Assessing the Efficacy of a Self-Stigma Reduction Mental Health Program with Mobile Biometrics: Work-in-Progress

Abstract

One of the strongest predictors of success in post-secondary education is student engagement. Unfortunately, people with psychiatric disabilities are less engaged in their campus communities. This work-in-progress paper details the disclosure-based self-stigma reduction program, Up To Me, which is developed to increase inclusion and engagement of people with mental illness on college campuses by teaching strategies to weigh costs and benefits of disclosing one's mental illness. Further, we elaborate on the program's evaluation mechanisms, which involve both self-reported and passively recorded smartphone sensor data. The latter reflects a unique merging of behavioral and computer sciences that serves to facilitate behavioral modeling using artificial intelligence as an objective measure of Up to Me outcomes. Similar to data collection for some activity and biometric recognition applications, we employ a publicly available and free-to-use smartphone sensor reading app to correlate self-reported well-being with Up to Me participant behaviors. We anticipate that the behavioral data gathered via smartphones will substantiate self-report data on Up to Me outcomes.