research-article

Tell me your apps and I will tell you your mood: correlation of apps usage with bipolar disorder state

Authors:

Jorge Alvarez-Lozano,

Maria Faurholt-Jepsen,

Lars Vedel KessingAuthors Info & Claims

PETRA '14: Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments

Article No.: 19, Pages 1 - 7

https://doi.org/10.1145/2674396.2674408

Published: 27 May 2014 Publication History

Abstract

Bipolar Disorder is a disease that is manifested with cycling periods of polar episodes, namely mania and depression. Depressive episodes are manifested through disturbed mood, psychomotor retardation, behaviour change, decrease in energy levels and length of sleep. Manic episodes are manifested through elevated mood, psychomotor acceleration and increase in intensity of social interactions. In this paper we report results of a clinical trial with bipolar patients that amongst other aspects, investigated whether changes in general behaviour of patients due to onset of a bipolar episode, can be captured through the analysis of smartphone usage. We have analysed changes in smartphone usage, specifically app usage and how these changes correlate with the self-reported patient state. We also used psychiatric evaluation scores provided by the clinic to understand correlation of the patient smartphone behaviour before the psychiatric evaluation and after the psychiatric evaluation. The results show that patients have strong correlation of patterns of app usage with different aspects of their self-reported state including mood, sleep and irritability. While, on the other hand, the patients' application usage shows discernable changes in the period before and after psychiatric evaluation.

References

[1]

S. Alqassim, M. Ganesh, S. Khoja, M. Zaidi, F. Aloul, and A. Sagahyroon. Sleep apnea monitoring using mobile phones. In e-Health Networking, Applications and Services (Healthcom), 2012 IEEE 14th International Conference on, pages 443--446, 2012.

[2]

Diagnostic and statistical manual of mental disorders: DSM-IV-TR. Washington, DC, American Psychiatric Association, 4th ed., text revision edition, 2000.

[3]

M. Bauer, T. Wilson, K. Neuhaus, J. Sasse, A. Pfennig, U. Lewitzka, P. Grof, T. Glenn, N. Rasgon, T. Bschor, and P. C. Whybrow. Self-reporting software for bipolar disorder: validation of chronorecord by patients with mania. Psychiatry research, 159: 359--66, 2008 Jun 30 2008.

[4]

J. Blum and E. Magill. M-psychiatry: Sensor networks for psychiatric health monitoring. In The 9th Annual Postgraduate Symposium on The Convergence of Telecommunications, Networking and Broadcasting, PGNET 2008, 2008.

[5]

J. Blum and E. Magill. The design and evaluation of personalised ambient mental health monitors. In Consumer Communications and Networking Conference (CCNC), 2010 7th IEEE, pages 1--5, 2010.

Digital Library

[6]

J. M. Bopp, D. J. Miklowitz, G. M. Goodwin, W. Stevens, J. M. Rendell, and J. R. Geddes. The longitudinal course of bipolar disorder as revealed through weekly text messaging: a feasibility study. Bipolar disorders, 12: 327--34, 2010 May 2010.

[7]

F. Buttussi and L. Chittaro. Mopet: A context-aware and user-adaptive wearable system for fitness training. Artif. Intell. Med., 42(2):153--163, Feb. 2008.

Digital Library

[8]

E. K. Choe, S. Consolvo, J. Jung, B. Harrison, and J. A. Kientz. Living in a glass house: A survey of private moments in the home. In Proceedings of the 13th International Conference on Ubiquitous Computing, UbiComp '11, pages 41--44, New York, NY, USA, 2011. ACM.

Digital Library

[9]

A. Gruenerbl, P. Oleksy, G. Bahle, C. Haring, J. Weppner, and P. Lukowicz. Towards smart phone based monitoring of bipolar disorder. In Proceedings of the Second ACM Workshop on Mobile Systems, Applications, and Services for HealthCare, mHealthSys '12, pages 3:1--3:6, New York, NY, USA, 2012. ACM.

Digital Library

[10]

N. Lathia, V. Pejovic, K. Rachuri, C. Mascolo, M. Musolesi, and P. Rentfrow. Smartphones for large-scale behavior change interventions. Pervasive Computing, IEEE, 12(3):66--73, 2013.

Digital Library

[11]

Aleksandar Matic, Venet Osmani, and Oscar Mayora-Ibarra. Analysis of social interactions through mobile phones. Mobile Networks and Applications, Vol. 17, Issue 6, pp. 808--819, 2012.

[12]

Venet Osmani, Alban Maxhuni, Agnes Grünerbl, Paul Lukowicz, Christian Haring, and Oscar Mayora. 2013. Monitoring activity of patients with bipolar disorder using smart phones. In Proceedings of International Conference on Advances in Mobile Computing & Multimedia (MoMM '13). ACM, New York, NY, USA, pp. 85--92, DOI=10.1145/2536853.2536882

Digital Library

[13]

O. Schleusing, P. Renevey, M. Bertschi, S. Dasen, J.-M. Koller, and R. Paradiso. Monitoring physiological and behavioral signals to detect mood changes of bipolar patients. In Medical Information Communication Technology (ISMICT), 2011 5th International Symposium on, pages 130--134, 2011.

[14]

M. Sung, C. Marci, and A. S. Pentland. Objective physiological and behavioral measures for identifying and tracking depression state in clinically depressed patients. Technical report, MIT Media Laboratory, Human Dynamics Group, 2005.

[15]

Trinh Minh Tri Do, Daniel Gatica-Perez, Where and what: Using smartphones to predict next locations and applications in daily life, Pervasive and Mobile Computing, Available online 23 April 2013, ISSN 1574-1192, http://dx.doi.org/10.1016/j.pmcj.2013.03.006.

[16]

Trinh-Minh-Tri Do and Daniel Gatica-Perez. 2010. By their apps you shall understand them: mining large-scale patterns of mobile phone usage. In Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia (MUM '10). ACM, New York, NY, USA, Article 27, 10 pages. DOI=10.1145/1899475.1899502

Digital Library

[17]

Ye Xu, Mu Lin, Hong Lu, Giuseppe Cardone, Nicholas Lane, Zhenyu Chen, Andrew Campbell, and Tanzeem Choudhury. 2013. Preference, context and communities: a multi-faceted approach to predicting smartphone app usage patterns. In Proceedings of the 2013 International Symposium on Wearable Computers (ISWC '13). ACM, New York, NY, USA, 69--76. DOI=10.1145/2493988.2494333

Digital Library

[18]

O. Schleusing, P. Renevey, M. Bertschi, S. Dasen, J. M. Koller, and R. Paradiso, "Monitoring physiological and behavioral signals to detect mood changes of bipolar patients," 2011 5th International Symposium on Medical Information and Communication Technology, pp. 130--134, 2011.

[19]

M. Bauer, T. Wilson, K. Neuhaus, J. Sasse, A. Pfennig, U. Lewitzka, P. Grof, T. Glenn, N. Rasgon, T. Bschor, and P. C. Whybrow, "Self-reporting software for bipolar disorder: validation of ChronoRecord by patients with mania.," Psychiatry research, vol. 159, no. 3, pp. 359--66, Jun. 2008.

[20]

A. Grünerbl, P. Oleksy, G. Bahle, C. Haring, J. Weppner, and P. Lukowicz, "Towards smart phone based monitoring of bipolar disorder," in Proceedings of the Second ACM Workshop on Mobile Systems, Applications, and Services for HealthCare, 2012, p. Article No. 3.

Digital Library

[21]

J. Blum and E. Magill, "M-psychiatry: Sensor networks for psychiatric health monitoring," in Proceedings of The 9th Annual Postgraduate Symposium The Convergence of Telecommunications, Networking and Broadcasting, Liverpool John Moores University, 2008, pp. 33--37.

[22]

S. Patel, B. Chen, and T. Buckley, "Home monitoring of patients with Parkinson's disease via wearable technology and a web-based application," Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2010, vol. 02139, 2010.

[23]

F. Buttussi and L. Chittaro, "MOPET: A context-aware and user-adaptive wearable system for fitness training," Artificial Intelligence in Medicine, vol. 42, no. 2, pp. 153--163, Feb. 2008.

Digital Library

[24]

J. M. Bopp, D. J. Miklowitz, G. M. Goodwin, W. Stevens, J. M. Rendell, and J. R. Geddes, "The longitudinal course of bipolar disorder as revealed through weekly text messaging: a feasibility study" Bipolar disorders, vol. 12, no. 3, pp. 327--34, May 2010.

[25]

Aleksandar Matic, Venet Osmani, Oscar Mayora-Ibarra, "Analysis of Social Interactions Through Mobile Phones", Mobile Networks and Applications (MONET), December 2012, Volume 17, Issue 6, pp 808--819

[26]

T. L. Simpson, D. R. Kivlahan, K. R. Bush, and M. E. McFall, "Telephone self-monitoring among alcohol use disorder patients in early recovery: a randomized study of feasibility and measurement reactivity.," Drug and alcohol dependence, vol. 79, no. 2, pp. 241--50, Aug. 2005.

[27]

"Optimism Apps," 2013. {Online}. Available: www.findingoptimism.com. {Accessed: 09-Jul-2013}.

[28]

M. Sung, C. Marci, and A. S. Pentland, "Objective Physiological and Behavioral Measures for Identifying and Tracking Depression State in Clinically Depressed Patients," 2005.

[29]

J. M. Blum and E. H. Magill, "The Design and Evaluation of Personalised Ambient Mental Health Monitors," in 7th IEEE Consumer Communications and Networking Conference, 2010, pp. 1--5.

Digital Library

[30]

R. Paradiso, a M. Bianchi, K. Lau, and E. P. Scilingo, "PSYCHE: personalised monitoring systems for care in mental health.," International Conference of the IEEE Engineering in Medicine and Biology Society., vol. 2010, pp. 3602--5, Jan. 2010.

[31]

Agnes Gruenerbl, Venet Osmani, Gernot Bahle, Jose C. Carrasco, Stefan Oehler, Oscar Mayora, Christian Haring, and Paul Lukowicz. 2014. "Using smart phone mobility traces for the diagnosis of depressive and manic episodes in bipolar patients" In Proceedings of the 5th Augmented Human International Conference (AH '14). ACM, New York, NY, USA, Article 38, 8 pages. DOI=10.1145/2582051.2582089

Digital Library

Cited By

Semente EHaragaes R(2024)Adopting of Smartphone Technologies Amongst Older Adults in Windhoek, NamibiaInternational Journal of Applied Management Sciences and Engineering10.4018/IJAMSE.33956711:1(1-23)Online publication date: 26-Feb-2024
https://doi.org/10.4018/IJAMSE.339567
Chan HCheng ZMendolia SPaloyo ATani MProulx DSavage DTorgler B(2024)Residential mobility restrictions and adverse mental health outcomes during the COVID-19 pandemic in the UKScientific Reports10.1038/s41598-024-51854-614:1Online publication date: 20-Jan-2024
https://doi.org/10.1038/s41598-024-51854-6
Volpe UElkholy HGargot TPinto da Costa MOrsolini L(2024)Devices, Mobile Health, and Digital PhenotypingTasman’s Psychiatry10.1007/978-3-030-51366-5_151(5191-5216)Online publication date: 5-Sep-2024
https://doi.org/10.1007/978-3-030-51366-5_151
Show More Cited By

Recommendations

Personalized versus Generic Mood Prediction Models in Bipolar Disorder
UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers

A number of studies have been investigating the use of mobile phone sensing to predict mood in unipolar (depression) and bipolar disorder. However, most of these studies included a small number of people making it difficult to understand the feasibility ...
Towards long term monitoring of electrodermal activity in daily life

Manic depression, also known as bipolar disorder, is a common and severe form of mental disorder. The European research project MONARCA aims at developing and validating mobile technologies for multi-parametric, long term monitoring of physiological and ...
Relationship between internet use and depression

A cross-sectional observational study was conducted on a sample of 336 adolescents.There was a positive correlation between internet addiction and depression.The detected relationship is independent from social networking activities. The aim of the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

PETRA '14: Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments

May 2014

408 pages

ISBN:9781450327466

DOI:10.1145/2674396

Conference Chair:
Fillia Makedon
University of Texas at Arlington
,
Program Chairs:
Mark Clements
Georgia Institute of Technology
,
Catherine Pelachaud
TELECOM ParisTech, France
,
Vana Kalogeraki
Athens University of Economics and Bus
,
Ilias Maglogiannis
University of Piraeus, Greece

Copyright © 2014 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 the author(s) 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

iPerform Center: iPerform Center for Assistive Technologies to Enhance Human Performance
CSE@UTA: Department of Computer Science and Engineering, The University of Texas at Arlington
HERACLEIA: HERACLEIA Human-Centered Computing Laboratory at UTA
U of Tex at Arlington: U of Tex at Arlington
NCRS: Demokritos National Center for Scientific Research
Fulbrigh, Greece: Fulbright Foundation, Greece

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 May 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

PETRA '14

Sponsor:

iPerform Center
CSE@UTA
HERACLEIA
U of Tex at Arlington
NCRS
Fulbrigh, Greece

PETRA '14: The 7th International Conference on PErvasive Technologies Related to Assistive Environments

May 27 - 30, 2014

Rhodes, Greece

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

52
Total Citations
View Citations
722
Total Downloads

Downloads (Last 12 months)50
Downloads (Last 6 weeks)7

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Semente EHaragaes R(2024)Adopting of Smartphone Technologies Amongst Older Adults in Windhoek, NamibiaInternational Journal of Applied Management Sciences and Engineering10.4018/IJAMSE.33956711:1(1-23)Online publication date: 26-Feb-2024
https://doi.org/10.4018/IJAMSE.339567
Chan HCheng ZMendolia SPaloyo ATani MProulx DSavage DTorgler B(2024)Residential mobility restrictions and adverse mental health outcomes during the COVID-19 pandemic in the UKScientific Reports10.1038/s41598-024-51854-614:1Online publication date: 20-Jan-2024
https://doi.org/10.1038/s41598-024-51854-6
Volpe UElkholy HGargot TPinto da Costa MOrsolini L(2024)Devices, Mobile Health, and Digital PhenotypingTasman’s Psychiatry10.1007/978-3-030-51366-5_151(5191-5216)Online publication date: 5-Sep-2024
https://doi.org/10.1007/978-3-030-51366-5_151
Gopalakrishnan AGururajan RZhou XVenkataraman RChan KHiggins N(2024)A survey of autonomous monitoring systems in mental healthWIREs Data Mining and Knowledge Discovery10.1002/widm.152714:3Online publication date: 24-Jan-2024
https://doi.org/10.1002/widm.1527
Xu TYu JDoyle DVoida S(2023)Technology-Mediated Strategies for Coping with Mental Health Challenges: Insights from People with Bipolar DisorderProceedings of the ACM on Human-Computer Interaction10.1145/36100317:CSCW2(1-31)Online publication date: 4-Oct-2023
https://dl.acm.org/doi/10.1145/3610031
Sarsenbayeva ZFleming CTag BWithana Avan Berkel NMcEwan A(2023)A Review on Mood Assessment Using SmartphonesHuman-Computer Interaction – INTERACT 202310.1007/978-3-031-42283-6_22(385-413)Online publication date: 25-Aug-2023
https://doi.org/10.1007/978-3-031-42283-6_22
Wen NMallavarapu ABiehl JWalker EBabichenko D(2023)Understanding Barriers of Missing Data in Personal Informatics SystemsPervasive Computing Technologies for Healthcare10.1007/978-3-031-34586-9_40(603-618)Online publication date: 11-Jun-2023
https://doi.org/10.1007/978-3-031-34586-9_40
Volpe UElkholy HGargot TPinto da Costa MOrsolini L(2023)Devices, Mobile Health and Digital PhenotypingTasman’s Psychiatry10.1007/978-3-030-42825-9_151-1(1-26)Online publication date: 2-Jun-2023
https://doi.org/10.1007/978-3-030-42825-9_151-1
Vega JBell BTaylor CXie JNg HHonary MMcNaney R(2022)Detecting Mental Health Behaviors Using Mobile Interactions: Exploratory Study Focusing on Binge EatingJMIR Mental Health10.2196/321469:4(e32146)Online publication date: 25-Apr-2022
https://doi.org/10.2196/32146
Currey DTorous J(2022)Digital phenotyping correlations in larger mental health samples: analysis and replicationBJPsych Open10.1192/bjo.2022.5078:4Online publication date: 3-Jun-2022
https://doi.org/10.1192/bjo.2022.507
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten