Findings
Verification involves testing the accelerometer's technical specifications (e.g., accuracy and precision) through peer-reviewed studies.
Validation of the algorithm relies on "ground truth" data, gathered through infrared video recordings and manual scoring of movements.
Cross-validation was used to assess the algorithm's performance, with additional validation in independent samples planned.
The separation of verification and validation allows greater flexibility, enabling the algorithm's use with multiple accelerometer devices that meet minimum standards.

Recommendations
Conduct separate verification and validation processes to ensure the reliability of both the device and the algorithm.
Use peer-reviewed publications to document the performance of DHTs and their limitations.
Ensure validation includes testing with representative populations to confirm the algorithm’s utility across diverse contexts.
Promote industry-wide standards to facilitate scalability and regulatory acceptance of DHTs in clinical trials.

Regulatory Considerations
Ensure DHTs undergo rigorous verification to meet accuracy and precision standards documented in peer-reviewed studies.
Validate algorithms using empirical "ground truth" data to demonstrate their ability to measure clinically meaningful outcomes.
Align the design and validation of DHTs with regulatory expectations for reliable and transferable performance across devices.

Findings
DHT selection should be guided by the trial's scientific goals, unmet needs, and potential to reduce participant burden.
Verification ensures the DHT accurately measures physical parameters, while validation confirms it reliably captures the desired clinical outcomes.
Conducting feasibility studies is essential to identify potential usability or compliance issues before full trial implementation.
Clear communication, training, and support plans for participants and sites are critical to the success of DHT-enabled trials.
Operational challenges, including DHT malfunctions, must be anticipated with robust management and mitigation plans.

Recommendations
Define Measurement Goals: Identify the scientific and patient-centered needs driving the decision to use DHTs.
Specification-Driven Selection: Tailor DHT selection based on technical performance, trial needs, and participant preferences, collaborating with manufacturers for transparency.
Verify and Validate Technologies: Conduct both verification and validation processes in controlled and real-world settings, focusing on the target population.
Pilot Feasibility Studies: Test the DHT in small-scale studies to assess usability, compliance, and real-world functionality.
Operational Planning: Develop detailed standard operating procedures (SOPs) for managing DHTs, addressing potential malfunctions, and supporting participants.

Regulatory Considerations
Regulatory status should not solely determine DHT selection; instead, focus on its fit-for-purpose performance in the trial context.
Maintain transparency with manufacturers to document DHT performance characteristics and limitations for regulatory submissions.
Validate endpoints and DHT data to meet evidentiary standards required by regulatory agencies.
Ensure clear roles and responsibilities for managing DHTs to align with regulatory compliance requirements.
Address interoperability, data privacy, and security concerns to adhere to ethical and legal standards in clinical trials.

Key Principles (Findings)
The central principle illustrated is that the regulatory pathway for data from a Digital Health Technology (DHT) depends on its role in the clinical trial. The flowchart shows that not all data collected via DHTs requires pre-market submission to the FDA. A critical decision point is whether the DHT itself meets the definition of a medical device and whether the data it generates will be used to support a labeling claim. The framework clarifies that the context of use is the primary determinant for the required regulatory interactions.

Recommended Actions (Recommendations)
The flowchart recommends a sequential decision-making process for sponsors. First, determine if the DHT is a medical device. Based on that outcome, the sponsor is guided to assess if the data will be used to evaluate a primary or secondary endpoint. The ultimate recommendation of the flowchart is that sponsors should formally engage with the FDA through mechanisms like the pre-submission (Q-Submission) process to gain clarity and feedback on their specific use case, particularly when the DHT data is intended to support a primary or secondary endpoint.

Regulatory Considerations
The flowchart visualizes key regulatory considerations for sponsors. It highlights that if a DHT is considered a medical device and is being used to support a pivotal trial, an Investigational Device Exemption (IDE) may be required. The data generated from the DHT is typically submitted to the FDA as part of an Investigational New Drug (IND) application, a New Drug Application (NDA), or a Biologics License Application (BLA). The resource makes it clear that even if a DHT itself does not require FDA clearance or approval, the data it generates is subject to FDA review when submitted in support of a medical product.

Findings
The MS digital health space is still largely uncharted.
Balancing the needs and desires of different users when creating a digital solution is challenging.
Insufficient adherence to remote digital health solutions presents a challenge to long-term engagement.
Creating a digital solution that is both meaningful to end users and aligned with regulatory standards involves challenges and compromises.

Recommendations
Employ an iterative development process to continually refine digital health solutions.
Collaborate closely with healthcare professionals and patients throughout the design process.
Use behavioral science strategies to enhance user engagement and adherence.
Ensure that digital solutions are scientifically robust and meet regulatory standards.
Implement a prescription-based model to improve adherence and integration into clinical practice.

Regulatory Considerations
Conduct technical verification and clinical validation for each assessment in digital health solutions.
Ensure data privacy and cybersecurity measures are robust and comply with local regulations.
Maintain ongoing post-marketing surveillance to monitor safety and effectiveness.
Adapt solutions to meet diverse regulatory requirements across different geographies.

Findings
Limited research on sleep disturbances in RA due to lack of suitable technologies.
Current assessment methods like PSG are not scalable for large studies.
Clinician-reported outcomes may not accurately reflect patients' daily lives due to recall bias.

Recommendations
Incorporate patient input early in the development process.
Select appropriate sensors and ensure they are analytically validated against reference standards.
Develop a regulatory-guided pathway for achieving clinical acceptance of NDEs.

Regulatory Considerations
Understand the pathways offered by FDA and EMA for developing NDEs.
Ensure transparency in the qualification process.
Recognize that agencies qualify the measure or endpoint, not the digital health technology tool itself.

Findings
Verification and validation are crucial for ensuring the accuracy and precision of DHT measurements.
Not all PD manifestations are easily measurable by DHTs; some require in-person assessments.
There is inconsistency in outcomes measured and methods of assessing validity.
Many DHT measurements are still in early research stages and need larger sample sizes.
Shortfalls in sensor specificity suggest a need for multiple measurement modalities.

Recommendations
Use multiple modalities of measurement to improve specificity.
Engage patients early to determine relevant endpoints for functional measurements.
Ensure DHTs are user-friendly and safe for participants.
Conduct randomized and blinded trials to confirm sensor effectiveness.
Consider decentralized trials to facilitate participation for patients with mobility challenges.

Regulatory Considerations
Ensure safety and welfare of subjects through FDA regulations.
Randomized and blinded trials are essential for confirming sensor-detected effects.
Decentralized trials can improve participation by overcoming mobility challenges.

Findings
The lack of standardization in vendor onboarding processes increases operational inefficiencies for sponsors and vendors.
Essential topics such as data security, quality management systems (QMS), and validation studies are under-addressed in ad hoc vendor assessments.
Cybersecurity and patient data privacy, especially compliance with GDPR, HIPAA, and global regulations, require enhanced focus during vendor evaluations.
Tailoring vendor assessments to specific trial requirements and patient populations is critical for effective implementation of digital health tools.
Greater collaboration between sponsors and vendors can improve operational alignment and mitigate risks during trials.

Recommendations
Utilize the 13 vendor assessment categories as a baseline for customizing questionnaires to meet specific project needs.
Establish standardized templates for evaluating data privacy, regulatory compliance, and patient-facing user experience.
Prioritize cybersecurity measures, including penetration testing, access management, and encryption standards, as a core assessment criterion.
Implement continuous feedback loops during vendor selection and onboarding to refine assessment processes and address emerging risks.
Encourage industry collaboration to evolve and expand the open-source framework based on practical implementation experiences.

Regulatory Considerations
Ensure all vendors adhere to relevant global standards, including 21 CFR Part 11, GDPR, and HIPAA, for data security and compliance.
Verify the regulatory status of medical devices and algorithms used in digital health solutions, including certifications such as ISO 13485 and IEC 62304.
Require documentation of informed consent processes and adherence to regional data protection regulations for patient data handling.
Align vendor capabilities with regulatory guidelines for clinical trial endpoints, emphasizing validation studies and clinical relevance.
Maintain transparent and audit-ready documentation for inspections and compliance verifications.

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 biomarkers allow continuous monitoring of PD symptoms in naturalistic settings, offering more patient-specific insights than traditional intermittent assessments.
Existing biomarker studies, such as PPMI and WATCH-PD, demonstrate promise but face limitations in sample sizes, device diversity, and data harmonization.
Current gaps include limited focus on non-motor symptoms, lack of collaboration across studies, and insufficient validation of digital biomarkers using gold-standard references.
The use of digital phenotyping to integrate clinical, behavioral, and neurophysiological data offers a transformative approach to disease monitoring.
Advanced analytics, such as AI and machine learning, are critical for deriving meaningful insights from large, diverse datasets but require rigorous validation.

Recommendations
Enhance Patient Engagement: Prioritize patient-centric outcomes, including user-friendly devices and symptom domains that reflect patient needs across the disease spectrum.
Foster Global Collaborations: Align efforts across stakeholders, including patient advocacy groups, technology developers, and regulators, to reduce duplication and accelerate progress.
Develop Data Standards: Create and adopt metadata frameworks to harmonize data collection and enable integration across studies and devices.
Validate Algorithms: Ensure analytical and clinical validation of machine learning algorithms to minimize the impact of confounding factors and enhance reliability.
Promote Open Science: Encourage data sharing and transparency to facilitate collaboration and build on existing research, with patient consent as a cornerstone.

Regulatory Considerations
Early Engagement with Regulators: Work with FDA and EMA to align on evidence requirements for validation and qualification of digital biomarkers.
Anchor-Based Validation: Use reference measures to establish clinical validity, particularly when introducing novel digital endpoints.
Focus on Generalizability: Validate digital biomarkers across diverse populations and settings to ensure broad applicability and acceptance.
Integrate Digital Biomarkers into Trials: Leverage digital endpoints to complement traditional measures in clinical trials, enhancing sensitivity and reducing patient and site burden.
Define Regulatory Standards: Collaborate with regulatory bodies to develop guidelines for the evaluation and approval of digital biomarkers.

Findings
A unified digital measurement lexicon is critical for clear communication across stakeholders during development and evaluation processes.
Stakeholder and patient engagement is essential for identifying meaningful aspects of health (MAH) and defining context-specific concepts of interest (COI).
Establishing proof of concept via observational studies or exploratory trial phases de-risks investment and demonstrates feasibility.
Evaluation frameworks such as V3 ensure that digital measures meet analytical and clinical validation requirements, even in the absence of established comparators.
The lack of robust comparators in underserved conditions creates both challenges and opportunities for the development of novel digital measures.

Recommendations
Engage Early and Continuously: Involve patients, caregivers, clinicians, and other stakeholders early to identify MAH and COI that align with patient needs and trial objectives.
Adopt V3 Framework: Follow the V3 process for verification, analytical validation, and clinical validation to ensure measures are fit for purpose.
Design Iterative Proof of Concept Studies: Use small-scale studies or exploratory trial phases to validate the technical and clinical feasibility of digital measures.
Seek Early Regulatory Engagement: Initiate discussions with regulatory agencies (e.g., FDA or EMA) early in the evaluation process to refine applications and address potential challenges.
Collaborate Across Stakeholders: Foster multi-stakeholder collaboration to pool expertise and resources, especially for challenging therapeutic areas or underserved populations.

Regulatory Considerations
Use tools like FDA's Drug Development Tool Qualification Program or EMA's Innovation Task Force to refine evidence requirements and address legal or technical challenges.
Understand COI and COU: Tailor digital measures to specific contexts of use and intended applications, which determine whether measures are classified as biomarkers or COAs.
Demonstrate that digital measures are valid and reliable through rigorous analytical and clinical validation studies.
Use longitudinal clinical studies to gather evidence supporting the use of digital measures for regulatory decision-making.
Work within consortia to align standards and generate shared evidence, particularly for challenging use cases or rare conditions.

Findings
SV95C allows continuous, objective assessment of ambulation in real-world settings, addressing biases and limitations of hospital-based assessments.
Wearable devices like ActiMyo® reduce patient and caregiver burden by enabling remote monitoring and decentralized clinical trials.
Digital endpoints like SV95C improve trial efficiency, potentially reducing required sample sizes and trial durations in rare diseases like DMD.
Regulatory qualification requires robust validation data, including comparisons with traditional measures, sensitivity to change, and precision.
Adoption of digital endpoints is dependent on stakeholder collaboration, patient engagement, and alignment with regulatory requirements.

Recommendations
Collaborate with regulatory bodies (e.g., EMA, FDA) early to align expectations for validation and qualification processes.
Focus on Patient-Centric Design: Develop wearable devices and endpoints with input from patients and caregivers to ensure usability and relevance to daily life.
Establish Robust Validation Protocols: Generate comprehensive data on precision, reliability, and sensitivity to change, including anchor-based approaches.
Provide training for patients, caregivers, and clinicians to enhance compliance and minimize missing data during trials.
Leverage Multi-Stakeholder Collaboration: Encourage partnerships among technology developers, drug developers, and patient groups to build normative datasets and refine measures.

Regulatory Considerations
Follow frameworks like the EMA qualification opinion process and FDA Drug Development Tools COA Qualification Program for validation.
Ensure validation studies demonstrate precision, reliability, and sensitivity to clinical changes, with comparisons to gold-standard assessments.
Use approaches that relate digital endpoint changes (e.g., SV95C) to meaningful clinical outcomes like loss of ambulation or other qualified measures.
Expand validation to include younger and nonambulant patients, ensuring endpoints are applicable across a broad spectrum of disease severity.
Adhere to Good Clinical Practice (GCP) and data protection regulations to ensure patient safety and trust.

Findings
A well-defined concept of interest (COI) and context of use (COU) are critical to selecting meaningful endpoints.
Digital tools must undergo usability assessments, verification of system performance, and validation against reference standards.
Measurement approaches should be tailored to reflect treatment benefits and detect meaningful changes.
Early and frequent regulatory engagement is essential throughout the development process.
Integration of validation for both the measure and the technology ensures alignment with clinical and operational goals.

Recommendations
Define the target population and conceptualize the treatment benefit and COU early in the development process.
Identify meaningful aspects of health and COIs that are relevant to patients and clinicians.
Evaluate and select digital tools based on evidence of usability, verification, and validation.
Engage with regulators early and often to align endpoints with evidentiary and regulatory requirements.
Finalize endpoint validation, including meaningful change thresholds and data analysis plans, to ensure readiness for pivotal trials.

Regulatory Considerations
Validate the selected digital health technology to meet performance standards for regulatory acceptance.
Demonstrate that the measurement reflects the intended COI and is effective in detecting meaningful change.
Obtain regulatory confirmation that the endpoint is aligned with requirements for pivotal trials and potential label claims.
Address usability and operational concerns, ensuring the DHT is acceptable and feasible for the trial population.
Use prior evidence where applicable, while integrating new validation data to support regulatory engagement.