The broader impact/commercial potential of this I-Corps project is to develop and test digital mental health interventions for children and families delivered through wearable devices. A first goal in developing this technology is to increase the effectiveness of traditional therapeutic modalities, resulting in increased psychological wellbeing of children and families. A second goal is to decrease barriers in access to health care by creating innovative health care service delivery models. Child and family mental health problems are economically and socially costly, resulting millions of dollars in lost revenue in each year. Developing mobile mental health interventions could help decrease health care costs and create viable health care delivery models by providing cost-effective mental health services to larger segments of the population.This I-Corps project is an artificial intelligence-assisted digital therapy designed to improve child mental health and family functioning. It is a software platform that runs through commercially available hardware devices, such as smartphones and smartwatches. The software collects multimodal data on clients' activities. Machine learning algorithms are then used to detect psychological states and the quality of interpersonal interactions in everyday life. This monitoring system then provides responsive and tailored feedback and support. For example, the system aims to detect negative mood states and send interventions designed to improve mood. This project is based on previous research using data collected from smartphones and wearable devices. Using these data, the developed machine learning algorithms are capable of automatically and passively detecting mood states and interpersonal conflicts with high levels of accuracy.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.
