Personal sensing refers to collecting data from networked sensors, such as in a smartphone, to understand individuals’ behaviors and experiences. Personal sensing has shown great promise in mental health research as sensed data have been used in a number of mental health conditions such as schizophrenia, bipolar disorder, and depression. For example, sensed data acquired from smartphones can be used to analyze sleep duration, which is linked to depression severity. The challenge though, is individuals' acceptance of collecting different types of sensed data and the people who have access to that information.
Dr. Katie Shilton, Associate Professor in the College of Information Studies at University of Maryland, along with a team of researchers, published “The Role of Data Type and Recipient in Individuals’ Perspectives on Sharing Passively Collected Smartphone Data for Mental Health: Cross-Sectional Questionnaire Study” at Northwestern University. Their research aims to investigate individual’s perspectives about sharing different types of sensor data beyond the research context, specifically with doctors, electronic health record (EHR) systems, and family members in order to design systems that harness sensor data for mental health treatment and support.