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 most promising aspects of mental health for digital measurement are sleep, physical activity, stress, and social behavior, which have the strongest scientific evidence. Core barriers to adoption include high cost and limited access, data privacy concerns, poor technological literacy, and a lack of technology adaptation for specific mental health needs. Essential technology characteristics for "fit-for-purpose" sDHTs include usability, reliable performance, strong data privacy and security, and long battery life.

Recommendations
Research and development should prioritize moving promising measures (sleep, activity, stress, social behavior) to large-scale clinical trials. Algorithms must be refined and clinically validated for mental health indications, and new sensor modalities should be explored. Infrastructure must be developed by creating standards and ontologies for mental health sensor data to ensure interoperability and scalability. To improve access and equity, financial support mechanisms and inclusive, culturally tailored design are critical.

Regulatory Considerations
The report does not provide a separate section for "Regulatory Considerations" but emphasizes that future development and funding should prioritize clinical validation across diverse populations. It notes the importance of a clear understanding of the intended measurement claims and the need for rigorous validation studies to move beyond pilot and feasibility stages to demonstrate real-world clinical utility.

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
Patient experience data provides critical context for regulatory review by illuminating disease burden, unmet medical needs, and the aspects of a condition that matter most to patients.
A systematic approach is necessary to ensure patient experience data is robust enough for regulatory consideration, moving beyond anecdotal evidence to scientifically rigorous data collection.
Early engagement between sponsors and the FDA is a key factor for successfully incorporating patient perspectives into a drug development program.
The value of patient-reported outcomes (PROs) and other clinical outcome assessments (COAs) is highly context-dependent, varying significantly across different diseases and patient populations.

Recommendations
Drug sponsors should leverage the FDA's meeting process to discuss their strategies for collecting and submitting patient experience data early in the development lifecycle.
Sponsors should utilize the repository of meeting reports as a learning resource to understand best practices and common challenges in patient-focused drug development for specific conditions.
Patient advocacy groups should actively participate in these discussions to ensure the full spectrum of patient experiences is captured and communicated to both regulators and developers.
Researchers should develop and validate novel tools and methodologies for capturing and analyzing patient experience data that are meaningful for both clinical and regulatory purposes.

Regulatory Considerations
Patient experience data is a key component of the benefit-risk assessment, providing evidence that can inform regulatory decisions regarding a drug's approval and labeling.
The FDA's review of patient experience data is guided by a commitment to patient-focused drug development, as mandated by the 21st Century Cures Act and supported by user fee agreements like PDUFA.
The scientific rigor of data collection and analysis is paramount; for patient experience data to be influential, it must meet high standards of validity and reliability.
Transparency is a core principle, and the publication of these meeting reports is intended to provide clear examples of how patient input can be effectively integrated into regulatory submissions.

Findings
The document introduces a risk-based credibility assessment framework for establishing and evaluating the credibility of an Artificial Intelligence (AI) model's output when used to support regulatory decisions regarding drug safety, effectiveness, or quality. The framework outlines a 7-step process beginning with defining the question of interest and the Context of Use (COU). Credibility is defined as trust, established through evidence, in the AI model's performance for a particular COU. The credibility assessment is tailored to the AI model risk, which is a combination of model influence (the AI model's evidence contribution relative to other evidence) and decision consequence (the significance of an adverse outcome from an incorrect decision). The document highlights challenges with AI use, including variability in development datasets (training/tuning), the need for methodological transparency due to model complexity, difficulty in quantifying and interpreting uncertainty in model output, and the potential for performance change over time (data drift), which necessitates life cycle maintenance.

Recommendations
Sponsors and interested parties should define the question of interest and clearly define the COU, detailing the AI model's specific role and scope and whether other information will be used. They should assess the AI model risk (low, medium, or high) to ensure that subsequent credibility assessment activities (Step 4) are commensurate with that risk and tailored to the COU. For Step 4, the credibility assessment plan should include a description of the model, model development process (including inputs, architecture, feature selection, and rationale), and data used (training and tuning data). Development data must be deemed fit for use (relevant and reliable) to mitigate issues like algorithmic bias. The plan should also detail the model evaluation process using independent test data and include performance metrics with confidence intervals, an estimate of uncertainty, and a description of model limitations. Early engagement with the FDA is strongly encouraged to discuss model risk and the adequacy of the credibility assessment plan.

Regulatory Considerations
The risk-based credibility assessment framework is intended to help organize and document information for regulatory submissions. The required stringency of assessment activities and the level of documentation should be commensurate with the AI model risk. For AI models whose performance can change over time (e.g., in pharmaceutical manufacturing or postmarketing), sponsors must implement life cycle maintenance plans to monitor performance and manage changes in a risk-based manner. Changes to AI models should be evaluated through the manufacturer's change management system and may require re-execution of parts of the credibility assessment plan. Early engagement can be facilitated through formal meetings (e.g., Pre-IND) or other specialized programs listed in the guidance, such as the Center for Clinical Trial Innovation (C3TI), the Model-Informed Drug Development (MIDD) Paired Meeting Program, and the Emerging Technology Program (ETP) or Advanced Technologies Team (CATT).

Findings
Cybersecurity is an integral part of medical device safety and effectiveness, and manufacturers are responsible for addressing it throughout the entire device lifecycle. The FDA considers a device's cybersecurity as part of its benefit-risk assessment for both premarket and postmarket activities. A lack of robust cybersecurity controls can lead to patient harm, compromised device functionality, and breaches of data privacy. The dynamic nature of cybersecurity threats requires ongoing monitoring, risk management, and timely implementation of mitigation strategies.

Recommendations
Manufacturers should build cybersecurity into devices from the design phase ("secure by design") and conduct a thorough risk analysis to identify and mitigate potential vulnerabilities. Premarket submissions should include comprehensive documentation of the device's cybersecurity controls, a risk management plan, and a plan for postmarket surveillance and response. Manufacturers should establish a robust postmarket surveillance program to monitor for, identify, and address new cybersecurity threats in a timely manner. Clear and informative labeling is essential to help users understand and manage cybersecurity risks.

Regulatory Considerations
The FDA has the authority to take action against devices with inadequate cybersecurity that pose a risk to public health. The agency recommends that manufacturers use the Q-submission process to discuss specific cybersecurity questions related to their device submissions. Compliance with recognized standards and best practices for cybersecurity is strongly encouraged. Manufacturers must report certain cybersecurity incidents to the FDA as part of their postmarket reporting requirements. The FDA collaborates with other government agencies and stakeholders to promote a coordinated approach to medical device cybersecurity.

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
The DHCoE works to strategically advance science and evidence for digital health technologies (DHTs).
Key areas of focus include Artificial Intelligence / Machine Learning (AI/ML) in Software as a Medical Device (SaMD), Cybersecurity, Augmented Reality (AR) and Virtual Reality (VR), and Wireless Medical Devices.
The DHCoE develops and publishes Guidances with Digital Health Content and maintains a Digital Health Policy Navigator to provide clarity on regulatory policies.
Digital health technologies are acknowledged as having the potential to facilitate decentralized clinical trial activities and allow for continuous or frequent measurements of clinical features remotely.
Programs and initiatives include the Software Precertification (Pre-Cert) Pilot Program, the Regulatory Accelerator, and the Diagnostic Data Program.
The center is also involved in international harmonization on device regulatory policy and standards.

Recommendations
The DHCoE recommends that stakeholders, including sponsors and DHT manufacturers, engage with the agency early to discuss the use of DHTs in drug development or for decentralized clinical trials (DCTs).
Stakeholders are encouraged to use the Digital Health Policy Navigator tool to assess whether a particular software function meets the device definition and is the focus of FDA oversight.
The DHCoE emphasizes the need for a patient-centered approach for AI/ML-enabled devices that considers issues like usability, equity, trust, and accountability, and promotes transparency.

Regulatory Considerations
The DHCoE's work includes innovating the regulatory paradigm for digital health, moving towards models that may include shifting scrutiny from the pre-market to the post-market phase and focusing on the capability of firms (Software Pre-Cert Pilot Program).
The FDA has committed, as part of PDUFA VII, to activities such as publishing a Framework for the Use of DHTs in Drug and Biological Product Development and establishing a DHT Steering Committee.
The center provides information to help determine the regulatory status of various digital health products, such as Software as a medical device (SaMD), mobile medical applications (MMA), and General Wellness products.
Submissions for products with device software functions must include recommended documentation for the FDA's evaluation of safety and effectiveness.
For questions regarding upcoming premarket submissions, stakeholders are directed to contact the appropriate review division through a Q-submission.

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
Cytokine Release Syndrome (CRS) is a common and potentially life-threatening adverse event of immunotherapies, particularly in Oncology, complicating patient care and increasing healthcare costs. Standard-of-care inpatient monitoring for CRS is manual, intermittent, costly, and restrictive, providing an incomplete view of the syndrome’s development and progression. The use of Digital Health Technologies (DHTs) for continuous, remote monitoring of vital signs (like heart rate, respiratory rate, skin temperature, SpO2, and activity) can capture early indicators of CRS up to two hours earlier than standard episodic monitoring. This ability to collect multivariate continuous data is valuable for informing robust model development for CRS risk prediction.

Recommendations
Investigators should deploy DHTs available today to monitor vital signs and symptoms currently observed in the hospital setting, but in an outpatient or home environment. The goal is to develop Early Warning Products that assess the probability of developing CRS, providing clinical decision support. Product developers should follow a strategic roadmap that outlines milestones for building products that are clinically relevant and commercially viable. Researchers should use a common set of digital clinical measures to gather high-quality datasets and ensure comparability across studies to build more robust predictive models. Predictive algorithms should be built on a robust reference measure for analytical validation and be clinically validated with sufficient data.

Regulatory Considerations
The resources are designed to help developers build products that are clinically appropriate, regulatory-acceptable, and commercially viable. Future regulatory submissions for CRS de-risking products will benefit from aligning with this industry-wide dialogue that is being built in collaboration with the FDA. Developing a robust CRS safety biomarker could enhance the safety profile of clinical trials, increase trial access, and streamline regulatory decision-making, possibly through a qualification pathway. Products that aim for a higher level of autonomy, such as a Diagnostic that redefines current CRS grading classes, may require very high clinical evidence and likely stringent regulatory review.

Findings
Clinically Acceptable Performance: A clinical study demonstrated that the weekly AFib burden estimates from the Apple AFib History Feature were in close agreement with a reference ECG patch, with an average difference of just 0.67%. The vast majority of measurements had paired differences within ±10% of the reference device.
Generalizable Across Subgroups: The device's accuracy was similar across various subgroups, including different sexes, races, ages, and skin tones.
Performance Post-Ablation is Uncertain: In a small subgroup of patients with a prior cardiac ablation, the device's performance, while still strong, showed slightly more variability and exceeded a pre-specified acceptance criterion. The study was not designed or powered to demonstrate equivalent performance in this specific group.
Technical Limitations Exist: The feature only provides a retrospective weekly estimate and does not give specific timestamps or durations of AFib episodes. It also does not detect other atrial tachyarrhythmias, like atrial flutter.

Recommendations
Appropriate Use: The document implicitly recommends using the tool precisely within its qualified context of use—as a secondary, not primary, endpoint for comparing AFib burden between study arms in cardiac ablation device trials.
Supplemental Data Collection: For studies involving patients who have had a prior ablation, it would be beneficial to assess the tool alongside other methods of determining AFib burden to better characterize its performance in this population.
Define Study-Specific Endpoints: Investigators using the tool are responsible for defining and justifying their specific study designs and what constitutes a clinically significant reduction in AFib burden.

Regulatory Considerations
MDDT Qualification: The Apple AFib History Feature is officially qualified by the FDA as a Medical Device Development Tool (MDDT), which reduces the burden on device developers, as they no longer need to independently justify its methodology for collecting weekly AFib burden estimates in their clinical studies.
Secondary Endpoint Only: A key limitation for its regulatory use is its qualification only as a secondary endpoint. It cannot, by itself, be used to evaluate the primary safety and effectiveness of cardiac ablation devices. This is partly because FDA typically requires the inclusion of any atrial tachyarrhythmia (not just AFib) for defining ablation success in pivotal studies.
Not a Replacement for Primary Endpoints: The tool's utility is intended to provide supplemental data and help better understand post-treatment AFib burden; it is not meant to replace more clinically well-defined primary endpoints.