Digital phenotyping data to predict symptom improvement and app personalization: Protocol for a prospective study (Preprint)

Abstract

UNSTRUCTURED Smartphone apps offering surveys and access to sensors are increasingly leveraged to collect data to provide insight into clinical conditions. As the mental health crisis in college students continues, apps provide a practical tool for students. Yet, uptake and engagement have remained limited. In this protocol, we present a study design to explore engagement with mental health apps in college students through the Technology Acceptance Model (TAM) as a theoretical framework. There are two main goals of this study. First, we present a logistic regression model fit on data from a prior study on college students prospectively test this model on a new student cohort. Second, we will provide users with data-driven activity suggestions every 4 days to determine whether this type of personalization will increase engagement or attitudes towards the app. This is one of the first digital phenotyping algorithms to be prospectively validated. Overall, our results will inform on the potential of digital phenotyping data to serve as tailoring data in adaptive interventions and to increase rates of engagement.