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Assessing social anxiety using gps trajectories and point-of-interest data

Published: 12 September 2016 Publication History

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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  • (2024)Use of Smartphone Sensor Data in Detecting and Predicting Depression and Anxiety in Young People (12-25 Years): A Scoping ReviewSSRN Electronic Journal10.2139/ssrn.4798261Online publication date: 2024
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      cover image ACM Conferences
      UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
      September 2016
      1288 pages
      ISBN:9781450344616
      DOI:10.1145/2971648
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 12 September 2016

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      Author Tags

      1. GPS
      2. location semantics
      3. mobile sensing
      4. social anxiety

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      UbiComp '16 Paper Acceptance Rate 101 of 389 submissions, 26%;
      Overall Acceptance Rate 764 of 2,912 submissions, 26%

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      Cited By

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      • (2024)Use of Smartphone Sensor Data in Detecting and Predicting Depression and Anxiety in Young People (12-25 Years): A Scoping ReviewSSRN Electronic Journal10.2139/ssrn.4798261Online publication date: 2024
      • (2024)A Tool for Capturing Smartphone Screen TextProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642347(1-24)Online publication date: 11-May-2024
      • (2024)Breaking the Flow: A Study of Interruptions During Software Engineering ActivitiesProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639079(1-12)Online publication date: 20-May-2024
      • (2024)Mental Fitness Tracker Using Regression Models2024 3rd International Conference for Innovation in Technology (INOCON)10.1109/INOCON60754.2024.10512049(1-5)Online publication date: 1-Mar-2024
      • (2024)Use of smartphone sensor data in detecting and predicting depression and anxiety in young people (12-25 years): A scoping reviewHeliyon10.1016/j.heliyon.2024.e35472(e35472)Online publication date: Jul-2024
      • (2023)Detecting Social Contexts from Mobile Sensing Indicators in Virtual Interactions with Socially Anxious IndividualsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36109167:3(1-26)Online publication date: 27-Sep-2023
      • (2023)A Self-supervised Framework for Improved Data-Driven Monitoring of Stress via Multi-Modal Passive Sensing2023 IEEE International Conference on Digital Health (ICDH)10.1109/ICDH60066.2023.00033(177-183)Online publication date: Jul-2023
      • (2023)Behavioural patterns discovery for lifestyle analysis from egocentric photo-streamsPervasive and Mobile Computing10.1016/j.pmcj.2023.10184695(101846)Online publication date: Oct-2023
      • (2023)Using digital phenotyping to understand health-related outcomes: A scoping reviewInternational Journal of Medical Informatics10.1016/j.ijmedinf.2023.105061174(105061)Online publication date: Jun-2023
      • (2022)Estimating Mental Health Using Human-generated Big Data and Machine Learning人が生み出すビッグデータと機械学習によるメンタルヘルスの推定The Brain & Neural Networks10.3902/jnns.29.7829:2(78-94)Online publication date: 5-Jun-2022
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