extended-abstract

SlumberBot: An Interactive Agent for Helping Users Investigate Disturbance Factors of Sleep Quality

Authors:

Yizhou Liu,

Da-jung Kim,

Ting Miao,

Yaliang ChuangAuthors Info & Claims

NordiCHI '20: Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society

Article No.: 112, Pages 1 - 4

https://doi.org/10.1145/3419249.3420091

Published: 26 October 2020 Publication History

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Abstract

As sleep health is increasingly becoming important in recent years, many wearable products and mobile apps have been developed to track users' sleep and interpret their sleep quality. Most of the available designs have mainly focused on objective measurements, such as body movement, heart rate, and/or bedroom light, noise level, and temperature. However, due to the lack of users' subjective experience measurements, sleep trackers often fail to provide useful suggestions for improving their sleep. In this study, we developed SlumberBot with conversational chatbot technology to help users capture their subjective sleep experiences and relevant factors in daytime activities as well. With SlumberBot, we conducted a preliminary field study with five participants in a 4-week time period. The result shows that SlumberBot is easy to stay engaged with and supportive of users' self-reflection on contextual factors related to sleep quality. Besides, SlumberBot has shown the potential of triggering short-term behavior changes that would impact their sleep positively.

References

[1]

Kelly Glazer Baron, Sabra Abbott, Nancy Jao, Natalie Manalo, and Rebecca Mullen. 2017. Orthosomnia: Are Some Patients Taking the Quantified Self Too Far? Journal of Clinical Sleep Medicine 13, 02: 351–354. https://jcsm.aasm.org/doi/10.5664/jcsm.6472

Crossref

Google Scholar

[2]

D. J. Buysse. 2014. Sleep health: can we define it? Does it matter?. Sleep, 37. 1, 9-17. http://dx.doi.org/10.5665/sleep.3298

Crossref

Google Scholar

[3]

C.E. Carney, D. J. Buysse, S. Ancoli-Israel, 2012. The consensus sleep diary: standardizing prospective sleep self-monitoring. Sleep, 35.2, 287-302.

Crossref

Google Scholar

[4]

E. K. Choe, S. Abdullah, M. Rabbi, 2017. Semi-Automated Tracking: A Balanced Approach for Self-Monitoring Applications. IEEE Pervasive Computing 16, 1: 74–84. https://ieeexplore.ieee.org/document/7807194

Digital Library

Google Scholar

[5]

Eun Kyoung Choe, Bongshin Lee, Matthew Kay, Wanda Pratt, and Julie A. Kientz. 2015. SleepTight: low-burden, self-monitoring technology for capturing and reflecting on sleep behaviors. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp ’15). Association for Computing Machinery, New York, NY, USA, 121–132.

Digital Library

Google Scholar

[6]

Young-Ho Kim, Jae Ho Jeon, Bongshin Lee, Eun Kyoung Choe, and Jinwook Seo. 2017. OmniTrack: A Flexible Self-Tracking Approach Leveraging Semi-Automated Tracking. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 3, Article 67 (September 2017), 28 pages.

Digital Library

Google Scholar

[7]

Rafal Kocielnik, Lillian Xiao, Daniel Avrahami, and Gary Hsieh. 2018. Reflection Companion: A Conversational System for Engaging Users in Reflection on Physical Activity. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 2, Article 70 (June 2018), 26 pages.

Digital Library

Google Scholar

[8]

National Sleep Foundation. 2016. Sleep Diary. https://www.sleepfoundation.org/sites/default/files/inline-files/SleepDiaryv6.pdf

Google Scholar

[9]

Ruth Ravichandran, Sang-Wha Sien, Shwetak N. Patel, Julie A. Kientz, and Laura R. Pina. 2017. Making Sense of Sleep Sensors: How Sleep Sensing Technologies Support and Undermine Sleep Health. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI ’17). Association for Computing Machinery, New York, NY, USA, 6864–6875.

Crossref

Google Scholar

[10]

Steven R. Rick, Aaron Paul Goldberg, and Nadir Weibel. 2019. SleepBot: encouraging sleep hygiene using an intelligent chatbot. In Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion (IUI ’19). Association for Computing Machinery, New York, NY, USA, 107–108.

Digital Library

Google Scholar

[11]

D. Schulman, T. Bickmore, C. Sidner. 2011. An intelligent conversational agent for promoting long-term health behavior change using motivational interviewing. In 2011 AAAI Spring Symposium Series. https://www.aaai.org/ocs/index.php/SSS/SSS11/paper/viewPaper/2401

Google Scholar

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De Souza AMariano PGuerino GChaves AValentim N(2023)Technologies for Hedonic Aspects Evaluation in Text-based Chatbots: A Systematic Mapping StudyProceedings of the XXII Brazilian Symposium on Human Factors in Computing Systems10.1145/3638067.3638089(1-11)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3638067.3638089
Son SLim Y(2023)Balancing Flexibility and Authority: Exploring Negotiation as an Interaction Strategy for Healthy Sleep BehaviorsProceedings of the 2023 ACM Designing Interactive Systems Conference10.1145/3563657.3596074(401-415)Online publication date: 10-Jul-2023
https://dl.acm.org/doi/10.1145/3563657.3596074
Karlgren KMcMillan D(2022)Designing for Extreme Sleepers: Rethinking the Rhythms of Sleep TechnologyNordic Human-Computer Interaction Conference10.1145/3546155.3546685(1-17)Online publication date: 8-Oct-2022
https://dl.acm.org/doi/10.1145/3546155.3546685
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SlumberBot: An Interactive Agent for Helping Users Investigate Disturbance Factors of Sleep Quality
1. Applied computing

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- Sleep efficiency and skin temperature play main roles in sleep quality assessment.

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Published In

NordiCHI '20: Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society

October 2020

1177 pages

ISBN:9781450375795

DOI:10.1145/3419249

General Chairs:
David Lamas
Tallinn University, Estonia
,
Hegle Sarapuu
Trinidad Wiseman, Estonia
,
Publications Chairs:
Ilja Šmorgun
Tallinn University, Estonia
,
Gerd Berget
Oslo Metropolitan University, Norway

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: 26 October 2020

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NordiCHI '20: Shaping Experiences, Shaping Society

October 25 - 29, 2020

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De Souza AMariano PGuerino GChaves AValentim N(2023)Technologies for Hedonic Aspects Evaluation in Text-based Chatbots: A Systematic Mapping StudyProceedings of the XXII Brazilian Symposium on Human Factors in Computing Systems10.1145/3638067.3638089(1-11)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3638067.3638089
Son SLim Y(2023)Balancing Flexibility and Authority: Exploring Negotiation as an Interaction Strategy for Healthy Sleep BehaviorsProceedings of the 2023 ACM Designing Interactive Systems Conference10.1145/3563657.3596074(401-415)Online publication date: 10-Jul-2023
https://dl.acm.org/doi/10.1145/3563657.3596074
Karlgren KMcMillan D(2022)Designing for Extreme Sleepers: Rethinking the Rhythms of Sleep TechnologyNordic Human-Computer Interaction Conference10.1145/3546155.3546685(1-17)Online publication date: 8-Oct-2022
https://dl.acm.org/doi/10.1145/3546155.3546685
Baumert MCowie MRedline SMehra RArzt MPépin JLinz D(2022)Sleep characterization with smart wearable devices: a call for standardization and consensus recommendationsSleep10.1093/sleep/zsac18345:12Online publication date: 1-Aug-2022
https://doi.org/10.1093/sleep/zsac183

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Abstract

References

Cited By

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Recommendations

Designing for Triggering Self-Investigations and Reflections on Factors Related to Sleep Health

Identifying Elderly with Poor Sleep Quality using Unobtrusive In-home Sensors for Early Intervention

Sleep quality prediction in caregivers using physiological signals

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