Findings
Fragmented and inconsistent measurement approaches currently hinder the generation of decision-grade evidence for pediatric rare diseases. Small and geographically dispersed patient populations make traditional site-based clinical assessments operationally difficult and burdensome for families. Digital health technologies can capture subtle functional changes and "functional fingerprints" in home settings that are often missed during infrequent clinic visits. Standardized core digital measures across conditions allow for the aggregation of data and the creation of a shared evidence base for rare disorders. Meaningful aspects of health identified by patients and caregivers include motor function, communication, sleep quality, and autonomic stability.

Recommendations
Sponsors should adopt the core set of digital clinical measures to reduce trial timelines, lower development costs, and decrease participant burden. Researchers should prioritize passive and objective data collection to minimize the need for manual tracking by caregivers. Clinical trial designs should transition toward decentralized or hybrid models to improve access for children and families regardless of their location. Stakeholders should use the project's conceptual model to identify and customize digital measures that align with the specific health priorities of their target population. Developers should focus on human-centered design to ensure digital tools are usable and sustainable for pediatric patients and their support networks.

Regulatory Considerations
The FDA and EMA provide specific pathways and interaction opportunities to accelerate the acceptance of digital endpoints in rare disease trials. Digital measures must be validated as "decision-grade" endpoints to meet the evidentiary requirements for regulatory submission and marketing approval. Alignment with industry standards for data elements and interoperability is necessary to ensure data integrity across multi-site studies. Early engagement with regulatory bodies through meetings and formal submissions is critical for confirming the suitability of new digital biomarkers. Compliance with data privacy and ethical standards is paramount when collecting continuous, real-world data from vulnerable pediatric populations.

Findings
The quality of evidence for the analytical validation of sensor-based digital health technologies (sDHTs), which is the evaluation of algorithms converting sensor data into a clinically interpretable measure, is often inconsistent and insufficient. The existing V3+ framework codifies the overall evaluation process, which includes verification, usability validation, analytical validation, and clinical validation. To improve the scientific rigor of analytical validation, a hierarchical framework for selecting reference measures is needed because not all potential reference measures are of equal quality. The framework classifies reference measures based on attributes that contribute to reduced measurement variability, with defining and principal measures being the most rigorous due to objective data acquisition and the ability to retain source data.

Recommendations
The proposed framework sequentially moves the investigator through four steps: (1) Compile preliminary information, including the digital clinical measure, context of use (COU), algorithm requirements, and sensor verification evidence . (2) Select an existing reference measure, develop a novel comparator, or identify a set of anchor measures, prioritizing measures with the highest scientific rigor (defining → principal → manual → reported) . (3) Consider the impact of the data collection environment to determine if the analytical validation study can be conducted in the intended use environment with the highest-order measure, or if in-lab validation is necessary, ensuring the results are generalizable . (4) Describe the rationale for key study design decisions to encourage transparency for evaluators, regulators, and payers . Investigators must justify passing over a higher-ranked reference measure, generally only acceptable if the higher-ranked measure poses unacceptable risk or is not applicable to the context of use.

Regulatory Considerations
The principles of the framework for analytical validation apply regardless of the regulatory status of the sDHT (regulated medical device, low-risk general wellness apps, or research product) or its intended use (clinical care or clinical research). The framework is intended to help investigators support the most rigorous claims regarding sDHT performance, which is important for acceptance by evaluators, peer-reviewers, regulators, and payers. The categorization of the digital clinical measure as a digital biomarker or an electronic clinical outcome assessment also does not change the framework's applicability.

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
Cytokine Release Syndrome (CRS) is a common and potentially life-threatening adverse event of immunotherapies, particularly in Oncology, complicating patient care and increasing healthcare costs. Standard-of-care inpatient monitoring for CRS is manual, intermittent, costly, and restrictive, providing an incomplete view of the syndrome’s development and progression. The use of Digital Health Technologies (DHTs) for continuous, remote monitoring of vital signs (like heart rate, respiratory rate, skin temperature, SpO2, and activity) can capture early indicators of CRS up to two hours earlier than standard episodic monitoring. This ability to collect multivariate continuous data is valuable for informing robust model development for CRS risk prediction.

Recommendations
Investigators should deploy DHTs available today to monitor vital signs and symptoms currently observed in the hospital setting, but in an outpatient or home environment. The goal is to develop Early Warning Products that assess the probability of developing CRS, providing clinical decision support. Product developers should follow a strategic roadmap that outlines milestones for building products that are clinically relevant and commercially viable. Researchers should use a common set of digital clinical measures to gather high-quality datasets and ensure comparability across studies to build more robust predictive models. Predictive algorithms should be built on a robust reference measure for analytical validation and be clinically validated with sufficient data.

Regulatory Considerations
The resources are designed to help developers build products that are clinically appropriate, regulatory-acceptable, and commercially viable. Future regulatory submissions for CRS de-risking products will benefit from aligning with this industry-wide dialogue that is being built in collaboration with the FDA. Developing a robust CRS safety biomarker could enhance the safety profile of clinical trials, increase trial access, and streamline regulatory decision-making, possibly through a qualification pathway. Products that aim for a higher level of autonomy, such as a Diagnostic that redefines current CRS grading classes, may require very high clinical evidence and likely stringent regulatory review.

Findings
While verification, analytical validation, and clinical validation have been well-established, usability validation has not been systematically incorporated into digital health technology evaluation.
Variability in device designs, patient populations, and regulatory environments creates barriers to widespread adoption of sensor-based digital health technologies.
Usability problems, such as poor user interfaces and technical errors, can lead to significant data loss in clinical trials and real-world applications.
While some guidance exists for usability in medical devices, there is no unified global standard for assessing usability in digital health products, leading to inconsistencies in implementation.
Stakeholders, including regulators, industry leaders, and researchers, recognize the need for usability validation to ensure the real-world effectiveness of digital health technologies.

Recommendations
Adopt the V3+ framework as a standardized method to ensure that usability is rigorously tested alongside verification, analytical validation, and clinical validation.
Establish clear protocols for usability testing, including use specification development, risk analysis, iterative formative evaluations, and summative evaluations.
Bring together regulators, technology developers, clinicians, and patients to create guidelines that ensure fit-for-purpose digital health solutions.
Work with regulatory agencies such as FDA, EMA, and MHRA to establish harmonized global standards for usability validation.
Encourage the publication of usability study results, including negative findings, to facilitate transparency and continuous improvement in digital health technologies.

Regulatory Considerations
Agencies like FDA and EMA increasingly require usability data for digital health technologies, but standardized methodologies are still evolving.
Usability validation should align with regulatory requirements for medical devices and digital biomarkers, ensuring clinical relevance and data integrity.
Digital health technologies must adhere to HIPAA, GDPR, and other data protection regulations while ensuring seamless usability.
Poor usability can lead to missing or unreliable data, which affects regulatory submissions and real-world evidence generation.
A consistent approach to usability evaluation is needed to support regulatory decision-making and digital health product approvals globally.

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
Usability is a multi-domain concept that requires a combination of methods for evaluation. Evaluations fall into two types: formative (for design modification of prototypes) and summative (for demonstrating usability of the final product to a representative user sample). The user experience metrics fall into several domains, including: Satisfaction, Usefulness, Ease of use, Learnability, Efficiency, Memorability, Understandability, Actionability, Readability, and Use-errors. Metrics related to Satisfaction and Usefulness are always subjectively reported by users.

Recommendations
Developers should select metrics based on the specific usability-related domain being evaluated.
Subjective Data (e.g., Satisfaction, Usefulness): Capture through qualitative surveys, quantitative surveys (scales), interviews, focus groups, and think-aloud evaluations .
Objective Data (e.g., Ease of use, Use-errors): Capture through direct or indirect observation (e.g., counting steps/attempts, timing task completion), or by using data generated by the sDHT (e.g., error reports, timestamps, page load times).
Time-based Metrics: Evaluate Learnability (ease of first use), Efficiency (ease with experience), and Memorability (ease after non-use) by measuring ease of use at different points in time .
Information Presentation: If the sDHT presents clinical data or written information (instructions, warnings), evaluate Understandability, Actionability, and Readability .
Use-errors: Objectively capture the number, type, and recoverability of use-errors (actions, or lack thereof, that may result in harm) via observation and sDHT data, noting that "use-error" is preferred to "user-error".

Regulatory Considerations
While this guide does not reference regulatory bodies like the FDA, it is part of the V3+ framework and recommends that researchers prioritize essential documents like the use specification and use-related risk analysis before designing a usability study. Summative evaluations demonstrating usability against a representative user sample under intended use conditions are the standard for demonstrating product fitness.

Findings
Usability gaps in sDHTs remain a barrier to adoption, with many technologies failing to prioritize ease of use, accessibility, and diverse user needs​
Human-centered design is critical for ensuring that digital health solutions are intuitive, functional, and scalable across different healthcare environments​
Standardized usability metrics for evaluating digital health technologies are lacking, leading to inconsistent reporting and validation of usability outcomes​
Use-related risk analysis is essential to identifying and mitigating risks associated with user errors, ensuring the safety and effectiveness of sDHTs​
The V3+ framework provides a structured approach to integrating usability validation into digital health technology development, aligning with global regulatory expectations​

Recommendations
Developers should incorporate human-centered design principles from the outset, ensuring that usability, accessibility, and user needs are central to sDHT development​
Usability validation should be standardized, with clear methodologies for measuring usability, including satisfaction, ease of use, efficiency, and error mitigation​
Regulatory and clinical stakeholders should collaborate on defining best practices for usability evaluation, ensuring that digital endpoints are both meaningful and scalable​
Risk analysis should be iterative, with developers continuously refining their technologies based on real-world user feedback and testing​
The usability validation component of V3+ should be widely adopted to ensure that digital clinical measures meet patient-centered, regulatory, and technical expectations​

Regulatory Considerations
Regulators are emphasizing the need for usability validation to ensure that digital endpoints are both clinically relevant and patient-friendly​
sDHTs must comply with human factors engineering guidelines, aligning with global regulatory frameworks such as ISO 9241-210 and FDA usability requirements​
Data security, privacy, and interoperability must be ensured, particularly as sDHTs become integrated into remote monitoring and decentralized clinical trials​
Real-world evidence (RWE) should support usability validation, helping to bridge the gap between regulatory approval and real-world adoption​
Regulatory bodies should work toward standardizing usability testing methodologies, ensuring consistency across clinical research, digital endpoints, and medical device evaluations​

Findings
For an sDHT to be considered fit-for-purpose, a researcher or healthcare provider must understand the alignment between the sDHT's intended use (What it does, who uses it, where/when/how) and their own context of use . Key information for this assessment comes from the developer's Use Specification (detailing hardware, software, accessories, training) and Use-Related Risk Analysis (detailing warnings, harms from use-errors, and risk avoidance) . Usability validation evidence should cover study objectives, protocols, participant characteristics, metrics, and collection methods.

Recommendations
Researchers/providers should use the checklist to:
The Basics: Compare the sDHT's intended use to their context of use; if there is substantial overlap, existing evidence may be sufficient.
Use Specification/Risk Analysis: Gather detailed descriptions of the sDHT's hardware, software, accessories, written materials, training, cautions, warnings, and potential harms from use-errors to update their own Use Specification and Use-Related Risk Analysis .
Existing Evidence: Access existing usability validation study results (objectives, methods, participant characteristics, metrics, etc.) to determine its applicability and generalizability to their context of use .
Collaboration: Consider establishing a collaborative relationship with the developer to provide feedback for next-generation sDHTs, ensure version control, and potentially collaborate on future usability validation studies .

Regulatory Considerations
The document notes that if the sDHT is a regulated medical device, the intended use statement should already capture the answers to the basic questions. The entire checklist is framed around the V3+ framework, which is designed to ensure the rigor necessary for a product to be considered fit-for-purpose by all stakeholders.

Findings
Sleep disturbances are common across multiple therapeutic areas, making standardized digital measures essential for cross-condition research.
Measurement accuracy varies depending on sensor placement, algorithms, and contextual factors such as sleep environment.
While home-based digital sleep tracking improves accessibility, challenges remain in ensuring consistency with clinical polysomnography.
Digital measures of sleep provide new opportunities for continuous and longitudinal monitoring, but standardization in data collection and interpretation is needed.
Stakeholders, including regulatory agencies, increasingly recognize digital sleep biomarkers, but additional validation is required to ensure widespread adoption.

Recommendations
Researchers and clinicians should integrate core digital sleep measures into study designs to improve data comparability across trials and clinical contexts.
Algorithm transparency and validation protocols should be established to enhance the accuracy of digital sleep monitoring tools.
Regulatory engagement should be prioritized early in the development process to ensure that digital sleep measures meet evidentiary standards.
Multi-stakeholder collaboration, including patient and care partner input, is essential to ensure sleep measures reflect meaningful aspects of health.
Further research is needed to refine wearable and sensor-based technologies to improve real-world applicability and clinical utility of digital sleep biomarkers.

Regulatory Considerations
The FDA and other regulatory bodies increasingly acknowledge sleep measures as potential clinical endpoints, but clear validation frameworks are necessary.
Digital sleep measures should align with industry standards such as HL7 to ensure interoperability and data integrity.
Data privacy and security regulations must be followed, particularly for continuous sleep monitoring in real-world settings.
Post-market validation and real-world evidence generation are critical to support regulatory acceptance of digital sleep biomarkers.
Developers must document the derivation of sleep measures, including algorithmic processing and sensor accuracy, to meet regulatory review requirements.

Findings
Data analytics are challenging due to diverse metrics and study aims.
Most devices lack built-in software for data output.
There is a lack of comparison and validation studies for different devices and metrics.
Validation of PA metrics is difficult due to the absence of a gold standard.
The integration of various databases is needed but challenging.

Recommendations
Conduct comparison and validation studies between different brands of devices and PA metrics.
Develop standardized metrics for measuring PA.
Improve data integration methods across different studies and databases.
Focus on developing built-in software for devices to facilitate data output.
Encourage research on the validation of PA metrics.

Regulatory Considerations
1Not mentioned

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.