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Lifelog-based estimation of activity diary for cognitive behavioral therapy

Authors:

Toshiaki FurukawaAuthors Info & Claims

UbiComp/ISWC'15 Adjunct: Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers

Pages 1251 - 1256

https://doi.org/10.1145/2800835.2807939

Published: 07 September 2015 Publication History

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Abstract

Cognitive Behavioral Therapy (CBT) shows promise for the effective treatment and prevention for depression. During the long-term treatment with CBT, users (e.g. patients with depression) are given some kind of homework in their daily lives to self-monitor their thoughts and behaviors by using some techniques taught by therapists (e.g. doctors) in the face-to-face session. In this paper, we present an application to support users to do their homework, especially self-monitoring of their behaviors. By collecting lifelogs of them via smart phones, the application estimates their behaviors (activities) based on their lifelogs and shows the results to support self-monitoring by themselves.

References

[1]

World Health Organization. 2008. The global burden of disease: 2004 update.

Google Scholar

[2]

Toshi A. Furukawa, Masaru Horikoshi, Norito Kawakami, Masayo Kadota, Megumi Sasaki, Yuki Sekiya, Hiroki Hosogoshi, Masami Kashimura, Kenichi Asano, Hitomi Terashima, Kazunori Iwasa, Minoru Nagasaku, and Louis C. Grothaus. 2012. Telephone Cognitive-Behavioral Therapy for Subthreshold Depression and Presenteeism in Workplace: A Randomized Controlled Trial. PLoS ONE, 7, e35330.

Google Scholar

[3]

Nao Kawanishi, Akio Hasegawa, Hissei Imai, Yoshio Takeuchi, and Toshiaki Furukawa. 2014. An Implementation of a Platform to Support Cognitive Behavioral Therapy Using Smart Phones. In Proceedings of 4th International Symposium on Pervasive Computing Paradigms for Mental Health (MindCare 2014) (poster).

Google Scholar

[4]

Psychology Tools. CBT DAILY ACTIVITY DIARY. Retrieved July 1, 2015 from http://psychology.tools/cbt-daily-activity-diary.html.

Google Scholar

[5]

Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of 2nd Conference on Knowledge Discovery and Data Mining (KDD-96), 226--231.

Google Scholar

Cited By

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Lepe-Salazar FSalgado-Torres S(2023)Multiple Composite Scenarios: A Game-Based Methodology for the Prevention of Mental DisordersEntertainment Computing10.1016/j.entcom.2022.10051944(100519)Online publication date: Jan-2023
https://doi.org/10.1016/j.entcom.2022.100519
Kumagai NTajika AHasegawa AKawanishi NHorikoshi MShimodera SKurata KChino BFurukawa T(2019)Predicting recurrence of depression using lifelog data: an explanatory feasibility study with a panel VAR approachBMC Psychiatry10.1186/s12888-019-2382-219:1Online publication date: 11-Dec-2019
https://doi.org/10.1186/s12888-019-2382-2
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
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Index Terms

Lifelog-based estimation of activity diary for cognitive behavioral therapy
1. Applied computing
  1. Life and medical sciences

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Published In

September 2015

1626 pages

ISBN:9781450335751

DOI:10.1145/2800835

General Chairs:
Kenji Mase
Nagoya University, JP
,
Marc Langheinrich
Università della Svizzera italiana (USI), CH
,
Daniel Gatica-Perez
IDIAP-EPFL, CH
,
Program Chairs:
Hans Gellersen
Lancaster University, UK
,
Tanzeem Choudhury
Cornell University
,
Koji Yatani
Univerisity of Tokyo, JP

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 September 2015

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UbiComp '15

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Yahoo! Japan
SIGMOBILE
FX Palo Alto Laboratory, Inc.
ACM
Rakuten Institute of Technology
Microsoft
Bell Labs
SIGCHI
Panasonic
Telefónica
ISTC-PC

UbiComp '15: The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 7 - 11, 2015

Osaka, Japan

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

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Lepe-Salazar FSalgado-Torres S(2023)Multiple Composite Scenarios: A Game-Based Methodology for the Prevention of Mental DisordersEntertainment Computing10.1016/j.entcom.2022.10051944(100519)Online publication date: Jan-2023
https://doi.org/10.1016/j.entcom.2022.100519
Kumagai NTajika AHasegawa AKawanishi NHorikoshi MShimodera SKurata KChino BFurukawa T(2019)Predicting recurrence of depression using lifelog data: an explanatory feasibility study with a panel VAR approachBMC Psychiatry10.1186/s12888-019-2382-219:1Online publication date: 11-Dec-2019
https://doi.org/10.1186/s12888-019-2382-2
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
Rew JHwang EChoi YRho S(2018)Monitoring skin condition using life activities on the SNS user documentsMultimedia Tools and Applications10.1007/s11042-017-4623-z77:8(9827-9847)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s11042-017-4623-z
Park YKang BChoo H(2017)A Digital Diary Making System Based on User Life-LogInternet of Vehicles – Technologies and Services10.1007/978-3-319-51969-2_17(206-213)Online publication date: 20-Jan-2017
https://doi.org/10.1007/978-3-319-51969-2_17

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Recommendations

Patient expectations and experiences from a clinical study in psychiatric care using a self-monitoring system

Creating salient summaries of home activity lifelog data

Classification of Daily Activities Based on the Amount of Social and Physical Activity for Behavioral Change Toward Wellbeing