extended-abstract

DEMONS: an integrated framework for examining associations between physiology and self-reported affect tied to depressive symptoms

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Laura E. BarnesAuthors Info & Claims

UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct

Pages 1139 - 1143

https://doi.org/10.1145/2968219.2968300

Published: 12 September 2016 Publication History

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Abstract

Depression is a prevalent and debilitating disorder among college students. Advances in mobile technology afford the opportunity to collect heterogeneous data while people are in their natural settings. The aim of the current paper is to propose an integrated framework, DEMONS (DEpression MONitoring Study), for combining passive and active data sources using a wearable sensor and a smartphone application. The ability to combine passive and active longitudinal data with mobile devices allows for better understanding of the temporal relations between self-reported affect and physiological variables (e.g., heart rate variability) linked to depressive symptoms. Adoption of the proposed framework will provide crucial information regarding the development and maintenance of depression in college students, as well as increased opportunities for early detection and intervention.

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Cited By

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Burgess EReddy MMohr D(2022)"I Just Can't Help But Smile Sometimes": Collaborative Self-Management of DepressionProceedings of the ACM on Human-Computer Interaction10.1145/35129176:CSCW1(1-32)Online publication date: 7-Apr-2022
https://dl.acm.org/doi/10.1145/3512917
Liu WHuang JPan WZhu YLi MXu H(2020)BraVo: A Physiological Indication System for Female College Students to Manage DepressionHuman Interaction, Emerging Technologies and Future Applications III10.1007/978-3-030-55307-4_19(122-127)Online publication date: 6-Aug-2020
https://doi.org/10.1007/978-3-030-55307-4_19
Pryss RSchlee WHoppenstedt BReichert MSpiliopoulou MLangguth BBreitmayer MProbst T(2019)An Ecological Momentary Assessment Study on Machine Learning to Predict the Used Mobile Operating System by the Daily Life Data of the TrackYourTinnitus mHealth Crowdsensing Platform. (Preprint)Journal of Medical Internet Research10.2196/15547Online publication date: 18-Jul-2019
https://doi.org/10.2196/15547
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Index Terms

DEMONS: an integrated framework for examining associations between physiology and self-reported affect tied to depressive symptoms
1. Applied computing
  1. Law, social and behavioral sciences
    1. Psychology
  2. Life and medical sciences
    1. Consumer health
    2. Health informatics

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UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct

September 2016

1807 pages

ISBN:9781450344623

DOI:10.1145/2968219

General Chairs:
Paul Lukowicz
DFKI and University of Kaiserslautern
,
Antonio Krüger
DFKI and Saarland University
,
Program Chairs:
Andreas Bulling
Max Planck Institute for Informatics
,
Youn-Kyung Lim
KAIST
,
Shwetak N. Patel
University of Washington

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 12 September 2016

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UbiComp '16: The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 12 - 16, 2016

Germany, Heidelberg

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Burgess EReddy MMohr D(2022)"I Just Can't Help But Smile Sometimes": Collaborative Self-Management of DepressionProceedings of the ACM on Human-Computer Interaction10.1145/35129176:CSCW1(1-32)Online publication date: 7-Apr-2022
https://dl.acm.org/doi/10.1145/3512917
Liu WHuang JPan WZhu YLi MXu H(2020)BraVo: A Physiological Indication System for Female College Students to Manage DepressionHuman Interaction, Emerging Technologies and Future Applications III10.1007/978-3-030-55307-4_19(122-127)Online publication date: 6-Aug-2020
https://doi.org/10.1007/978-3-030-55307-4_19
Pryss RSchlee WHoppenstedt BReichert MSpiliopoulou MLangguth BBreitmayer MProbst T(2019)An Ecological Momentary Assessment Study on Machine Learning to Predict the Used Mobile Operating System by the Daily Life Data of the TrackYourTinnitus mHealth Crowdsensing Platform. (Preprint)Journal of Medical Internet Research10.2196/15547Online publication date: 18-Jul-2019
https://doi.org/10.2196/15547
Sanches PJanson AKarpashevich PNadal CQu CDaudén Roquet CUmair MWindlin CDoherty GHöök KSas CBrewster SFitzpatrick GCox AKostakos V(2019)HCI and Affective HealthProceedings of the 2019 CHI Conference on Human Factors in Computing Systems10.1145/3290605.3300475(1-17)Online publication date: 2-May-2019
https://dl.acm.org/doi/10.1145/3290605.3300475
Morrison WGuerdan LKanugo JTrull TShang Y(2018)TigerAware: An Innovative Mobile Survey and Sensor Data Collection and Analytics System2018 IEEE Third International Conference on Data Science in Cyberspace (DSC)10.1109/DSC.2018.00025(115-122)Online publication date: Jun-2018
https://doi.org/10.1109/DSC.2018.00025
Chow PFua KHuang YBonelli WXiong HBarnes LTeachman B(2017)Using Mobile Sensing to Test Clinical Models of Depression, Social Anxiety, State Affect, and Social Isolation Among College StudentsJournal of Medical Internet Research10.2196/jmir.682019:3(e62)Online publication date: 3-Mar-2017
https://doi.org/10.2196/jmir.6820
Wang RWang WAung MBen-Zeev DBrian RCampbell AChoudhury THauser MKane JScherer EWalsh M(2017)Predicting Symptom Trajectories of Schizophrenia using Mobile SensingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/31309761:3(1-24)Online publication date: 11-Sep-2017
https://dl.acm.org/doi/10.1145/3130976

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Experiences in mHealth for chronic disease management in 4 countries

The relationship between clinical, momentary, and sensor-based assessment of depression

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