Eric Hurwitz

Research Fellow, University of North Carolina, Chapel Hill

5 active projects

Digital mental health

Mental illness impacts more than 1 in 5 adults in the U.S. This research aims to investigate the feasibility of utilizing digital biomarkers derived from wearables to facilitate early detection and improved monitoring of individuals with psychiatric and mental health…

Scientific Questions Being Studied

Mental illness impacts more than 1 in 5 adults in the U.S. This research aims to investigate the feasibility of utilizing digital biomarkers derived from wearables to facilitate early detection and improved monitoring of individuals with psychiatric and mental health conditions.

Project Purpose(s)

  • Disease Focused Research (Psychiatric and mental health disorders)
  • Social / Behavioral

Scientific Approaches

We will create cohorts of individuals with different mental health disorders. Then, we will use statistical and machine learning models to determine whether digital biomarkers from wearables may be used for early disease prediction, detection, and patient monitoring.

Anticipated Findings

The expected results involve potential machine learning algorithms that address various factors influencing patients before, during, and after their diagnosis with mental health conditions.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Eric Hurwitz - Research Fellow, University of North Carolina, Chapel Hill

Collaborators:

  • Evan Connelly - Research Associate, University of North Carolina, Chapel Hill

Heart disease and digital health

Heart disease is the leading cause of death globally. This study seeks to investigate digital biomarkers from wearables in patients before, during, and after records of cardiovascular disease (CVD) and/or other heart-related conditions to facilitate early disease detection and enhanced…

Scientific Questions Being Studied

Heart disease is the leading cause of death globally. This study seeks to investigate digital biomarkers from wearables in patients before, during, and after records of cardiovascular disease (CVD) and/or other heart-related conditions to facilitate early disease detection and enhanced patient monitoring.

Project Purpose(s)

  • Commercial

Scientific Approaches

We will create cohorts of individuals with different heart conditions. Then, we will use statistical and machine learning models to determine whether digital biomarkers from wearables may be used for early disease prediction, detection, and patient monitoring.

Anticipated Findings

The anticipated findings are potentially developing detection algorithms for heart-related conditions.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Eric Hurwitz - Research Fellow, University of North Carolina, Chapel Hill

Pregnancy and postpartum

Maternal mortality is responsible for 700 deaths annually in the United States. It has been demonstrated that 80% of pregnancy related deaths are preventable, highlighting the need for improved pregnancy and postpartum patient care. This study seeks to investigate digital…

Scientific Questions Being Studied

Maternal mortality is responsible for 700 deaths annually in the United States. It has been demonstrated that 80% of pregnancy related deaths are preventable, highlighting the need for improved pregnancy and postpartum patient care. This study seeks to investigate digital biomarkers from wearables in patients during pregnancy and the postpartum period with several conditions to facilitate early disease detection and enhanced patient monitoring.

Project Purpose(s)

  • Commercial

Scientific Approaches

We will create cohorts of individuals with different conditions that impact patients during pregnancy and postpartum. Then, we will use statistical models to determine whether digital biomarkers from wearables may be used for early disease prediction, detection, and patient monitoring.

Anticipated Findings

The anticipated findings are potentially developing detection algorithms for severe conditions that impact patients during pregnancy and postpartum.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Eric Hurwitz - Research Fellow, University of North Carolina, Chapel Hill
  • Hiral Master - Project Personnel, All of Us Program Operational Use

Cancer and digital health

Cancer is the 2nd leading cause of death in the U.S. This study focuses on using digital biomarkers from wearable technology to investigate cancer prevention and continuous patient monitoring.

Scientific Questions Being Studied

Cancer is the 2nd leading cause of death in the U.S. This study focuses on using digital biomarkers from wearable technology to investigate cancer prevention and continuous patient monitoring.

Project Purpose(s)

  • Disease Focused Research (cancer)

Scientific Approaches

We will create cohorts of individuals with different types of cancer. Then, we will use statistical and machine learning models to determine whether digital biomarkers from wearables may be used for early disease prediction, detection, and patient monitoring.

Anticipated Findings

The anticipated findings are potentially developing detection machine learning algorithms for different aspects that impact patients before, during, and after cancer care.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Eric Hurwitz - Research Fellow, University of North Carolina, Chapel Hill

COVID-19 and digital health

According to the WHO, COVID-19 has affected over 770 million patients and is responsible for approximately 7 million deaths globally as of September 27, 2023. This study focuses on using digital biomarkers from wearable technology to investigate COVID-19 detection and…

Scientific Questions Being Studied

According to the WHO, COVID-19 has affected over 770 million patients and is responsible for approximately 7 million deaths globally as of September 27, 2023. This study focuses on using digital biomarkers from wearable technology to investigate COVID-19 detection and continuous patient monitoring.

Project Purpose(s)

  • Disease Focused Research (COVID-19)
  • Population Health
  • Social / Behavioral

Scientific Approaches

We will create a cohort of individuals with COVID-19. Then, we will use statistical and machine learning models to determine whether digital biomarkers from wearables may be used for early disease prediction, detection, and patient monitoring.

Anticipated Findings

The anticipated findings are potentially developing detection algorithms for COVID-19 or it's symptoms that impact patients.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Eric Hurwitz - Research Fellow, University of North Carolina, Chapel Hill
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