short-paper

Comparison of in-situ mood input methods on mobile devices

Authors:

Torben Wallbaum,

Susanne BollAuthors Info & Claims

MUM '16: Proceedings of the 15th International Conference on Mobile and Ubiquitous Multimedia

Pages 123 - 127

https://doi.org/10.1145/3012709.3012724

Published: 12 December 2016 Publication History

Abstract

The exchange of daily moods is an important part of interpersonal communication over a distance. Mobile devices offer a platform for sharing on the go, and keeping in touch regularly. However, it is still a challenge to design an input for mood, such that the sharing easily integrates into the daily life and is engaging for a longer time. To design an input, which overcomes these challenges, we explore the use of four different methods for expressing mood on mobile devices, drawn from previous work in psychology. We aim to verify the usefulness of these methods and want to investigate this by conducting a comparative study of these four input methods, regarding the following factors: intuitiveness, inconvenience, speed of input, everyday use, expressiveness and overall suitability. Results show that use of photographs and emotion terms are suitable to describe the many facets of moods for most participants. Most of the participants prefer personalized input methods as well as combined methods. We discuss the outcomes with regard to the implications on future designs of messenger applications.

References

[1]

Roy F Baumeister and Mark R Leary. 1995. The need to belong: desire for interpersonal attachments as a fundamental human motivation. Psychological bulletin 117, 3 (1995), 497.

[2]

Margaret M Bradley and Peter J Lang. 1994. Measuring emotion: the self-assessment manikin and the semantic differential. Journal of behavior therapy and experimental psychiatry 25, 1 (1994), 49--59.

[3]

Yi-Ru Regina Chen and Peter J Schulz. 2016. The effect of information communication technology interventions on reducing social isolation in the elderly: A systematic review. Journal of medical Internet research 18, 1 (2016).

[4]

Karen Church, Eve Hoggan, and Nuria Oliver. 2010. A study of mobile mood awareness and communication through MobiMood. In Proceedings of the 6th Nordic Conference on Human-Computer Interaction: Extending Boundaries. ACM, 128--137.

Digital Library

[5]

Sheldon Cohen and Thomas A Wills. 1985. Stress, social support, and the buffering hypothesis. Psychological bulletin 98, 2 (1985), 310.

[6]

John R Crawford and Julie D Henry. 2004. The Positive and Negative Affect Schedule (PANAS): Construct validity, measurement properties and normative data in a large non-clinical sample. British Journal of Clinical Psychology 43, 3 (2004), 245--265.

[7]

Fabian Hemmert, Ulrike Gollner, Matthias Löwe, Anne Wohlauf, and Gesche Joost. 2011. Intimate mobiles: grasping, kissing and whispering as a means of telecommunication in mobile phones. In Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services. ACM, 21--24.

Digital Library

[8]

Julia R Henly, Sandra K Danziger, and Shira Offer. 2005. The contribution of social support to the material well-being of low-income families. Journal of Marriage and Family 67, 1 (2005), 122--140.

[9]

Joris H Janssen, Wijnand A IJsselsteijn, and Joyce HDM Westerink. 2014. How affective technologies can influence intimate interactions and improve social connectedness. International Journal of Human-Computer Studies 72, 1 (2014), 33--43.

Digital Library

[10]

Joseph'Jofish' Kaye. 2006. I just clicked to say I love you: rich evaluations of minimal communication. In CHI'06 extended abstracts on human factors in computing systems. ACM, 363--368.

Digital Library

[11]

Ian Li. 2015. MoodJam. Your moods. In color. (2015). http://www.moodjam.com/

[12]

Heinz-Herbert Noll. 2002. Towards a European system of social indicators: Theoretical framework and system architecture. Social Indicators Research 58, 1--3 (2002), 47--87.

[13]

John P Pollak, Phil Adams, and Geri Gay. 2011. PAM: a photographic affect meter for frequent, in situ measurement of affect. In Proceedings of the SIGCHI conference on Human factors in computing systems. ACM, 725--734.

Digital Library

[14]

James A Russell. 1980. A circumplex model of affect. Journal of personality and social psychology 39, 6 (1980), 1161.

[15]

James A Russell and Albert Mehrabian. 1977. Evidence for a three-factor theory of emotions. Journal of research in Personality 11, 3 (1977), 273--294.

[16]

James A Russell, Anna Weiss, and Gerald A Mendelsohn. 1989. Affect grid: a single-item scale of pleasure and arousal. Journal of personality and social psychology 57, 3 (1989), 493.

[17]

Jaime Snyder, Mark Matthews, Jacqueline Chien, Pamara F Chang, Emily Sun, Saeed Abdullah, and Geri Gay. 2015. MoodLight: Exploring Personal and Social Implications of Ambient Display of Biosensor Data. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing. ACM, 143--153.

Digital Library

[18]

Petra Sundström, Anna Ståhl, and Kristina Höök. 2007. In situ informants exploring an emotional mobile messaging system in their everyday practice. International journal of human-computer studies 65, 4 (2007), 388--403.

Digital Library

[19]

Keiko Takahashi, Junko Tamura, and Makiko Tokoro. 1997. Patterns of social relationships and psychological well-being among the elderly. International Journal of Behavioral Development 21, 3 (1997), 417--430.

[20]

Rongrong Wang and Francis Quek. 2010. Touch & talk: contextualizing remote touch for affective interaction. In Proceedings of the fourth international conference on Tangible, embedded, and embodied interaction. ACM, 13--20.

Digital Library

[21]

David Watson, Lee A Clark, and Auke Tellegen. 1988. Development and validation of brief measures of positive and negative affect: the PANAS scales. Journal of personality and social psychology 54, 6 (1988), 1063.

[22]

Lois B Wexner. 1954. The degree to which colors (hues) are associated with mood-tones. Journal of applied psychology 38, 6 (1954), 432.

Cited By

Grimme SSpoerl SBoll SKoelle M(2024)My Data, My Choice, My Insights: Women's Requirements when Collecting, Interpreting and Sharing their Personal Health DataProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642851(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642851
Scherenberg V(2023)Psychische Gesundheit via Mood-Tracking?Public Health Forum10.1515/pubhef-2023-006531:3(234-237)Online publication date: 7-Sep-2023
https://doi.org/10.1515/pubhef-2023-0065
Torkamaan H(2023)Mood Measurement on SmartphonesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35808647:1(1-35)Online publication date: 28-Mar-2023
https://dl.acm.org/doi/10.1145/3580864
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Index Terms

Comparison of in-situ mood input methods on mobile devices
1. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Other conferences

MUM '16: Proceedings of the 15th International Conference on Mobile and Ubiquitous Multimedia

December 2016

366 pages

ISBN:9781450348607

DOI:10.1145/3012709

General Chairs:
Jonna Häkkila
University of Lapland, Finland
,
Timo Ojala
University of Oulu, Finland

Copyright © 2016 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].

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New York, NY, United States

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Published: 12 December 2016

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Bundesministerium für Bildung und Forschung

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MUM '16

MUM '16: 15th International Conference on Mobile and Ubiquitous Multimedia

December 12 - 15, 2016

Rovaniemi, Finland

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MUM '16 Paper Acceptance Rate 35 of 77 submissions, 45%;

Overall Acceptance Rate 190 of 465 submissions, 41%

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Cited By

Grimme SSpoerl SBoll SKoelle M(2024)My Data, My Choice, My Insights: Women's Requirements when Collecting, Interpreting and Sharing their Personal Health DataProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642851(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642851
Scherenberg V(2023)Psychische Gesundheit via Mood-Tracking?Public Health Forum10.1515/pubhef-2023-006531:3(234-237)Online publication date: 7-Sep-2023
https://doi.org/10.1515/pubhef-2023-0065
Torkamaan H(2023)Mood Measurement on SmartphonesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35808647:1(1-35)Online publication date: 28-Mar-2023
https://dl.acm.org/doi/10.1145/3580864
Senette CBuzzi MParatore M(2023)Self-assess Momentary Mood in Mobile Devices: a Case Study with Mature Female Participants2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)10.1109/ICCT56969.2023.10075891(1-7)Online publication date: 19-Jan-2023
https://doi.org/10.1109/ICCT56969.2023.10075891
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
Schueller SNeary MLai JEpstein D(2021)Understanding People’s Use of and Perspectives on Mood-Tracking Apps: Interview StudyJMIR Mental Health10.2196/293688:8(e29368)Online publication date: 11-Aug-2021
https://doi.org/10.2196/29368
Yan XLi YHuang BPark SNewman M(2021)User Burden of Microinteractions: An In-lab Experiment Examining User Performance and Perceived Burden Related to In-situ Self-reportingProceedings of the 23rd International Conference on Mobile Human-Computer Interaction10.1145/3447526.3472046(1-14)Online publication date: 27-Sep-2021
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Torkamaan HZiegler J(2020)Mobile Mood TrackingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34322074:4(1-30)Online publication date: 18-Dec-2020
https://dl.acm.org/doi/10.1145/3432207
Zhang TEl Ali AWang CHanjalic ACesar PBernhaupt RMueller FVerweij DAndres JMcGrenere JCockburn AAvellino IGoguey ABjørn PZhao SSamson BKocielnik R(2020)RCEA: Real-time, Continuous Emotion Annotation for Collecting Precise Mobile Video Ground Truth LabelsProceedings of the 2020 CHI Conference on Human Factors in Computing Systems10.1145/3313831.3376808(1-15)Online publication date: 21-Apr-2020
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Umair MSas CLatif MHarrison SBardzell SNeustaedter CTatar D(2019)Towards Affective ChronometryProceedings of the 2019 on Designing Interactive Systems Conference10.1145/3322276.3322367(1479-1494)Online publication date: 18-Jun-2019
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