Gamified Double-Edged Sword: Exploring the Different Social Comparison Motives of Mobile Fitness App Users - Research in Progress

Mobile fitness applications (a.k.a. “apps”) are widely used to manage personal health records. The success of fitness apps hinges on their ability in promoting users’ exercise activities. The gamified design element has been widely employed by fitness apps as an effective approach to motivate users to exercise more. However, the efficacy of different gamified elements in influencing users’ subsequent exercise behaviors is still under debate in both research and practice. In this research-in-progress paper, we anchor the social comparison mechanisms to accordingly design gamification elements and demonstrate the dual impact of gamification on users’ exercise behavior change. In addition, we argue that the improvement of users’ exercise performance hinges on the extent to which users’ dispositional approach avoidance temperament is aligned with user’ gamification-enabled social comparison motives. The theoretical inference will guide a future field experiment by testing the effect of gamification on the users’ exercise performance change

DESIGNING QUANTIFIED-SELF 2.0 RUNNING PLATFORM TO ENSURE PHYSICAL ACTIVITY MAINTENANCE: THE ROLE OF ACHIEVEMENT GOALS AND ACHIEVEMENT MOTIVATIONAL AFFORDANCE

With the rapid development of wearable technologies, people can nowadays easily track and record their health-related information—particularly their athletic performance. The quantified-self 2.0 (QS 2.0) movement encourages running website or mobile application users to share their athletic information with other online community members to ensure the sustainable use of the technology and the maintenance of physical activity. However, the health literature claims that health behavior maintenance is difficult because it is easy for people to give up on the regular physical exercise during the maintenance stage, considering the unforeseen barriers and temptations that may occur in the long term. Drawing upon a motivational affordance perspective and the achievement goal theory (AGT), this theory-based manuscript provides design principles for QS 2.0 running platforms, with the purpose to increase users’ physical activity maintenance (PAM). Additionally, we propose a conceptual model explaining the underlying mechanism in terms of how these affordance design principles serve as the sources of two kinds of achievement goals, namely mastery goals and performance goals, which has distinct roles in determining users’ longitudinal exercise performances

Data Privacy, What Still Need Consideration in Online Application System?

This paper aims to conduct an analysis and exploration of matters that still needs to be considered in relation to data privacy in the online application system. This research is still a preliminary study. We conduct research related to data privacy using systematic literature review approach (SLR). Bt using SLR stages, we made a synthesis of 44 publications from Scopus Database Online that were released in the range 2015 - 2019. Based on this study, we found six things points to consider in data privacy, namely security and data protection, user awareness, risk managment, control setting, ethics, and transparency

Understanding the Disclosure of Private Healthcare Information within Online Quantified Self 2.0 Platforms

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