Findings
The quality of evidence for the analytical validation of sensor-based digital health technologies (sDHTs), which is the evaluation of algorithms converting sensor data into a clinically interpretable measure, is often inconsistent and insufficient. The existing V3+ framework codifies the overall evaluation process, which includes verification, usability validation, analytical validation, and clinical validation. To improve the scientific rigor of analytical validation, a hierarchical framework for selecting reference measures is needed because not all potential reference measures are of equal quality. The framework classifies reference measures based on attributes that contribute to reduced measurement variability, with defining and principal measures being the most rigorous due to objective data acquisition and the ability to retain source data.

Recommendations
The proposed framework sequentially moves the investigator through four steps: (1) Compile preliminary information, including the digital clinical measure, context of use (COU), algorithm requirements, and sensor verification evidence . (2) Select an existing reference measure, develop a novel comparator, or identify a set of anchor measures, prioritizing measures with the highest scientific rigor (defining → principal → manual → reported) . (3) Consider the impact of the data collection environment to determine if the analytical validation study can be conducted in the intended use environment with the highest-order measure, or if in-lab validation is necessary, ensuring the results are generalizable . (4) Describe the rationale for key study design decisions to encourage transparency for evaluators, regulators, and payers . Investigators must justify passing over a higher-ranked reference measure, generally only acceptable if the higher-ranked measure poses unacceptable risk or is not applicable to the context of use.

Regulatory Considerations
The principles of the framework for analytical validation apply regardless of the regulatory status of the sDHT (regulated medical device, low-risk general wellness apps, or research product) or its intended use (clinical care or clinical research). The framework is intended to help investigators support the most rigorous claims regarding sDHT performance, which is important for acceptance by evaluators, peer-reviewers, regulators, and payers. The categorization of the digital clinical measure as a digital biomarker or an electronic clinical outcome assessment also does not change the framework's applicability.

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
Sensor-generated health data must be collected in a way that ensures completeness, contextual metadata, and fit-for-purpose accuracy to support clinical applications.
Data security and privacy regulations vary globally, necessitating the implementation of adaptable frameworks such as the FAIR data principles and cybersecurity best practices.
Standardized data transmission and processing protocols are required to ensure interoperability across digital health platforms and prevent data loss or corruption.
Validation frameworks, such as DiMe’s V3 (Verification, Analytical Validation, and Clinical Validation), are essential to confirm the reliability of digital clinical measures.
Equity and accessibility considerations must be prioritized, ensuring that digital health solutions work across diverse populations and healthcare settings.

Recommendations
Digital health developers should follow standardized methodologies for data collection, leveraging frameworks such as the EVIDENCE checklist and DiMe’s V3 validation process.
Privacy-by-design principles should be embedded into sensor-based data systems to comply with HIPAA, GDPR, and emerging digital health privacy regulations.
Data processing workflows must be transparent, well-documented, and validated to ensure consistent, unbiased, and reproducible results in clinical applications.
Organizations should adopt cybersecurity best practices, including end-to-end encryption, authentication protocols, and risk mitigation strategies, to protect sensor data.
Sensor data integration strategies should be aligned with industry standards and open-source protocols to promote interoperability and scalability in healthcare ecosystems.

Regulatory Considerations
Regulatory agencies such as the FDA encourage the use of validated digital biomarkers and structured sensor data processing methodologies to support regulatory submissions.
Sensor data privacy policies must comply with local and international regulations, requiring clear user agreements, informed consent, and transparent data governance.
Secure data transmission protocols must be implemented to prevent unauthorized access, aligning with industry standards for encryption, authentication, and network security.
Organizations deploying sensor-based health technologies should conduct risk assessments and audits to ensure compliance with evolving regulatory requirements for AI and digital health.
Global harmonization of data security and transmission standards is necessary to support cross-border data exchange, facilitating regulatory approval and market access for digital health innovations.

Findings
Adherence: Participants achieved an overall adherence rate of 90.18%, demonstrating the feasibility of home-based data collection over a 30-day period.
Participant Feedback: Most participants found the technology easy to use, though some reported difficulties with specific devices, such as sleeping with a wearable watch.
Device Selection: Precision, consistency, and participant preferences guided the selection of spirometry devices, with single-blow spirometry favored for ease of use.
Accuracy: Home spirometry measurements underestimated forced vital capacity (FVC) compared to historical in-clinic data, possibly due to device differences or disease progression.
Future Participation: Nine out of ten participants expressed interest in joining longer virtual studies using similar technologies.

Recommendations
Evaluate Adherence and Usability: Conduct feasibility studies to assess adherence rates and identify usability challenges before full-scale implementation.
Incorporate Participant Feedback: Use cross-over designs to gather participant preferences and feedback on device usability, data sharing, and frequency of data collection.
Validate Accuracy and Consistency: Ensure that DHTs provide precise, reliable measurements comparable to in-clinic standards and assess their performance in real-world settings.
Optimize Technology for Long-Term Use: Address issues such as wearability and participant burden to improve device acceptance and compliance.
Refine Training and Communication: Provide clear instructions and training to participants, setting expectations for using and troubleshooting the technologies.

Regulatory Considerations
Validate Home-Based Data Collection: Demonstrate that data collected remotely with DHTs are accurate, reliable, and clinically relevant for trial endpoints.
Pilot Studies for Regulatory Submissions: Use feasibility data to strengthen regulatory submissions, ensuring endpoints are validated for use in pivotal trials.
Address Technology Limitations: Acknowledge and mitigate potential discrepancies between home and clinic data, using feasibility study insights to refine protocols.

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
Lack of security and confidence in digital health technologies hampers adoption.
Absence of suitable guidance for selecting BioMeTs based on clinical requirements.
BioMeTs (DHTs) and algorithms are often created without expert guidance and transparency.
No standardized evaluation resources for testing, verifying, and validating BioMeTs.
Inconsistencies in algorithm application across different cohorts.

Recommendations
Develop a standardized BioMeT and algorithm evaluation framework.
Create professionally tailored standardized guidelines for BioMeT use.
Implement a framework with unique identifiers for BioMeTs and algorithms.
Establish mechanisms for dynamic updates of hardware or software.
Use systematic reviews and Delphi processes to inform framework development.

Regulatory Considerations
Assign unique identifier numbers to BioMeTs and algorithms.
Provide mechanisms for dynamic hardware or software updates.
Ensure robust deployment through standardized evaluation protocols.

Findings
The complexity of selecting appropriate technology due to an increasing array of devices and sensors.
Risks associated with choosing inappropriate MSTs, including susceptibility to missing data or erroneous data transmission.
The need for both manufacturers and clinical trial sponsors to ensure analytical validation supports MST use.

Recommendations
Identify a digital outcome that meets an unmet need for the planned trial or population.
Determine whether the technology is fit-for-purpose based on the measure, context of use, and classification as a medical device.
Ensure devices are reliable and reproducible for collecting required data.
Conduct statistical analysis according to a predefined analysis plan.
Consider adaptive designs to reduce resource requirements and increase study success.

Regulatory Considerations
Compliance with medical device classifications such as 510(k)s and CE marks.
Ensure devices and platforms comply with HIPAA, GDPR, and data privacy regulations.
Be aware of potential updates to technology or software that could impact trials.

Findings
Operational inefficiencies in participant recruitment and data acquisition inflate costs and extend timelines.
Disparities in access to research due to geographic and mobility constraints limit participant diversity.
Many digital biomarkers require further validation for use in clinical trials.
Heightened need for security measures to protect against data breaches in digital trials.
Opportunities exist to improve clinical trials using real-world data from EHRs and IoT technologies.

Recommendations
Leverage existing technologies and research platforms to transform clinical trials.
Develop partnerships with technology and computational communities.
Create standard protocol templates for automation in recruitment, retention, and data collection.
Develop validation models for new devices and analyses using existing trials.
Invest in the next generation workforce in medicine, technology, and clinical research.

Regulatory Considerations
Address data privacy and security concerns in digital trials.
Provide guidance for IRBs on consenting requirements, reporting, and oversight in digital trials.
Develop empirical research on the risks and benefits of digital trials.
Educate IRBs on digital technology and its implications for clinical trials.

Findings
Successful deployment of digital clinical measures requires a shared foundation of standardized methodologies, terminology, and best practices.
The selection of digital measures must prioritize patient-centered outcomes and align with meaningful aspects of health.
Technology validation processes, including the Verification, Analytical Validation, and Clinical Validation (V3) framework, are crucial to ensuring data accuracy and reliability.
Interoperability, data security, and governance remain key challenges for digital health technologies in both research and clinical applications.
Case studies demonstrate the real-world utility of digital clinical measures in clinical research, patient care, and public health initiatives.

Recommendations
Stakeholders should follow a structured, stepwise approach to selecting and validating digital clinical measures, starting with identifying meaningful health aspects.
Digital health tools must undergo rigorous verification and validation to ensure they are fit-for-purpose and meet clinical and regulatory standards.
Patient engagement should be integrated into every stage of digital measure development to ensure the relevance and usability of selected endpoints.
Regulatory and payer engagement should occur early in the process to streamline market access and reimbursement pathways.
Organizations should adopt a proactive approach to data privacy, security, and governance, ensuring compliance with regulations such as HIPAA and GDPR.

Regulatory Considerations
The FDA and other regulatory bodies emphasize the need for clinical validation of digital measures before they can be used as primary endpoints in trials.
Standardization of digital health technologies is critical to regulatory approval, requiring alignment with frameworks such as HL7 and ISO standards.
Data security and privacy regulations must be strictly adhered to, particularly in decentralized clinical trials where remote monitoring is used.
Digital endpoint validation must include real-world evidence (RWE) to support regulatory decision-making and post-market surveillance.
Organizations must consider the evolving regulatory landscape for AI-driven health technologies, ensuring compliance with best practices for algorithmic transparency and bias mitigation.

Findings
Traditional endpoints like HbA1c are insufficient to assess hypoglycemia's impact on quality of life and daily function for diabetes patients.
CGM offers continuous, objective glucose monitoring, enabling the detection of glycemic variability and hypoglycemic episodes in real-time.
Stakeholders, including regulators, industry, and patients, emphasize the need for CGM-derived endpoints to complement traditional biomarkers.
Challenges include standardizing hypoglycemia definitions, creating shared databases for CGM data, and addressing technical limitations at lower glucose levels.
Patient-reported outcomes (PROs) combined with CGM data can provide a comprehensive view of treatment effects but require further validation.

Recommendations
Establish consensus definitions of hypoglycemia and standardized metrics for CGM-based endpoints, such as percent reduction in hypoglycemia duration or frequency.
Create shared CGM databases to facilitate data analysis and validation of novel endpoints across clinical trials.
Conduct CGM-based studies to correlate hypoglycemia metrics with meaningful patient outcomes, including wellness, disease burden, and functional impacts.
Integrate CGM endpoints into regulatory submissions alongside traditional measures like HbA1c to demonstrate comprehensive treatment effects.
Collaborate with stakeholders to address technical challenges, such as CGM accuracy at lower glucose levels, and explore their application in pediatric populations in the future.

Regulatory Considerations
Validate CGM-derived endpoints to align with regulatory requirements, demonstrating their predictive value for severe hypoglycemia and other meaningful outcomes.
Engage regulators early to ensure CGM metrics complement existing endpoints like HbA1c and address unmet needs in diabetes trials.
Address technical limitations, such as CGM calibration and data accuracy at low glucose levels, to meet evidentiary standards for clinical trial endpoints.
Develop and document statistical methodologies for analyzing CGM-derived endpoints, including handling missing data and variability.
Include patient-reported outcomes and quality-of-life measures to contextualize CGM data in regulatory submissions.