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
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
There is a need for rapid development of solutions for monitoring Long COVID symptoms due to their variability and lack of treatment options.
Barriers include patient acceptability and the healthcare system's readiness for new technologies like vocal biomarkers.
The health status of patients, particularly those with severe symptoms, may limit their ability to participate in regular voice recordings, affecting adherence.

Recommendations
Involve end users in the co-design of digital health solutions to ensure they meet needs and expectations.
Develop telemonitoring solutions that allow for accurate follow-up and complement on-site evaluations.
Implement feedback loops to improve both the solution and the algorithm through lessons learned in population studies.
Ensure that voice data collection is diverse enough to represent the target population and decrease systemic biases.
Obtain explicit consent prior to voice data collection to comply with data protection regulations.

Regulatory Considerations
Voice data is considered identifying and sensitive, requiring compliance with various data protection laws.
Explicit consent is necessary for voice data collection to minimize future risks.
Validation through clinical trials is required to prove clinical benefit, effectiveness, and security.
CE marking or FDA certification will be mandatory to bring the solution to market.
Requests for reimbursement can be made after proving the clinical and economic interest of the digital system.

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
Digital health measures must be grounded in patient priorities, ensuring that they capture meaningful aspects of health.
Variability in symptoms, patient experiences, and disease progression necessitates adaptable and inclusive digital measurement strategies.
Sensor technologies must be carefully evaluated for accuracy, reliability, and suitability for specific clinical applications.
Digital measures can support multiple endpoints, requiring clear definitions to ensure consistency and interoperability.
The validation of digital measures must integrate statistical and clinical significance to support regulatory acceptance.

Recommendations
Patient perspectives should be prioritized when designing and selecting digital clinical measures.
Digital endpoints should align with clinical goals and be clearly defined to ensure relevance across different conditions.
Technical specifications of sensors must be assessed rigorously to ensure appropriate data quality and integrity.
Developers should collaborate with regulatory agencies early to streamline the validation and approval of digital measures.
Standardized methodologies should be established to ensure consistency in evaluating digital health technologies.

Regulatory Considerations
Digital endpoints should be validated using rigorous scientific and regulatory frameworks to ensure clinical applicability.
Sensor-based measures must comply with data integrity standards and regulatory requirements for digital health technologies.
Interoperability and standardization of digital measures are necessary to facilitate regulatory submissions and cross-study comparisons.
Stakeholders should leverage real-world evidence (RWE) to support regulatory decision-making for digital health innovations.
Privacy and security considerations must be addressed to ensure compliance with HIPAA, GDPR, and other data protection regulations.

Findings
The PPG-based Wrist-monitor provides comparable measurements to traditional devices with less inconvenience.
Further research is needed to confirm accuracy in specific subpopulations.
Current ABPM devices may impact long-term adherence due to discomfort.

Recommendations
Conduct further studies on the device's accuracy in various subpopulations.
Consider the PPG-based device for continuous BP monitoring.
Use the device for hypertension diagnosis and treatment.
Explore the device's use in other inpatient settings.

Regulatory Considerations
The device is FDA cleared for BP measurements.
It is undergoing validation for other inpatient settings.

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
The CardiacSense PPG device can reliably detect heart rate in various situations, but noise suppression during activity remains a challenge.
The study did not directly address the device's ability to detect atrial fibrillation in ambulatory patients, indicating a gap in current research.
Further studies are needed to confirm the device's effectiveness in detecting AF during ambulatory conditions.

Recommendations
Improve noise suppression technology to enhance the device's accuracy during motion.
Conduct further studies to validate the device's ability to detect atrial fibrillation in ambulatory patients.
Continue research to address the limitations identified in the current study.

Regulatory Considerations
Adherence to FDA guidance for new medical device applications is crucial.
Ensure compliance with regulatory standards for digital health technologies.

Findings
There are significant risks associated with connected sensor technologies that exceed current evaluation capabilities, including validation, security practices, data rights and governance, utility and usability, and economic feasibility.
Existing evaluation frameworks are inadequate for the unique challenges posed by digital health technologies.
The regulatory environment for digital specimens is not well-established, leading to ambiguity in oversight.

Recommendations
Develop a systematic and standardized evaluation framework for connected sensor technologies.
Implement a connected sensor technology label to improve transparency and decision-making.
Encourage innovation and modern regulatory oversight through updated guidelines.
Address the evolving distinction between regulated and unregulated digital health technologies.
Enhance communication infrastructure to make information more accessible to stakeholders.

Regulatory Considerations
The regulatory environment for digital health technologies is evolving, with a need for modern oversight.
There is ambiguity in the regulation of digital specimens, requiring clearer guidelines.
The FDA has issued guidances to encourage innovation and efficient oversight.

Findings
The term "clinically validated" is frequently used in marketing but lacks a clear, standardized meaning, leading to confusion. The rapid development of BioMeTs has outpaced the creation of systematic, evidence-based evaluation frameworks, creating a knowledge gap. Existing validation standards from software, hardware, and clinical development are often applied in silos and are not fully sufficient for modern BioMeTs. Evaluating a BioMeT requires assessing the entire "data supply chain," from the sensor hardware (verification) and data processing algorithms (analytical validation) to its performance against a meaningful clinical concept (clinical validation).

Recommendations
The digital medicine field should adopt the V3 (Verification, Analytical Validation, Clinical Validation) framework as a foundational evaluation standard for all BioMeTs to ensure they are fit-for-purpose. Technology manufacturers, clinical trial sponsors, and researchers should transparently report their V3 processes and results to overcome "black box" approaches and build a common evidence base. Technology manufacturers are primarily responsible for verification , while the entity developing the algorithm (e.g., manufacturer or sponsor) is responsible for analytical validation. The sponsor or clinical team using the BioMeT for a specific purpose is responsible for clinical validation in that context of use.

Regulatory Considerations
The V3 framework is designed to inform and align with the current regulatory landscape, although the regulatory pathway for a specific BioMeT depends on its intended use and marketing claims, not just its underlying technology. The 21st Century Cures Act and the concept of Software as a Medical Device (SaMD) have created new regulatory paradigms that decouple software from specific hardware. BioMeTs used to support drug development may follow a tool qualification pathway, while those marketed as standalone medical devices are subject to device clearance or approval processes. Stakeholders should engage with regulatory agencies early to determine appropriate validation approaches.

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.