Well-being tracking via smartphone-measured activity and sleep: Cohort study

Abstract

Background: Automatically tracking mental well-being could facilitate personalization of treatments for mood disorders such as depression and bipolar disorder. Smartphones present a novel and ubiquitous opportunity to track individuals’ behavior and may be useful for inferring and automatically monitoring mental well-being. Objective: The aim of this study was to assess the extent to which activity and sleep tracking with a smartphone can be used for monitoring individuals’ mental well-being. Methods: A cohort of 106 individuals was recruited to install an app on their smartphone that would track their well-being with daily surveys and track their behavior with activity inferences from their phone’s accelerometer data. Of the participants recruited, 53 had sufficient data to infer activity and sleep measures. For this subset of individuals, we related measures of activity and sleep to the individuals’ well-being and used these measures to predict their well-being. Results: We found that smartphone-measured approximations for daily physical activity were positively correlated with both mood (P=.004) and perceived energy level (P