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
Patient experience data provides critical context for regulatory review by illuminating disease burden, unmet medical needs, and the aspects of a condition that matter most to patients.
A systematic approach is necessary to ensure patient experience data is robust enough for regulatory consideration, moving beyond anecdotal evidence to scientifically rigorous data collection.
Early engagement between sponsors and the FDA is a key factor for successfully incorporating patient perspectives into a drug development program.
The value of patient-reported outcomes (PROs) and other clinical outcome assessments (COAs) is highly context-dependent, varying significantly across different diseases and patient populations.

Recommendations
Drug sponsors should leverage the FDA's meeting process to discuss their strategies for collecting and submitting patient experience data early in the development lifecycle.
Sponsors should utilize the repository of meeting reports as a learning resource to understand best practices and common challenges in patient-focused drug development for specific conditions.
Patient advocacy groups should actively participate in these discussions to ensure the full spectrum of patient experiences is captured and communicated to both regulators and developers.
Researchers should develop and validate novel tools and methodologies for capturing and analyzing patient experience data that are meaningful for both clinical and regulatory purposes.

Regulatory Considerations
Patient experience data is a key component of the benefit-risk assessment, providing evidence that can inform regulatory decisions regarding a drug's approval and labeling.
The FDA's review of patient experience data is guided by a commitment to patient-focused drug development, as mandated by the 21st Century Cures Act and supported by user fee agreements like PDUFA.
The scientific rigor of data collection and analysis is paramount; for patient experience data to be influential, it must meet high standards of validity and reliability.
Transparency is a core principle, and the publication of these meeting reports is intended to provide clear examples of how patient input can be effectively integrated into regulatory submissions.

Findings
MCID represents the smallest change that someone living with Alzheimer's disease would identify as important, but faces several universal application challenges. Alzheimer's disease progresses differently for each individual, complicating the establishment of universal standards that account for individual-level issues. The disease is gradual and evolving, with what is perceived as clinically meaningful varying significantly at early and late disease stages. People living with Alzheimer's disease and caregivers may have differing perspectives on treatment benefits, making it challenging to establish appropriate MCID. Current Alzheimer's trials rely on various tests to evaluate cognitive and functional impairments, but these tests often lack sensitivity to early-stage changes and are affected by variability in rater rankings. Digital biomarkers offer promising approaches for detecting real-time, objective clinical differences and improving patient outcomes through continuous monitoring, individualized assessments, and artificial intelligence learning for complex analytical predictions.

Recommendations
Digital biomarkers and advanced health technologies should be leveraged to enable continuous monitoring and individualized assessments that can better capture meaningful change in Alzheimer's disease. The primary focus must remain on outcomes that truly matter to people living with Alzheimer's disease and their caregivers, ensuring that the principle of clinical meaningfulness is not lost as new technologies are introduced.

Regulatory Considerations
Important considerations around standardization, accuracy, and integration into current clinical frameworks must be addressed as digital biomarkers are adopted. As new technologies are introduced alongside evolving regulatory frameworks, maintaining focus on clinically meaningful outcomes for patients and caregivers is essential.

Findings
A comprehensive catalogue of over 340 symptoms and impacts was identified across ten symptom domains and two functional impact domains. Strongest evidence for relevance in early disease was found for tremor, fine motor dexterity, gait, stiffness, and slowed movements. Common non-motor symptoms include cognitive alterations, mood changes such as anxiety or depression, sleep disturbances, fatigue, and urinary dysfunction. Significant variability exists in how these concepts are currently measured and classified in literature, often confounding symptoms with functional impacts. There is a notable lack of diversity in existing research, with over 93% of qualitative data originating from white populations in the US, UK, and Canada.

Recommendations
Researchers and clinicians should utilize the proposed Domain-Category-Concept-Experience schema to ensure consistency and parsimoniousness in outcome selection. Selection of concepts for clinical trials should be evidence-based, focusing on those demonstrated to be both prevalent and bothersome to patients. Future research must prioritize the inclusion of culturally, racially, and geographically diverse populations to ensure the model's universal applicability. Stakeholders should adopt lay-friendly terminology, such as using ""slow movements"" instead of ""bradykinesia,"" to better reflect the patient perspective. Continuous re-evaluation of the model is necessary to maintain alignment with emerging biological staging systems for neuronal synuclein disease.

Regulatory Considerations
The consensus model was developed to align specifically with FDA guidance on patient-focused drug development (PFDD) to support regulatory-ready endpoints. Meaningful aspects of health should be identified through direct patient report to satisfy evidentiary requirements for ""fit-for-purpose"" clinical outcome assessments. Evidence-based SOFT report cards provide a transparent method for justifying the selection of concepts of interest in regulatory submissions. Early engagement with agencies is encouraged to ensure selected endpoints are sensitive to treatment effects and reflect what matters most to patients. Harmonization of concept definitions is a critical prerequisite for the successful qualification of new drug development tools.

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.

Key objectives of the initiative include:
Enhancing patient engagement and experience in clinical trials
Providing practical guidance for implementing patient-facing technologies
Addressing regulatory considerations and challenges
Facilitating collaboration among sponsors, sites, patients, and technology vendors

Findings
The development of this standardized questionnaire highlights a critical gap in clinical research: the lack of a consistent method for collecting participant feedback. It implicitly finds that understanding the patient experience is essential for addressing issues like high dropout rates and patient burden. The tool's detailed sections suggest that factors from communication and scheduling to technology usability and visit burden are key determinants of a participant's trial experience.

Recommendations
The resource strongly recommends that sponsors and research sites proactively gather structured feedback directly from study participants. It advises using this tool to identify specific pain points in trial design and execution. The underlying recommendation is to adopt a more patient-centric and human-centered approach by integrating participant feedback into the continuous improvement of clinical trial protocols and operations, ultimately boosting recruitment and retention.

Regulatory Considerations
While not a formal regulatory guidance document, the tool supports the principles of patient-focused drug development (PFDD) encouraged by regulatory bodies like the FDA. Collecting data on the patient experience can help demonstrate that a trial's design and conduct minimizes undue burden and is ethically sound. This feedback can be a valuable component of submissions, illustrating a commitment to patient centricity and potentially improving the assessment of a trial's overall quality and integrity

Findings
The DTRA Best Practice Evaluation Rubric uses five dimensions to determine if a DCT practice should be considered a "best practice":
Evidence of Success: Requires measurable and demonstrable success using KPIs and tangible outcomes.
Improving Patient Experience: Must address the needs of patients, caregivers, and therapeutic experts, demonstrating improved experience and engagement.
Site Impact: Must consider the implications of adoption and the practical impact on site burden and working practices.
Operational and Technical Feasibility: Ensures operational and technical aspects (including ongoing support, security, integrity, scaling, and reuse) have been fully considered when deploying new technologies.
Regulatory & Ethical Compliance: Requires appropriate consideration of global and local regulations and guidance (e.g., ICH E6/E8, GDPR, HIPAA), including adherence to privacy, consent, and ethical safeguards.

Recommendations
A practice should demonstrate several key factors across the dimensions:
Patient-Centricity: Reduce patient burden by offering the option to reduce physical visits and enable greater patient empowerment and access to information. It should strive to increase the diversity of recruited patients while mitigating bias toward technologically literate patients.
Site Support: Achieve a net reduction in burden for sites, utilizing simple, intuitive technology with minimal, on-demand training. It must provide clarity of fiduciary responsibility and use technology to increase risk-based monitoring without sacrificing data integrity.
Technical Rigor: Have a clear problem statement and a thoroughly defined strategy to mitigate operational and technical risks. It should take a holistic approach and ensure the solution is fit for use for the specific patient population, aligning with data privacy and security standards.

Regulatory Considerations
Practices must ensure compliance with both global and local regulations and Health Authority guidance. Explicit attention must be given to aligning with ICH E6 (Good Clinical Practice) and privacy laws like GDPR and HIPAA. The design must protect stakeholders providing sensitive or personal data with safeguards to ensure ethical safety.

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
Qualitative Methods: One-on-one interviews provide in-depth individual insights, while focus groups capture diverse perspectives through participant interaction.
Approaches such as Delphi panels and observational methods can complement interviews and focus groups in understanding patient experiences.
Quantitative Methods: Surveys provide structured, quantifiable data and are effective for large populations.
Careful design of questions and response options minimizes bias and improves data quality.
Mixed Methods:Combining qualitative and quantitative techniques enhances understanding and validates findings.
Sequential and concurrent designs can address complex research questions and improve robustness.
Barriers to Self-Report: Special adaptations may be needed for patients with disabilities, pediatric populations, or those with language or cultural differences.
Proxy reporting by caregivers is sometimes necessary but may introduce bias.
Social Media: Useful for real-time or retrospective insights into patient perspectives. Limitations include lack of verified identities and potential bias in user demographics.

Recommendations
Choose data collection methods aligned with research objectives and the target population.
Use open-ended questions for qualitative research to elicit unbiased responses; avoid leading or judgmental prompts.
Pilot test interview guides, surveys, and response options to ensure clarity and relevance.
Integrate cultural and linguistic adaptations for diverse populations in multinational studies.
For mixed-method research, establish clear objectives for combining qualitative and quantitative components and address conflicting findings systematically.

Regulatory Considerations
Data collected through qualitative or quantitative methods must meet regulatory standards for integrity and reliability when submitted to the FDA.
Screening and exit interviews should not interfere with the integrity of ongoing clinical trials; use trained third-party interviewers where appropriate.
Researchers should follow ethical standards and federal regulations when using social media data, ensuring informed consent and data privacy.

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
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.