Syndication in science: Curated collaboration.

Development and validation of digital measures require dedicated clinical studies, which can be conducted by a single study sponsor or a precompetitive collaboration. In this perspective, we propose an alternative model, data syndication, a curated collaboration, which foresees a technology provider being a founding member with biopharmaceutical sponsors and other stakeholders joining. Its main advantages are the speed of the study startup and the opportunity for real-time data streaming.

Accelerating Parkinson's Disease drug development with federated learning approaches.

Parkinson's Disease is a progressive neurodegenerative disorder afflicting almost 12 million people. Increased understanding of its complex and heterogenous disease pathology, etiology and symptom manifestations has resulted in the need to design, capture and interrogate substantial clinical datasets. Herein we advocate how advances in the deployment of artificial intelligence models for Federated Data Analysis and Federated Learning can help spearhead coordinated and sustainable approaches to address this grand challenge.

Implementing sensor-based digital health technologies in clinical trials: Key considerations from the eCOA Consortium.

The increased use of sensor-based digital health technologies (DHTs) in clinical trials brought to light concerns about implementation practices that might introduce burden on trial participants, resulting in suboptimal compliance and become an additional complicating factor in clinical trial conduct. These concerns may contribute to the lower-than-anticipated uptake of DHT deployment and data use for regulatory decision-making, despite well-articulated benefits. The Electronic Clinical Outcome Assessment (eCOA) Consortium gathered collective experience on deploying sensor-based DHTs and supplemented this with relevant literature focusing on mechanisms that may enhance participant compliance.

Impacts on study design when implementing digital measures in Parkinson's disease-modifying therapy trials.

Introduction: Parkinson's Disease affects over 8.5 million people and there are currently no medications approved to treat underlying disease. Clinical trials for disease modifying therapies (DMT) are hampered by a lack of sufficiently sensitive measures to detect treatment effect.

Regulatory Pathways for Qualification and Acceptance of Digital Health Technology-Derived Clinical Trial Endpoints: Considerations for Sponsors.

Despite widespread interest and substantial investment in the adoption of sensor-based digital health technologies (sDHTs) for remote data capture in drug development trials, no drug has been approved based on an sDHT-derived primary endpoint in the United States (US). One reason for this lack of advancement is the complexity of obtaining regulatory endorsement for those endpoints within current US regulatory pathways. The goal of our review is to describe the two choices currently available to pharmaceutical study Sponsors: (i) they may navigate the traditional route of compiling the evidence to support the sDHT-derived endpoint in their investigational new drug (IND) application, requiring specific expertise and substantial resources; or (ii) they may navigate the drug development tool (DDT) pathway with the goal of qualifying their sDHT-derived endpoint as a biomarker or clinical outcome assessment applicable to a broader context of use (COU), either alone or as part of a partnership or consortium.

A proposed multi-domain, digital model for capturing functional status and health-related quality of life in oncology.

Whereas traditional oncology clinical trial endpoints remain key for assessing novel treatments, capturing patients' functional status is increasingly recognized as an important aspect for supporting clinical decisions and assessing outcomes in clinical trials. Existing functional status assessments suffer from various limitations, some of which may be addressed by adopting digital health technologies (DHTs) as a means of collecting both objective and self-reported outcomes. In this mini-review, we propose a device-agnostic multi-domain model for oncology capturing functional status, which includes physical activity data, vital signs, sleep variables, and measures related to health-related quality of life enabled by connected digital tools.

Study protocol: A comparison of mobile and clinic-based spirometry for capturing the treatment effect in moderate asthma.

Several inefficiencies in drug development trial implementation may be improved by moving data collection from the clinic to mobile, allowing for more frequent measurements and therefore increased statistical power while aligning to a patient-centric approach to trial design. Sensor-based digital health technologies such as mobile spirometry (mSpirometry) are comparable to clinic spirometry for capturing outcomes, such as forced expiratory volume in 1 s (FEV1); however, the impact of remote spirometry measurements on the detection of treatment effect has not been investigated. A protocol for a multicenter, single-arm, open-label interventional trial of long-acting beta agonist (LABA) therapy among 60 participants with uncontrolled moderate asthma is described.

Reverse Engineering of Digital Measures: Inviting Patients to the Conversation.

Background: Digital measures offer an unparalleled opportunity to create a more holistic picture of how people who are patients behave in their real-world environments, thereby establishing a better connection between patients, caregivers, and the clinical evidence used to drive drug development and disease management. Reaching this vision will require achieving a new level of co-creation between the stakeholders who design, develop, use, and make decisions using evidence from digital measures.

Summary: In September 2022, the second in a series of meetings hosted by the Swiss Federal Institute of Technology in Zürich, the Foundation for the National Institutes of Health Biomarkers Consortium, and sponsored by Wellcome Trust, entitled "Reverse Engineering of Digital Measures," was held in Zurich, Switzerland, with a broad range of stakeholders sharing their experience across four case studies to examine how patient centricity is essential in shaping development and validation of digital evidence generation tools.

Digital technologies: Innovations that transform the face of drug development.

Recently, digital health technologies (DHTs) and digital biomarkers have gained a lot of traction in clinical investigations, motivating sponsors, investigators, and regulators to discuss and implement integrated approaches for deploying DHTs. These new tools present new and unique challenges for optimal technology integration in clinical trial processes, including operational, ethical, and regulatory issues. In this paper, we gathered different perspectives to discuss challenges and perspectives from three different stakeholders: industry, US regulators, and a public-private partnership consortium.

Digital health technology derived measures: Biomarkers or clinical outcome assessments?

Digital health technologies (DHTs) present unique opportunities for clinical evidence generation but pose certain challenges. These challenges stem, in part, from existing definitions of drug development tools, which were not created with DHT-derived measures in mind. DHT-derived measures can be leveraged as either clinical outcome assessments (COAs) or as biomarkers since they share properties with both categories of drug development tools.

Empowering drug development: Leveraging insights from imaging technologies to enable the advancement of digital health technologies.

The US Food and Drug Administration (FDA) has publicly recognized the importance of improving drug development efficiency, deeming translational biomarkers a top priority. The use of imaging biomarkers has been associated with increased rates of drug approvals. An appropriate level of validation provides a pragmatic way to choose and implement these biomarkers.

Sensor Verification and Analytical Validation of Algorithms to Measure Gait and Balance and Pronation/Supination in Healthy Volunteers.

Numerous studies have sought to demonstrate the utility of digital measures of motor function in Parkinson’s disease. Frameworks, such as V3, document digital measure development: technical verification, analytical and clinical validation. We present the results of a study to (1) technically verify accelerometers in an Apple iPhone 8 Plus and ActiGraph GT9X versus an oscillating table and (2) analytically validate software tasks for walking and pronation/supination on the iPhone plus passively detect walking measures with the ActiGraph in healthy volunteers versus human raters.

Considerations for Conducting Bring Your Own "Device" (BYOD) Clinical Studies.

Background: Digital health technologies are attracting attention as novel tools for data collection in clinical research. They present alternative methods compared to in-clinic data collection, which often yields snapshots of the participants' physiology, behavior, and function that may be prone to biases and artifacts, e.g.

Recommendations for Defining and Reporting Adherence Measured by Biometric Monitoring Technologies: Systematic Review.

Background: Suboptimal adherence to data collection procedures or a study intervention is often the cause of a failed clinical trial. Data from connected sensors, including wearables, referred to here as biometric monitoring technologies (BioMeTs), are capable of capturing adherence to both digital therapeutics and digital data collection procedures, thereby providing the opportunity to identify the determinants of adherence and thereafter, methods to maximize adherence.

Objective: We aim to describe the methods and definitions by which adherence has been captured and reported using BioMeTs in recent years.

Multimodal biometric monitoring technologies drive the development of clinical assessments in the home environment.

Biometric monitoring technologies (BioMeTs) have attracted the attention of the health care community because of their user-friendly form factor and multi-sensor data-collection capabilities. The potential benefits of remote monitoring for collecting comprehensive, longitudinal, and contextual datasets span therapeutic areas, and both chronic and acute disease settings. Importantly, multimodal BioMeTs unlock the ability to generate rich contextual data to augment digital measures.

EVIDENCE Publication Checklist for Studies Evaluating Connected Sensor Technologies: Explanation and Elaboration.

The EVIDENCE (EValuatIng connecteD sENsor teChnologiEs) checklist was developed by a multidisciplinary group of content experts convened by the Digital Medicine Society, representing the clinical sciences, data management, technology development, and biostatistics. The aim of EVIDENCE is to promote high quality reporting in studies where the primary objective is an evaluation of a digital measurement product or its constituent parts. Here we use the terms digital measurement product and connected sensor technology interchangeably to refer to tools that process data captured by mobile sensors using algorithms to generate measures of behavioral and/or physiological function.

Remote Cardiac Safety Monitoring through the Lens of the FDA Biomarker Qualification Evidentiary Criteria Framework: A Case Study Analysis.

Clinical safety findings remain one of the reasons for attrition of drug candidates during clinical development. Cardiovascular liabilities are not consistently detected in early-stage clinical trials and often become apparent when drugs are administered chronically for extended periods of time. Vital sign data collection outside of the clinic offers an opportunity for deeper physiological characterization of drug candidates and earlier safety signal detection.

Remote FEV1 Monitoring in Asthma Patients: A Pilot Study.

Forced expiratory volume in one second (FEV ) is a critical parameter for the assessment of lung function for both clinical care and research in patients with asthma. While asthma is defined by variable airflow obstruction, FEV is typically assessed during clinic visits. Mobile spirometry (mSpirometry) allows more frequent measurements of FEV , resulting in a more continuous assessment of lung function over time and its variability.

An Evaluation of Biometric Monitoring Technologies for Vital Signs in the Era of COVID-19.

The novel coronavirus disease 2019 (COVID-19) global pandemic has shifted how many patients receive outpatient care. Telehealth and remote monitoring have become more prevalent, and measurements taken in a patient's home using biometric monitoring technologies (BioMeTs) offer convenient opportunities to collect vital sign data. Healthcare providers may lack prior experience using BioMeTs in remote patient care, and, therefore, may be unfamiliar with the many versions of BioMeTs, novel data collection protocols, and context of the values collected.

Fit-for-Purpose Biometric Monitoring Technologies: Leveraging the Laboratory Biomarker Experience.

Biometric monitoring technologies (BioMeTs) are becoming increasingly common to aid data collection in clinical trials and practice. The state of BioMeTs, and associated digitally measured biomarkers, is highly reminiscent of the field of laboratory biomarkers 2 decades ago. In this review, we have summarized and leveraged historical perspectives, and lessons learned from laboratory biomarkers as they apply to BioMeTs.