Findings
Data analytics are challenging due to diverse metrics and study aims.
Most devices lack built-in software for data output.
There is a lack of comparison and validation studies for different devices and metrics.
Validation of PA metrics is difficult due to the absence of a gold standard.
The integration of various databases is needed but challenging.

Recommendations
Conduct comparison and validation studies between different brands of devices and PA metrics.
Develop standardized metrics for measuring PA.
Improve data integration methods across different studies and databases.
Focus on developing built-in software for devices to facilitate data output.
Encourage research on the validation of PA metrics.

Regulatory Considerations
1Not mentioned

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
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
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
High rates of missing data in DHTs compared to traditional measures due to technical and software difficulties.
Practical issues such as unfamiliarity with platforms, connectivity difficulties, and lack of data visibility.
Specific technical issues like blocking of websites by firewalls and failed software updates leading to data loss.

Recommendations
Ensure appropriate workforce training for those involved in DHT deployment.
Conduct pilot evaluations in study sites to identify potential issues early.
Improve data visibility at both site and central coordinating team levels.
Implement robust feasibility testing before full-scale deployment.
Co-design DHTs with study staff and patients to enhance usability.

Regulatory Considerations
The FDA requires adequate training, education, and experience for those responsible for data capture using mobile technologies.
Ensure data integrity through oversight responsibilities as recommended by the Clinical Trials Transformation Initiative.

Findings
Despite progress, DHTs are not yet fully accepted in clinical research.
Challenges include small study samples, unrepresentative samples, lack of normative data sets, feature selection bias, and replication issues due to sensor variability.
There is a need for a framework to identify and mitigate sources of variability in DHTs.

Recommendations
Develop a conceptual framework to identify and mitigate sources of variability.
Consider both active and passive monitoring approaches in study designs.
Align knowledge and data sharing across consortia to improve DHTs.
Emphasize normative data sets to establish ground truths for variability.
Encourage precompetitive collaborations to advance regulatory maturity.

Regulatory Considerations
Collaborative efforts like the 3DT project are essential for regulatory maturity.
Global regulatory agencies encourage data-driven engagement through consortia.

Findings
Verification involves testing the accelerometer's technical specifications (e.g., accuracy and precision) through peer-reviewed studies.
Validation of the algorithm relies on "ground truth" data, gathered through infrared video recordings and manual scoring of movements.
Cross-validation was used to assess the algorithm's performance, with additional validation in independent samples planned.
The separation of verification and validation allows greater flexibility, enabling the algorithm's use with multiple accelerometer devices that meet minimum standards.

Recommendations
Conduct separate verification and validation processes to ensure the reliability of both the device and the algorithm.
Use peer-reviewed publications to document the performance of DHTs and their limitations.
Ensure validation includes testing with representative populations to confirm the algorithm’s utility across diverse contexts.
Promote industry-wide standards to facilitate scalability and regulatory acceptance of DHTs in clinical trials.

Regulatory Considerations
Ensure DHTs undergo rigorous verification to meet accuracy and precision standards documented in peer-reviewed studies.
Validate algorithms using empirical "ground truth" data to demonstrate their ability to measure clinically meaningful outcomes.
Align the design and validation of DHTs with regulatory expectations for reliable and transferable performance across devices.

Findings
SV95C allows continuous, objective assessment of ambulation in real-world settings, addressing biases and limitations of hospital-based assessments.
Wearable devices like ActiMyo® reduce patient and caregiver burden by enabling remote monitoring and decentralized clinical trials.
Digital endpoints like SV95C improve trial efficiency, potentially reducing required sample sizes and trial durations in rare diseases like DMD.
Regulatory qualification requires robust validation data, including comparisons with traditional measures, sensitivity to change, and precision.
Adoption of digital endpoints is dependent on stakeholder collaboration, patient engagement, and alignment with regulatory requirements.

Recommendations
Collaborate with regulatory bodies (e.g., EMA, FDA) early to align expectations for validation and qualification processes.
Focus on Patient-Centric Design: Develop wearable devices and endpoints with input from patients and caregivers to ensure usability and relevance to daily life.
Establish Robust Validation Protocols: Generate comprehensive data on precision, reliability, and sensitivity to change, including anchor-based approaches.
Provide training for patients, caregivers, and clinicians to enhance compliance and minimize missing data during trials.
Leverage Multi-Stakeholder Collaboration: Encourage partnerships among technology developers, drug developers, and patient groups to build normative datasets and refine measures.

Regulatory Considerations
Follow frameworks like the EMA qualification opinion process and FDA Drug Development Tools COA Qualification Program for validation.
Ensure validation studies demonstrate precision, reliability, and sensitivity to clinical changes, with comparisons to gold-standard assessments.
Use approaches that relate digital endpoint changes (e.g., SV95C) to meaningful clinical outcomes like loss of ambulation or other qualified measures.
Expand validation to include younger and nonambulant patients, ensuring endpoints are applicable across a broad spectrum of disease severity.
Adhere to Good Clinical Practice (GCP) and data protection regulations to ensure patient safety and trust.

Findings
There is a wide variability in PA measurement methodologies in existing literature, which complicates comparisons across studies.
The use of digital tools like activity monitors complicates the regulatory process due to non-interchangeability and varying technical and regulatory requirements.
There is a need for standardized procedures to ensure data comparability and integrity.

Recommendations
Implement a standardized methodology for PA data collection and reporting.
Use a standard operating procedure for data collection regarding PA outcomes.
Ensure that activity monitors meet safety, usability, and acceptability criteria for COPD patients.
Encourage widespread adoption of the proposed recommendations to facilitate further research.
Consider device agnosticism while ensuring device sensitivity and accuracy.

Regulatory Considerations
Devices should be device agnostic but must ensure sensitivity, accuracy, and data verification.
Regulatory requirements vary across jurisdictions and need to be met for device approval.
Safety, usability, and acceptability of devices for COPD patients are critical criteria.

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
The integration of mobile devices into interventional research, specifically RCTs, is evolving slowly.
There is a lack of standardization in mobile outcome assessments, making it difficult to compare results across studies.
Current definitions for conventionally measured outcomes do not adequately reflect the novelty of mobile outcome assessments.

Recommendations
Emphasize validating the clinical meaningfulness of mobile outcome assessments.
Develop new mobile outcome assessments for future clinical research.
Use CTTI's recommendations and tools for selecting appropriate mobile outcomes as trial endpoints.

Regulatory Considerations
Standardization is necessary to facilitate the acceptance and use of mobile outcomes in regulatory interventional research.
Validation of mobile outcomes is crucial for their use in regulatory decision-making.
The development of new categories or modification of current definitions may be needed to accommodate novel measures using mobile devices.