Abstract
Recent years have witnessed a growing popularity of activity tracking applications. Previously work has focused on three major types of social interaction features in such applications: cooperation, competition and community. Such features motivate users to be more active in exercise and stay within the track of positive behavior change. Online fitness communities such as Strava encourage users to connect to peers and provide a rich set of social interaction features. Utilizing a large-scale behavioral trace data set, this work aims to analyze the dynamics of online fitness behaviors and network subscription as well as the relationship between them. Our results indicate that activeness of fitness behaviors not only has seasonal variations, but also vary by user group and how well users are connected in an online fitness community. These results provide important implications for studies on network-based health and design of application features for health promotion.
| Original language | English |
|---|---|
| Title of host publication | Social computing and social media. Technologies and analytics |
| Subtitle of host publication | SCSM 2018. Lecture notes in computer science |
| Editors | G. Meiselwitz |
| Publisher | Springer |
| Pages | 137-147 |
| Volume | 10914 |
| DOIs | |
| Publication status | Published - 2018 |
| Externally published | Yes |