Findings
Digital endpoints must not only support regulatory approval but also provide evidence that meets payer expectations for reimbursement and value-based care. The lack of early engagement with payers and health technology assessment (HTA) agencies is a key barrier to the adoption of digital clinical measures. Digital measures can enhance value-based care models by capturing patient-centered outcomes, reducing healthcare costs, and improving early disease detection. The scalability and generalizability of digital endpoints remain challenges, particularly for diverse populations and real-world healthcare settings. Technical and systematic barriers—such as data heterogeneity, stakeholder knowledge gaps, and inconsistent regulatory-payer alignment—are slowing the adoption of digital endpoint data for reimbursement decisions.

Recommendations
Pharma and medical product developers should engage early with payers and regulators to ensure digital endpoints align with reimbursement expectations. Payers and HTA bodies should establish clear evidence thresholds for digital endpoint validation, ensuring consistency in market access decisions. Digital endpoints should be validated against health-related quality of life (HRQoL) measures and patient-reported outcomes (PROs) to demonstrate clinical relevance. Real-world evidence (RWE) should be incorporated into clinical trials alongside digital endpoints to strengthen reimbursement applications. Stakeholders should prioritize scalable, patient-centered digital measures that capture disease progression over time and across different care settings.

Regulatory Considerations
Integrated Evidence Plans (IEPs) should be developed early to align digital endpoint evidence with regulatory and payer requirements. Digital endpoints should be assessed through multi-stakeholder collaboration, ensuring validation across pharmaceutical, regulatory, and reimbursement frameworks. Payers and regulators should work together to create aligned pathways for digital measure acceptance, reducing delays in market access. Data security, privacy, and interoperability must be addressed to support regulatory approval and patient trust in digital health solutions. The industry should leverage international regulatory-payer collaboration models, such as the HTA-EMA partnership and the FDA Payor Communication Task Force, to accelerate global digital endpoint adoption.

Findings
Collaborative Communities are sustained, multi-stakeholder forums (including patients, industry, academia, and the FDA) dedicated to solving shared challenges in the medical device ecosystem. These communities are not intended to replace formal regulatory mechanisms. They are equipped to perform activities such as:
Developing best practices and strategies.
Generating and evaluating evidence to support novel approaches.
Clarifying ill-defined challenges and generating consensus on definitions.
Addressing issues related to product quality and safety.

Recommendations
The FDA/CDRH does not establish or fund these communities. Instead, the FDA recommends that interested stakeholders convene and lead these groups. The FDA reviews opportunities on a case-by-case basis for participation, considering:
The community's potential public health impact.
Alignment with the CDRH mission, priorities, and resources.
The existence of a formal governance structure, a convener, a plan to measure success, and a mechanism for sustained engagement.

Regulatory Considerations
The FDA's participation in these communities is a strategic priority for advancing regulatory science and fostering responsible medical device innovation. Examples of digital health-related collaborations include those focused on AI/ML, Digital Biomarkers, Digital Health Technologies (DHTs), and Real-World Data (RWD). The outcomes developed by these groups can inform and accelerate the development of science-based solutions to policy and scientific challenges.

Findings
MCID represents the smallest change that someone living with Alzheimer's disease would identify as important, but faces several universal application challenges. Alzheimer's disease progresses differently for each individual, complicating the establishment of universal standards that account for individual-level issues. The disease is gradual and evolving, with what is perceived as clinically meaningful varying significantly at early and late disease stages. People living with Alzheimer's disease and caregivers may have differing perspectives on treatment benefits, making it challenging to establish appropriate MCID. Current Alzheimer's trials rely on various tests to evaluate cognitive and functional impairments, but these tests often lack sensitivity to early-stage changes and are affected by variability in rater rankings. Digital biomarkers offer promising approaches for detecting real-time, objective clinical differences and improving patient outcomes through continuous monitoring, individualized assessments, and artificial intelligence learning for complex analytical predictions.

Recommendations
Digital biomarkers and advanced health technologies should be leveraged to enable continuous monitoring and individualized assessments that can better capture meaningful change in Alzheimer's disease. The primary focus must remain on outcomes that truly matter to people living with Alzheimer's disease and their caregivers, ensuring that the principle of clinical meaningfulness is not lost as new technologies are introduced.

Regulatory Considerations
Important considerations around standardization, accuracy, and integration into current clinical frameworks must be addressed as digital biomarkers are adopted. As new technologies are introduced alongside evolving regulatory frameworks, maintaining focus on clinically meaningful outcomes for patients and caregivers is essential.

Findings
A comprehensive catalogue of over 340 symptoms and impacts was identified across ten symptom domains and two functional impact domains. Strongest evidence for relevance in early disease was found for tremor, fine motor dexterity, gait, stiffness, and slowed movements. Common non-motor symptoms include cognitive alterations, mood changes such as anxiety or depression, sleep disturbances, fatigue, and urinary dysfunction. Significant variability exists in how these concepts are currently measured and classified in literature, often confounding symptoms with functional impacts. There is a notable lack of diversity in existing research, with over 93% of qualitative data originating from white populations in the US, UK, and Canada.

Recommendations
Researchers and clinicians should utilize the proposed Domain-Category-Concept-Experience schema to ensure consistency and parsimoniousness in outcome selection. Selection of concepts for clinical trials should be evidence-based, focusing on those demonstrated to be both prevalent and bothersome to patients. Future research must prioritize the inclusion of culturally, racially, and geographically diverse populations to ensure the model's universal applicability. Stakeholders should adopt lay-friendly terminology, such as using ""slow movements"" instead of ""bradykinesia,"" to better reflect the patient perspective. Continuous re-evaluation of the model is necessary to maintain alignment with emerging biological staging systems for neuronal synuclein disease.

Regulatory Considerations
The consensus model was developed to align specifically with FDA guidance on patient-focused drug development (PFDD) to support regulatory-ready endpoints. Meaningful aspects of health should be identified through direct patient report to satisfy evidentiary requirements for ""fit-for-purpose"" clinical outcome assessments. Evidence-based SOFT report cards provide a transparent method for justifying the selection of concepts of interest in regulatory submissions. Early engagement with agencies is encouraged to ensure selected endpoints are sensitive to treatment effects and reflect what matters most to patients. Harmonization of concept definitions is a critical prerequisite for the successful qualification of new drug development tools.

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
Analytical validation is critical for ensuring digital clinical measures align with regulatory and scientific expectations, particularly when no established reference measures exist.
Novel digital measures require flexible validation approaches, as traditional clinical reference measures often do not directly correspond to digital endpoints​
Statistical methodologies must be tailored to the nature of digital measures, using approaches such as factor analysis, regression modeling, and latent variable estimation​
Regulatory engagement is crucial early in the validation process to align evidentiary standards and facilitate market adoption​
The validation process must be context-specific, considering population characteristics, data collection settings, and sensor variability to ensure reliability across diverse applications​.

Recommendations
Developers should follow a stepwise approach in designing validation studies, incorporating existing reference measures, novel comparators, and statistical validation techniques.
Regulatory authorities should provide clearer guidance on acceptable validation methodologies, particularly for novel digital endpoints.
Analytical validation must be tailored to the intended use environment, ensuring that sensor-based measures capture meaningful health outcomes in real-world settings​.
Multi-stakeholder collaboration (regulators, payers, researchers, and patients) should be prioritized to create consensus on validation strategies and market access pathways.
Machine learning and AI models used for digital clinical measures should undergo rigorous evaluation to mitigate bias and improve interpretability in healthcare decision-making​.

Regulatory Considerations
Digital endpoint validation must incorporate both traditional statistical measures and novel validation frameworks, ensuring credibility in regulatory submissions.
FDA and international regulators encourage early engagement to discuss validation plans, data requirements, and evidentiary thresholds for digital measures​.
Real-world evidence (RWE) and real-world data (RWD) should be leveraged to support regulatory submissions and post-market surveillance of digital health innovations​.
Validation studies should align with global regulatory standards, such as ISO, FDA’s digital health guidance, and European Medical Device Regulations (MDR).
Data privacy, security, and compliance with regulations like HIPAA and GDPR are critical considerations when deploying and validating digital clinical measures​

Findings
There is a lack of standardized methodologies for deriving meaningful change thresholds for digital endpoints (DEs).
Challenges exist in identifying DEs that capture the most meaningful concepts to patients.
There is a need for further unification and synergy of efforts in the field, especially given the absence of clear cross-agency regulatory frameworks.

Recommendations
Form multidisciplinary task forces to develop consensus expert guidance recommendations.
Improve transparency and sharing of learnings within the industry.
Engage with regulatory bodies early and frequently throughout the DHT development process.
Use anchor-based methods as the primary approach for deriving meaningful change thresholds.
Ensure DEs reflect concepts that are meaningful to patients.

Regulatory Considerations
Early and frequent engagement with regulators is crucial.
DEs must reflect meaningful patient concepts and be validated early in the development process.
Anchor-based methods are preferred by regulatory authorities for deriving meaningful change thresholds.

Findings
Digital endpoints must not only support regulatory approval but also provide evidence that meets payer expectations for reimbursement and value-based care.
The lack of early engagement with payers and health technology assessment (HTA) agencies is a key barrier to the adoption of digital clinical measures.
Digital measures can enhance value-based care models by capturing patient-centered outcomes, reducing healthcare costs, and improving early disease detection.
The scalability and generalizability of digital endpoints remain challenges, particularly for diverse populations and real-world healthcare settings.
Technical and systematic barriers—such as data heterogeneity, stakeholder knowledge gaps, and inconsistent regulatory-payer alignment—are slowing the adoption of digital endpoint data for reimbursement decisions.

Recommendations
Pharma and medical product developers should engage early with payers and regulators to ensure digital endpoints align with reimbursement expectations.
Payers and HTA bodies should establish clear evidence thresholds for digital endpoint validation, ensuring consistency in market access decisions.
Digital endpoints should be validated against health-related quality of life (HRQoL) measures and patient-reported outcomes (PROs) to demonstrate clinical relevance.
Real-world evidence (RWE) should be incorporated into clinical trials alongside digital endpoints to strengthen reimbursement applications.
Stakeholders should prioritize scalable, patient-centered digital measures that capture disease progression over time and across different care settings.

Regulatory Considerations
Integrated Evidence Plans (IEPs) should be developed early to align digital endpoint evidence with regulatory and payer requirements.
Digital endpoints should be assessed through multi-stakeholder collaboration, ensuring validation across pharmaceutical, regulatory, and reimbursement frameworks.
Payers and regulators should work together to create aligned pathways for digital measure acceptance, reducing delays in market access.
Data security, privacy, and interoperability must be addressed to support regulatory approval and patient trust in digital health solutions.
The industry should leverage international regulatory-payer collaboration models, such as the HTA-EMA partnership and the FDA Payor Communication Task Force, to accelerate global digital endpoint adoption.

Findings
Meaningful outcome measures should align with patient priorities and clinical relevance, emphasizing aspects of health that impact daily life.
Digital tools must demonstrate value over traditional methods in capturing outcomes, especially in remote or decentralized contexts.
Questions about therapeutic benefit and endpoint sensitivity must address how these measures reflect patient improvements or disease progression.
Stakeholder collaboration is critical to selecting and validating concepts of interest and corresponding outcome measures.
Challenges include ensuring data privacy, operational feasibility, and addressing potential gaps in endpoint validation.

Recommendations
Engage patients and caregivers to identify meaningful aspects of health and concepts of interest relevant to their daily lives and goals.
Collaborate with clinicians to determine the clinical validity and utility of proposed measures and tools for endpoint development.
Ensure that DHTs selected for measurement add value beyond traditional methods and are feasible for clinical and real-world use.
Incorporate payer perspectives to align outcome measures with cost-benefit evaluations and reimbursement criteria.
Use the question bank as a flexible guide, adapting it to the specific needs and context of individual clinical trials.

Regulatory Considerations
Ensure endpoints and their measures meet regulatory standards for clinical relevance and sensitivity to therapeutic changes.
Align outcome measures with accepted core sets (e.g., COMET database) and validate them through stakeholder engagement.
Address concerns related to data privacy, scalability, and operational feasibility in the use of DHTs for endpoint development.
Plan for regulatory engagement to demonstrate the robustness of digitally-derived endpoints in pivotal clinical trials.
Provide evidence to support the incorporation of novel endpoints into regulatory and payer frameworks for decision-making.

Findings
Research is more impactful and relevant when patients and other stakeholders are treated as equal partners throughout the entire study lifecycle.
A lack of shared vision, clear communication protocols, and defined roles are significant barriers to the success of multi-stakeholder research teams.
Engaging diverse stakeholders leads to the development of more patient-centered research questions and outcome measures that reflect real-world priorities.
Institutional barriers, such as inflexible policies on compensation and data access for non-researcher team members, frequently undermine effective collaboration.
Successful patient-centered outcomes research (PCOR) requires specific skills in collaborative problem-solving, conflict navigation, and leading productive team meetings.

Recommendations
Integrate patients, caregivers, clinicians, and other stakeholders into research teams from the initial planning stages to ensure alignment with patient needs.
Establish a shared vision and clear ground rules for communication, decision-making, and responsibilities to foster a cohesive and productive team environment.
Provide training and resources for all team members on best practices for stakeholder engagement, collaborative teamwork, and patient-centered research methods.
Institutions should develop supportive infrastructure, including fair compensation policies and streamlined onboarding processes, to facilitate meaningful stakeholder participation.
Research plans should be flexible, allowing teams to adapt their engagement strategies and methodologies in response to stakeholder feedback and changing circumstances.

Regulatory Considerations
Evidence generated through patient-centered outcomes research can strengthen regulatory submissions by demonstrating that a product's benefits are meaningful to patients.
The inclusion of diverse patient populations in research, a core tenet of PCOR, helps generate real-world evidence that is more generalizable and relevant for post-market surveillance.
Regulatory bodies are increasingly emphasizing the use of patient-experience data and patient-reported outcomes, which are central to the PCORI research model.
Engaging stakeholders in the selection of clinical trial endpoints helps ensure alignment with patient priorities, which can facilitate more efficient regulatory review.
The collaborative, transparent methods promoted by PCORI can help build trust and align expectations among researchers, patients, and regulatory agencies.

Findings
Digital health measures must be grounded in patient priorities, ensuring that they capture meaningful aspects of health.
Variability in symptoms, patient experiences, and disease progression necessitates adaptable and inclusive digital measurement strategies.
Sensor technologies must be carefully evaluated for accuracy, reliability, and suitability for specific clinical applications.
Digital measures can support multiple endpoints, requiring clear definitions to ensure consistency and interoperability.
The validation of digital measures must integrate statistical and clinical significance to support regulatory acceptance.

Recommendations
Patient perspectives should be prioritized when designing and selecting digital clinical measures.
Digital endpoints should align with clinical goals and be clearly defined to ensure relevance across different conditions.
Technical specifications of sensors must be assessed rigorously to ensure appropriate data quality and integrity.
Developers should collaborate with regulatory agencies early to streamline the validation and approval of digital measures.
Standardized methodologies should be established to ensure consistency in evaluating digital health technologies.

Regulatory Considerations
Digital endpoints should be validated using rigorous scientific and regulatory frameworks to ensure clinical applicability.
Sensor-based measures must comply with data integrity standards and regulatory requirements for digital health technologies.
Interoperability and standardization of digital measures are necessary to facilitate regulatory submissions and cross-study comparisons.
Stakeholders should leverage real-world evidence (RWE) to support regulatory decision-making for digital health innovations.
Privacy and security considerations must be addressed to ensure compliance with HIPAA, GDPR, and other data protection regulations.

Findings
Digital health technologies (DHTs) enable the creation of novel endpoints that can represent the patient experience more objectively and accurately than traditional measures.
Endpoints derived from DHTs may be more meaningful to patients, healthcare providers, and other stakeholders.
The CTTI pathway for developing novel endpoints is applicable across various chronic conditions, with specific case studies developed for Duchenne Muscular Dystrophy, Diabetes, Parkinson's Disease, and Heart Failure.

Recommendations
A systematic approach should be used to identify and develop key novel endpoints from digital health technologies.
Development should focus on creating measures that are meaningful to patients.
Stakeholders—including patients, regulators, and investigative site personnel—should be engaged early and often in the planning process.
Biostatisticians and data scientists should be involved in key decisions regarding protocol design, data collection, and analysis.
Novel endpoints should be incorporated as exploratory endpoints in existing clinical trials and observational studies to gather evidence

Regulatory Considerations
Developers are advised to engage with regulators like the FDA early and frequently when planning the development of a novel endpoint.
There are established processes for interacting with the FDA, and resources are available to guide developers through these interactions.
The principles of adaptive trial design are the same for studies using mobile technologies as they are for traditional clinical trials.