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 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.

To be released.

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
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
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
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
Limited evidence of clinical validity from pilot trials due to cost, time, and regulatory complexities.
Lack of standards for data integration across different tools and platforms.
Potential biases introduced by pre-existing algorithms.
Opaque data processing methods in BioMeTs.

Recommendations
Develop data, hardware, and software standards for BioMeTs.
Improve regulations for data rights, access, privacy, and governance.
Provide guidance on analytical methodologies for BioMeT data validation.

Regulatory Considerations
Early regulatory interactions with agencies like the FDA and EMA.
Ensuring data quality, integrity, reliability, and robustness.
Understanding regulatory pathways for BioMeTs in clinical trials.

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.

Findings
The glossary establishes consistent terminology for digital health technologies, improving clarity in clinical research.
Definitions cover key aspects of digital measurement, including accuracy, precision, and validation.
Data integrity, security, and authentication are emphasized, particularly regarding structured and real-time data.
The glossary distinguishes between raw and processed data, providing clarity on data attribution and authenticity.
It includes terms relevant to both consumer-grade and regulated medical devices, supporting their appropriate use in clinical trials.

Findings
Scope of Requirements: The requirement applies to NDAs, ANDAs, certain BLAs, and INDs.
Study data must conform to FDA-supported standards listed in the Data Standards Catalog.
Noncommercial INDs (e.g., investigator-sponsored or expanded access INDs) are exempt but may voluntarily comply.
Supported Standards: FDA currently supports standards like SDTM, ADaM, and SEND for tabulation and analysis.
Controlled terminology standards (e.g., MedDRA, CDISC Controlled Terminology) are critical for semantic data interoperability.
Implementation Timelines: New standards become mandatory 24 months after the transition date announced in the Federal Register.
Updates to existing standards are required for studies starting 12 months after their transition date.
Waivers: Waivers may be granted to allow submission using unsupported standard versions, but not for non-standardized data formats.
FDA-Sponsor Interactions: Sponsors should engage with the FDA early in the development process to align on data standardization plans.
Pre-submission technical reviews and Type C meetings can be used to resolve data standardization issues.

Recommendations
Ensure compliance with FDA-supported standards as listed in the Data Standards Catalog.
Begin using the latest supported standards early in the study lifecycle to avoid non-compliance.
Engage with FDA during early-phase development to confirm data standardization plans.
Use tools like the Study Data Technical Conformance Guide for additional implementation support.
Submit waiver requests early if specific standard versions cannot be used.

Regulatory Considerations
Submissions that do not meet the electronic format and data standard requirements may be refused filing (NDAs and BLAs) or refused receipt (ANDAs).
Compliance with standardized formats is mandatory unless explicitly exempted or a waiver is granted.
Updates to supported standards are announced in the Federal Register, with defined implementation periods to allow sponsors to transition.
Sponsors must include critical files like demographic datasets and define.xml files in their submissions to demonstrate standard conformance.