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.

Findings
Measurement variability arises from different wearable sensor placements, algorithms, and environmental contexts.
Standardized ontologies are needed to ensure consistency in physical activity measurement across digital health studies.
Regulatory agencies, including the FDA, have endorsed specific digital measures such as MVPA as clinical trial endpoints.
Advances in sensor technology and data analysis have improved the feasibility of measuring real-world physical activity with high accuracy.
Additional validation efforts are required for postural sway measures, as current technologies primarily rely on force plates and laboratory-based assessments.

Recommendations
Researchers and developers should adopt standardized ontologies to enhance the comparability of digital measures in clinical research.
Sensor placement and algorithm transparency must be considered to minimize measurement variability in digital endpoints.
Stakeholders should engage with regulatory bodies early to ensure that digital biomarkers meet evidentiary requirements for clinical trials.
Digital health technology developers should prioritize usability and patient-centered design to increase adoption and adherence.
Further research is needed to expand real-world applicability and validation of postural sway measures for clinical and therapeutic use.

Regulatory Considerations
FDA has recognized certain digital measures, such as time spent in MVPA, as valid clinical trial endpoints.
Digital measures used in clinical research should align with HL7 and industry standards for interoperability and data integrity.
Transparency in data processing, including raw data versus processed metrics, is essential for regulatory acceptance.
Developers must ensure compliance with data privacy regulations when collecting real-world physical activity data.
Post-market monitoring of digital endpoints is recommended to ensure continued accuracy and reliability in diverse patient populations.

Findings
Cybersecurity threats in healthcare are increasingly frequent and severe, posing risks to device safety and clinical care.
Many vulnerabilities arise from third-party software components and interconnected device ecosystems.
Legacy devices often lack adequate cybersecurity controls, leading to increased patient and organizational risks.
Cybersecurity risk management processes must integrate safety and security assessments throughout the device lifecycle.
Transparency in device cybersecurity is crucial for enabling safe integration and use by healthcare providers and end users.

Recommendations
Implement a Secure Product Development Framework (SPDF) for comprehensive cybersecurity throughout the product lifecycle.
Include a Software Bill of Materials (SBOM) for all premarket submissions to track software dependencies and vulnerabilities.
Perform robust cybersecurity testing, including penetration testing and vulnerability assessments.
Enhance device labeling with clear cybersecurity-related instructions and risks for users.
Develop a coordinated vulnerability disclosure plan for postmarket cybersecurity management.

Regulatory Considerations
Adherence to 21 CFR Part 820 Quality System regulation requirements, including design controls and risk management.
Compliance with Section 524B of the FD&C Act for cybersecurity of cyber devices.
Submission of SBOMs and detailed security risk management reports for premarket applications.
Provision of cybersecurity information as part of device labeling to prevent misbranding under Section 502 of the FD&C Act.
Integration of security testing and validation as part of the FDA review process.

Findings
No uniform definition exists for nocturnal scratching, leading to inconsistencies in data interpretation and measurement across studies.
There are significant differences in how scratching behaviors are defined, recorded, and analyzed, making cross-study comparisons difficult.
The term “nocturnal” is often used imprecisely, as sleep periods vary among individuals (e.g., shift workers, patients with disrupted sleep patterns).
Traditional methods such as videography and clinician observations are expensive, labor-intensive, and impractical for widespread use.
Advances in sensor-based wearables and machine learning present opportunities to create objective, scalable, and patient-centric digital measurement tools.

Recommendations
Define nocturnal scratching as a rhythmic and repetitive skin-contact movement occurring within a delimited sleep period, rather than restricting it to nighttime.
Implement standardized ontologies to guide measurement definitions, ensuring consistency across studies and clinical applications.
Encourage the creation and validation of wearables and machine learning algorithms for objective, scalable measurement of scratching.
Engage researchers, clinicians, patients, and regulatory bodies to drive consensus on measurement definitions and methodologies.
Establish digital measures as key endpoints in clinical trials, supporting their validation and regulatory acceptance.

Findings
Standard assessments lack sensitivity in early stages of neurodegenerative diseases.
Challenges with the validity and quality of RMT measurements.
Issues related to equity and inclusion in deploying digital tools.
Importance of considering feasibility, acceptance, usability, and ecological validity of digital endpoints.

Recommendations
Develop regulatory strategies early on.
Ensure equity and inclusion in deploying digital tools.
Address challenges related to the validity and usability of digital endpoints.
Promote public-private partnerships to address privacy and security concerns.
Involve patients and stakeholders in the design and implementation of digital tools.

Regulatory Considerations
Acceptance of digital endpoints by regulatory authorities is crucial.
Validation with current gold standards and clinically meaningful legacy endpoints.
Ensure data security and privacy.

Findings
Regulatory inconsistencies across FDA divisions create uncertainty and inefficiencies in the approval process for digital health products.
Misalignment between FDA regulatory requirements and payer expectations hinders the commercialization and adoption of digital health innovations.
The absence of clear alternative regulatory pathways for novel digital health products discourages investment and innovation.
The lack of standardized regulatory frameworks for AI-driven healthcare technologies, including large language models (LLMs), poses challenges for industry adoption.
Limited international harmonization in digital health regulation makes it difficult for companies to scale innovations globally.

Recommendations
FDA should improve communication and coordination across divisions to ensure consistent regulatory interpretations and processes.
Regulatory pathways for novel digital health products should be modernized, including the introduction of alternative approval mechanisms tailored to iterative software development and AI-enabled devices.
A regulatory framework for third-party large language models (LLMs) should be developed to support their integration into digital health applications.
Greater alignment between FDA and payer decision-makers is needed to streamline market access and ensure reimbursement for digital health products.
International regulatory harmonization efforts should be expanded to facilitate global adoption of digital health technologies.

Regulatory Considerations
The FDA should clarify and refine regulatory requirements for AI-driven digital health products, including predefined change control plans for software updates.
Cloud-based health platforms require clear regulatory guidance on security, data ownership, and compliance with HIPAA and international privacy laws.
Real-world evidence (RWE) should be incorporated into regulatory decision-making to facilitate faster approvals and post-market surveillance of digital health products.
Standardized regulatory frameworks for digital biomarkers and digital drug development tools (DDDTs) should be developed to support clinical research applications.
Policymakers should collaborate with industry stakeholders to establish education and training programs on digital health innovation and regulatory science.

Findings
There is widespread confusion among digital health developers regarding the complex and evolving regulatory landscape, with many uncertain about whether their products require regulation or which pathway to pursue. This lack of a clear regulatory strategy acts as a significant barrier to market access, investor confidence, and user trust. The heterogeneity of the digital health sector, coupled with varying international requirements, further complicates the path to market for innovators, hindering the scalability of effective solutions.

Recommendations
Digital health innovators should proactively integrate a tailored regulatory strategy into their core business plan, viewing it as a commercial differentiator rather than a hurdle. Developers are encouraged to utilize resources like DiMe’s regulatory pathway tools to navigate the U.S. and global landscapes effectively. Early and continuous engagement with regulators and collaborative efforts across the industry are essential to ensure products are developed to meet both market needs and regulatory standards, ultimately accelerating the delivery of high-quality digital health solutions to patients.

Regulatory Considerations
A comprehensive policy framework is necessary for the successful integration of digital health technologies, encompassing regulatory authorization, value assessment, and reimbursement. Developers must understand the nuances of different regulatory classifications, such as Software as a Medical Device (SaMD), and their specific evidentiary requirements. Greater international harmonization of regulatory standards is crucial for enabling global scalability. Regulatory bodies should continue to develop agile frameworks that can accommodate the rapid pace of innovation in digital health while ensuring patient safety and product effectiveness.

Findings
There is a need for comprehensive validation and verification processes for DHTs.
Ensuring data security and privacy is a significant concern.
Usability issues for diverse populations need to be addressed.
There is a lack of clarity on whether certain DHTs meet the definition of a device under the FD&C Act.
The guidance does not establish legally enforceable responsibilities.

Recommendations
Ensure DHTs are fit-for-purpose for clinical investigations.
Implement robust data security measures to protect participant information.
Conduct usability evaluations to ensure DHTs can be used by intended populations.
Engage with FDA early to discuss the use of DHTs in clinical investigations.
Develop a risk management plan to address potential issues with DHT use.

Regulatory Considerations
Verification and validation should be addressed regardless of device classification.
Sponsors should ensure compliance with data protection and privacy regulations.
FDA evaluates DHT data based on endpoints, medical products, and patient populations. Sponsors can engage with FDA’s Q-Submission Program for feedback on DHT usage in clinical trials.
Sponsors should understand the legal implications of using DHTs in clinical investigations.
The guidance provides recommendations but does not establish legally enforceable responsibilities.

Findings
Limited number of drugs approved using DHT data for labeling claims.
Lack of clarity on definitions and regulatory pathways for DHT-derived endpoints.
Challenges in global studies due to varying definitions among regulatory authorities.
Fine line between using DHT-derived measures for therapy response and quality of life assessments.

Recommendations
Create clear definitions for DHT-derived tools and measures.
Define specific evidentiary criteria for DHT-based tools.
Leverage precompetitive public-private partnerships to advance DHT development.
Utilize existing regulatory pathways like the iSTAND pilot program.

Regulatory Considerations
Need for harmonized global definitions and pathways for DHT-derived measures.
Use of existing programs like the iSTAND pilot program to integrate new digital measures.
Clear guidance from FDA and EMA for qualifying biomarkers or COAs in drug development.

Findings
There is a need for more rigorous research beyond technology validation to ensure reliable real-world data capture and improved patient outcomes.
Limited translation of AI tools into medical practice despite their success in retrospective studies.
Insufficient application of social factors in clinical decision-making and DHT research.
Need for more rigorous and reproducible research designs with larger sample sizes and longer follow-up times.

Recommendations
Use the study's repository to inform future research by healthcare providers, policymakers, and the life sciences industry.
Consider how data collection methods (active or passive) complement primary study outcomes.
Conduct targeted systematic reviews to assess factors contributing to the digital divide.
Ensure greater consistency in metrics used across DHT research.

Regulatory Considerations
Manufacturers need to demonstrate the ongoing value of their products using real-world evidence.
Regulatory approvals for AI-based products are increasing, particularly for machine learning applications.

Findings
The need for rigorous verification and validation of DHT-generated measurements before they can be relied upon for safety, efficacy, or effectiveness.
The risk of widening health inequities due to disparities in access to healthcare and technology.
Challenges in ensuring data quality, privacy, and security.
The necessity for improved interoperability to facilitate data sharing.
The requirement for developing AI and machine learning algorithms for real-time data evaluation.

Recommendations
Improve the reach and effectiveness of DHTs, particularly among marginalized groups.
Develop and validate AI and machine learning algorithms for real-time evaluation of DHT data.
Ensure systematic protections for data privacy and security.
Enhance interoperability to unlock the full potential of DHTs.
Engage with stakeholders, including patients, to create efficient pathways for DHT adoption.

Regulatory Considerations
Compliance with rapidly changing digital health policies.
Utilization of FDA guidance documents and tools for understanding digital health regulations.
Consideration of regulatory oversight as DHTs become more integral to clinical trial design.

Findings
The US regulatory landscape is more suitable for promoting innovation in digital health compared to Europe.
Traditional regulatory approaches are not keeping pace with technological advancements.
There is a lack of specific guidance on the use of wearables and software in clinical drug trials.
The US has a more advanced regulatory framework for drug development tools than Europe.

Recommendations
Use approved solutions or consider early qualification of drug development tools.
Engage early with FDA and EMA to define evidentiary standards and regulatory pathways.
Ensure correct regulatory classification of digital tools.
Engage early with regulatory authorities to navigate the regulatory landscape.

Regulatory Considerations
Digital tools must be fit-for-purpose for their intended use.
Sponsors must ensure conformity with GxP and local data privacy and cybersecurity laws.
Data from digital tools must deliver reliable data with tangible clinical benefits.
The context of use drives the benefit-risk assessment and evidentiary criteria for regulatory acceptability.