Quantified-self tools track personal data as well as emotional and psychological scores, in real time, of people who use such tools. Such data have potential uses for initiating interactions to induce personal health-related behavior changes. However, notwithstanding this potential, not much benefit has been derived from the data tracked using various devices such as smartphones and fitness trackers. The main research goal of this study is to investigate how interactions of quantified-self tools should be designed for inducing user perception and behavior change. Particularly, this study uses two message representation formats (MRF) for users to perceive self-tracking tools as companion devices because the MRFs of smartphones and fitness trackers are important to interact with users in conversational interaction. This study developed a message expression algorithm, “Samantha,” to deliver personalized-messages automatically in real time about the values tracked by these devices to their users. The study studied the effect of the four message representation formats on the perception of companion and to induce behavior change.
|Number of pages||18|
|Journal||International Journal of Human-Computer Interaction|
|Publication status||Published - 2020 Jan 20|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2017R1C1B2011377). Also, this work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (NRF-2016R1D1A1B02015987).
© 2019, © 2019 Taylor & Francis Group, LLC.
All Science Journal Classification (ASJC) codes
- Human Factors and Ergonomics
- Human-Computer Interaction
- Computer Science Applications