StudentLife: Using Smartphones to Assess Mental Health and Academic Performance of College Students
Much of the stress and strain of student life remains hidden. The StudentLife continuous sensing app assesses the day to day and week by week impact of workload on stress, sleep, activity, mood, sociability, mental well being and academic performance of a single class of 48 students across a 10 weeks term at Dartmouth College using Android phones. Results from the StudentLife study show a number of significant correlations between the automatic objective sensor data from smartphones and mental health and educational outcomes of the student body. We propose a simple model based on linear regression with lasso regularization that can accurately predict cumulative GPA. We also identify a Dartmouth term lifecycle in the data that shows students start the term with high positive affect and conversation levels, low stress, and healthy sleep and daily activity patterns. As the term progresses and the workload increases, stress appreciably rises while positive affect, sleep, conversation and activity drops off. The StudentLife dataset is publicly available on the web.
Mobile Health Sensors, Analytic Methods, and Applications