@inproceedings{b1fc0561d8234945a01bea2a8ac353bf,
title = "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{\textquoteright} 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{\textquoteright} intrinsic motivation, perceived competence, and social relatedness in physical activity.",
keywords = "Health, Motivation, Personal informatics, Physical activity, Quantified-self, Social interaction",
author = "Nan Yang and {van Hout}, Gerbrand and Loe Feijs and Wei Chen and Jun Hu",
year = "2020",
doi = "10.1007/978-3-030-42029-1_7",
language = "English",
isbn = "978-3-030-42028-4",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (LNICST)",
publisher = "Springer",
pages = "92--107",
editor = "Garcia, {Nuno M.} and Pires, {Ivan Miguel} and Rossitza Goleva",
booktitle = "IoT Technologies for HealthCare",
address = "Germany",
note = "6th EAI International Conference on IoT Technologies for HealthCare, HealthyIoT 2019 ; Conference date: 04-12-2019 Through 06-12-2019",
}