Findings
Digital health measures for ADRD must align with patient and care partner priorities, including functional daily activities such as remembering object locations and maintaining speech fluency.
Sensor placement, data collection modalities, and algorithmic interpretation significantly impact the accuracy and reliability of digital measures.
While digital cognitive and behavioral assessments have strong potential as clinical endpoints, standardization is needed to ensure regulatory acceptance.
Sleep and mobility disruptions in ADRD can be measured with actigraphy, EEG, and ambient sensor-based approaches, but usability considerations are crucial.
Metadata, including environmental conditions and patient comorbidities, must be accounted for to ensure valid interpretations of digital measures in both research and clinical practice.

Recommendations
Researchers and technology developers should adopt standardized ontologies for digital measures to improve consistency across studies and regulatory submissions.
Digital biomarkers should be selected and validated with reference to patient and care partner needs, ensuring they reflect meaningful aspects of health.
Considerations such as sensor placement, data processing methods, and cultural neutrality of cognitive assessments must be accounted for in study designs.
Clinical trials should incorporate digital health technologies as both exploratory endpoints and potential screening tools for ADRD progression.
Further research is needed to refine algorithms for sleep, mobility, and speech-based digital biomarkers to enhance their predictive power for cognitive decline.

Regulatory Considerations
Digital measures of sleep and mobility have been recognized as potential clinical trial endpoints by regulatory agencies such as the FDA.
Standardized reporting and frameworks should be followed to ensure interoperability and data integrity in digital health studies.
Developers must document and validate scoring algorithms used for cognitive and behavioral assessments to meet regulatory expectations.
Data privacy and security regulations must be adhered to, particularly when collecting real-world behavioral and biometric data.
Ongoing validation and real-world evidence generation are necessary to establish digital measures as reliable clinical and regulatory endpoints in ADRD research.

Findings
Sleep disturbances are common across multiple therapeutic areas, making standardized digital measures essential for cross-condition research.
Measurement accuracy varies depending on sensor placement, algorithms, and contextual factors such as sleep environment.
While home-based digital sleep tracking improves accessibility, challenges remain in ensuring consistency with clinical polysomnography.
Digital measures of sleep provide new opportunities for continuous and longitudinal monitoring, but standardization in data collection and interpretation is needed.
Stakeholders, including regulatory agencies, increasingly recognize digital sleep biomarkers, but additional validation is required to ensure widespread adoption.

Recommendations
Researchers and clinicians should integrate core digital sleep measures into study designs to improve data comparability across trials and clinical contexts.
Algorithm transparency and validation protocols should be established to enhance the accuracy of digital sleep monitoring tools.
Regulatory engagement should be prioritized early in the development process to ensure that digital sleep measures meet evidentiary standards.
Multi-stakeholder collaboration, including patient and care partner input, is essential to ensure sleep measures reflect meaningful aspects of health.
Further research is needed to refine wearable and sensor-based technologies to improve real-world applicability and clinical utility of digital sleep biomarkers.

Regulatory Considerations
The FDA and other regulatory bodies increasingly acknowledge sleep measures as potential clinical endpoints, but clear validation frameworks are necessary.
Digital sleep measures should align with industry standards such as HL7 to ensure interoperability and data integrity.
Data privacy and security regulations must be followed, particularly for continuous sleep monitoring in real-world settings.
Post-market validation and real-world evidence generation are critical to support regulatory acceptance of digital sleep biomarkers.
Developers must document the derivation of sleep measures, including algorithmic processing and sensor accuracy, to meet regulatory review requirements.

Findings
Data analytics are challenging due to diverse metrics and study aims.
Most devices lack built-in software for data output.
There is a lack of comparison and validation studies for different devices and metrics.
Validation of PA metrics is difficult due to the absence of a gold standard.
The integration of various databases is needed but challenging.

Recommendations
Conduct comparison and validation studies between different brands of devices and PA metrics.
Develop standardized metrics for measuring PA.
Improve data integration methods across different studies and databases.
Focus on developing built-in software for devices to facilitate data output.
Encourage research on the validation of PA metrics.

Regulatory Considerations
1Not mentioned

Findings
Challenges in patient adherence and acceptability of digital endpoints.
Issues with algorithm validation and use in diverse populations.
Barriers due to proprietary software and lack of transparency.
Vast heterogeneity in digital endpoints and lack of standards.
Need for ongoing ethical support and consideration of environmental impact.

Recommendations
Foster multi-stakeholder cooperation and open-forum discussions.
Integrate patient needs into the design of digital health technologies.
Include implementation science expertise in research proposals.
Develop standards for selecting and reporting digital endpoints.
Provide ongoing ethical support throughout the research lifecycle.

Regulatory Considerations
Early engagement with regulators is crucial.
Understanding regulatory requirements for clinical trials versus clinical care.
Need for harmonised terminology and guidelines for digital endpoints.

Findings
The field lacks a centralized, standardized database of validated digital health technologies, making it difficult for researchers and clinicians to select appropriate tools.
Non-wearable sensors and software applications are the most common types of DHTs, with 83% of ambient technologies categorized as software or applications.
Most DHTs focus on mild cognitive impairment (MCI) and early Alzheimer’s disease, with fewer technologies validated for moderate or severe dementia stages.
Uneven Distribution of Dementia Subtypes – The review identified a gap in DHT validation for frontotemporal dementia (FTD) and Lewy Body dementia, with Alzheimer’s disease being the predominant focus.

Recommendations
Expand and maintain an open-access database of validated DHTs to improve accessibility and standardization.
Increase research on digital measures applicable to moderate and severe stages of dementia, as well as non-Alzheimer’s dementias.
Promote integration of wearable, ambient, and cognitive assessment tools to generate comprehensive digital phenotypes of patients.
Follow clear guidelines for analytical and clinical validation of DHTs to improve regulatory acceptance and research applicability.
Conduct more usability and feasibility assessments, especially for populations with cognitive decline, to ensure DHTs are accessible and effective in real-world settings.

Findings
FDA considers electronic records and signatures to be equivalent to paper records and handwritten signatures when they meet the requirements of 21 CFR part 11. Advances in technology, including Digital Health Technologies (DHTs) and cloud computing, necessitate updated guidance on ensuring the authenticity, integrity, and confidentiality of electronic data in clinical investigations. Records submitted to the FDA under predicate rules (e.g., marketing applications) are subject to part 11. FDA does not intend to assess the compliance of external Real-World Data (RWD) sources like Electronic Health Record (EHR) systems with part 11, but the sponsor remains responsible for the quality and integrity of all submitted data.

Recommendations
Risk-Based Validation: Regulated entities should use a risk-based approach to validation for all electronic systems deployed, proportionate to the risks to participant safety and reliability of trial results. Validation must cover system functionality, trial-specific configurations, customizations, and interoperability.
Data Retention & Audit Trails: Electronic records must be retained for the applicable period in a secure and traceable manner. Audit trails must capture all changes (old/new value, user ID, date/time) and should be protected from modification.
Security & Access Controls: Logical and physical access controls (e.g., strong login credentials, multi-factor authentication) must limit system access to authorized users based on a documented risk assessment. Security safeguards (e.g., encryption, antivirus) must be in place to protect data at rest and in transit.
DHT Use: DHTs should be selected and validated to be fit for purpose. The data originator (person, system, or DHT itself) must be associated with every data element as part of the audit trail. The final location of source data for inspection is the durable electronic data repository, not the individual DHT.
Outsourcing: Regulated entities must have a written agreement with IT service providers (including for cloud computing) detailing roles, responsibilities, and the service provider's ability to provide data integrity and security safeguards. The sponsor must maintain oversight.

Regulatory Considerations
FDA does not certify electronic systems or signature methods; they are evaluated during inspection. Users of electronic signatures must submit a letter of non-repudiation to the FDA certifying that the electronic signature is the legally binding equivalent of a handwritten signature. Security breaches impacting participant safety or privacy should be reported to the IRB and FDA in a timely manner.

Findings
The FDA considers electronic records and signatures equivalent to their paper counterparts when they meet the requirements of 21 CFR Part 11. Due to technological advances, electronic systems and digital health technologies (DHTs) are now integral to clinical trials, requiring a modern, risk-based approach to ensure data integrity. Sponsors remain ultimately responsible for the quality and integrity of all data submitted, even when using third-party IT service providers or data from real-world sources like EHRs. The authenticity, integrity, and confidentiality of electronic data are paramount and must be maintained through robust system controls throughout the data lifecycle.

Recommendations
Regulated entities should use a justified and documented risk-based approach to validate all electronic systems before and during a clinical trial, with the level of validation depending on the system's potential to impact participant safety and trial result reliability. Secure, computer-generated, time-stamped audit trails must be implemented to track the creation, modification, and deletion of all electronic records without obscuring original data. Robust logical and physical access controls are necessary to limit system access to authorized individuals. Entities should have written agreements with IT service providers that clearly define roles, responsibilities, and procedures for ensuring data security and long-term retention.

Regulatory Considerations
The requirements of 21 CFR Part 11 apply to all electronic records created, modified, or submitted to the FDA under predicate rules for clinical investigations, including those from foreign sites under an IND or IDE. While the FDA does not intend to assess the Part 11 compliance of external source systems like EHRs, data becomes subject to these regulations once transferred into the sponsor's electronic system. During inspections, the FDA will focus on system validation, data handling procedures, security protocols, audit trails, and documentation of sponsor oversight. Users must certify to the FDA that their electronic signatures are the legally binding equivalent of handwritten signatures.

Findings
Diverse Pathways to Market Exist: The case studies demonstrate there is no single "right" way to approach the FDA; successful strategies are highly varied and include De Novo requests, 510(k) clearances, and leveraging established pathways for new indications.
Early FDA Engagement is Crucial: A consistent theme across the successful case studies is the value of engaging with the FDA early and often. This collaborative approach helps de-risk the development process, clarify evidentiary requirements, and build trust.
"Drug-like" Evidence Can Be a Differentiator: For novel software-based interventions, particularly digital therapeutics, generating a robust body of evidence similar to that of a pharmaceutical (i.e., randomized controlled trials) is a key strategy for gaining regulatory and commercial success.
Platform-Based Approaches are Emerging: Companies are finding success by moving from single-product solutions to integrated platforms that can monitor multiple health aspects, which requires a more holistic regulatory strategy.

Recommendations
Leverage Pre-Submission (Pre-Sub) Meetings: Sponsors are strongly encouraged to use the Q-Submission program to gain valuable, early feedback from the FDA on their validation plans and overall regulatory strategy.
Build a Multi-faceted Commercialization Plan: Regulatory clearance is only one step. The case studies recommend developing a comprehensive strategy that considers market access, reimbursement, and payer engagement from the outset.
Address Underserved Markets: The examples highlight opportunities for innovation in underserved areas, such as pediatrics and behavioral health, where DHTs can fill significant gaps in care.
Innovate on Evidence Generation: Sponsors should be prepared to innovate not just in their technology, but also in their approach to clinical evidence, tailoring their trial designs to best demonstrate the unique value of their digital product.

Regulatory Considerations
Understand the Risk Classification: The regulatory pathway for a DHT is determined by its intended use and associated risk level. Sponsors must correctly classify their device to determine if a 510(k), De Novo, or other pathway is appropriate.
AI/ML Devices Have Unique Needs: For products incorporating artificial intelligence or machine learning, sponsors must address specific regulatory considerations, such as predetermined change control plans (PCCPs), to manage algorithm updates post-market.
Interoperability is a Key Factor: For devices intended to be part of a connected health ecosystem (e.g., automated insulin dosing systems), demonstrating interoperability and cybersecurity is a critical component of the regulatory submission.

Findings
There is a lack of systematic approaches to guide the processes of collecting, interpreting, analyzing, and translating health data from wearables into digital biomarkers.
Most wearables have fixed measurement capabilities, limiting their translation to digital biomarkers.
Current guidance lacks study design and conduct elements that involve all stakeholders in an iterative approach for implementing digital biomarkers in practice.
Researchers and health professionals often rely on limited guidance for using wearable data in clinical practice and chronic disease management.

Recommendations
Implement the DACIA framework to provide interdisciplinary guidance on using wearable sensor data for digital biomarker development.
Focus on participant needs as a crucial factor for study success, applicable to both short and long-duration studies.
Involve relevant stakeholders in each key step of the DACIA framework in an iterative manner.
Apply the DACIA framework to explore digital biomarkers using various devices or signal measurements.
Reduce participant burden through support and continuous feedback.

Regulatory Considerations
Not mentioned

Findings
Regulatory inconsistencies across different FDA divisions and international jurisdictions create inefficiencies in the approval process for digital health products.
Lack of alignment between regulatory approval and payer reimbursement requirements poses a significant barrier to commercialization and widespread adoption of digital health innovations.
There are limited regulatory pathways for novel digital health products, including AI-enabled solutions, requiring new frameworks to address iterative software development and real-world data integration.
Existing health technology assessment (HTA) models do not fully accommodate digital health technologies, limiting their inclusion in reimbursement decisions.
Industry stakeholders emphasize the need for clearer guidelines on cloud-based infrastructure, third-party AI model validation, and digital health interoperability.

Recommendations
FDA and international regulatory bodies should improve coordination to establish standardized approval processes and consistent clinical evidence requirements.
New regulatory pathways should be introduced for AI-driven and software-based digital health products, considering their unique lifecycle and iterative development models.
Greater transparency and communication between FDA divisions should be established to ensure consistent decision-making and regulatory interpretations across centers.
Policymakers should prioritize payer alignment strategies, incorporating real-world evidence (RWE) to streamline reimbursement and market access processes.
The digital health industry should collaborate with regulators to create standardized best practices for AI validation, cloud security, and digital biomarker evaluation.

Regulatory Considerations
FDA should clarify the evidentiary standards for AI-enabled medical devices and establish predefined change control plans for software updates.
Digital health products should adhere to globally recognized standards such as HL7 for interoperability and ISO regulations for data security.
Market access pathways must integrate pricing and reimbursement considerations to facilitate the commercial viability of digital health technologies.
The use of real-world data (RWD) should be expanded in regulatory decision-making, supporting the approval and post-market surveillance of digital health innovations.
Regulatory frameworks should be updated to accommodate cloud-based health platforms, addressing issues such as data privacy, operational security, and compliance with HIPAA and GDPR.

Findings
The Library of Human Factors Resources compiles external documents related to human factors, human-centered design, and usability in the context of Digital Health Technologies (DHTs), especially sensor-based DHTs (sDHTs). The resources in the library are categorized by usability validation activity:
Use-related risk assessment (topics include user tasks, use-errors, use-related hazards, and risk mitigation).
Design considerations (topics relate to product design).
Formative evaluation (topics related to usability evaluation of a prototype product).
Summative evaluation (topics related to usability evaluation of a final-version or marketed product).

Recommendations
The page recommends that users, developers, and evaluators utilize the library's interactive index to find relevant documents to their DHT:
Use the Search by Activity tab to filter by one of the usability validation activities listed above.
Use the human factors topic column to further narrow the search.
Review the document name, issuing body, and product of focus columns to identify the most relevant document.
Click the hyperlink to access the selected document, then use the information in the relevant sections column to locate the specific topics of interest.

Regulatory Considerations
The library is a collection of resources for accelerating sDHT adoption, and it specifically includes documents like regulatory guidance and industry standards. This emphasizes the importance of understanding and applying human factors principles for validation and achieving regulatory acceptance for sDHTs.

Findings
Only a small number of novel digital measures have matured into regulatory qualification or efficacy endpoints.
Demonstrating that digital measures are meaningful to patients is a key challenge.
There is resistance from sponsors due to uncertainty about the value of DHT-derived endpoints in regulatory discussions.
Patient experiences are highly heterogeneous, making it difficult to generalize meaningful aspects of health.
Challenges exist in defining clinical significance and classifying digital measures as COAs vs biomarkers.

Recommendations
Engage patients and caregivers in facilitated discussions to incorporate their voices.
Determine the best method for gathering patient input on a case-by-case basis.
Engage patients to inform evidence needs, implementation, and value delivery.
Return summarized health data to participants to motivate and encourage communication with clinicians.

Regulatory Considerations
Understand the FDA's recent guidance on patient engagement in drug development.
Recognize the shift in evidence rigor required by the FDA for demonstrating meaningfulness.
Provide evidence that DHTs are usable, acceptable, and clinically relevant.
Utilize early engagement channels like CPIM and pre-LOI programs offered by the FDA.