Key Principles (Findings)
The central principle of the FDA's program is that Digital Health Technologies (DHTs) offer significant potential to make clinical trials more efficient, patient-centric, and capable of capturing novel data. A key finding is that a collaborative, multifaceted approach is necessary to address the challenges of incorporating DHT-derived data into regulatory decision-making. The program acknowledges that ensuring data quality, validating new endpoints, and establishing clear regulatory expectations are critical for the successful adoption of these technologies in drug development.

Program Activities (Recommendations)
The FDA's activities in this area function as implicit recommendations for the industry. The agency is actively:
Developing a Framework: Creating and publishing a clear framework to guide the use of DHTs in drug and biological product development.
Engaging Stakeholders: Convening public meetings and workshops to foster collaboration and share learning among patients, biopharmaceutical companies, DHT manufacturers, and academia.
Supporting Demonstration Projects: Funding and overseeing research projects to address critical gaps and demonstrate the reliability and validity of specific digital measures.
Building Internal Expertise: Establishing a DHT Steering Committee and enhancing internal knowledge to ensure consistent and expert review of submissions containing DHT-derived data.

Regulatory Considerations
This webpage emphasizes the FDA's commitment to creating a clear regulatory framework for the use of DHTs in drug development. It highlights that while DHTs offer great promise, they also present new regulatory challenges related to data integrity, validation, and analysis. The FDA's approach involves a combination of issuing new regulatory guidance, promoting stakeholder collaboration, and advancing regulatory science. Sponsors are encouraged to engage with the FDA to discuss their use of DHTs in clinical trials to ensure alignment with the agency's expectations. The establishment of the CDRH Digital Health Center of Excellence provides a dedicated resource for such engagement.

Findings
Existing guidelines lack pragmatic application and systematic approach for device selection.
Device choice is dependent on measurement objectives, user population, and available resources.
Current frameworks do not systematically consider verification, validation, feasibility, and protocol design.
Rapid obsolescence of digital devices due to technological advancements.
Need to incorporate social/psychological factors into device selection.

Recommendations
Develop a practical guide with a systematic approach for selecting wearable devices.
Use five core criteria: continuous monitoring capability, device suitability and availability, technical performance, feasibility of use, and cost evaluation.
Prioritize feasibility of use to ensure user needs are incorporated into the selection process.
Adapt guide criteria to accommodate novel innovations.
Foster clarity and transparency in decision-making among researchers, HCPs, and device users.

Regulatory Considerations
Follow FDA guidance for digital health technology usage in clinical investigations.
Consider CTTI recommendations for improving clinical trial quality and efficiency.
Use ePRO Consortium's factors for device suitability in regulatory trials.
Apply international guidelines for specific measurements when available.

Findings
Usability is a multi-domain concept that requires a combination of methods for evaluation. Evaluations fall into two types: formative (for design modification of prototypes) and summative (for demonstrating usability of the final product to a representative user sample). The user experience metrics fall into several domains, including: Satisfaction, Usefulness, Ease of use, Learnability, Efficiency, Memorability, Understandability, Actionability, Readability, and Use-errors. Metrics related to Satisfaction and Usefulness are always subjectively reported by users.

Recommendations
Developers should select metrics based on the specific usability-related domain being evaluated.
Subjective Data (e.g., Satisfaction, Usefulness): Capture through qualitative surveys, quantitative surveys (scales), interviews, focus groups, and think-aloud evaluations .
Objective Data (e.g., Ease of use, Use-errors): Capture through direct or indirect observation (e.g., counting steps/attempts, timing task completion), or by using data generated by the sDHT (e.g., error reports, timestamps, page load times).
Time-based Metrics: Evaluate Learnability (ease of first use), Efficiency (ease with experience), and Memorability (ease after non-use) by measuring ease of use at different points in time .
Information Presentation: If the sDHT presents clinical data or written information (instructions, warnings), evaluate Understandability, Actionability, and Readability .
Use-errors: Objectively capture the number, type, and recoverability of use-errors (actions, or lack thereof, that may result in harm) via observation and sDHT data, noting that "use-error" is preferred to "user-error".

Regulatory Considerations
While this guide does not reference regulatory bodies like the FDA, it is part of the V3+ framework and recommends that researchers prioritize essential documents like the use specification and use-related risk analysis before designing a usability study. Summative evaluations demonstrating usability against a representative user sample under intended use conditions are the standard for demonstrating product fitness.

Findings
Sites must establish effective data privacy and security plans, especially considering regional and global regulations like GDPR.
Risk mitigation is critical, including plans to address unanticipated issues and potential patient disengagement due to technology challenges.
Budgeting and contracting often involve additional considerations, such as storage, training, and technical support requirements for connected devices.
Sites require adequate training to ensure staff and patients are prepared to use connected devices efficiently.
Companion applications or services often play an essential role in device functionality and data transmission.

Recommendations
Develop a clear plan for data pathways, including storage, security, and regulatory compliance.
Establish detailed risk mitigation and management strategies to handle unexpected challenges.
Ensure comprehensive training programs for site staff and patients to enhance device usability.
Incorporate device storage and resource allocation into budgeting and contracting processes.
Facilitate effective communication with sponsors and vendors to resolve operational and technical issues promptly.

Regulatory Considerations
Ensure connected devices comply with CFR 21, Part 11, and other relevant data collection and transmission regulations.
Understand and adhere to local and regional data privacy laws, such as GDPR, when managing patient data.
Verify that appropriate licenses and regulatory approvals are in place for device data transmission and storage.
Assess and address shipping and handling regulations for devices, ensuring safe and compliant transportation.

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
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
Misclassification of wakefulness during sleep periods and issues with tracking outside main sleep bouts.
Bias in performance evaluation studies due to limited representation of diverse populations.
Hidden complexities in consumer-grade devices related to data access, fees, privacy, and security.

Recommendations
Carefully interpret study results based on wearable sleep-tracking technology data.
Address biases in study populations by including diverse cohorts.
Ensure proper preprocessing of data from consumer-grade devices.
Avoid inserting personally identifiable information in device settings.
Evaluate issues related to specific populations like minors.

Regulatory Considerations
Complexity of privacy laws across different countries.
Need for strategies to protect personal information in device settings.
Consideration of specific population issues, such as minors, in regulatory frameworks.

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
A proactive risk assessment is essential for optimizing study quality by identifying and mitigating risks to human subject protection and data integrity before and during a trial. Monitoring should be comprehensive, addressing not only likely risks identified initially but also less probable, high-impact risks and unanticipated issues that emerge. The effectiveness of a monitoring strategy depends on tailoring its timing, frequency, and methods to study-specific factors like complexity and site experience. Centralized monitoring, as part of a risk-based approach, can detect systemic issues like data omissions or protocol deviations more rapidly than traditional on-site visits alone.

Recommendations
Sponsors should formally document their risk assessment methodologies and ensure these assessments directly inform the creation and revision of monitoring plans. Monitoring plans must be detailed, outlining the study design, specific data sampling strategies, and clear protocols for escalating significant issues. When significant problems are identified, sponsors must conduct a timely root cause analysis and implement corrective and preventive actions. All monitoring activities, findings, and subsequent actions should be thoroughly documented and communicated to sponsor management, clinical site staff, and other relevant parties.

Regulatory Considerations
FDA regulations mandate sponsor oversight and proper monitoring but do not prescribe specific methods, providing the flexibility for sponsors to adopt a risk-based approach. The FDA may request a sponsor's risk assessment and monitoring plan documentation during an inspection. This guidance represents the Agency's current thinking and is nonbinding, allowing sponsors to use alternative approaches if they satisfy regulatory requirements. A key focus of monitoring should be to ensure critical trial processes, such as the maintenance of blinding, are protected to maintain overall data and trial integrity.

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
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
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.