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.
|Title of host publication||NordiCHI 2020 - Proceedings of the 11th Nordic Conference on Human-Computer Interaction|
|Subtitle of host publication||Shaping Experiences, Shaping Society|
|Publisher||Association for Computing Machinery, Inc|
|Publication status||Published - 25 Oct 2020|
|Event||11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society, NordiCHI 2020 - Virtual, Online, Estonia|
Duration: 25 Oct 2020 → 29 Oct 2020
|Name||ACM International Conference Proceeding Series|
|Conference||11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society, NordiCHI 2020|
|Period||25/10/20 → 29/10/20|
Bibliographical notePublisher Copyright:
© 2020 Owner/Author.
- Sleep health
- Sleep quality
- Subjective measurement