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  1. Science and Research Special Topics

Digital Health Technologies for Drug Development: Demonstration Projects

To promote shared learning and understanding with external stakeholders, FDA continues to oversee and support numerous demonstration (i.e., research) projects on Digital Health Technologies (DHTs) through multiple venues and programs including FDA’s Broad Agency Announcement, CERSI Program, and other funding opportunities.

Below lists some of our current FDA-funded projects on DHTs:

  • Developing a Composite Score with Actigraphy Physical Activity and Heart Rate Measurements as a Sensitive Study Endpoint to Facilitate the Use of Digital Health Technology in Pediatric Clinical Trials and Drug Development

    Awardee: The National Institute of Standards and Technology (NIST)

    Project Description: This project aims to use the power of the scientific computation approach to advance the use of actigraphy endpoints in pediatric pulmonary arterial hypertension (PAH) clinical trials. We plan to achieve three primary objectives: (1) Develop a composite score with actigraphy physical activity and heart rate measurements; (2) Examine the use of the composite score in children with PAH to assess disease severity and detect disease progression; (3) Introduce a framework to expand the use of actigraphy to a broader digital health technology space and other pediatric diseases. The goal is to use actigraphy data in a measured way to address the unmet need in pediatric PAH and other pediatric rare diseases.

  • Developing an Objective and Quantitative Endpoint for Atopic Dermatitis in Pediatric and Adult Populations

    Awardee: Emerald Innovations, Inc.

    Project Description: The goal of this project is to develop an objective, quantitative, and user-friendly digital endpoint to assess nocturnal scratching in children with atopic dermatitis. This endpoint will fulfill a crucial unmet need by for assessing the efficacy and performance of FDA-regulated products for atopic dermatitis and pruritus. Currently, these assessments heavily rely on subjective self-reported endpoints like the NRS (Numeric Rating Scale). A machine learning model for assessing nocturnal scratching from radio signals will be developed to draw comparison to healthy controls in terms of scratching and disease burden.

  • Using mHealth to Measure Impact in Functionality Behavior, Activity and Sleep Patterns in Children and Adolescents Treated with Psychotropics

    CERSI Program: University of Maryland

    Project Description: This project aims to leverage the power of mobile technology to address three primary objectives: (1) fill the gap in the capture of behavioral, functional and activity data; (2) ease the burden on patients, caregivers, and providers struggling to capture relevant and actionable data related to treatment and care management; (3) identify severity of behavioral changes after treatment initiation or change, rates of adverse events/secondary effects, along with patterns of medication use and adherence. The ultimate goal is to integrate these data with larger data systems and existing data sources to support changes in labeling or in practice patterns.

  • Evaluation Mobile Health Tool Use for Capturing Patient-Centered Outcome Measures in Heart Failure Patients

    CERSI Program: Yale-Mayo

    Project Description: The goal of this project is to test the feasibility and reliability of capturing quantifiable measures of functional capacity and quality of life (QoL) using a wearable sensor in heart failure (HF) patients for a period of 60 days. Acute Decompensated Heart Failure (ADHF) patients will be recruited post-discharge from the National Heart Centre and National University Hospital in Singapore. Patients will be monitored at home using the Biofourmis' BiovitalsHF™ platform which will capture biosensor data from two wearable devices: Everion® and Apple Watch Series 4. Patients will also use the BiovitalsHF™ smartphone application to capture electronic patient reported outcomes (ePROs) such as medication adherence, symptoms, the Kansas City Cardiomyopathy Questionnaire (KCCQ) responses, and perform the guided mobile-based 2-minute-step-test.

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