Assessing social anxiety using gps trajectories and point-of-interest data
Open accessPublished version of a preprint
Abstract
Mental health problems are highly prevalent and appear to be increasing in frequency and severity among the college student population. The upsurge in mobile and wearable wireless technologies capable of intense, longitudinal tracking of individuals, provide valuable opportunities to examine temporal patterns and dynamic interactions of key variables in mental health research. In this paper, we present a feasibility study leveraging non-invasive mobile sensing technology to passively assess college students' social anxiety, one of the most common disorders in the college student population. We have first developed a smartphone application to continuously track GPS locations of college students, then we built an analytic infrastructure to collect the GPS trajectories and finally we analyzed student behaviors (e.g. studying or staying at home) using Point-Of-Interest (POI). The whole framework supports intense, longitudinal, dynamic tracking of college students to evaluate how their anxiety and behaviors change in the college campus environment. The collected data provides critical information about how students' social anxiety levels and their mobility patterns are correlated. Our primary analysis based on 18 college students demonstrated that social anxiety level is significantly correlated with places students' visited and location transitions.
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Significant technical work has advanced the use of sensors to detect anxious states, opening the possibility of triggering and tailoring interventions using passive data collection, which reduces burden on the user.
“This is despite significant technical work advancing the use of sensors to detect anxious states, which opens the possibility of triggering and tailoring interventions using passive data collection, which reduces the burden on the user (Huang et al., 2016;Miranda, Calderon, & Favela, 2014;Rennert & Karapanos, 2013).”
From Ecological momentary interventions for depression and anxiety (2017) · cites this paper as Huang et al., 2016
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References 17 · Cited by 109
- Authors
- Huang, Y., Xiong, H., Leach, K., Zhang, Y., Chow, P., Fua, K., Teachman, B. A., & Barnes, L. E.
- Affiliations
- University of Virginia (US)
- ORCID
- Venue
- Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
- Publisher
- ACM
- Published
- 2016-09-12
- Pages
- 898-903
- OpenAlex
- W2516478977
- Cited by
- 109
- References
- 17
- Citations by year
- 2026: 6 · 2025: 2 · 2024: 8 · 2023: 4 · 2022: 22 · 2021: 16 · 2020: 23 · 2019: 12 · 2018: 8 · 2017: 8
- FWCI
- 13.72
- Type
- conference paper
- Language
- en
- Open access
- green
- doi.org
- Locations
- ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) (publishedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- social anxiety, GPS tracks, point of interest data, mobile sensing, mobile application, university students, mental health, movement patterns, campus environment
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