT development and validation.
Provide detailed study protocols, data analyses, and evidence in submissions to support DDT reliability and applicability.
Utilize FDA’s NextGen Portal for submission tracking and ensure compliance with recommended data standards.
Regulatory Considerations
Compliance with the three-stage qualification process: Letter of Intent (LOI), Qualification Plan (QP), and Full Qualification Package (FQP).
Adherence to transparency provisions under the 21st Century Cures Act, including public disclosure of qualification information and Determination Letters.
Rescission or modification of a qualified DDT or COU if new evidence challenges its validity.
Clear distinction between qualification for regulatory decision-making and approval for clinical use or marketing.
Encouragement to use study data standards (e.g., Clinical Data Interchange Standards Consortium) for submissions to ensure consistency and quality.
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.
Questions and answers: Qualification of digital technology-based methodologies to support approval of medicinal products
This document provides a guide for the qualification of digital health technologies (DHTs) intended to support the development and approval of medicinal products. It highlights the importance of regulatory compliance, clinical validation, and robust methodologies for the qualification process, ensuring that digital tools meet EMA standards for benefit-risk evaluation. The guidelines address the selection of digital endpoints, clinical utility, and the iterative development of qualification plans.
Questions and answers: Qualification of digital technology-based methodologies to support approval of medicinal products
2020
EMA
Recommendations / best practices
Authors
European Medicines Agency
This document provides a guide for the qualification of digital health technologies (DHTs) intended to support the development and approval of medicinal products. It highlights the importance of regulatory compliance, clinical validation, and robust methodologies for the qualification process, ensuring that digital tools meet EMA standards for benefit-risk evaluation. The guidelines address the selection of digital endpoints, clinical utility, and the iterative development of qualification plans.
Findings
The EMA emphasizes early engagement to align on regulatory pathways and qualification processes for DHTs.
Context of Use (CoU) is critical in assessing digital technologies, requiring clear justification for their application in clinical trials.
The selection of digital endpoints must demonstrate clinical relevance, reliability, and sensitivity to change.
Validation of digital biomarkers must include data supporting their relationship to clinical outcomes of interest.
Changes to technology during development require a risk-based management approach to maintain the validity of data.
Recommendations
Begin early consultations with the EMA to determine the most suitable regulatory pathways and to define the Context of Use.
Ensure that qualification submissions provide robust evidence on clinical validity, reliability, and sensitivity to change.
Develop best practice guides for the implementation of digital technologies in clinical trials, including training for users and compliance monitoring.
Use an iterative approach for technology qualification, allowing adjustments based on emerging data and findings.
Provide clear risk management strategies for handling technology updates and assessing their impact on data integrity.
Regulatory Considerations
Adhere to applicable regulations, including the Medical Devices Regulation (MDR) and ISO standards, for technologies used in medicinal product development.
Implement data protection measures compliant with EU regulations, ensuring the integrity and security of sensitive health data.
Submit validation documentation demonstrating compliance with Good Clinical Practice (GCP) and Computer System Validation (CSV) standards.
Incorporate statistical planning aligned with ICH guidelines, including pre-planned analyses for endpoints supported by digital technologies.
Engage with multidisciplinary teams and potentially parallel processes with other regulatory agencies (e.g., FDA, PMDA) for a comprehensive qualification process.
The EMA emphasizes early engagement to align on regulatory pathways and qualification processes for DHTs.
Context of Use (CoU) is critical in assessing digital technologies, requiring clear justification for their application in clinical trials.
The selection of digital endpoints must demonstrate clinical relevance, reliability, and sensitivity to change.
Validation of digital biomarkers must include data supporting their relationship to clinical outcomes of interest.
Changes to technology during development require a risk-based management approach to maintain the validity of data.
Recommendations
Begin early consultations with the EMA to determine the most suitable regulatory pathways and to define the Context of Use.
Ensure that qualification submissions provide robust evidence on clinical validity, reliability, and sensitivity to change.
Develop best practice guides for the implementation of digital technologies in clinical trials, including training for users and compliance monitoring.
Use an iterative approach for technology qualification, allowing adjustments based on emerging data and findings.
Provide clear risk management strategies for handling technology updates and assessing their impact on data integrity.
Regulatory Considerations
Adhere to applicable regulations, including the Medical Devices Regulation (MDR) and ISO standards, for technologies used in medicinal product development.
Implement data protection measures compliant with EU regulations, ensuring the integrity and security of sensitive health data.
Submit validation documentation demonstrating compliance with Good Clinical Practice (GCP) and Computer System Validation (CSV) standards.
Incorporate statistical planning aligned with ICH guidelines, including pre-planned analyses for endpoints supported by digital technologies.
Engage with multidisciplinary teams and potentially parallel processes with other regulatory agencies (e.g., FDA, PMDA) for a comprehensive qualification process.
Keywords
Clinical outcome assessment (COA)
Clinical trials
Clinical validation
Context of use (COU)
Data collection
Data management
Data privacy and security
Digital biomarkers
Digital endpoints
Digital health technologies (DHTs)
Drug development
EMA guidance
Good Clinical Practice (GCP)
Marketing authorization application
Real-world data (RWD)
Regulatory framework
Regulatory science
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.
Questions and answers: Qualification of digitaltechnology-based methodologies to support approval ofmedicinal products
This document provides a guide for the qualification of digital health technologies (DHTs) intended to support the development and approval of medicinal products. It highlights the importance of regulatory compliance, clinical validation, and robust methodologies for the qualification process, ensuring that digital tools meet EMA standards for benefit-risk evaluation. The guidelines address the selection of digital endpoints, clinical utility, and the iterative development of qualification plans.
Questions and answers: Qualification of digitaltechnology-based methodologies to support approval ofmedicinal products
2020
EMA
Recommendations / best practices
Authors
European Medicines Agency
This document provides a guide for the qualification of digital health technologies (DHTs) intended to support the development and approval of medicinal products. It highlights the importance of regulatory compliance, clinical validation, and robust methodologies for the qualification process, ensuring that digital tools meet EMA standards for benefit-risk evaluation. The guidelines address the selection of digital endpoints, clinical utility, and the iterative development of qualification plans.
Findings
The EMA emphasizes early engagement to align on regulatory pathways and qualification processes for DHTs.
Context of Use (CoU) is critical in assessing digital technologies, requiring clear justification for their application in clinical trials.
The selection of digital endpoints must demonstrate clinical relevance, reliability, and sensitivity to change.
Validation of digital biomarkers must include data supporting their relationship to clinical outcomes of interest.
Changes to technology during development require a risk-based management approach to maintain the validity of data.
Recommendations
Begin early consultations with the EMA to determine the most suitable regulatory pathways and to define the Context of Use.
Ensure that qualification submissions provide robust evidence on clinical validity, reliability, and sensitivity to change.
Develop best practice guides for the implementation of digital technologies in clinical trials, including training for users and compliance monitoring.
Use an iterative approach for technology qualification, allowing adjustments based on emerging data and findings.
Provide clear risk management strategies for handling technology updates and assessing their impact on data integrity.
Regulatory Considerations
Adhere to applicable regulations, including the Medical Devices Regulation (MDR) and ISO standards, for technologies used in medicinal product development.
Implement data protection measures compliant with EU regulations, ensuring the integrity and security of sensitive health data.
Submit validation documentation demonstrating compliance with Good Clinical Practice (GCP) and Computer System Validation (CSV) standards.
Incorporate statistical planning aligned with ICH guidelines, including pre-planned analyses for endpoints supported by digital technologies.
Engage with multidisciplinary teams and potentially parallel processes with other regulatory agencies (e.g., FDA, PMDA) for a comprehensive qualification process.
The EMA emphasizes early engagement to align on regulatory pathways and qualification processes for DHTs.
Context of Use (CoU) is critical in assessing digital technologies, requiring clear justification for their application in clinical trials.
The selection of digital endpoints must demonstrate clinical relevance, reliability, and sensitivity to change.
Validation of digital biomarkers must include data supporting their relationship to clinical outcomes of interest.
Changes to technology during development require a risk-based management approach to maintain the validity of data.
Recommendations
Begin early consultations with the EMA to determine the most suitable regulatory pathways and to define the Context of Use.
Ensure that qualification submissions provide robust evidence on clinical validity, reliability, and sensitivity to change.
Develop best practice guides for the implementation of digital technologies in clinical trials, including training for users and compliance monitoring.
Use an iterative approach for technology qualification, allowing adjustments based on emerging data and findings.
Provide clear risk management strategies for handling technology updates and assessing their impact on data integrity.
Regulatory Considerations
Adhere to applicable regulations, including the Medical Devices Regulation (MDR) and ISO standards, for technologies used in medicinal product development.
Implement data protection measures compliant with EU regulations, ensuring the integrity and security of sensitive health data.
Submit validation documentation demonstrating compliance with Good Clinical Practice (GCP) and Computer System Validation (CSV) standards.
Incorporate statistical planning aligned with ICH guidelines, including pre-planned analyses for endpoints supported by digital technologies.
Engage with multidisciplinary teams and potentially parallel processes with other regulatory agencies (e.g., FDA, PMDA) for a comprehensive qualification process.
Keywords
Clinical outcome assessment (COA)
Clinical trials
Clinical validation
Context of use (COU)
Data collection
Data management
Data privacy and security
Digital biomarkers
Digital endpoints
Digital health technologies (DHTs)
Drug development
EMA guidance
Good Clinical Practice (GCP)
Marketing authorization application
Real-world data (RWD)
Regulatory framework
Regulatory science
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.
The Use of Wearables in Clinical Trials During Cancer Treatment: Systematic Review
An overview of the use of wearable devices in clinical trials for patients with solid tumors receiving cancer treatment, examining adherence, types of outcomes measured, and variability in definitions and methods across studies.
The Use of Wearables in Clinical Trials During Cancer Treatment: Systematic Review
2020
JMIR Mhealth Uhealth
Review
Authors
Ulrikke Lyng Beauchamp, Helle Pappot, Cecilie Holländer-Mieritz
An overview of the use of wearable devices in clinical trials for patients with solid tumors receiving cancer treatment, examining adherence, types of outcomes measured, and variability in definitions and methods across studies.
Findings
There is a lack of consensus on outcome measures and adherence definitions across studies using wearables in oncology.
There is significant heterogeneity in study designs and outcomes, making comparisons difficult.
Limited guidelines exist for designing or reporting trials using wearables in oncology.
Recommendations
Establish standardized definitions for wearable outcomes and adherence to improve study comparisons.
Encourage research using advanced wearable devices and active data use.
Conduct more randomized clinical trials to create consensus on implementing wearables in oncological practice.
Develop guidelines for designing and reporting trials using wearables.
Regulatory Considerations
The Clinical Transformation Initiative (CTTI) provides recommendations for the use of mobile technology in clinical trials, which could inform regulatory frameworks.
There is a lack of consensus on outcome measures and adherence definitions across studies using wearables in oncology.
There is significant heterogeneity in study designs and outcomes, making comparisons difficult.
Limited guidelines exist for designing or reporting trials using wearables in oncology.
Recommendations
Establish standardized definitions for wearable outcomes and adherence to improve study comparisons.
Encourage research using advanced wearable devices and active data use.
Conduct more randomized clinical trials to create consensus on implementing wearables in oncological practice.
Develop guidelines for designing and reporting trials using wearables.
Regulatory Considerations
The Clinical Transformation Initiative (CTTI) provides recommendations for the use of mobile technology in clinical trials, which could inform regulatory frameworks.
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.
Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs)
This paper addresses the lack of a standardized, evidence-based evaluation framework for Biometric Monitoring Technologies (BioMeTs) in the rapidly growing field of digital medicine. It proposes a foundational three-component framework—Verification, Analytical Validation, and Clinical Validation (V3)—to establish a common vocabulary and best practices for stakeholders across engineering, clinical science, data science, and regulation. The goal is to provide a systematic process for determining if a BioMeT is fit-for-purpose, particularly for use in clinical trials, thereby enabling more effective collaboration and improving trust in digital measurement tools.
Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs)
2020
npj Digital Medicine
Framework or roadmap
,
Review
Authors
Jennifer C. Goldsack, Andrea Coravos, Jessie P. Bakker, Brinnae Bent, Ariel V. Dowling, Cheryl Fitzer-Attas, Alan Godfrey, Job G. Godino, Ninad Gujar, Elena Izmailova, Christine Manta, Barry Peterson, Benjamin Vandendriessche, William A. Wood, Ke Will Wang, Jessilyn Dunn
This paper addresses the lack of a standardized, evidence-based evaluation framework for Biometric Monitoring Technologies (BioMeTs) in the rapidly growing field of digital medicine. It proposes a foundational three-component framework—Verification, Analytical Validation, and Clinical Validation (V3)—to establish a common vocabulary and best practices for stakeholders across engineering, clinical science, data science, and regulation. The goal is to provide a systematic process for determining if a BioMeT is fit-for-purpose, particularly for use in clinical trials, thereby enabling more effective collaboration and improving trust in digital measurement tools.
Findings
The term "clinically validated" is frequently used in marketing but lacks a clear, standardized meaning, leading to confusion. The rapid development of BioMeTs has outpaced the creation of systematic, evidence-based evaluation frameworks, creating a knowledge gap. Existing validation standards from software, hardware, and clinical development are often applied in silos and are not fully sufficient for modern BioMeTs. Evaluating a BioMeT requires assessing the entire "data supply chain," from the sensor hardware (verification) and data processing algorithms (analytical validation) to its performance against a meaningful clinical concept (clinical validation).
Recommendations
The digital medicine field should adopt the V3 (Verification, Analytical Validation, Clinical Validation) framework as a foundational evaluation standard for all BioMeTs to ensure they are fit-for-purpose. Technology manufacturers, clinical trial sponsors, and researchers should transparently report their V3 processes and results to overcome "black box" approaches and build a common evidence base. Technology manufacturers are primarily responsible for verification , while the entity developing the algorithm (e.g., manufacturer or sponsor) is responsible for analytical validation. The sponsor or clinical team using the BioMeT for a specific purpose is responsible for clinical validation in that context of use.
Regulatory Considerations
The V3 framework is designed to inform and align with the current regulatory landscape, although the regulatory pathway for a specific BioMeT depends on its intended use and marketing claims, not just its underlying technology. The 21st Century Cures Act and the concept of Software as a Medical Device (SaMD) have created new regulatory paradigms that decouple software from specific hardware. BioMeTs used to support drug development may follow a tool qualification pathway, while those marketed as standalone medical devices are subject to device clearance or approval processes. Stakeholders should engage with regulatory agencies early to determine appropriate validation approaches.
The term "clinically validated" is frequently used in marketing but lacks a clear, standardized meaning, leading to confusion. The rapid development of BioMeTs has outpaced the creation of systematic, evidence-based evaluation frameworks, creating a knowledge gap. Existing validation standards from software, hardware, and clinical development are often applied in silos and are not fully sufficient for modern BioMeTs. Evaluating a BioMeT requires assessing the entire "data supply chain," from the sensor hardware (verification) and data processing algorithms (analytical validation) to its performance against a meaningful clinical concept (clinical validation).
Recommendations
The digital medicine field should adopt the V3 (Verification, Analytical Validation, Clinical Validation) framework as a foundational evaluation standard for all BioMeTs to ensure they are fit-for-purpose. Technology manufacturers, clinical trial sponsors, and researchers should transparently report their V3 processes and results to overcome "black box" approaches and build a common evidence base. Technology manufacturers are primarily responsible for verification , while the entity developing the algorithm (e.g., manufacturer or sponsor) is responsible for analytical validation. The sponsor or clinical team using the BioMeT for a specific purpose is responsible for clinical validation in that context of use.
Regulatory Considerations
The V3 framework is designed to inform and align with the current regulatory landscape, although the regulatory pathway for a specific BioMeT depends on its intended use and marketing claims, not just its underlying technology. The 21st Century Cures Act and the concept of Software as a Medical Device (SaMD) have created new regulatory paradigms that decouple software from specific hardware. BioMeTs used to support drug development may follow a tool qualification pathway, while those marketed as standalone medical devices are subject to device clearance or approval processes. Stakeholders should engage with regulatory agencies early to determine appropriate validation approaches.
Keywords
Algorithms
Analytical validation
Biometric monitoring technologies
Clinical validation
Context of use (COU)
Data validity and reliability
Digital health technologies (DHTs)
Regulatory framework
Software as a medical device (SaMD)
Standards development
Technology evaluation
V3+ framework
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.
A roadmap for implementation of patient-centered digital outcome measures in Parkinson’s disease obtained using mobile health technologies
A roadmap for the implementation of patient-centered digital outcome measures in Parkinson's disease using mobile health technologies, addressing key needs such as defining relevant patient-centered digital targets, selecting appropriate device combinations, developing an open-source platform for data integration and analysis, and establishing a pathway for regulatory approval and adoption by healthcare organizations.
A roadmap for implementation of patient-centered digital outcome measures in Parkinson’s disease obtained using mobile health technologies
2019
Mov Disord
Framework or roadmap
Authors
Alberto J Espay, Jeffrey M Hausdorff, Alvaro Sánchez-Ferro, Jochen Klucken, Aristide Merola, Paolo Bonato, Serene S Paul, Fay B Horak, Joaquin A Vizcarra, Tiago A Mestre, Ralf Reilmann, Alice Nieuwboer, E Ray Dorsey, Lynn Rochester, Bastiaan R Bloem, Walter Maetzler, Joan A Gardner
A roadmap for the implementation of patient-centered digital outcome measures in Parkinson's disease using mobile health technologies, addressing key needs such as defining relevant patient-centered digital targets, selecting appropriate device combinations, developing an open-source platform for data integration and analysis, and establishing a pathway for regulatory approval and adoption by healthcare organizations.
Findings
Lack of consensus on the type and scope of digital outcome measures.
Partial integration of mobile health technologies into clinical practice.
Challenges in data presentation and interpretation.
Poorly addressed patient compliance and technology illiteracy.
Validation challenges for mobile health technologies.
Recommendations
Target deficits confirmed to be relevant to patients.
Use a combination of devices with an acceptable benefit-to-burden ratio.
Integrate data into patient management platform standards.
Ensure regulatory approval and adoption by healthcare organizations.
Consider pilot use of competing platforms for better integration.
Regulatory Considerations
Understand and overcome regulatory hurdles.
Ensure sustainable financial models.
Consider pilot use of platforms for regulatory integration.
Lack of consensus on the type and scope of digital outcome measures.
Partial integration of mobile health technologies into clinical practice.
Challenges in data presentation and interpretation.
Poorly addressed patient compliance and technology illiteracy.
Validation challenges for mobile health technologies.
Recommendations
Target deficits confirmed to be relevant to patients.
Use a combination of devices with an acceptable benefit-to-burden ratio.
Integrate data into patient management platform standards.
Ensure regulatory approval and adoption by healthcare organizations.
Consider pilot use of competing platforms for better integration.
Regulatory Considerations
Understand and overcome regulatory hurdles.
Ensure sustainable financial models.
Consider pilot use of platforms for regulatory integration.
Keywords
Algorithms
Clinical trials
Clinical validation
Data platforms
Data privacy and security
Digital health technologies (DHTs)
Digital outcome measures
Human factors engineering (HFE)
Interoperability
Neurodegenerative diseases
Patient centricity
Patient-centered outcomes
Real-world data (RWD)
Regulatory framework
Scalability
Stakeholder collaboration
Standards development
Technology adoption
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.