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Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:UNIVERSITY OF VERMONT & STATE AGRICULTURAL COLLEGE
Doing Business As Name:University of Vermont & State Agricultural College
PD/PI:
  • Ryan McGinnis
  • (802) 656-3660
  • ryan.mcginnis@uvm.edu
Award Date:07/16/2021
Estimated Total Award Amount: $ 50,000
Funds Obligated to Date: $ 50,000
  • FY 2021=$50,000
Start Date:07/15/2021
End Date:12/31/2021
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.041
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:I-Corps: Mobile Phone Based Vital Sign Detection
Federal Award ID Number:2138673
DUNS ID:066811191
Parent DUNS ID:066811191
Program:I-Corps
Program Officer:
  • Ruth Shuman
  • (703) 292-2160
  • rshuman@nsf.gov

Awardee Location

Street:85 South Prospect Street
City:Burlington
State:VT
ZIP:05405-0160
County:Burlington
Country:US
Awardee Cong. District:00

Primary Place of Performance

Organization Name:University of Vermont & State Agricultural College
Street:85 South Prospect Street
City:Burlington
State:VT
ZIP:05405-0160
County:Burlington
Country:US
Cong. District:00

Abstract at Time of Award

The broader impact/commercial potential of this I-Corps project involves the development of an accessible, evidence-based, biofeedback intervention to allow individuals suffering with mental health disorders to have immediate access to effective treatment. A first use case is for the approximately 11% of Americans who experience reoccurring panic attacks, which has significant societal cost such that these individuals are more likely to visit emergency rooms, miss work, and experience subsequent comorbidity. This segment of the population currently has limited access to evidence-based treatment due to long therapy waitlists and often ineffective offerings. The proposed digital therapeutic technology aims to answer this unmet need by providing immediate, inexpensive, evidence-based intervention available on a smartphone. Advances made with this technology could be expanded to also inform biofeedback intervention for other mental health problems including general anxiety, post-traumatic stress disorder, substance abuse disorders, and even chronic pain. This I-Corps project develops new approaches for characterizing physiological states from mobile phone videoes and delivering personalized therapies where and when they are most needed. The novel computational algorithms leverage machine learning to enable accurate estimations of physiological signals that are robust to imperfect data quality inherent in measurements made during daily life. The technology may also be feasible for use during episodes of high emotional reactivity. This project builds on innovations in the clinical measurement and intervention by integrating algorithms into a mobile application, using it to collect datasets of real-world suffering and to inform personalized interventions. The feasibility of the tool has been demonstrated as a digital therapeutic for panic attacks. This project will explore the commercialization potential of this approach. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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