Healthier Life with Digital Companions: Effects of Reflection-Level and Statement-Type of Messages on Behavior Change via a Perceived Companion

ABSTRACT

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.

Additional information

Funding

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).

Notes on contributors

Jinkyu Jang

Jinkyu Jang is a professor in Companoid Labs at Yonsei University, where he studies how humans and computers coexist through interaction technologies.

Jinwoo Kim

Jinwoo Kim is a professor in HCI Lab at Yonsei University, where he investigates the user experience in terms of human-centered innovation.