Presented By: Precision Health
Precision Health Seminar: Sept. 2019
Mobile Technology for Precision Mental Health Care
Presenters:
Amy Bohnert, PhD, MHS, Associate Professor, Psychiatry, and Co-PI of Precision Health mental health research project
Corey Lester, MS, PhD, PharmD, Research Assistant Professor, Pharmacy
Srijan Sen, MD, PhD, Associate Professor, Psychiatry; Associate Chair for Research and Research Faculty Development; Frances and Kenneth Eisenberg Professor of Depression and Neurosciences; and Co-PI of Precision Health mental health research project
More than any other advancement that has emerged in the past four decades, mobile technology has the potential to address the dual problems of limited clinical capacity and inadequate and untimely data in mental health care. As part of Precision Health at the University of Michigan, this project will test wearable and mobile technology as a means to reduce mental health symptoms among patients waiting for mental health care. Furthermore, data derived from mobile technology, genomics, and patient reports will be used to predict response to clinic-based treatments.
Amy Bohnert, PhD, MHS, Associate Professor, Psychiatry, and Co-PI of Precision Health mental health research project
Corey Lester, MS, PhD, PharmD, Research Assistant Professor, Pharmacy
Srijan Sen, MD, PhD, Associate Professor, Psychiatry; Associate Chair for Research and Research Faculty Development; Frances and Kenneth Eisenberg Professor of Depression and Neurosciences; and Co-PI of Precision Health mental health research project
More than any other advancement that has emerged in the past four decades, mobile technology has the potential to address the dual problems of limited clinical capacity and inadequate and untimely data in mental health care. As part of Precision Health at the University of Michigan, this project will test wearable and mobile technology as a means to reduce mental health symptoms among patients waiting for mental health care. Furthermore, data derived from mobile technology, genomics, and patient reports will be used to predict response to clinic-based treatments.
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