Key objectives of the initiative include:
Developing a step-by-step framework (PE Digital Roadmap) for implementing meaningful patient engagement in digital health
Clarifying the role of patients in designing and developing digital health solutions
Addressing challenges in digital health stakeholder alignment through the Stakeholder Expectations Matrix
Promoting transparency in patient involvement processes for digital health solutions

Findings
The most promising aspects of mental health for digital measurement are sleep, physical activity, stress, and social behavior, which have the strongest scientific evidence. Core barriers to adoption include high cost and limited access, data privacy concerns, poor technological literacy, and a lack of technology adaptation for specific mental health needs. Essential technology characteristics for "fit-for-purpose" sDHTs include usability, reliable performance, strong data privacy and security, and long battery life.

Recommendations
Research and development should prioritize moving promising measures (sleep, activity, stress, social behavior) to large-scale clinical trials. Algorithms must be refined and clinically validated for mental health indications, and new sensor modalities should be explored. Infrastructure must be developed by creating standards and ontologies for mental health sensor data to ensure interoperability and scalability. To improve access and equity, financial support mechanisms and inclusive, culturally tailored design are critical.

Regulatory Considerations
The report does not provide a separate section for "Regulatory Considerations" but emphasizes that future development and funding should prioritize clinical validation across diverse populations. It notes the importance of a clear understanding of the intended measurement claims and the need for rigorous validation studies to move beyond pilot and feasibility stages to demonstrate real-world clinical utility.

Findings
The goal of sDHT design is to create tools that are functional, intuitive, accessible, and enjoyable to use, moving beyond merely minimizing use-errors. Human-centered design (HCD) is the preferred term over user-centered design, emphasizing the impact on many user groups beyond just the end-users. "Users" encompass end-users (patients/participants), carepartners, clinicians, investigators, and administrators.

Recommendations
Developers of sDHTs should adhere to the following HCD principles:
Empathetic: Take time to deeply understand users' needs, behaviors, and emotions, capturing this in the use specification.
Holistic: Consider the entire end-to-end user journey, including hardware, software, accessories, packaging, instructions for use, and training.
Iterative: Employ an iterative approach to designing, prototyping, testing, and refining, using formative evaluations to identify use-errors and gather usability data, capturing this in the use-related risk analysis.
User-centric: Improve usability by capturing user feedback in real-world settings, gradually recruiting larger, more diverse samples that represent the intended use population.
Inclusive: Collaborate with individuals representing all user groups by hiring them as consultants or creating user advisory panels to influence design decisions (co-design).
Multidisciplinary: Ensure the development team includes colleagues from various disciplines to bring diverse perspectives and innovative solutions.

Regulatory Considerations
The document ties the HCD process to risk management and eventual validation by recommending that findings from formative evaluations (used to identify use-errors) be captured in a use-related risk analysis. The approach aligns with the principles of the overarching V3+ framework.

Findings
Challenges in patient adherence and acceptability of digital endpoints.
Issues with algorithm validation and use in diverse populations.
Barriers due to proprietary software and lack of transparency.
Vast heterogeneity in digital endpoints and lack of standards.
Need for ongoing ethical support and consideration of environmental impact.

Recommendations
Foster multi-stakeholder cooperation and open-forum discussions.
Integrate patient needs into the design of digital health technologies.
Include implementation science expertise in research proposals.
Develop standards for selecting and reporting digital endpoints.
Provide ongoing ethical support throughout the research lifecycle.

Regulatory Considerations
Early engagement with regulators is crucial.
Understanding regulatory requirements for clinical trials versus clinical care.
Need for harmonised terminology and guidelines for digital endpoints.

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
Meaningful outcome measures should align with patient priorities and clinical relevance, emphasizing aspects of health that impact daily life.
Digital tools must demonstrate value over traditional methods in capturing outcomes, especially in remote or decentralized contexts.
Questions about therapeutic benefit and endpoint sensitivity must address how these measures reflect patient improvements or disease progression.
Stakeholder collaboration is critical to selecting and validating concepts of interest and corresponding outcome measures.
Challenges include ensuring data privacy, operational feasibility, and addressing potential gaps in endpoint validation.

Recommendations
Engage patients and caregivers to identify meaningful aspects of health and concepts of interest relevant to their daily lives and goals.
Collaborate with clinicians to determine the clinical validity and utility of proposed measures and tools for endpoint development.
Ensure that DHTs selected for measurement add value beyond traditional methods and are feasible for clinical and real-world use.
Incorporate payer perspectives to align outcome measures with cost-benefit evaluations and reimbursement criteria.
Use the question bank as a flexible guide, adapting it to the specific needs and context of individual clinical trials.

Regulatory Considerations
Ensure endpoints and their measures meet regulatory standards for clinical relevance and sensitivity to therapeutic changes.
Align outcome measures with accepted core sets (e.g., COMET database) and validate them through stakeholder engagement.
Address concerns related to data privacy, scalability, and operational feasibility in the use of DHTs for endpoint development.
Plan for regulatory engagement to demonstrate the robustness of digitally-derived endpoints in pivotal clinical trials.
Provide evidence to support the incorporation of novel endpoints into regulatory and payer frameworks for decision-making.

Findings
Clinical trial protocols designed without patient input often result in a high participant burden and a poor patient experience, leading to challenges in trial enrollment, adherence, and retention.
A lack of early patient engagement can lead to study designs that are not feasible, collect data on outcomes that aren't meaningful to patients, and require costly protocol amendments later in the process.
Many sponsors and research teams lack a structured, systematic process and standardized tools for effectively planning and executing patient engagement activities.
Meaningful patient partnerships can lead to research of greater quality and relevance, as patients provide unique insights into living with their condition and the practicality of trial procedures.

Recommendations
Adopt a structured toolkit and systematic process to plan patient engagement, define objectives, select appropriate methods, and apply learnings to the protocol.
Engage with patients and caregivers as early as possible in the protocol development lifecycle to ensure their insights can meaningfully influence the study design.
Carefully select diverse patient partners based on criteria like their disease experience, and choose appropriate engagement methods (e.g., advisory boards, focus groups, surveys) to meet defined goals.
Use provided guides, templates, and visual aids to facilitate clear communication, manage expectations, and effectively gather, assess, and implement patient feedback.

Regulatory Considerations
Patient engagement in trial design is strongly encouraged by global regulatory bodies, including the U.S. Food and Drug Administration (FDA).
These activities align with regulatory initiatives like the FDA's Patient-Focused Drug Development (PFDD) guidance, which emphasizes collecting data that reflects patient experiences, needs, and priorities.
Incorporating patient feedback helps ensure that a clinical trial is designed to capture meaningful endpoints and outcomes, which supports subsequent regulatory and Health Technology Assessment (HTA) review.

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
Patient technologies were used across 55 countries, with mobile applications (53%) and wearable devices (33%) being the most common technologies.
Common data issues included data transmission failures, duplicate or missing data, and integration challenges with other datasets.
Factors like technical literacy, device usability, and preferences for paper-based alternatives affected adoption rates, particularly in elderly populations.
Varying broadband connectivity, importation hurdles, and compliance with regulations like GDPR posed significant challenges.
Most sponsors (54%) were willing to reuse technologies, citing improved retention, compliance, and remote monitoring capabilities as key benefits.

Recommendations
Consider patient demographics, such as age and technical literacy, when selecting and implementing technologies.
Offer multi-format training for sites, patients, and monitors, and provide robust support systems to address technical and compliance issues.
Risk Mitigation: Anticipate potential issues like data loss, non-compliance, and technical failures by incorporating backup processes into protocols.
Conduct feasibility assessments for site infrastructure and regulatory compliance in target regions to minimize delays.
Regularly gather experiential feedback from patients to refine technologies and improve future trial designs.

Regulatory Considerations
Seek advice from regulators to ensure patient technologies align with clinical trial protocols and data submission requirements.
Ensure Compliance with GDPR and Local Regulations: Address privacy concerns and adapt technologies to meet country-specific requirements.
Prepare Documentation for Importation: Account for additional time and costs related to import licenses and customs requirements.
Plan for the impact of technical updates on clinical data reliability and regulatory submissions.

Findings
There is a lack of research on patient perspectives regarding the use of digital health technologies in clinical trials.
Digital health technologies offer opportunities to reduce participant burden and streamline operations but require effective engagement strategies.
Protocols need to address safety signals and data contexts not covered by traditional designs.

Recommendations
Engage patients and research sites early and often in planning digital health trials.
Ensure that outcome measurements meaningful to patients are identified before selecting digital health technologies.
Conduct feasibility or pilot studies with representative patient populations prior to trial launch.
Provide thorough descriptions of digital health technologies in informed consent documents.
Ensure sites have appropriate infrastructure and training for digital health trials.

Regulatory Considerations
Protocols should address safety signals not previously observed with traditional designs.
Communicate clearly about data confidentiality risks to participants.
Ensure informed consent documents provide clear guidance on expectations and responsibilities.

Findings
Current PD endpoints, such as UPDRS and PDQ scales, rely on subjective assessments and may not fully capture disease fluctuations or treatment effects.
Accelerometer technology offers objective and continuous data, addressing limitations of traditional endpoints.
The proposed endpoint focuses on "bothersome tremor," validated through observational studies and patient input.
Collaboration among stakeholders, including regulators and technology manufacturers, is crucial for endpoint development and standardization.
Developing a basket of endpoints, including accelerometer-derived measures, can provide a more comprehensive picture of PD burden and treatment impact.

Recommendations
Define the context of use (COU) for accelerometer-derived endpoints, focusing on patient-centered measures like "number of episodes and total duration of bothersome tremor."
Validate accelerometer data through real-world and controlled studies, correlating it with existing PD measures and clinical outcomes.
Collaborate with technology manufacturers to optimize accelerometer placement, signal detection, and patient usability.
Engage with regulators early to align novel endpoints with evidentiary and regulatory requirements.
Establish frameworks for data sharing and standardization to facilitate endpoint development and adoption.

Regulatory Considerations
Validate endpoints to ensure alignment with existing PD measures and regulatory standards for clinical trials.
Incorporate patient feedback during endpoint development to support meaningful and relevant measures.
Address intellectual property (IP) concerns by redefining IP as the execution of algorithms rather than the algorithms themselves.
Align trial design with regulatory requirements, ensuring endpoints can reliably measure treatment impact on PD symptoms.
Engage regulators early to obtain feedback and confirm endpoint readiness for regulatory submission.