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 9 of 9 results
State of the science and recommendations for using wearable technology in sleep and circadian research
A comprehensive review of the current state of wearable sleep-tracking technology, including an overview of traditional actigraphy, consumer-grade devices, and contemporary research/clinical-grade devices, as well as discussions of their performance, accuracy, limitations, and considerations for use in sleep and circadian research.
State of the science and recommendations for using wearable technology in sleep and circadian research
2024
Sleep
Recommendations / best practices
,
Review
Authors
Massimiliano De Zambotti, Cathy Goldstein, Jesse Cook, Luca Menghini, Marco Altini, Philip Cheng, Rebecca Robillard
A comprehensive review of the current state of wearable sleep-tracking technology, including an overview of traditional actigraphy, consumer-grade devices, and contemporary research/clinical-grade devices, as well as discussions of their performance, accuracy, limitations, and considerations for use in sleep and circadian research.
Findings
Misclassification of wakefulness during sleep periods and issues with tracking outside main sleep bouts.
Bias in performance evaluation studies due to limited representation of diverse populations.
Hidden complexities in consumer-grade devices related to data access, fees, privacy, and security.
Recommendations
Carefully interpret study results based on wearable sleep-tracking technology data.
Address biases in study populations by including diverse cohorts.
Ensure proper preprocessing of data from consumer-grade devices.
Avoid inserting personally identifiable information in device settings.
Evaluate issues related to specific populations like minors.
Regulatory Considerations
Complexity of privacy laws across different countries.
Need for strategies to protect personal information in device settings.
Consideration of specific population issues, such as minors, in regulatory frameworks.
Misclassification of wakefulness during sleep periods and issues with tracking outside main sleep bouts.
Bias in performance evaluation studies due to limited representation of diverse populations.
Hidden complexities in consumer-grade devices related to data access, fees, privacy, and security.
Recommendations
Carefully interpret study results based on wearable sleep-tracking technology data.
Address biases in study populations by including diverse cohorts.
Ensure proper preprocessing of data from consumer-grade devices.
Avoid inserting personally identifiable information in device settings.
Evaluate issues related to specific populations like minors.
Regulatory Considerations
Complexity of privacy laws across different countries.
Need for strategies to protect personal information in device settings.
Consideration of specific population issues, such as minors, in regulatory frameworks.
Keywords
& inclusion (DEI)
Actigraphy
Algorithms
Clinical trials
Clinical validation
Consumer-grade wearables
Data collection
Data management
Data privacy and security
Data quality
Data validity and reliability
Device selection
Diversity
equity
Ethics
FDA
Patient-generated data
Sensor-based digital health technologies (sDHTs)
Software as a medical device (SaMD)
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.
Digital Health Technology for Real-World Clinical Outcome Measurement Using Patient-Generated Data: Systematic Scoping Review
A comprehensive systematic scoping review of the use of digital health technologies (DHTs) to measure real-world clinical outcomes using patient-generated data.
Digital Health Technology for Real-World Clinical Outcome Measurement Using Patient-Generated Data: Systematic Scoping Review
2023
JMIR
Review
Authors
Evelyn Pyper, Sarah Mckeown, Jamie Hartmann-Boyce, John Powell
A comprehensive systematic scoping review of the use of digital health technologies (DHTs) to measure real-world clinical outcomes using patient-generated data.
Findings
There is a need for more rigorous research beyond technology validation to ensure reliable real-world data capture and improved patient outcomes.
Limited translation of AI tools into medical practice despite their success in retrospective studies.
Insufficient application of social factors in clinical decision-making and DHT research.
Need for more rigorous and reproducible research designs with larger sample sizes and longer follow-up times.
Recommendations
Use the study's repository to inform future research by healthcare providers, policymakers, and the life sciences industry.
Consider how data collection methods (active or passive) complement primary study outcomes.
Conduct targeted systematic reviews to assess factors contributing to the digital divide.
Ensure greater consistency in metrics used across DHT research.
Regulatory Considerations
Manufacturers need to demonstrate the ongoing value of their products using real-world evidence.
Regulatory approvals for AI-based products are increasing, particularly for machine learning applications.
There is a need for more rigorous research beyond technology validation to ensure reliable real-world data capture and improved patient outcomes.
Limited translation of AI tools into medical practice despite their success in retrospective studies.
Insufficient application of social factors in clinical decision-making and DHT research.
Need for more rigorous and reproducible research designs with larger sample sizes and longer follow-up times.
Recommendations
Use the study's repository to inform future research by healthcare providers, policymakers, and the life sciences industry.
Consider how data collection methods (active or passive) complement primary study outcomes.
Conduct targeted systematic reviews to assess factors contributing to the digital divide.
Ensure greater consistency in metrics used across DHT research.
Regulatory Considerations
Manufacturers need to demonstrate the ongoing value of their products using real-world evidence.
Regulatory approvals for AI-based products are increasing, particularly for machine learning applications.
Keywords
Artificial intelligence (AI) / Machine learning (ML)
Clinical research
Data collection
Digital biomarkers
Digital health technologies (DHTs)
Mobile applications
Patient-generated data
Real-world data (RWD)
Real-world evidence (RWE)
Sensor-based digital health technologies (sDHTs)
Technology evaluation
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.
Library of Digital Measurement Products
This resource is an interactive, searchable library that catalogs sensor-based digital health technologies, high-quality digital clinical measures, and validated measurement tools. It is designed to help clinicians, developers, and researchers identify high-quality instruments for monitoring disease and health in real-world settings. The library aims to streamline evidence generation, support the efficacy of new treatments, and accelerate research by centralizing information to minimize redundant work. It focuses on enabling superior monitoring of disease progression and treatment responses, ultimately ensuring that the field can identify and utilize the best digital measurement tools to improve patient outcomes.
Library of Digital Measurement Products
2023
Digital Medicine Society
Library
Authors
Digital Medicine Society
This resource is an interactive, searchable library that catalogs sensor-based digital health technologies, high-quality digital clinical measures, and validated measurement tools. It is designed to help clinicians, developers, and researchers identify high-quality instruments for monitoring disease and health in real-world settings. The library aims to streamline evidence generation, support the efficacy of new treatments, and accelerate research by centralizing information to minimize redundant work. It focuses on enabling superior monitoring of disease progression and treatment responses, ultimately ensuring that the field can identify and utilize the best digital measurement tools to improve patient outcomes.
N/A
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.
Considerations for Conducting Bring Your Own “Device” (BYOD) Clinical Studies
A summary of the benefits and challenges of the "bring your own device" (BYOD) model for collecting data in clinical research using digital health technologies, and considerations for implementing BYOD studies.
Considerations for Conducting Bring Your Own “Device” (BYOD) Clinical Studies
2022
Digit Biomark
Framework or roadmap
,
Recommendations / best practices
,
Review
Authors
Charmaine Demanuele, Cynthia Lokker, Krishna Jhaveri, Pirinka Georgiev, Emre Sezgin, Cindy Geoghegan, Kelly H Zou, Elena Izmailova, Marie Mccarthy
A summary of the benefits and challenges of the "bring your own device" (BYOD) model for collecting data in clinical research using digital health technologies, and considerations for implementing BYOD studies.
Findings
Limited use of BYOD in clinical trials and evolving regulatory guidance.
Potential biases due to participant preselection based on technology access and literacy.
Challenges in technology availability for generating study endpoints.
Recommendations
Ensure appropriate technology selection to meet study objectives.
Address potential biases in study population and data variability.
Implement mitigation strategies like provisioned technologies to avoid digital divide.
Develop a comprehensive statistical analysis plan for BYOD data.
Engage stakeholders early in the study design process.
Regulatory Considerations
Manage interactions with regulatory authorities on trial design and approval.
Prepare evidence dossiers for novel assessments via digital health technology.
Ensure compliance with guidelines like those from the Agency for Health Research and Quality.
Limited use of BYOD in clinical trials and evolving regulatory guidance.
Potential biases due to participant preselection based on technology access and literacy.
Challenges in technology availability for generating study endpoints.
Recommendations
Ensure appropriate technology selection to meet study objectives.
Address potential biases in study population and data variability.
Implement mitigation strategies like provisioned technologies to avoid digital divide.
Develop a comprehensive statistical analysis plan for BYOD data.
Engage stakeholders early in the study design process.
Regulatory Considerations
Manage interactions with regulatory authorities on trial design and approval.
Prepare evidence dossiers for novel assessments via digital health technology.
Ensure compliance with guidelines like those from the Agency for Health Research 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.
Digitally Enabled, Patient-Centric Clinical Trials: Shifting the Drug Development Paradigm
An overview of Merck's digitally enabled clinical trials initiative, which aims to shift the clinical trial paradigm from a site-centric to a patient-centric model through the use of digital technologies in four key areas: smart dosing, patient-centric sampling, digital biomarkers, and digital platforms.
Digitally Enabled, Patient-Centric Clinical Trials: Shifting the Drug Development Paradigm
2020
Clin Transl Sci
Example or case study
,
Perspective or viewpoint
Authors
Marissa F Dockendorf, Bryan J Hansen, Kevin P Bateman, Matthew Moyer, Jyoti K Shah, Lisa A Shipley
An overview of Merck's digitally enabled clinical trials initiative, which aims to shift the clinical trial paradigm from a site-centric to a patient-centric model through the use of digital technologies in four key areas: smart dosing, patient-centric sampling, digital biomarkers, and digital platforms.
Findings
1. Challenges related to patient privacy and lack of sufficient validation for digital endpoints.
2. Lack of transparency in endpoint calculations and operational challenges.
3. Added complexities due to software version changes and regulatory unknowns.
Recommendations
1. Increase patient-centricity and reduce patient burden through digital health technologies.
2. Foster collaboration among pharmaceutical companies, regulators, academia, and technology companies.
3. Embrace innovation and ensure senior leadership support for digital health initiatives.
4. Utilize real-time data access to enrich clinical trial data sets.
5. Implement outpatient sampling to augment decision-making processes.
Regulatory Considerations
1. Request feedback from regulatory agencies as part of the development plan for outpatient sampling.
2. Consider the FDA's guidance on bioanalytical method validation for dried blood sampling.
3. Note examples of regulatory acceptance of digital biomarkers as primary or secondary endpoints.
1. Challenges related to patient privacy and lack of sufficient validation for digital endpoints.
2. Lack of transparency in endpoint calculations and operational challenges.
3. Added complexities due to software version changes and regulatory unknowns.
Recommendations
1. Increase patient-centricity and reduce patient burden through digital health technologies.
2. Foster collaboration among pharmaceutical companies, regulators, academia, and technology companies.
3. Embrace innovation and ensure senior leadership support for digital health initiatives.
4. Utilize real-time data access to enrich clinical trial data sets.
5. Implement outpatient sampling to augment decision-making processes.
Regulatory Considerations
1. Request feedback from regulatory agencies as part of the development plan for outpatient sampling.
2. Consider the FDA's guidance on bioanalytical method validation for dried blood sampling.
3. Note examples of regulatory acceptance of digital biomarkers as primary or secondary endpoints.
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.
Developing and adopting safe and effective digital biomarkers to improve patient outcomes
An overview of digital biomarkers, their development and evaluation, their role in clinical research and patient care, and the challenges associated with their use, such as ensuring privacy and security.
Developing and adopting safe and effective digital biomarkers to improve patient outcomes
2019
NPJ Digit Med
Framework or roadmap
,
Perspective or viewpoint
,
Review
Authors
Andrea Coravos, Sean Khozin, Kenneth D Mandl
An overview of digital biomarkers, their development and evaluation, their role in clinical research and patient care, and the challenges associated with their use, such as ensuring privacy and security.
Findings
A systematic approach is needed to assess the quality and utility of digital biomarkers.
Challenges exist in ensuring patient privacy and autonomy with remote monitoring.
Evaluating the validity and fit-for-purpose of digital biomarkers is difficult due to the interplay among hardware, sensors, and algorithms.
Cybersecurity challenges pose risks to privacy and safety.
Transparency of algorithms and interoperability of components are necessary for a safe digital biomarker ecosystem.
Recommendations
Promote transparency of algorithms used in digital biomarkers.
Ensure interoperability of components with open interfaces.
Establish strong incentive structures for the safe and effective use of digital biomarkers.
Continuously collect data and handle modifications over time in a learning digital health system.
Encourage collaboration among industry, researchers, regulators, clinicians, and patients.
Regulatory Considerations
The FDA's regulatory process can address modular components of digital biomarkers.
The FDA is piloting a pre-certification program for streamlined product approvals.
New frameworks are emerging for security, ethics, and informed consent challenges in digital phenotyping technologies.
A systematic approach is needed to assess the quality and utility of digital biomarkers.
Challenges exist in ensuring patient privacy and autonomy with remote monitoring.
Evaluating the validity and fit-for-purpose of digital biomarkers is difficult due to the interplay among hardware, sensors, and algorithms.
Cybersecurity challenges pose risks to privacy and safety.
Transparency of algorithms and interoperability of components are necessary for a safe digital biomarker ecosystem.
Recommendations
Promote transparency of algorithms used in digital biomarkers.
Ensure interoperability of components with open interfaces.
Establish strong incentive structures for the safe and effective use of digital biomarkers.
Continuously collect data and handle modifications over time in a learning digital health system.
Encourage collaboration among industry, researchers, regulators, clinicians, and patients.
Regulatory Considerations
The FDA's regulatory process can address modular components of digital biomarkers.
The FDA is piloting a pre-certification program for streamlined product approvals.
New frameworks are emerging for security, ethics, and informed consent challenges in digital phenotyping technologies.
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.
Accelerating Adoption of Patient-Facing Technologies in Clinical Trials: A Pharmaceutical Industry Perspective on Opportunities and Challenges
An overview of the opportunities and challenges associated with the adoption of patient-facing technologies in clinical trials from the perspective of various stakeholders.
Accelerating Adoption of Patient-Facing Technologies in Clinical Trials: A Pharmaceutical Industry Perspective on Opportunities and Challenges
2018
Ther Innov Regul Sci
Perspective or viewpoint
Authors
Ashley M Polhemus, Hassan Kadhim, Shelly Barnes, Susan E Zebrowski, Alex Simmonds, Shirley N Masand, Jaclyn Banner, Melissa Dupont
An overview of the opportunities and challenges associated with the adoption of patient-facing technologies in clinical trials from the perspective of various stakeholders.
Findings
Organizational challenges such as risk-averse corporate culture and lack of strategy hinder PT adoption.
Business-related challenges include unclear ROI and limited willingness to invest.
External challenges involve regulatory implications and technology landscape issues.
Internal disconnections within companies lead to inefficiencies in PT initiatives.
Recommendations
Improve understanding and communication between all clinical trial stakeholders.
Engage with sites and patients to inform trial design.
Address internal disconnections within companies to facilitate PT adoption.
Develop a clear business case for PT to encourage investment.
Enhance training and support for technology use in clinical trials.
Regulatory Considerations
Lack of specific guidance for PT use in clinical trials.
Geographic variability in regulations and interpretations.
Privacy and security concerns related to data management.
Organizational challenges such as risk-averse corporate culture and lack of strategy hinder PT adoption.
Business-related challenges include unclear ROI and limited willingness to invest.
External challenges involve regulatory implications and technology landscape issues.
Internal disconnections within companies lead to inefficiencies in PT initiatives.
Recommendations
Improve understanding and communication between all clinical trial stakeholders.
Engage with sites and patients to inform trial design.
Address internal disconnections within companies to facilitate PT adoption.
Develop a clear business case for PT to encourage investment.
Enhance training and support for technology use in clinical trials.
Regulatory Considerations
Lack of specific guidance for PT use in clinical trials.
Geographic variability in regulations and interpretations.
Privacy and security concerns related to data management.
Keywords
Clinical trial endpoints
Clinical trials
Data privacy and security
Data quality
Decentralized clinical trials
Digital endpoints
Digital health technologies (DHTs)
Mobile applications
Patient burden
Patient centricity
Patient experience
Patient-generated data
Regulatory compliance
Regulatory guidance
Risk management
Scalability
Stakeholder collaboration
Technology adoption
Usability
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.
Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia
An overview of using non-intrusive sensor-based information and communication technology to collect real-world evidence on cognitive function and daily activities in people with prodromal and manifest stages of dementia, and discusses the requirements for using this technology in future clinical trials and observational studies.
Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia
2018
Alzheimers Dement
Framework or roadmap
,
Perspective or viewpoint
,
Review
Authors
Stefan Teipel, Alexandra K€ Onig, Jesse Hoey, Jeff Kaye, Frank Kr€ Uger, Julie M Robillard, Thomas Kirste, Claudio Babiloni
An overview of using non-intrusive sensor-based information and communication technology to collect real-world evidence on cognitive function and daily activities in people with prodromal and manifest stages of dementia, and discusses the requirements for using this technology in future clinical trials and observational studies.
Findings
Challenges related to data safety, quality, privacy, and regulatory requirements in smart sensor technologies.
Bias in standard RCTs due to exclusion of participants with language or motor barriers.
Need for ICT systems to detect smooth transitions in cognitive abilities and everyday functions.
Recommendations
Develop ICT-based procedures that capture relevant clinical features validly.
Ensure data fidelity and robustness in ICT systems.
Incorporate user needs into ICT solutions.
Address data safety and privacy concerns.
Develop international policies for access, security, and privacy in ICT solutions.
Regulatory Considerations
Need for international efforts to address gaps in policies around access, security, and privacy.
Current laws do not cover health information on mobile apps or the Internet.
Lack of regulation could undermine the credibility of RWE results.
Challenges related to data safety, quality, privacy, and regulatory requirements in smart sensor technologies.
Bias in standard RCTs due to exclusion of participants with language or motor barriers.
Need for ICT systems to detect smooth transitions in cognitive abilities and everyday functions.
Recommendations
Develop ICT-based procedures that capture relevant clinical features validly.
Ensure data fidelity and robustness in ICT systems.
Incorporate user needs into ICT solutions.
Address data safety and privacy concerns.
Develop international policies for access, security, and privacy in ICT solutions.
Regulatory Considerations
Need for international efforts to address gaps in policies around access, security, and privacy.
Current laws do not cover health information on mobile apps or the Internet.
Lack of regulation could undermine the credibility of RWE results.
Keywords
Actigraphy
Algorithms
Artificial intelligence (AI) / Machine learning (ML)
Clinical trials
Clinical validation
Data privacy and security
Data quality
Ethics
Neurodegenerative diseases
Patient-generated data
Real-world evidence (RWE)
Regulatory framework
Sensor-based digital health technologies (sDHTs)
Technology adoption
Usability
User-centered design
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.