The accuracy of passive phone sensors in predicting daily mood.

ABSTRACT:

Background

Smartphones provide a low-cost and efficient means to collect population level data. Several small studies have shown promise in predicting mood variability from smartphone-based sensor and usage data, but have not been generalized to nationally recruited samples. This study used passive smartphone data, demographic characteristics, and baseline depressive symptoms to predict prospective daily mood.

Method

Daily phone usage data were collected passively from 271 Android phone users participating in a fully remote randomized controlled trial of depression treatment (BRIGHTEN). Participants completed daily Patient Health Questionnaire-2. A machine learning approach was used to predict daily mood for the entire sample and individual participants.

Results

Sampl

SUBMITTER: Pratap A 

PROVIDER: S-EPMC8491547 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature