SocialBike: Quantified-Self Data as Social Cue in Physical Activity

Abstract

Quantified-self application is widely used in sports and health management; the type and amount of data that can be fed back to the user are growing rapidly. However, only a few studies discussed the social attributes of quantified-self data, especially in the context of cycling. In this study, we present “SocialBike,” a digital augmented bicycle that aims to increase cyclists’ motivation and social relatedness in physical activity by showing their quantified-self data to each other. To evaluate the concept through a rigorous control experiment, we built a cycling simulation system to simulate a realistic cycling experience with SocialBike. A within-subjects experiment was conducted through the cycling simulation system with 20 participants. Quantitative data were collected with the Intrinsic Motivation Inventory (IMI) and data recorded by the simulation system; qualitative data were collected through user interviews. The result showed that SocialBike increase cyclists’ intrinsic motivation, perceived competence, and social relatedness in physical activity.