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 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
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
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
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
While verification, analytical validation, and clinical validation have been well-established, usability validation has not been systematically incorporated into digital health technology evaluation.
Variability in device designs, patient populations, and regulatory environments creates barriers to widespread adoption of sensor-based digital health technologies.
Usability problems, such as poor user interfaces and technical errors, can lead to significant data loss in clinical trials and real-world applications.
While some guidance exists for usability in medical devices, there is no unified global standard for assessing usability in digital health products, leading to inconsistencies in implementation.
Stakeholders, including regulators, industry leaders, and researchers, recognize the need for usability validation to ensure the real-world effectiveness of digital health technologies.

Recommendations
Adopt the V3+ framework as a standardized method to ensure that usability is rigorously tested alongside verification, analytical validation, and clinical validation.
Establish clear protocols for usability testing, including use specification development, risk analysis, iterative formative evaluations, and summative evaluations.
Bring together regulators, technology developers, clinicians, and patients to create guidelines that ensure fit-for-purpose digital health solutions.
Work with regulatory agencies such as FDA, EMA, and MHRA to establish harmonized global standards for usability validation.
Encourage the publication of usability study results, including negative findings, to facilitate transparency and continuous improvement in digital health technologies.

Regulatory Considerations
Agencies like FDA and EMA increasingly require usability data for digital health technologies, but standardized methodologies are still evolving.
Usability validation should align with regulatory requirements for medical devices and digital biomarkers, ensuring clinical relevance and data integrity.
Digital health technologies must adhere to HIPAA, GDPR, and other data protection regulations while ensuring seamless usability.
Poor usability can lead to missing or unreliable data, which affects regulatory submissions and real-world evidence generation.
A consistent approach to usability evaluation is needed to support regulatory decision-making and digital health product approvals globally.

Findings
Usability gaps in sDHTs remain a barrier to adoption, with many technologies failing to prioritize ease of use, accessibility, and diverse user needs​
Human-centered design is critical for ensuring that digital health solutions are intuitive, functional, and scalable across different healthcare environments​
Standardized usability metrics for evaluating digital health technologies are lacking, leading to inconsistent reporting and validation of usability outcomes​
Use-related risk analysis is essential to identifying and mitigating risks associated with user errors, ensuring the safety and effectiveness of sDHTs​
The V3+ framework provides a structured approach to integrating usability validation into digital health technology development, aligning with global regulatory expectations​

Recommendations
Developers should incorporate human-centered design principles from the outset, ensuring that usability, accessibility, and user needs are central to sDHT development​
Usability validation should be standardized, with clear methodologies for measuring usability, including satisfaction, ease of use, efficiency, and error mitigation​
Regulatory and clinical stakeholders should collaborate on defining best practices for usability evaluation, ensuring that digital endpoints are both meaningful and scalable​
Risk analysis should be iterative, with developers continuously refining their technologies based on real-world user feedback and testing​
The usability validation component of V3+ should be widely adopted to ensure that digital clinical measures meet patient-centered, regulatory, and technical expectations​

Regulatory Considerations
Regulators are emphasizing the need for usability validation to ensure that digital endpoints are both clinically relevant and patient-friendly​
sDHTs must comply with human factors engineering guidelines, aligning with global regulatory frameworks such as ISO 9241-210 and FDA usability requirements​
Data security, privacy, and interoperability must be ensured, particularly as sDHTs become integrated into remote monitoring and decentralized clinical trials​
Real-world evidence (RWE) should support usability validation, helping to bridge the gap between regulatory approval and real-world adoption​
Regulatory bodies should work toward standardizing usability testing methodologies, ensuring consistency across clinical research, digital endpoints, and medical device evaluations​

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
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 V3+ Use Specification must contain a detailed description of the user groups, use environments, and the sDHT user interface. The user groups include end-users (individuals from whom data is captured) as well as carepartners, clinicians, researchers, and administrators. Characteristics of users (e.g., demographics, literacy, physical/cognitive capabilities, disease characteristics) and use environments (e.g., temperature, network availability, clutter) must be considered for risk management .

Recommendations
Developers must follow these four steps to create the Use Specification:
Identify all user groups: Create a list of users, including sub-categories (e.g., different types of researchers), and describe the characteristics of each group (e.g., health literacy, physical capabilities) to create detailed descriptions of representative users.
Identify all likely use environments: Create a list of typical environments (e.g., Home, Hospitals) and describe their characteristics (e.g., temperature, noise, network availability), also considering "edge cases" (e.g., extreme weather).
Describe the sDHT user interface: Detail all aspects of the hardware and software (visual, auditory, tactile cues), accessories (e.g., packaging, chargers), and all written materials and training (e.g., instructions for use, helpdesk troubleshooting).
Keep it up to date: The Use Specification is a living document that requires ongoing updates and maintenance throughout the sDHT development and usability validation process.

Regulatory Considerations
The development of the Use Specification is presented as the foundational step for the usability validation component of the V3+ framework. This document directly informs the subsequent Use-Related Risk Analysis.

Findings
Usability gaps in sDHTs remain a barrier to adoption, with many technologies failing to prioritize ease of use, accessibility, and diverse user needs​
Human-centered design is critical for ensuring that digital health solutions are intuitive, functional, and scalable across different healthcare environments​
Standardized usability metrics for evaluating digital health technologies are lacking, leading to inconsistent reporting and validation of usability outcomes​
Use-related risk analysis is essential to identifying and mitigating risks associated with user errors, ensuring the safety and effectiveness of sDHTs​
The V3+ framework provides a structured approach to integrating usability validation into digital health technology development, aligning with global regulatory expectations​

Recommendations
Developers should incorporate human-centered design principles from the outset, ensuring that usability, accessibility, and user needs are central to sDHT development​
Usability validation should be standardized, with clear methodologies for measuring usability, including satisfaction, ease of use, efficiency, and error mitigation​
Regulatory and clinical stakeholders should collaborate on defining best practices for usability evaluation, ensuring that digital endpoints are both meaningful and scalable​
Risk analysis should be iterative, with developers continuously refining their technologies based on real-world user feedback and testing​
The usability validation component of V3+ should be widely adopted to ensure that digital clinical measures meet patient-centered, regulatory, and technical expectations​

Regulatory Considerations
Regulators are emphasizing the need for usability validation to ensure that digital endpoints are both clinically relevant and patient-friendly​
sDHTs must comply with human factors engineering guidelines, aligning with global regulatory frameworks such as ISO 9241-210 and FDA usability requirements​
Data security, privacy, and interoperability must be ensured, particularly as sDHTs become integrated into remote monitoring and decentralized clinical trials​
Real-world evidence (RWE) should support usability validation, helping to bridge the gap between regulatory approval and real-world adoption​
Regulatory bodies should work toward standardizing usability testing methodologies, ensuring consistency across clinical research, digital endpoints, and medical device evaluations​

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​