NSF Award
2414218
The broader impact/commercial potential of this I-Corps project is the development of a new scalable, objective assessment that relies on a child’s physiological and behavioral responses, rather than solely upon observation of symptoms by a guardian to transform the identification and screening of pre-adolescent children. This tool is scalable, brief (~5 minutes), accessible and automated for no/low provider effort, easily usable (no language barriers for users as it is tone/beep-prompted), with underlying models and biomarkers validated in published peer-reviewed research. Providing annual endpoints satisfies parental and provider interest in longitudinal tracking of childhood mental health, subsequently decreasing the number of children overlooked during this vulnerable age phase (4-10 years old) without greatly extending annual well-visit appointments. The outcomes of this work would be revolutionary in its availability, as there is no other technology-based routine mental health assessment tool for young children currently on the market. There is no other research or commercial institution that has aggregated as much data on early childhood mental health behavioral assessments and associated biomarkers. These specifics give a competitive edge over any potential forthcoming competitor in this space.<br/><br/>This I-Corps project is based on the development of routine screening of young adults and children to lay the necessary foundation for better clinician connectivity. The significant technical innovations include the development of novel, machine learning based algorithms for robustly gathering quantitative information about pre-adolescent children’s physiological responses during structured mood induction tasks using only data from a mobile phone. This approach leverages numerous studies to demonstrate the feasibility of this digital screening method and in so doing collected one of the largest pre-adolescent mental health physiological response datasets. The combination of this novel measurement modality and a unique, large, and expanding clinical dataset provide an innovative foundation prepared to develop the proposed transformative digital identification tool.<br/><br/>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.
