Research Article
SocialBike: Quantified-Self Data as Social Cue in Physical Activity
@INPROCEEDINGS{10.1007/978-3-030-42029-1_7, author={Nan Yang and Gerbrand Hout and Loe Feijs and Wei Chen and Jun Hu}, title={SocialBike: Quantified-Self Data as Social Cue in Physical Activity}, proceedings={IoT Technologies for HealthCare. 6th EAI International Conference, HealthyIoT 2019, Braga, Portugal, December 4--6, 2019, Proceedings}, proceedings_a={HEALTHYIOT}, year={2020}, month={6}, keywords={Social interaction Quantified-self Personal informatics Motivation Physical activity Health}, doi={10.1007/978-3-030-42029-1_7} }- Nan Yang
Gerbrand Hout
Loe Feijs
Wei Chen
Jun Hu
Year: 2020
SocialBike: Quantified-Self Data as Social Cue in Physical Activity
HEALTHYIOT
Springer
DOI: 10.1007/978-3-030-42029-1_7
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.
