Exploring the Impacts of Elaborateness and Indirectness in a Behavior Change Support System

Zhihua Zhang, Juliana Miehle, Yuki Matsuda, Manato Fujimoto, Yutaka Arakawa, Keiichi Yasumoto, Wolfgang Minker

Research output: Contribution to journalArticlepeer-review

Abstract

Numerous technologies exist for promoting a healthier lifestyle. These technologies collectively referred to as 'Behavior Change Support Systems'. However, the majority of existing apps use quantitative data representation. Since it is difficult to understand the meaning behind quantitative data, this approach has been suggested to lower users' motivation and fail to promote behavior change. Therefore, an interpretation of quantitative data needs to be provided as a supplement. However, different descriptions of the same data may lead to different outcomes. In this paper, we explore the impact of different communication styles for interpretations of quantitative data on behavior change by developing and evaluating Walkeeper - a web-based app that provides interpretations of the users' daily step counts using different levels of elaborateness and indirectness with the aim of promoting walking. Through the quantitative analysis and results of a user study, we contribute new knowledge on designing such interpretations for quantitative data.

Original languageEnglish
Article number9429258
Pages (from-to)74778-74788
Number of pages11
JournalIEEE Access
Volume9
DOIs
Publication statusPublished - 2021

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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