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short-paper

Discovery of Behavioral Markers of Social Anxiety from Smartphone Sensor Data

Authors:

Matthew S. Gerber,

Bethany Teachman,

Laura E. BarnesAuthors Info & Claims

DigitalBiomarkers '17: Proceedings of the 1st Workshop on Digital Biomarkers

Pages 9 - 14

https://doi.org/10.1145/3089341.3089343

Published: 23 June 2017 Publication History

Abstract

Better understanding of an individual's smartphone use can help researchers to understand the relationship between behaviors and mental health, and ultimately improve methods for early detection, evaluation, and intervention. This relationship may be particularly significant for individuals with social anxiety, for whom stress from social interactions may arise repeatedly and unexpectedly over the course of a day. For this reason, we present an exploratory study of behavioral markers extracted from smartphone data. We examine fine-grained behaviors before and after smartphone communication events across social anxiety levels. To discover behavioral markers, we model the smartphone as a linear dynamical system with the accelerometer data as output. In a two-week study of 52 college students, we find substantially different behavioral markers prior to outgoing phone calls when comparing individuals with high and low social anxiety.

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Cited By

Lee UJung GMa EKim JKim HAlikhanov JNoh YKim H(2023)Toward Data-Driven Digital Therapeutics Analytics: Literature Review and Research DirectionsIEEE/CAA Journal of Automatica Sinica10.1109/JAS.2023.12301510:1(42-66)Online publication date: Jan-2023
https://doi.org/10.1109/JAS.2023.123015
Lee KLee TYefimova MKumar SPuga FAzuero AKamal ABakitas MWright ADemiris GRitchie CPickering CNicholas Dionne-Odom J(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
https://doi.org/10.1016/j.ijmedinf.2023.105061
Zlatintsi AFilntisis PGaroufis CEfthymiou NMaragos PMenychtas AMaglogiannis ITsanakas PSounapoglou TKalisperakis EKarantinos TLazaridi MGaryfalli VMantas AMantonakis LSmyrnis N(2022)E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video CapturesSensors10.3390/s2219754422:19(7544)Online publication date: 5-Oct-2022
https://doi.org/10.3390/s22197544
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Index Terms

Discovery of Behavioral Markers of Social Anxiety from Smartphone Sensor Data
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Empirical studies in ubiquitous and mobile computing
    2. Ubiquitous and mobile devices
      1. Mobile phones
      2. Smartphones

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Information & Contributors

Information

Published In

cover image ACM Conferences

DigitalBiomarkers '17: Proceedings of the 1st Workshop on Digital Biomarkers

June 2017

44 pages

ISBN:9781450349635

DOI:10.1145/3089341

General Chairs:
Deborah Estrin
Cornell NYC Tech, USA
,
JP Pollak
Cornell NYC Tech, USA
,
Tauhidur Rahman
Cornell University, USA

Copyright © 2017 ACM.

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]

Sponsors

SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

In-Cooperation

SIGOPS: ACM Special Interest Group on Operating Systems

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 June 2017

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Author Tags

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Short-paper

Funding Sources

Hobby Postdoctoral and Predoctoral Fellows in Computational Science

Conference

MobiSys'17

Sponsor:

SIGMOBILE

MobiSys'17: The 15th Annual International Conference on Mobile Systems, Applications, and Services

June 23, 2017

New York, Niagara Falls, USA

Acceptance Rates

DigitalBiomarkers '17 Paper Acceptance Rate 6 of 9 submissions, 67%;

Overall Acceptance Rate 14 of 19 submissions, 74%

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Citations

Cited By

Lee UJung GMa EKim JKim HAlikhanov JNoh YKim H(2023)Toward Data-Driven Digital Therapeutics Analytics: Literature Review and Research DirectionsIEEE/CAA Journal of Automatica Sinica10.1109/JAS.2023.12301510:1(42-66)Online publication date: Jan-2023
https://doi.org/10.1109/JAS.2023.123015
Lee KLee TYefimova MKumar SPuga FAzuero AKamal ABakitas MWright ADemiris GRitchie CPickering CNicholas Dionne-Odom J(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
https://doi.org/10.1016/j.ijmedinf.2023.105061
Zlatintsi AFilntisis PGaroufis CEfthymiou NMaragos PMenychtas AMaglogiannis ITsanakas PSounapoglou TKalisperakis EKarantinos TLazaridi MGaryfalli VMantas AMantonakis LSmyrnis N(2022)E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video CapturesSensors10.3390/s2219754422:19(7544)Online publication date: 5-Oct-2022
https://doi.org/10.3390/s22197544
Gooding PKariotis T(2021)Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping ReviewJMIR Mental Health10.2196/246688:6(e24668)Online publication date: 10-Jun-2021
https://doi.org/10.2196/24668
Burghardt KTavabi NFerrara ENarayanan SLerman K(2021)Having a Bad Day? Detecting the Impact of Atypical Events Using Wearable SensorsSocial, Cultural, and Behavioral Modeling10.1007/978-3-030-80387-2_25(257-267)Online publication date: 4-Jul-2021
https://doi.org/10.1007/978-3-030-80387-2_25
Jacobson NSummers BWilhelm S(2020)Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone SensorsJournal of Medical Internet Research10.2196/1687522:5(e16875)Online publication date: 29-May-2020
https://doi.org/10.2196/16875
Boukhechba MBaglione ABarnes L(2020)Leveraging Mobile Sensing and Machine Learning for Personalized Mental Health CareErgonomics in Design: The Quarterly of Human Factors Applications10.1177/106480462092049428:4(18-23)Online publication date: 17-May-2020
https://doi.org/10.1177/1064804620920494
Rashid HMendu SDaniel KBeltzer MTeachman BBoukhechba MBarnes L(2020)Predicting Subjective Measures of Social Anxiety from Sparsely Collected Mobile Sensor DataProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34118234:3(1-24)Online publication date: 4-Sep-2020
https://dl.acm.org/doi/10.1145/3411823
Mandalapu VGong J(2019)Understanding Affective Dynamics of Learning Toward a Ubiquitous Learning SystemGetMobile: Mobile Computing and Communications10.1145/3372300.337230323:2(9-15)Online publication date: 14-Nov-2019
https://dl.acm.org/doi/10.1145/3372300.3372303
Salekin AEberle JGlenn JTeachman BStankovic J(2018)A Weakly Supervised Learning Framework for Detecting Social Anxiety and DepressionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/32142842:2(1-26)Online publication date: 5-Jul-2018
https://dl.acm.org/doi/10.1145/3214284
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