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
Sleep disturbances are common across multiple therapeutic areas, making standardized digital measures essential for cross-condition research.
Measurement accuracy varies depending on sensor placement, algorithms, and contextual factors such as sleep environment.
While home-based digital sleep tracking improves accessibility, challenges remain in ensuring consistency with clinical polysomnography.
Digital measures of sleep provide new opportunities for continuous and longitudinal monitoring, but standardization in data collection and interpretation is needed.
Stakeholders, including regulatory agencies, increasingly recognize digital sleep biomarkers, but additional validation is required to ensure widespread adoption.

Recommendations
Researchers and clinicians should integrate core digital sleep measures into study designs to improve data comparability across trials and clinical contexts.
Algorithm transparency and validation protocols should be established to enhance the accuracy of digital sleep monitoring tools.
Regulatory engagement should be prioritized early in the development process to ensure that digital sleep measures meet evidentiary standards.
Multi-stakeholder collaboration, including patient and care partner input, is essential to ensure sleep measures reflect meaningful aspects of health.
Further research is needed to refine wearable and sensor-based technologies to improve real-world applicability and clinical utility of digital sleep biomarkers.

Regulatory Considerations
The FDA and other regulatory bodies increasingly acknowledge sleep measures as potential clinical endpoints, but clear validation frameworks are necessary.
Digital sleep measures should align with industry standards such as HL7 to ensure interoperability and data integrity.
Data privacy and security regulations must be followed, particularly for continuous sleep monitoring in real-world settings.
Post-market validation and real-world evidence generation are critical to support regulatory acceptance of digital sleep biomarkers.
Developers must document the derivation of sleep measures, including algorithmic processing and sensor accuracy, to meet regulatory review requirements.

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
Product development firms are hesitant to increase DHT use despite regulatory support.
Conventional hardware-based technologies are preferred over newer digital tools.
Operational barriers contribute to the low adoption of DHTs in product development trials.

Recommendations
Reduce operational barriers to facilitate DHT adoption.
Provide additional regulatory clarity to encourage DHT use.
Encourage the incorporation of more DHTs and patient-centric endpoints in clinical trials.

Regulatory Considerations
The FDA's guidance on DHT use is evolving and not yet fully formalized.
There is a need for harmonization between US and non-US regulatory agencies.
The impact of recent regulatory support may take years to be fully realized.

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
There is a notable lack of reports on the use of digital health technology in pediatric patients.
Challenges exist in selecting the design, metrics, and types of sensors best suited for disease evaluation.
False positive detection remains problematic in seizure detection using DHTs.
There is a lack of information on the use of DHTs in infants.
Unique design challenges for pediatric DHTs arise from size, anatomy, physiology, activity levels, and cognitive development.

Recommendations
Further research and evaluation are needed to realize the full potential of remote monitoring in pediatric trials.
Creative approaches, including machine learning, should be employed to identify optimal measurement methods.
Training for caregivers is necessary to ensure DHTs are worn correctly and data are complete.

Regulatory Considerations
Confirming the reliability and clinical relevance of DHT measurements is essential.
Ensuring privacy and confidentiality of patient data must be prioritized.

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
There is a lack of consensus on outcome measures and adherence definitions across studies using wearables in oncology.
There is significant heterogeneity in study designs and outcomes, making comparisons difficult.
Limited guidelines exist for designing or reporting trials using wearables in oncology.

Recommendations
Establish standardized definitions for wearable outcomes and adherence to improve study comparisons.
Encourage research using advanced wearable devices and active data use.
Conduct more randomized clinical trials to create consensus on implementing wearables in oncological practice.
Develop guidelines for designing and reporting trials using wearables.

Regulatory Considerations
The Clinical Transformation Initiative (CTTI) provides recommendations for the use of mobile technology in clinical trials, which could inform regulatory frameworks.

Findings
TOMs are underutilized in clinical trials for neurodegenerative disorders.
Challenges include relevance of measured targets, standardization of parameters, costs, and patient compliance.
Lack of validation studies for TOMs' clinical meaningfulness and issues with proprietary platform integration.

Recommendations
Validate TOMs output to ensure clinical meaningfulness.
Standardize clinically relevant measures and procedures.
Establish a single platform for data integration from various proprietary platforms.
Assist in regulatory approvals to facilitate wider use of TOMs.
Enhance the ecological validity of TOMs by using them in natural settings.

Regulatory Considerations
Overcome regulatory roadblocks for wider use of TOMs.
Assist manufacturers in obtaining regulatory approvals for TOMs.
Address integration issues with proprietary platforms from different manufacturers.

Findings
Challenges related to data safety, quality, privacy, and regulatory requirements in smart sensor technologies.
Bias in standard RCTs due to exclusion of participants with language or motor barriers.
Need for ICT systems to detect smooth transitions in cognitive abilities and everyday functions.

Recommendations
Develop ICT-based procedures that capture relevant clinical features validly.
Ensure data fidelity and robustness in ICT systems.
Incorporate user needs into ICT solutions.
Address data safety and privacy concerns.
Develop international policies for access, security, and privacy in ICT solutions.

Regulatory Considerations
Need for international efforts to address gaps in policies around access, security, and privacy.
Current laws do not cover health information on mobile apps or the Internet.
Lack of regulation could undermine the credibility of RWE results.

Findings
Researchers face challenges in scientific methodology, regulatory, legal, and operational aspects.
Many consumer-grade devices lack scientific evidence for their health claims.
There are significant challenges in data management, infrastructure, analysis, and security.
Lack of mobile technology data standards and transparency in data processing algorithms.
The need for a shared understanding of methodologies and terminology.

Recommendations
Develop industry-wide standards for data and terminology.
Foster dialogue between biopharmaceutical industry and device manufacturers for methodological development.
Ensure a patient-centric approach in clinical trials using wearable devices.
Conduct well-powered studies with clear medical problem statements.
Implement rigorous analytical and clinical validation processes.

Regulatory Considerations
Separate marketing approval paths for drugs and devices in the US.
Most wearable devices are classified as Class II devices requiring 510(k) clearance.
Compliance with HIPAA for data obtained via medical devices.
Need for device performance validation in specific populations relevant to device label claims.
Differences in US and EU regulations regarding data protection and consent requirements.