Citation

Ware S, Yue C, Morillo R, Lu J, Shang C, Bi J, Kamath J, Russell A, Bamis A, Wang B. Smart Health (Amst) 2020; 15: e100093.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.smhl.2019.100093

PMID

unavailable

Abstract

Depression is a serious mental illness. The symptoms associated with depression are both behavioral (in appetite, energy level, sleep) and cognitive (in interests, mood, concentration). Currently, survey instruments are commonly used to keep track of depression symptoms, which are burdensome and difficult to execute on a continuous basis. In this paper, we explore the feasibility of predicting all major categories of depressive symptoms automatically using smartphone data. Specifically, we consider two types of smartphone data, one collected passively on smartphones (through an app running on the phones) and the other collected from an institution's WiFi infrastructure (that does not require direct data capture on the phones), and construct a family of machine learning based models for the prediction. Both scenarios require no efforts from the users, and can provide objective assessment on depressive symptoms. Using smartphone data collected from 182 college students in a two-phase study, our results demonstrate that smartphone data can be used to predict both behavioral and cognitive symptoms effectively, with F1 score as high as 0.86. Our study makes a significant step forward over existing studies that only focus on predicting overall depression status (i.e., whether one is depressed or not).

Language: en

Keywords

Machine learning; Depressive symptom prediction; Smartphone sensing