Abstract
The quantified self-movement encourages a continuous tracking of data points regarding a person’s daily activities through wearable sensors, and thus has important implications for health and wellness. With the advent of sophisticated low-cost wearable computing devices, online communities that facilitate social interaction and exchange of wearable data (Quantified Self 2.0 platforms) have also emerged. Although security and privacy disclosure has been studied within online social networks and online health communities, little has been done to understand how individual and group characteristics influence the disclosure behaviour regarding highly sensitive personal information gathered from wearable sensors (e.g., sleep, nutrition, mood, performance, ambient conditions). Using data collected from 43 Fitbit groups which consist of 5300 Asian users within the Fitbit online community, we examine the influence of group characteristics (size, posts, average steps) and individual attributes on privacy disclosure behaviour. Results from our hierarchical linear modelling analysis suggests that attributes such as group size and individual posts are associated with increased privacy data disclosure, whilst we surprisingly find that when other group members have higher health performance or are more active, individuals are more likely to disclose less healthcare information. Based on these findings, theoretical and practical implications are discussed.
Recommended Citation
Zhang, Jun; Dibia, Victor; Sodnomov, Alexey; and Lowry, Paul Benjamin, "Understanding the Disclosure of Private Healthcare Information within Online Quantified Self 2.0 Platforms" (2015). PACIS 2015 Proceedings. 140.
https://aisel.aisnet.org/pacis2015/140