Using Mobile Sensing to Test Clinical Models of Depression, Social Anxiety, State Affect, and Social Isolation

Article
Authors:Barnes, Laura, Department of Systems and Information EngineeringUniversity of Virginia Teachman, Bethany, Department of PsychologyUniversity of Virginia Chow, Philip, Department of PsychologyUniversity of Virginia Huang, Yu, Engineering Graduate-hengUniversity of Virginia Xiong, Haoyi, Computer ScienceMissouri University of Science and Technology Bonelli, Wesley, Arts & Sciences Undergraduate-basuUniversity of Virginia Fua, Karl, Arts & Sciences Graduate-fasgUniversity of Virginia
Abstract:

Background: Research in psychology demonstrates a strong link between state affect (moment-to-moment experiences of positive or negative emotionality) and trait affect (eg, relatively enduring depression and social anxiety symptoms), and a tendency to withdraw (eg, spending time at home). However, existing work is based almost exclusively on static, self-reported descriptions of emotions and behavior that limit generalizability. Despite adoption of increasingly sophisticated research designs and technology (eg, mobile sensing using a global positioning system [GPS]), little research has integrated these seemingly disparate forms of data to improve understanding of how emotional experiences in everyday life are associated with time spent at home, and whether this is influenced by depression or social anxiety symptoms.
Objective: We hypothesized that more time spent at home would be associated with more negative and less positive affect.
Methods: We recruited 72 undergraduate participants from a southeast university in the United States. We assessed depression and social anxiety symptoms using self-report instruments at baseline. An app (Sensus) installed on participants’ personal mobile phones repeatedly collected in situ self-reported state affect and GPS location data for up to 2 weeks. Time spent at home was a proxy for social isolation.
Results: We tested separate models examining the relations between state affect and time spent at home, with levels of depression and social anxiety as moderators. Models differed only in the temporal links examined. One model focused on associations between changes in affect and time spent at home within short, 4-hour time windows. The other 3 models focused on associations between mean-level affect within a day and time spent at home (1) the same day, (2) the following day, and (3) the previous day. Overall, we obtained many of the expected main effects (although there were some null effects), in which higher social anxiety was associated with more time or greater likelihood of spending time at home, and more negative or less positive affect was linked to longer homestay. Interactions indicated that, among individuals higher in social anxiety, higher negative affect and lower positive affect within a day was associated with greater likelihood of spending time at home the following day.
Conclusions: Results demonstrate the feasibility and utility of modeling the relationship between affect and homestay using fine-grained GPS data. Although these findings must be replicated in a larger study and with clinical samples, they suggest that integrating repeated state affect assessments in situ with continuous GPS data can increase understanding of how actual homestay is related to affect in everyday life and to symptoms of anxiety and depression.

Keywords:
mental health, depression, social anxiety, affect, homestay, mobile health, mHealth
Rights:
All rights reserved (no additional license for public reuse)
Contributor:Barnes, Laura, Department of Systems and Information EngineeringUniversity of Virginia
Language:
English
Source Citation:

Chow PI, Fua K, Huang Y, Bonelli W, Xiong H, Barnes LE, Teachman BA Using Mobile Sensing to Test Clinical Models of Depression, Social Anxiety, State Affect, and Social Isolation Among College Students J Med Internet Res 2017;19(3):e62

Publisher:
University of Virginia
Published Date:
March 3, 2017
Sponsoring Agency:
Hobby Postdoctoral and Predoctoral Fellowships in Computational Science