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 Integration of new draft guidance on remote health monitoring with existing regulatory workflows is necessary to reduce uncertainty in trial evaluations.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 traditional medical device regulatory paradigm is not designed for the adaptive nature of AI/ML technologies, which can learn and change after they are on the market. A key benefit of AI/ML is its ability to improve performance by learning from real-world data, but this also presents a unique regulatory challenge. To ensure patient safety and device effectiveness, a new, flexible regulatory framework is required that can accommodate these iterative improvements. Transparency and robust monitoring are essential to manage the risks associated with evolving algorithms.

Recommendations
The FDA proposes a "Predetermined Change Control Plan" (PCCP) to be included in premarket submissions. This plan would specify the anticipated modifications to the device (the "what") and the methodology for implementing and validating those changes (the "how"). The development of "Good Machine Learning Practice" (GMLP) is encouraged to ensure that AI/ML algorithms are developed and validated using best practices. Manufacturers should implement robust real-world performance monitoring to ensure that their devices remain safe and effective after deployment.

Regulatory Considerations
The FDA is developing a new regulatory framework tailored to the unique aspects of AI/ML-based SaMD, which will leverage a TPLC approach. The agency has issued an "AI/ML SaMD Action Plan" that outlines its multi-pronged approach, including issuing draft guidance on PCCPs and promoting the harmonization of GMLP. The FDA is actively collaborating with stakeholders to foster innovation while ensuring patient safety. The agency maintains a public list of authorized AI/ML-enabled medical devices to enhance transparency.

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
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
Coordination challenges with multiple locations in DCTs.
Variability in data collection across decentralized locations and remote tools.
Challenges in implementing certain statistical approaches in DCTs.
Need for DHTs to be accessible and suitable for all trial participants.
Ensuring compliance with local laws and regulations.

Recommendations
Develop clear protocols for integrating decentralized elements into clinical trials, specifying remote and in-person activities.
Use digital health technologies (DHTs) and electronic systems to streamline data acquisition, informed consent, and investigational product tracking.
Provide training for all stakeholders, including trial personnel, local health care providers, and participants, on decentralized processes.
Implement robust safety monitoring plans to address adverse events in decentralized settings.
Ensure compliance with local and international laws governing telehealth, data privacy, and investigational product use.

Regulatory Considerations
Maintain compliance with FDA requirements under 21 CFR parts 312 and 812 for drug and device trials, respectively.
Document all trial activities and data flows in trial protocols and data management plans, ensuring traceability and integrity.
Ensure informed consent processes meet FDA standards and provide clear communication to participants about decentralized trial activities and data handling.
Address investigational product accountability by documenting IP distribution, storage, and return or disposal.
Design electronic systems for decentralized trials to comply with 21 CFR part 11 requirements for data reliability, security, and confidentiality.

Findings
The FDA considers electronic records and signatures equivalent to their paper counterparts when they meet the requirements of 21 CFR Part 11. Due to technological advances, electronic systems and digital health technologies (DHTs) are now integral to clinical trials, requiring a modern, risk-based approach to ensure data integrity. Sponsors remain ultimately responsible for the quality and integrity of all data submitted, even when using third-party IT service providers or data from real-world sources like EHRs. The authenticity, integrity, and confidentiality of electronic data are paramount and must be maintained through robust system controls throughout the data lifecycle.

Recommendations
Regulated entities should use a justified and documented risk-based approach to validate all electronic systems before and during a clinical trial, with the level of validation depending on the system's potential to impact participant safety and trial result reliability. Secure, computer-generated, time-stamped audit trails must be implemented to track the creation, modification, and deletion of all electronic records without obscuring original data. Robust logical and physical access controls are necessary to limit system access to authorized individuals. Entities should have written agreements with IT service providers that clearly define roles, responsibilities, and procedures for ensuring data security and long-term retention.

Regulatory Considerations
The requirements of 21 CFR Part 11 apply to all electronic records created, modified, or submitted to the FDA under predicate rules for clinical investigations, including those from foreign sites under an IND or IDE. While the FDA does not intend to assess the Part 11 compliance of external source systems like EHRs, data becomes subject to these regulations once transferred into the sponsor's electronic system. During inspections, the FDA will focus on system validation, data handling procedures, security protocols, audit trails, and documentation of sponsor oversight. Users must certify to the FDA that their electronic signatures are the legally binding equivalent of handwritten signatures.

Findings
Decentralized Clinical Trials (DCTs), which shift activities away from sites, rely heavily on technology to reduce participant burden and improve access to trials. Digital platforms are essential for this shift, providing centralized data capture, remote monitoring, and streamlined workflows. Benefits include allowing participants to be monitored remotely, which can improve self-management and clinical outcomes, and giving researchers better insight into the real-world variability of disease activity. Currently, commercial platforms are often limited in functionality and face major challenges due to a lack of interoperability and specific data standardization protocols for clinical trial platforms, making it difficult to integrate third-party modules.

Recommendations
The paper strongly recommends the adoption of unified, integrated, and DCT-specific digital platforms to fully realize the benefits of decentralization. Platform developers should adopt international standards for health data exchange, such as HL7 FHIR and CDISC standards (PRM, CDASH, ADaM), to address the lack of data standardization and improve interoperability and modularity. Platforms should incorporate features that enhance participant engagement and adherence, such as customization options, simple user interfaces (UIs), push notifications, gamification, and allowing access to participant data . Security and governance teams are paramount to manage risks associated with malware, lost devices, and ensuring compliance with local legislation and data security protocols.

Regulatory Considerations
Digital platform design must maintain digital security and compliance with local legislation and data standards. The paper notes that a fully integrated, unified digital platform in a best-case scenario would use pre-existing standards (like CDISC and HL7) to guarantee interoperability. Adopting these standards and recommendations for data sharing, privacy, and security, as recommended by organizations like the Healthcare Information and Management Systems Society, is critical for future digital components used in DCTs. Improved data integrity and accountability in platforms could be further explored using technologies like blockchain to create an immutable ledger.

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​

Findings
There is a lack of alignment in concepts, definitions, and terminology related to digital health technologies, which hinders global drug development programs.
Different regulatory agencies interpret common terms like "monitoring" differently, leading to confusion and inconsistency.
The classification of digital measures impacts evidentiary requirements and regulatory acceptance, but detailed guidance on these requirements is lacking.

Recommendations
Align terminology and definitions across stakeholders to ensure consistency in understanding and communication.
Reuse existing terms where possible to avoid unnecessary complexity.
Focus on what is measured rather than how it is measured to streamline regulatory processes.
Encourage companies and regulators to reflect on and adopt a common lexicon within their organizations.
Move quickly to address critical questions about evidence needed for validation of digital measures.

Regulatory Considerations
Regulatory authorities should apply consistent standards for all endpoints, regardless of data acquisition methods.
The classification of DHTs as medical devices or not will impact their regulatory pathway and requirements.
There is a need for dialogue with regulators to clarify source data requirements for data acquired by DHTs.

Findings
The adoption of BYOD in clinical trials has been accelerated by the COVID-19 pandemic and supportive regulatory guidance, which now recognize it as an acceptable means for remote data collection. Studies have shown high measure completion and equivalent data quality between provisioned devices and BYOD, supporting its use in diverse patient populations. Key challenges to BYOD implementation include ensuring data equivalence across a wide variety of personal devices, managing participant technical support, and addressing data privacy and security concerns. The choice between native apps and web-based solutions involves trade-offs in usability, data security, and operational complexity.

Recommendations
Sponsors should develop a clear BYOD strategy that considers the target patient population, the complexity of the required data collection, and the global regulatory landscape. A robust training and support plan is essential for both participants and site staff to ensure proper device use and troubleshooting. Sponsors should work with technology vendors to ensure their platforms are user-friendly, secure, and capable of handling data from a variety of devices. It is crucial to establish clear communication channels for participants to report technical issues and receive timely assistance.

Regulatory Considerations
Both the FDA and EMA have issued guidance that supports the use of BYOD in clinical trials, provided that data integrity, security, and privacy are maintained. Sponsors must be able to demonstrate the equivalence of data collected via BYOD with data from provisioned devices. All BYOD solutions must comply with relevant data protection regulations, such as GDPR and HIPAA. The regulatory submission should include a clear description of the BYOD strategy and a justification for its use in the trial.

Findings
Enhanced documentation is required for high-risk device software where flaws could result in serious injury or death.
Risk management plans should include robust risk assessments, including residual risk evaluations.
Verification and validation activities are critical to confirm software functionality and mitigate risks.
The lack of traceability between software design and requirements can undermine device safety and effectiveness.
Unresolved software anomalies must be carefully documented and justified based on a risk assessment.

Recommendations
Use a risk-based approach to determine whether basic or enhanced documentation levels are required for premarket submissions.
Include comprehensive risk management documentation, detailing hazard identification, risk control measures, and residual risk evaluations.
Provide detailed system and software architecture diagrams, highlighting relationships between modules and external systems.
Document unresolved software anomalies and justify their impact on safety and effectiveness using a risk-based rationale.
Align software development, configuration management, and maintenance practices with FDA-recognized standards like ANSI/AAMI/IEC 62304.

Regulatory Considerations
Adherence to 21 CFR Part 820 Quality System regulations, emphasizing design controls and risk management.
Submission of risk management files and unresolved software anomalies as part of premarket documentation.
Use of system and software architecture diagrams to demonstrate software functionality and risk mitigation.
Implementation of cybersecurity measures as part of software validation and risk management processes.
Documentation of premarket changes and interactions between device functions and external systems, particularly in multi-function devices.