Findings
In a progressive neurodegenerative illness like Alzheimer's disease, slowing the rate of disease progression is considered a clinically meaningful outcome for patients and their caregivers.
The assessment of what constitutes a clinical benefit is highly dependent on the specific stage of AD being studied, the drug's mechanism of action, and the symptoms present in that patient population.
Direct input from patients and caregivers is critical for understanding disease burden and defining treatment benefits that are truly meaningful from their perspective.
The interpretation of score changes on Clinical Outcome Assessments (COAs) requires full context; an absolute point difference must be considered relative to the study's duration, the expected placebo decline, and the specific disease stage.
Evidence from biomarkers that show an effect on underlying disease pathology provides additional support and increases the persuasiveness of the changes observed on clinical endpoints.

Recommendations
Drug developers should implement multiple "fit-for-purpose" COAs that use different reporters (e.g., clinicians, observers) and methods to generate broad and diverse evidence of a drug's clinical benefit.
Sponsors should utilize both qualitative and quantitative methodologies to explore clinical meaningfulness, including assessing "meaningful within-patient change" throughout the development process.
Developers are encouraged to create and validate new COAs and leverage innovative approaches, such as digital health technologies, to better capture concepts that are relevant to patients, especially in the earliest stages of AD.
Throughout the drug development lifecycle, stakeholders should systematically collect and incorporate patient experience data to ensure that the perspectives, needs, and priorities of patients are meaningfully captured.

Regulatory Considerations
For a drug to gain approval, it must meet the regulatory standard of "substantial evidence of effectiveness," which is typically derived from adequate and well-controlled investigations designed to minimize bias.
The FDA defines clinical benefit as a clinically meaningful effect of a drug on how an individual feels, functions, or survives.
An assessment of clinical benefit is not limited to the primary endpoint; the consistency of findings across multiple endpoints (primary and secondary) is a key consideration during regulatory review.
The accelerated approval pathway may be used for serious conditions with unmet needs based on a surrogate endpoint, but traditional approval requires verification of clinical benefit in confirmatory trials.
The FDA's evaluation includes a benefit-risk analysis, which considers the severity of the disease and the availability of alternative therapies, recognizing that patients and physicians may accept greater risks for life-threatening illnesses.

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
Digital health measures for ADRD must align with patient and care partner priorities, including functional daily activities such as remembering object locations and maintaining speech fluency.
Sensor placement, data collection modalities, and algorithmic interpretation significantly impact the accuracy and reliability of digital measures.
While digital cognitive and behavioral assessments have strong potential as clinical endpoints, standardization is needed to ensure regulatory acceptance.
Sleep and mobility disruptions in ADRD can be measured with actigraphy, EEG, and ambient sensor-based approaches, but usability considerations are crucial.
Metadata, including environmental conditions and patient comorbidities, must be accounted for to ensure valid interpretations of digital measures in both research and clinical practice.

Recommendations
Researchers and technology developers should adopt standardized ontologies for digital measures to improve consistency across studies and regulatory submissions.
Digital biomarkers should be selected and validated with reference to patient and care partner needs, ensuring they reflect meaningful aspects of health.
Considerations such as sensor placement, data processing methods, and cultural neutrality of cognitive assessments must be accounted for in study designs.
Clinical trials should incorporate digital health technologies as both exploratory endpoints and potential screening tools for ADRD progression.
Further research is needed to refine algorithms for sleep, mobility, and speech-based digital biomarkers to enhance their predictive power for cognitive decline.

Regulatory Considerations
Digital measures of sleep and mobility have been recognized as potential clinical trial endpoints by regulatory agencies such as the FDA.
Standardized reporting and frameworks should be followed to ensure interoperability and data integrity in digital health studies.
Developers must document and validate scoring algorithms used for cognitive and behavioral assessments to meet regulatory expectations.
Data privacy and security regulations must be adhered to, particularly when collecting real-world behavioral and biometric data.
Ongoing validation and real-world evidence generation are necessary to establish digital measures as reliable clinical and regulatory endpoints in ADRD research.

Findings
The field lacks a centralized, standardized database of validated digital health technologies, making it difficult for researchers and clinicians to select appropriate tools.
Non-wearable sensors and software applications are the most common types of DHTs, with 83% of ambient technologies categorized as software or applications.
Most DHTs focus on mild cognitive impairment (MCI) and early Alzheimer’s disease, with fewer technologies validated for moderate or severe dementia stages.
Uneven Distribution of Dementia Subtypes – The review identified a gap in DHT validation for frontotemporal dementia (FTD) and Lewy Body dementia, with Alzheimer’s disease being the predominant focus.

Recommendations
Expand and maintain an open-access database of validated DHTs to improve accessibility and standardization.
Increase research on digital measures applicable to moderate and severe stages of dementia, as well as non-Alzheimer’s dementias.
Promote integration of wearable, ambient, and cognitive assessment tools to generate comprehensive digital phenotypes of patients.
Follow clear guidelines for analytical and clinical validation of DHTs to improve regulatory acceptance and research applicability.
Conduct more usability and feasibility assessments, especially for populations with cognitive decline, to ensure DHTs are accessible and effective in real-world settings.

Findings
There is a lack of systematic approaches to guide the processes of collecting, interpreting, analyzing, and translating health data from wearables into digital biomarkers.
Most wearables have fixed measurement capabilities, limiting their translation to digital biomarkers.
Current guidance lacks study design and conduct elements that involve all stakeholders in an iterative approach for implementing digital biomarkers in practice.
Researchers and health professionals often rely on limited guidance for using wearable data in clinical practice and chronic disease management.

Recommendations
Implement the DACIA framework to provide interdisciplinary guidance on using wearable sensor data for digital biomarker development.
Focus on participant needs as a crucial factor for study success, applicable to both short and long-duration studies.
Involve relevant stakeholders in each key step of the DACIA framework in an iterative manner.
Apply the DACIA framework to explore digital biomarkers using various devices or signal measurements.
Reduce participant burden through support and continuous feedback.

Regulatory Considerations
Not mentioned

Findings
Standard assessments lack sensitivity in early stages of neurodegenerative diseases.
Challenges with the validity and quality of RMT measurements.
Issues related to equity and inclusion in deploying digital tools.
Importance of considering feasibility, acceptance, usability, and ecological validity of digital endpoints.

Recommendations
Develop regulatory strategies early on.
Ensure equity and inclusion in deploying digital tools.
Address challenges related to the validity and usability of digital endpoints.
Promote public-private partnerships to address privacy and security concerns.
Involve patients and stakeholders in the design and implementation of digital tools.

Regulatory Considerations
Acceptance of digital endpoints by regulatory authorities is crucial.
Validation with current gold standards and clinically meaningful legacy endpoints.
Ensure data security and privacy.

Findings
Digital health technologies can provide more granular, continuous, and sensitive measures compared to traditional clinical assessments.
Digital measurements can detect treatment responses earlier and with smaller sample sizes than traditional methods.
There is a lack of standardized endpoints and insufficient data to contextualize findings from digital measurements.

Recommendations
Utilize digital health technologies to increase research efficiency and reduce trial participation burden.
Develop frameworks for regulatory acceptance of digital endpoints.
Continue research to establish meaningful changes9 and standardize endpoints based on digital measurements.

Regulatory Considerations
Establish evidentiary criteria for using digital measurements as surrogate endpoints.
Address the need for regulatory frameworks to support the use of digital health technologies in clinical trials.

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
Novel digitally-derived endpoints can provide more reliable data, increase trial efficiency, and enhance patient centricity.
Selecting appropriate outcome measures that are meaningful to patients and clinicians is critical to success.
Developing these endpoints requires a resource-intensive, systematic approach to meet stakeholder needs.
Demonstrating validity and utility of novel endpoints poses unique challenges, especially for new measures without established validation standards.
Sharing lessons learned and promoting transparency can advance the field by enabling collaboration and establishing standards.

Recommendations
Focus on measures that are meaningful to patients and clinically relevant by incorporating both patient and clinician perspectives.
Select technology after identifying the appropriate outcome to ensure alignment between the technology and trial objectives.
Engage with regulators early and often to ensure endpoint acceptance and alignment with regulatory requirements.
Include digitally-derived endpoints in early-phase trials and observational studies to validate their fit-for-purpose status.
Encourage knowledge sharing and collaboration among stakeholders to establish shared standards and accelerate adoption.

Regulatory Considerations
Engage with FDA, EMA, or other regulatory bodies during early stages of endpoint development to gather critical input.
Use established regulatory frameworks, such as Investigational New Drug (IND) or Investigational Device Exemption (IDE), for guidance on endpoint use in pivotal trials.
Validate technologies to meet performance characteristics, ensuring outputs correspond to clinical concepts of interest.
Include digitally-derived endpoints in exploratory studies to build evidence for their regulatory approval.
Reference resources such as the FDA and EMA guides for navigating endpoint-related regulatory interactions.

Findings
Despite progress, DHTs are not yet fully accepted in clinical research.
Challenges include small study samples, unrepresentative samples, lack of normative data sets, feature selection bias, and replication issues due to sensor variability.
There is a need for a framework to identify and mitigate sources of variability in DHTs.

Recommendations
Develop a conceptual framework to identify and mitigate sources of variability.
Consider both active and passive monitoring approaches in study designs.
Align knowledge and data sharing across consortia to improve DHTs.
Emphasize normative data sets to establish ground truths for variability.
Encourage precompetitive collaborations to advance regulatory maturity.

Regulatory Considerations
Collaborative efforts like the 3DT project are essential for regulatory maturity.
Global regulatory agencies encourage data-driven engagement through consortia.

Findings
There is a need for appropriate training and infrastructure support to address challenges in implementing digital health technologies.
User acceptance is hindered by discomfort with technology among some participants.
Physicians face time constraints and question the utility of digital health technologies over current practices.
Concerns about data confidentiality among participants need to be addressed.
The complexity of digital health technology affects patient acceptance.

Recommendations
Provide appropriate training to staff and patients.
Ensure availability of appropriate infrastructure support.
Conduct pilot studies before scaling up to larger trials.
Address data confidentiality concerns.
Select devices with FDA clearance to minimize regulatory hurdles.

Regulatory Considerations
The FDA's Digital Health program provides regulatory advice for digital health technology applications.
Choosing devices with FDA 501(k) clearance can minimize regulatory hurdles.