Findings
Digital biomarkers, such as DaTscan enrichment and SV95C-like endpoints, can reduce sample sizes and improve study performance when used effectively.
Digital endpoints help address challenges in neurodegenerative disorder trials, such as variability in subjective outcome measures.
Quantifying the benefits of digital technologies in clinical trials supports stakeholder alignment and resource prioritization.
Current gaps include the lack of robust economic models, evidence for clinical impact, and standardized frameworks for biomarker integration.
A business-oriented, data-driven approach facilitates collaboration between pharmaceutical sponsors, technology providers, and regulatory bodies.

Recommendations
Develop quantified frameworks like Moneyball to simulate the impact of digital biomarkers on study performance metrics, including sample size and probability of success (PoSS).
Prioritize early alignment between biomarkers and therapeutic objectives to streamline validation and regulatory approval processes.
Build multi-stakeholder collaborations to address gaps in evidence requirements and create shared value frameworks for digital technology investments.
Enhance decision-making with real-time simulations of clinical trial scenarios to optimize patient selection and endpoint design.
Expand quantitative models to include diverse biomarker types, heterogeneous patient populations, and long-term treatment outcomes.

Regulatory Considerations
Align digital biomarker validation processes with the V3 framework (verification, analytical validation, clinical validation) to meet regulatory standards.
Demonstrate robust evidence of clinical utility and reproducibility to support regulatory submissions for novel digital endpoints.
Develop standardized protocols for integrating biomarkers into trial designs, ensuring compliance with EMA and FDA guidelines.
Address patient privacy and data security concerns in the use of wearable and digital health technologies.
Collaborate with regulatory bodies to establish frameworks for evaluating novel biomarker modalities and endpoints in clinical trials.

Findings
The use of Intended Use and Indication for Use is crucial for digital health products to ensure the product is used appropriately and effectively to meet the needs of the intended population. This information helps establish clear expectations for a product's performance and safety, facilitates regulatory approval, and ensures compliance. The Intended Use provides a general description of the digital health product's purpose or function. The Indication for Use describes the disease or condition the device will diagnose, treat, prevent, cure, or mitigate, including a description of the patient population. A change in a product's indication for use from general to specific usually results in a narrower indication concerning function, target population, or disease entity. Levels of specificity for diagnostic and therapeutic products can be categorized, ranging from the identification of a physical parameter (most general) to the identification of an effect on the clinical outcome (most specific).

Recommendations
The Intended Use statement should include the name of the product, the medical purpose, and what it is trying to do for the user. The Indication for Use statement should include the name of the product, the specific condition or disease state it is addressing, the patient population being targeted, what the product features do, whether other technology components are used with the product, and whether it is for "prescription" or "over-the-counter" use. Developers should characterize the users (e.g., by age, knowledge, or language) and describe the usage context (e.g., hospital ward, emergency room, web-based app). The Indication for Use statement should clearly state the product's clinical capabilities and what it is not intended for (e.g., not intended to provide a diagnosis or replace traditional methods).

Regulatory Considerations
The information provided in the Intended Use and Indication for Use statements is used to inform the product's design and development, as well as to guide regulatory decisions about its approval and marketing. Defining these statements facilitates the regulatory approval process and helps ensure compliance with relevant regulations and standards. The FDA defines the levels of specificity as a qualitative ranking of the proposed indications for use. The document provides examples of FDA's "Indications for Use" from submissions, such as the use of an Atrial Fibrillation History Feature, illustrating the necessary detail for regulatory submissions like a 510(k).

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
The FDA has distinct engagement pathways depending on whether the product is a Standalone Digital Health Product or a Combination Product. The pathways are further categorized into Informal and Formal advice.
Informal Pathways include:
Digital Health Inquiry (Digital health inbox, DICE mailbox inquiry).
General Inquiry for combination products.
CDRH List & Learn.
Formal Pathways for digital health and combination products include:
513(g) request (for classification information).
Q-Submission Program (Pre-Submission, Submission Issue Request (SIR), Information meeting).
CDRH-payor connection (Early payor feedback program, Parallel review with CMS).
RFD/Pre-RFD process (Request for Designation).

Recommendations
Developers should use this map to identify the appropriate mechanism for seeking regulatory advice. The initial decision point is whether the engagement is related to the design, development, or deployment of a digital health product. Once the product type is identified, the map directs the user to the appropriate formal or informal path. The various mechanisms are used for different purposes, such as an Information meeting (to share information without expecting feedback) or a Pre-Submission program (for formal feedback on a planned product).

Regulatory Considerations
The pathways involve different Centers and Offices, including the Center for Devices and Radiological Health (CDRH), Centers for Medicare & Medicaid Services (CMS), and the Office of Combination Products (OCP). The FDA's focus is on engagement pathways for digital health products. For information on developing digitally derived endpoints for drugs, developers are directed to the DiMe and CTTI guides. Engagement is related to design, development, or deployment of the product

Findings
Decentralized clinical trials (DCTs) improve trial accessibility, efficiency, and patient-centricity but require careful planning and stakeholder engagement.
Digitally-derived novel endpoints remain underutilized in clinical trials due to challenges in validation, regulatory acceptance, and stakeholder collaboration.
Updated recommendations emphasize meaningful measures, early regulatory engagement, and sharing lessons learned to advance endpoint development.
Resources like the question bank and process map help sponsors and investigators align measures with patient needs and regulatory expectations.
Addressing interoperability, privacy concerns, and diverse participant needs is critical for broad implementation of DHTs in trials.

Recommendations
Engage all stakeholders early, including patients, regulators, and technology providers, to address trial needs and risks effectively.
Develop endpoints that are meaningful to patients and clinically relevant by incorporating stakeholder input and aligning with regulatory expectations.
Use digitally-derived endpoints in early-phase trials to validate their fit-for-purpose status and optimize their positioning in pivotal trials.
Provide training and support for investigators, sites, and participants to enhance DCT implementation and reduce burdens.
Promote collaboration and data sharing to advance standardization and facilitate the adoption of novel endpoints.

Regulatory Considerations
Use the revised Regulatory Engagement Guide to clarify when and how to engage with FDA and EMA for digitally-derived endpoint validation.
Align trial designs with regulatory requirements for data reliability, privacy, and endpoint applicability.
Leverage evidentiary considerations to demonstrate endpoint reliability, meaningfulness, and clinical relevance.
Integrate feedback from regulators early to avoid delays in endpoint acceptance and ensure compliance with regional laws.
Address concerns related to data privacy, interoperability, and participant diversity to meet regulatory and ethical standards.

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
Advantages of MCTs: Investigators highlighted remote data capture, access to real-time data for monitoring, and improved data quality as major benefits. They also noted reduced participant burden due to fewer in-person visits and increased participant engagement through real-time data access.
Challenges of MCTs: Increased site burden due to additional time required for technology setup, troubleshooting, and managing high data volumes was a common theme. Participants faced challenges such as technology unfamiliarity, device management, and potential behavior changes from real-time data access.
Support Needs: Investigators emphasized the need for technical support, staff training, and increased budgetary resources to manage devices and train participants. They also highlighted the importance of clear communication about device selection and capabilities from sponsors.

Recommendations
Sponsors should supply comprehensive device training, including hands-on and supplemental materials, and establish systems for ongoing technical support throughout the trial.
Include funds for device management, staff training, and participant support to accommodate the additional demands of MCTs.
Prioritize user-friendly devices to minimize participant burden and improve adherence.
Collaborate with investigators and participants during trial planning to ensure technologies align with study objectives and participant needs.
Focus on in-person, hands-on training to ensure staff and participants are comfortable with the technologies.

Regulatory Considerations
Devices must meet data security and safety requirements to address Institutional Review Board (IRB) concerns.
Provide detailed information about device safety, storage, and capabilities to ensure compliance with regulatory standards.
Clearly communicate data access levels to participants, minimizing risks of data misinterpretation.

Findings
Quantitative imaging extracts numerical values from medical data that are subject to systematic error and random variation. The utility of these values depends on well-characterized performance and sufficient user information for interpretation. Performance specifications often change throughout the operating range of a device, such as volumetric reproducibility varying by structure size. Fully automated functions require more robust analytical validation than manual or semi-automated functions because they lack the opportunity for expert user correction. While phantoms serve as high-quality reference standards for ground truth, they are simplifications that may not fully reflect clinical performance.

Recommendations
Manufacturers should provide a detailed technical description of the quantitative imaging function, including the measurand, algorithm training paradigms, and level of automation. Performance specifications should incorporate objective reference values when available to allow for comparisons between subject and predicate devices. A sensitivity analysis should be conducted to determine the impact of sources of error like patient characteristics, image acquisition protocols, and image processing. Labeling must include clear instructions for user-performed quality assurance and specify any limitations where the function has been found ineffective. For automated devices, manufacturers should help users understand scenarios where the function might generate an incorrect output that is not easily identifiable.

Regulatory Considerations
The FDA recommends following a ten-step technical performance assessment process, ranging from defining the measurand to comparing statistical results against pre-defined acceptance criteria. Premarket submissions should include performance data demonstrating that the device meets claims regarding bias, precision, linearity, and limits of quantitation. Uncertainty should be reported in units of the measurand and cover the entire operating range of the function. Manufacturers are encouraged to use the Q-Submission process to address questions regarding regulatory status or specific requirements. Software implementation details should align with existing FDA guidance for the content of premarket software documentation.

Findings
Vendor selection should assess 13 categories, including General organizational operation and products, Quality Management Principles, Practices of product or service design, Device supply and provisioning, Account provisioning, Study specific materials, Live trial support, Trial closeout activities, Data handling/processing and data flow (GDPs), Device and Data (sensors + raw data) algorithm accessibility, Interoperability/Integration, Validation/Clinical Relevance/standard of documentation, and Cybersecurity. Key aspects to consider include having Validation and verification of the device and algorithm in place, ability to support multiple countries, an established quality management system. Vendors must assure maintained data integrity and quality, and provide evidence of Good Clinical Practice (GCP) compliance. Robust practices in Good manufacturing practice, Good product development practices (for hardware and software, including software lifecycle documentation), and Good scientific practices are required.

Recommendations
Sponsors should engage with vendors early in study design to tailor the technology capabilities and data requirements to patient needs and preferences. Vendors are typically responsible for device verification and analytical validation, but collaboration with sponsors and other stakeholders on clinical validation is beneficial to establish validation thresholds, specific needs of target clinical populations, and acceptability and usability of the technology. Sponsors should enable a feedback loop from patients back to vendors to improve technology for specific target populations. Sponsors or researchers should prioritize access to high-fidelity and sensor-level data to enable novel research and assessment of additional health aspects. Specific inquiries for vendors should cover: measurements offered (Accelerometry output for scratch detection, sleep measurement, environmental factors, and vitals), device material and safety testing (irritation/sensitization), usability/patient burden (e.g., disturbance during sleep), and applicability to Pediatrics, different ethnic groups, and different skin colors.

Regulatory Considerations
For software development, vendors should document and monitor the software lifecycle for quality, and demonstrate that algorithms have been tested with appropriate datasets. Assurances of GCP compliance are necessary. Vendors should demonstrate that their manufacturing practices ensure devices from different batches provide the same result measurements. Collaboration on clinical validation with sponsors and other stakeholders is a key component to generate the necessary evidence for the Validation/Clinical Relevance/standard of documentation requirement for regulatory purposes.

Findings
The review includes 275 studies, with neurology, musculoskeletal disorders, and cardiology as the most common therapeutic areas.
The studies focused on sensor performance (48%), algorithm development (86%), operational feasibility (46%), and software development (9%).
Gaps in reporting included insufficient details on software used (27%), comparator measures (17%), and participant demographics (e.g., age and gender were missing in 9% and 15% of studies, respectively).
Sixty-seven percent of the studies used wearable sensors, while others incorporated smartphones, tablets, cameras, and implantable devices.
The lack of methodological and reporting standards across studies hinders reproducibility and broader applicability.

Recommendations
Develop methodological and reporting standards to improve consistency across feasibility studies.
Include comprehensive participant demographic data, including sociodemographics and health indicators, to ensure inclusivity and generalizability.
Conduct small feasibility studies to validate sensors, optimize algorithms, and identify operational challenges before launching full-scale trials. Use the database created from this review to inform trial design and technology selection, ensuring alignment with specific research goals.
Encourage collaboration among investigators, sponsors, and regulators to standardize methods and share insights to avoid redundant studies.

Regulatory Considerations
Align sensor verification and algorithm validation processes with regulatory requirements for reliable clinical endpoints.
Ensure secure and ethical data transfer, storage, and sharing practices for compliance with privacy regulations.
Address barriers to participation for underrepresented populations by assessing and reporting equity-related data during feasibility studies.

Findings
Research is more impactful and relevant when patients and other stakeholders are treated as equal partners throughout the entire study lifecycle.
A lack of shared vision, clear communication protocols, and defined roles are significant barriers to the success of multi-stakeholder research teams.
Engaging diverse stakeholders leads to the development of more patient-centered research questions and outcome measures that reflect real-world priorities.
Institutional barriers, such as inflexible policies on compensation and data access for non-researcher team members, frequently undermine effective collaboration.
Successful patient-centered outcomes research (PCOR) requires specific skills in collaborative problem-solving, conflict navigation, and leading productive team meetings.

Recommendations
Integrate patients, caregivers, clinicians, and other stakeholders into research teams from the initial planning stages to ensure alignment with patient needs.
Establish a shared vision and clear ground rules for communication, decision-making, and responsibilities to foster a cohesive and productive team environment.
Provide training and resources for all team members on best practices for stakeholder engagement, collaborative teamwork, and patient-centered research methods.
Institutions should develop supportive infrastructure, including fair compensation policies and streamlined onboarding processes, to facilitate meaningful stakeholder participation.
Research plans should be flexible, allowing teams to adapt their engagement strategies and methodologies in response to stakeholder feedback and changing circumstances.

Regulatory Considerations
Evidence generated through patient-centered outcomes research can strengthen regulatory submissions by demonstrating that a product's benefits are meaningful to patients.
The inclusion of diverse patient populations in research, a core tenet of PCOR, helps generate real-world evidence that is more generalizable and relevant for post-market surveillance.
Regulatory bodies are increasingly emphasizing the use of patient-experience data and patient-reported outcomes, which are central to the PCORI research model.
Engaging stakeholders in the selection of clinical trial endpoints helps ensure alignment with patient priorities, which can facilitate more efficient regulatory review.
The collaborative, transparent methods promoted by PCORI can help build trust and align expectations among researchers, patients, and regulatory agencies.

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.