Findings
Fragmented and inconsistent measurement approaches currently hinder the generation of decision-grade evidence for pediatric rare diseases. Small and geographically dispersed patient populations make traditional site-based clinical assessments operationally difficult and burdensome for families. Digital health technologies can capture subtle functional changes and "functional fingerprints" in home settings that are often missed during infrequent clinic visits. Standardized core digital measures across conditions allow for the aggregation of data and the creation of a shared evidence base for rare disorders. Meaningful aspects of health identified by patients and caregivers include motor function, communication, sleep quality, and autonomic stability.

Recommendations
Sponsors should adopt the core set of digital clinical measures to reduce trial timelines, lower development costs, and decrease participant burden. Researchers should prioritize passive and objective data collection to minimize the need for manual tracking by caregivers. Clinical trial designs should transition toward decentralized or hybrid models to improve access for children and families regardless of their location. Stakeholders should use the project's conceptual model to identify and customize digital measures that align with the specific health priorities of their target population. Developers should focus on human-centered design to ensure digital tools are usable and sustainable for pediatric patients and their support networks.

Regulatory Considerations
The FDA and EMA provide specific pathways and interaction opportunities to accelerate the acceptance of digital endpoints in rare disease trials. Digital measures must be validated as "decision-grade" endpoints to meet the evidentiary requirements for regulatory submission and marketing approval. Alignment with industry standards for data elements and interoperability is necessary to ensure data integrity across multi-site studies. Early engagement with regulatory bodies through meetings and formal submissions is critical for confirming the suitability of new digital biomarkers. Compliance with data privacy and ethical standards is paramount when collecting continuous, real-world data from vulnerable pediatric populations.

Findings
The rapid advancement of sensor technology and connectivity has enabled high-frequency, longitudinal monitoring of physiological processes, yet the infrastructure for large-scale deployment remains resource-intensive. Current challenges include a lack of standardized terminology for digital decision-making tools and significant variability in environmental factors that affect sensor performance. Proprietary algorithms and device-specific barriers often hinder the verification and validation processes necessary for regulatory approval. Additionally, there is a distinct gap between granular digital features and their clinical relevance or meaningfulness to patients. Ethical concerns are emerging around data management, patient anxiety in psychiatric contexts, and the responsibility for addressing adverse events detected by remote monitoring.

Recommendations
Stakeholders should develop consensus-driven frameworks for standardized device performance reporting and environmental testing to streamline evaluations for specific contexts of use. The community should adopt a modular approach to data standards that bins requirements by concept of interest and disease-specific needs. Collaborative efforts between patients and developers are essential to bridge the gap between technical metrics and meaningful aspects of health. It is recommended to implement ""bring-your-own-device"" (BYOD) frameworks that ensure data reliability while supporting the inevitable evolution of technology during long-term studies. Researchers and clinicians must be trained in the ethical, legal, and social implications of digital health technology use, particularly regarding data privacy and the management of remote-detected safety signals.

Regulatory Considerations
Digital health technologies used to collect endpoints must meet high evidentiary requirements for validation, with complexity increasing when multiple sensors or complex software are bundled. Regulatory agencies like the FDA and EMA have established pathways for the qualification of drug development tools, including biomarkers and clinical outcome assessments. Integration of new draft guidance on remote health monitoring with existing regulatory workflows is necessary to reduce uncertainty in trial evaluations. While many digital health technologies do not qualify as medical devices unless they have a specific medical purpose, synergies between device risk assessments and drug trial data integrity frameworks should be explored. Early engagement with regulators remains a critical step for obtaining feedback on novel digital endpoints and ensuring the suitability of evidentiary support.

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
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
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.

Findings
Evaluating patient outcomes on a case-by-case basis within individual drug programs is an inefficient use of resources and creates regulatory unpredictability. This approach frequently leads to redundant efforts to validate the same assessment tools across different development programs. The lack of a standardized, transparent process for accepting Clinical Outcome Assessments (COAs) hinders the development and use of novel, patient-centric endpoints, ultimately slowing the delivery of therapies that address outcomes that matter most to patients.

Recommendations
Developers of COAs, including patient groups, academic researchers, and pharmaceutical sponsors, are encouraged to collaborate with the FDA through the qualification program. This engagement should occur early to ensure that the measures are developed with sufficient rigor to meet regulatory standards. Stakeholders should leverage the program to validate a wide range of COAs, particularly Patient-Reported Outcomes (PROs), making them publicly available to advance patient-focused drug development across the entire industry and reduce redundant validation work.

Regulatory Considerations
The COA Qualification Program offers a formal regulatory pathway for the FDA to review and accept a COA for a specific Context of Use (COU). This qualification is separate from the review of an individual drug application, making the validated tool accessible for any sponsor to use in their clinical trials without re-adjudicating the COA's fitness for that purpose. Qualification requires a comprehensive submission demonstrating the measure is well-defined and reliable, ensuring that it appropriately captures the patient's experience or functional status.

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
Limited evidence of clinical validity from pilot trials due to cost, time, and regulatory complexities.
Lack of standards for data integration across different tools and platforms.
Potential biases introduced by pre-existing algorithms.
Opaque data processing methods in BioMeTs.

Recommendations
Develop data, hardware, and software standards for BioMeTs.
Improve regulations for data rights, access, privacy, and governance.
Provide guidance on analytical methodologies for BioMeT data validation.

Regulatory Considerations
Early regulatory interactions with agencies like the FDA and EMA.
Ensuring data quality, integrity, reliability, and robustness.
Understanding regulatory pathways for BioMeTs in clinical trials.

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
Digitally-derived endpoints must align with trial goals, reflect the concept of interest (COI), and demonstrate clinical relevance.
Validation involves both verification of the digital tool's performance and ensuring the endpoint measures what it claims to measure.
Early-phase trials should assess usability, tolerability, and data privacy to ensure tools are operationally feasible for the intended population.
Regulatory alignment on endpoints, including their ability to demonstrate meaningful change, is critical before pivotal trials.
Statistical analysis plans must account for the unique aspects of digital endpoints, such as data quality and missing data considerations.

Recommendations
Define target populations and meaningful aspects of health (MAH) early in development to guide endpoint selection.
Conduct gap assessments of existing endpoints and propose clinically meaningful differences for patient outcomes.
Validate digital tools through verification (e.g., accuracy, reliability) and usability studies specific to the intended population.
Engage with regulators to align endpoints with evidentiary requirements for pivotal trials and label claims.
Prepare statistical plans and supporting evidence to justify the inclusion of digitally-derived endpoints in pivotal trials.

Regulatory Considerations
Verification and validation of DHTs should meet FDA and EMA standards, ensuring endpoints are fit-for-purpose and clinically relevant.
Align endpoints with regulatory requirements, demonstrating meaningful change that reflects treatment benefit.
Compile evidence of clinical validation, including how endpoints detect meaningful changes during treatment.
Address privacy, scalability, and operational feasibility to meet regulatory expectations for pivotal trials.
Consult regulatory guidance documents, such as FDA’s draft guidance on DHTs for remote data acquisition and EMA's methodologies for drug development.

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
Patient-centeredness is a key criterion, with higher importance assigned to endpoints identified by patients as meaningful.
The tool includes predefined weighting criteria to ensure that endpoints addressing unmet needs are prioritized.
A structured rating scale facilitates the comparison of novel endpoints across different therapeutic areas.
Digital measurement technologies, such as wearables and ePROs, are increasingly considered viable novel endpoints.
The tool provides a standardized approach to endpoint selection but requires user input to tailor scores to specific trial needs.

Recommendations
Clinical researchers should use the tool to systematically evaluate novel endpoints before incorporating them into study designs.
Weighting criteria should be adapted based on the specific therapeutic area and patient population to reflect real-world priorities.
Endpoint selection should incorporate regulatory and scientific considerations to ensure alignment with study objectives.
Digital health technologies should be leveraged where appropriate to support novel endpoint validation and implementation.
Stakeholder engagement, including patient advocacy groups, should be integrated into the endpoint selection process.

Regulatory Considerations
Novel endpoints should align with FDA and regulatory body expectations for evidence generation and validation.
The tool does not replace regulatory guidance but can serve as a structured framework for early-phase endpoint assessment.
Sponsors should document endpoint selection rationale in submissions to regulatory agencies.
Digital health endpoints should comply with data integrity and privacy regulations, including HIPAA and GDPR.
Ongoing validation and post-market evidence generation may be required for novel digital endpoints used in pivotal trials.