Understanding and Designing for Sharing and Privacy in Wearable Fitness Platforms

Commercial wearable devices that collect health and fitness data are widely used. These devices sense and collect a variety of personal data, which can be shared by users with other people and with third parties. Yet, the collection of personal data by these sensor devices and the sharing of it poses several risks, including stalking, secondary use, aggregation, and inferences. In this dissertation, I present a new and an increased understanding of fitness tracker users’ sharing practices, concerns, awareness, and needs. The main goal is to design controls and features that empower users over the sharing and privacy of their information.
My research utilized different approaches, including semi-structured interview, survey, and participatory design studies. Overall, the findings uncover several sharing patterns by fitness tracker users, with practices in each pattern based on the intended audiences. While users do not consider much of the data collected by their devices sensitive, they have concerns about the possibility of abusing their data. However, users have limited awareness about the potential to infer personal information from the primary data collected by activity trackers. My research provides several factors that might impact users’ perceptions and attitudes towards inferences in the context of IoT wearable devices. Lastly, my research presents a set of taxonomies for sharing and privacy controls and mechanisms in fitness tracker platforms and contributes several design guidelines.