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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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.

Original languageEnglish
Title of host publicationNordiCHI 2020 - Proceedings of the 11th Nordic Conference on Human-Computer Interaction
Subtitle of host publicationShaping Experiences, Shaping Society
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450375795
DOIs
Publication statusPublished - 25 Oct 2020
Event11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society, NordiCHI 2020 - Virtual, Online, Estonia
Duration: 25 Oct 202029 Oct 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society, NordiCHI 2020
CountryEstonia
CityVirtual, Online
Period25/10/2029/10/20

Keywords

  • Orthosomnia
  • Quantified-self
  • Sleep health
  • Sleep quality
  • Subjective measurement

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