Findings
The quality of evidence for the analytical validation of sensor-based digital health technologies (sDHTs), which is the evaluation of algorithms converting sensor data into a clinically interpretable measure, is often inconsistent and insufficient. The existing V3+ framework codifies the overall evaluation process, which includes verification, usability validation, analytical validation, and clinical validation. To improve the scientific rigor of analytical validation, a hierarchical framework for selecting reference measures is needed because not all potential reference measures are of equal quality. The framework classifies reference measures based on attributes that contribute to reduced measurement variability, with defining and principal measures being the most rigorous due to objective data acquisition and the ability to retain source data.

Recommendations
The proposed framework sequentially moves the investigator through four steps: (1) Compile preliminary information, including the digital clinical measure, context of use (COU), algorithm requirements, and sensor verification evidence . (2) Select an existing reference measure, develop a novel comparator, or identify a set of anchor measures, prioritizing measures with the highest scientific rigor (defining → principal → manual → reported) . (3) Consider the impact of the data collection environment to determine if the analytical validation study can be conducted in the intended use environment with the highest-order measure, or if in-lab validation is necessary, ensuring the results are generalizable . (4) Describe the rationale for key study design decisions to encourage transparency for evaluators, regulators, and payers . Investigators must justify passing over a higher-ranked reference measure, generally only acceptable if the higher-ranked measure poses unacceptable risk or is not applicable to the context of use.

Regulatory Considerations
The principles of the framework for analytical validation apply regardless of the regulatory status of the sDHT (regulated medical device, low-risk general wellness apps, or research product) or its intended use (clinical care or clinical research). The framework is intended to help investigators support the most rigorous claims regarding sDHT performance, which is important for acceptance by evaluators, peer-reviewers, regulators, and payers. The categorization of the digital clinical measure as a digital biomarker or an electronic clinical outcome assessment also does not change the framework's applicability.

Findings
In a progressive neurodegenerative illness like Alzheimer's disease, slowing the rate of disease progression is considered a clinically meaningful outcome for patients and their caregivers.
The assessment of what constitutes a clinical benefit is highly dependent on the specific stage of AD being studied, the drug's mechanism of action, and the symptoms present in that patient population.
Direct input from patients and caregivers is critical for understanding disease burden and defining treatment benefits that are truly meaningful from their perspective.
The interpretation of score changes on Clinical Outcome Assessments (COAs) requires full context; an absolute point difference must be considered relative to the study's duration, the expected placebo decline, and the specific disease stage.
Evidence from biomarkers that show an effect on underlying disease pathology provides additional support and increases the persuasiveness of the changes observed on clinical endpoints.

Recommendations
Drug developers should implement multiple "fit-for-purpose" COAs that use different reporters (e.g., clinicians, observers) and methods to generate broad and diverse evidence of a drug's clinical benefit.
Sponsors should utilize both qualitative and quantitative methodologies to explore clinical meaningfulness, including assessing "meaningful within-patient change" throughout the development process.
Developers are encouraged to create and validate new COAs and leverage innovative approaches, such as digital health technologies, to better capture concepts that are relevant to patients, especially in the earliest stages of AD.
Throughout the drug development lifecycle, stakeholders should systematically collect and incorporate patient experience data to ensure that the perspectives, needs, and priorities of patients are meaningfully captured.

Regulatory Considerations
For a drug to gain approval, it must meet the regulatory standard of "substantial evidence of effectiveness," which is typically derived from adequate and well-controlled investigations designed to minimize bias.
The FDA defines clinical benefit as a clinically meaningful effect of a drug on how an individual feels, functions, or survives.
An assessment of clinical benefit is not limited to the primary endpoint; the consistency of findings across multiple endpoints (primary and secondary) is a key consideration during regulatory review.
The accelerated approval pathway may be used for serious conditions with unmet needs based on a surrogate endpoint, but traditional approval requires verification of clinical benefit in confirmatory trials.
The FDA's evaluation includes a benefit-risk analysis, which considers the severity of the disease and the availability of alternative therapies, recognizing that patients and physicians may accept greater risks for life-threatening illnesses.

Findings
The traditional process of evaluating biomarkers within the context of a single drug development program is inefficient and creates uncertainty for sponsors. This case-by-case approach leads to redundant efforts, slows down the development of novel therapies, and hinders the broad adoption of promising scientific tools. There is a clear need for a centralized, collaborative pathway to formally validate biomarkers, which can de-risk drug development, encourage innovation, and make the process more predictable and cost-effective for all stakeholders.

Recommendations
Drug developers, academic researchers, and other stakeholders should proactively engage with the FDA through the formal Biomarker Qualification Program to validate biomarkers for specific contexts of use. It is recommended to form public-private partnerships and other collaborations to pool resources and data, which strengthens the evidence package for a biomarker's utility. Developers should use the qualification process to establish a biomarker's value early, making it a publicly available and reliable tool that can accelerate the development of multiple drug products.

Regulatory Considerations
The Biomarker Qualification Program provides a distinct regulatory pathway for establishing a biomarker's validity for a specific Context of Use (COU), separate from an individual Investigational New Drug (IND) or New Drug Application (NDA). The process involves a three-stage submission and review cycle: the Letter of Intent, the Qualification Plan, and the Full Qualification Package. Once qualified, a biomarker is publicly listed and can be incorporated into multiple drug development programs without the need for sponsors to re-submit and re-justify the validation data for that specific COU, streamlining subsequent regulatory reviews.

Key Principles (Findings)
The central principle of the FDA's program is that Digital Health Technologies (DHTs) offer significant potential to make clinical trials more efficient, patient-centric, and capable of capturing novel data. A key finding is that a collaborative, multifaceted approach is necessary to address the challenges of incorporating DHT-derived data into regulatory decision-making. The program acknowledges that ensuring data quality, validating new endpoints, and establishing clear regulatory expectations are critical for the successful adoption of these technologies in drug development.

Program Activities (Recommendations)
The FDA's activities in this area function as implicit recommendations for the industry. The agency is actively:
Developing a Framework: Creating and publishing a clear framework to guide the use of DHTs in drug and biological product development.
Engaging Stakeholders: Convening public meetings and workshops to foster collaboration and share learning among patients, biopharmaceutical companies, DHT manufacturers, and academia.
Supporting Demonstration Projects: Funding and overseeing research projects to address critical gaps and demonstrate the reliability and validity of specific digital measures.
Building Internal Expertise: Establishing a DHT Steering Committee and enhancing internal knowledge to ensure consistent and expert review of submissions containing DHT-derived data.

Regulatory Considerations
This webpage emphasizes the FDA's commitment to creating a clear regulatory framework for the use of DHTs in drug development. It highlights that while DHTs offer great promise, they also present new regulatory challenges related to data integrity, validation, and analysis. The FDA's approach involves a combination of issuing new regulatory guidance, promoting stakeholder collaboration, and advancing regulatory science. Sponsors are encouraged to engage with the FDA to discuss their use of DHTs in clinical trials to ensure alignment with the agency's expectations. The establishment of the CDRH Digital Health Center of Excellence provides a dedicated resource for such engagement.

Findings
The guidance applies to four types of Clinical Outcome Assessments (COAs): Patient-Reported Outcomes (PROs), Observer-Reported Outcomes (ObsROs), Clinician-Reported Outcomes (ClinROs), and Performance Outcomes (PerfOs). A COA is considered fit-for-purpose when the validation evidence is sufficient to support its context of use (COU). To determine if a COA is fit-for-purpose, sponsors must clearly describe the Concept of Interest (COI) and the COU, and present sufficient evidence to support a clear rationale for the COA's proposed interpretation and use. The rationale for using a COA should include up to eight components, such as justification for the COA type, capturing the important parts of the COI, appropriate administration and scoring, minimal influence from irrelevant factors or measurement error, and correspondence with the Meaningful Aspect of Health (MAH). The most direct assessment of how a patient feels or functions (MAH) should be used as the COI whenever possible.

Recommendations
Sponsors should use the Roadmap to Patient-Focused Outcome Measurement to guide the selection, modification, or development of a COA. The process begins with understanding the disease/condition (including patient perspectives) and conceptualizing clinical benefits and risks (defining the MAH, COI, and COU). When feasible, existing COAs are generally preferred, especially for well-established COIs, as this approach is often the least burdensome. If an existing COA is modified or used in a different context, additional evidence (e.g., cognitive interviews, psychometric studies) must be collected to justify its fitness for the new context of use. For new COA development, sponsors should involve patients, document all steps, and generally avoid using the new COA for the first time in a registration (pivotal) trial; a standalone observational study or early phase trial is recommended for evaluation.

Regulatory Considerations
Sponsors are encouraged to interact early and throughout medical product development with the relevant FDA review division to ensure COAs are appropriate for the intended COU. Sponsors should communicate their proposed COA-based endpoint approach, including the MAH, COI, COA type/name/score, and the final COA-based endpoint, ideally using the suggested format. The type and amount of evidence required to support the rationale for a COA's use is weighed against the degree of uncertainty regarding that part of the rationale. For ClinROs, it is recommended to use an assessor masked to treatment assignment and study visit for primary endpoints, if feasible. FDA strongly discourages proxy-reported measures for concepts known only to the patient (e.g., pain) and recommends using an ObsRO to measure observable behaviors instead when the patient cannot self-report.

Recommendations
Clearly define the concept of interest and its context of use to ensure COAs align with trial objectives.
Use conceptual and measurement frameworks to communicate how COAs measure patient experiences and generate interpretable scores.
Leverage existing COAs where possible, modifying them only when justified, and document all modifications rigorously.
Ensure COAs are accessible and inclusive, incorporating features like large fonts, touch interfaces, or audio assistance for diverse populations.
Conduct early engagement with FDA to discuss COA selection, development, and validation plans.

Regulatory Considerations
Fit-for-purpose validation requires evidence of conceptual alignment, scoring reliability, and sensitivity to clinically meaningful changes.
Digital health technologies used for COAs must comply with FDA’s guidance on data integrity, usability, and technical performance.
COAs intended for regulatory submissions must be developed and validated before pivotal trials to avoid jeopardizing trial outcomes.
Modifications to COAs or scoring methods during trials necessitate justification and revalidation.
Sponsors should submit comprehensive documentation on COA development, including scoring algorithms and item tracking matrices.

Findings
Regulatory Acceptance is Complex: Gaining regulatory acceptance for endpoints derived from Digital Health Technologies (DHTs) is a lengthy, multifaceted, and costly process that requires a global strategy and early health authority consultation.
"Fit-for-Purpose" is Key: A DHT's clearance or approval as a medical device does not automatically ensure it is fit-for-purpose in a clinical trial; its intended use must align with the specific context of use (COU) in the study.
Meaningfulness is a Hurdle: Demonstrating the clinical meaningfulness of novel digital endpoints, especially for abstract concepts like cognitive decline in Alzheimer's Disease, remains a significant challenge for regulatory acceptance.
International Harmonization is Lacking: Differences in regulatory requirements for DHT validation between major health authorities can delay or prevent the successful implementation of digital measures in global clinical trials.
Technology Changes Pose Risks: Software and hardware updates to DHTs during a clinical trial can have significant implications, potentially invalidating study results if not managed through a predetermined change-control plan.

Recommendations
Engage Health Authorities Early and Often: Sponsors should conduct multiple consultations with major health authorities (e.g., FDA, EMA) early in the development process to align on the Concept of Interest (COI), COU, and the validation roadmap.
Develop a Comprehensive Regulatory Strategy: A global regulatory strategy should be an integral part of the overall development plan, tailored to the program's objectives and endpoint hierarchy.
Establish "Fit-for-Purpose" Criteria: Before selecting a DHT, sponsors should establish the minimum technical and performance specifications required for the specific COU to guide the selection of a fit-for-purpose device.
Create a Conceptual Framework: For novel endpoints, sponsors should develop a conceptual framework that visualizes how the DHT-derived measure relates to meaningful health concepts and patient experiences.
Plan for Change and Missing Data: Sponsors should establish predetermined change-control plans with manufacturers to manage DHT updates and create risk management plans to minimize and handle missing data from remote acquisition.

Regulatory Considerations
Distinct Pathways in US vs. EU: The US FDA uses a risk-based approach for DHTs that are medical devices, while in Europe, CE marking for the intended COU is generally expected by the EMA.
Qualification is an Option, Not a Requirement: Both the FDA and EMA offer voluntary qualification programs for Drug Development Tools (DDTs), which can validate a DHT for a specific COU across multiple drug programs, though the process is resource-intensive.
Scientific Advice for Individual Programs: For DHTs used within a single drug development program, engaging with health authorities through scientific advice meetings is a more targeted and confidential pathway for gaining feedback and agreement.
Data Privacy and Security are Paramount: Sponsors must ensure that the collection, transfer, and storage of personal data via DHTs comply with all applicable regulations, such as GDPR in the EU, including cybersecurity and data transfer measures.

Findings
Different Digital Health Technologies (DHTs) estimate sleep staging using data from various sensor-based sources (e.g., EEG, actigraphy, ballistocardiography), each with different properties impacting the estimation. Sleep staging algorithms are often proprietary. DHTs interpret sleep staging at different time intervals, or epochs (e.g., polysomnography uses 30-second epochs). DHT vendors transmit data at different levels, ranging from epoch-level data to pre-calculated summary data (e.g., "total sleep time").

Recommendations
Method and Signals: Ask the vendor about their method of sleep monitoring and which signals are being recorded and used, and understand the strengths and limitations of the technology.
Granularity and Epochs: Inquire about the granularity of sleep data estimated (coarse to fine grain) and the epoch length used for sleep annotations, as this informs interpretation and comparability to other research.
Thresholds and Rules: Ask what rules and thresholds are set for confirming events like sleep onset and offset to ensure certainty in the data and inform future interpretation of results.
Data Level: To align with the Core Digital Measures of Sleep, epoch-level data is preferred for further analysis and comparison between measurement systems. If only summary data is offered, ask for a detailed description of the estimation process.
Algorithms and Evidence: Ask for evidence to support the validity and reliability of the estimated sleep stages, which may include peer-reviewed manuscripts, technical documentation, and conference abstracts.

Regulatory Considerations
While not a regulatory document, the recommendations emphasize the need for vendors to provide evidence for the validity and reliability of their proprietary sleep staging algorithms. This evidence, which can be found in peer-reviewed literature or technical documentation, is crucial for establishing confidence in the results arising from the technology, and can be used for inclusion in, for example, regulatory documents.

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
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
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
The adoption of BYOD in clinical trials has been accelerated by the COVID-19 pandemic and supportive regulatory guidance, which now recognize it as an acceptable means for remote data collection. Studies have shown high measure completion and equivalent data quality between provisioned devices and BYOD, supporting its use in diverse patient populations. Key challenges to BYOD implementation include ensuring data equivalence across a wide variety of personal devices, managing participant technical support, and addressing data privacy and security concerns. The choice between native apps and web-based solutions involves trade-offs in usability, data security, and operational complexity.

Recommendations
Sponsors should develop a clear BYOD strategy that considers the target patient population, the complexity of the required data collection, and the global regulatory landscape. A robust training and support plan is essential for both participants and site staff to ensure proper device use and troubleshooting. Sponsors should work with technology vendors to ensure their platforms are user-friendly, secure, and capable of handling data from a variety of devices. It is crucial to establish clear communication channels for participants to report technical issues and receive timely assistance.

Regulatory Considerations
Both the FDA and EMA have issued guidance that supports the use of BYOD in clinical trials, provided that data integrity, security, and privacy are maintained. Sponsors must be able to demonstrate the equivalence of data collected via BYOD with data from provisioned devices. All BYOD solutions must comply with relevant data protection regulations, such as GDPR and HIPAA. The regulatory submission should include a clear description of the BYOD strategy and a justification for its use in the trial.