Facilitating Physical Activity Through On-Site Quantified-Self Data Sharing

by | Jun 16, 2020 | Research Publications

Author(s):

  • Nan Yang
  • Gerbrand van Hout
  • Loe Feijs
  • Wei Chen
  • Jun Hu

Abstract:


With the development of sensing technology and the popularization of quantified-self devices, there are increasing types of health-related data that can be sensed, visualized and presented to the user. However, most existing quantified-self applications are designed to support self-management and self-reflection; only a few studies so far have investigated the social aspect of quantified-self data. In this study, we investigated the social role of quantified-self data by introducing the design and evaluation of SocialBike—a digitally augmented bicycle that aims to increase the user’s intrinsic motivation in physical activity through on-site quantified-self data sharing. We conducted a controlled experiment on a cycling simulation system. Two forms of SocialBike’s on-bike display were evaluated with 36 participants. We used the Intrinsic Motivation Inventory to collect quantitative data about users’ intrinsic motivation in physical activity; the cycling simulation system recorded quantitative data about user behavior. Qualitative data was collected through semi-structured interviews. We conducted paired sample t-test to analyze both types of quantitative data; qualitative data were analyzed by the method of thematic analysis. The results show that SocialBike’s front display significantly increased users’ intrinsic motivation in physical activity. A total of nine themes were identified from the qualitative analysis, providing supplementary explanations for the quantitative results and additional insights into the overall design.

Documentation:

https://doi.org/10.3390/su12124904

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