Enhancing Intrinsic Motivation in Physical Activity through Quantified-self Data Sharing

Abstract

INTRODUCTION: Quantified-self application is widely used in sports and health management; the type and amount of data that can be detected and 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.

OBJECTIVES: To design and evaluate SocialBike - a digitally augmented bicycle that aims to increase user’s motivation in physical activity by showing their quantified-self data to each other.

METHODS: We conducted a within-subject experiment on a cycling simulation system with 20 participants. The on-bike display of SocialBike was compared to a control display to evaluate its impact on two dependent variables, including user’s intrinsic motivation in physical activity and social relatedness with other cyclists nearby. Intrinsic Motivation Inventory (IMI) was used to measure users’ self-reported intrinsic motivation and social relatedness. The user’s cycling behaviour data were also recorded by the simulation system. We extracted six indicators about the two dependent variables from the behaviour data. Qualitative data were collected through semi-structured interviews. We conducted a paired sample T-test on both types of quantitative data. Qualitative data were analysed by the method of thematic analysis.

RESULTS: The results of the Intrinsic Motivation Inventory show that SocialBike’s on-bike display has significant positive effects on four subscales, including intrinsic motivation, perceived competence, values/usefulness, and relatedness. The results of user cycling behaviour indicate that SocialBike increased user’s social relatedness with other cyclists nearby by significantly affecting three indicators. Three themes were identified from qualitative data, including motivation, social interaction, and data visualization. The qualitative result provides supplementary evidence for the quantitative result and additional insights into the overall design of SocialBike.

CONCLUSION: SocialBike as a platform for sharing quantified-self data have positive effects in enhancing users’ intrinsic motivation in physical activity and social relatedness with other cyclists nearby.