Personalization Paradox in Behavior Change Apps

Abstract

Social comparison-based features are widely used in social computing apps. However, most existing apps are not grounded in social comparison theories and do not consider individual differences in social comparison preferences and reactions. This paper is among the first to automatically personalize social comparison targets. In the context of an m-health app for physical activity, we use artificial intelligence (AI) techniques of multi-armed bandits. Results from our user study (n=53) indicate that there is some evidence that motivation can be increased using the AI-based personalization of social comparison. The detected effects achieved small-to-moderate effect sizes, illustrating the real-world implications of the intervention for enhancing motivation and physical activity. In addition to design implications for social comparison features in social apps, this paper identified the personalization paradox, the conflict between user modeling and adaptation, as a key design challenge of personalized applications for behavior change. Additionally, we propose research directions to mitigate this Personalization Paradox.

Mitigating Issues With/of/for True Personalization

“…Even with a plethora of academic research into personalization in general, strong personalization is yet to gain notable momentum. Personalizing software functionality is much more complex than personalizing contents via , e.g., feedback, simulation (Mcalpine and Flatla, 2016 ), or social comparison (Zhu et al, 2021 ). Thus, “personal” and “personalized” are different concepts.…”

“…This can occur in two major ways. The system may adapt to the user preferences and directly influence the interaction, or the user interacts with the app in such manner that it changes the user model based on user's behavior (Zhu et al, 2021 ). If the system dynamically adapts to the user, then the level or spectrum of personalization offered by the system will vary at different points in time.…”

Trading Data for Health

“…If users cherish their own privacy, they become reluctant to use an mHealth app but if they are high in social comparison orientation, this will increase their likelihood to use it. Even so, these two processes are well studied, for instance with respect to the privacy-calculus model for app use in general (Dienlin & Metzger, 2016;Trepte et al, 2020) and some few studies on the impact of social comparison on mHealth app use (Li et al, 2019;Arigo & Suls, 2018;Arigo et al, 2020, Zhu et al, 2021. To our understanding, no study has investigated both potential effects at the same time.…”

“…Even though this and comparable approaches (Schneider & Schupp, 2014) have been employed to understand differences in social comparison orientation, the concept has rarely been applied towards the use of mHealth apps (Zhu et al, 2021). This is surprising, as Arigo and Suls (2018) highlight the importance of considering social comparison orientation to understand the use and effects of mHealth apps.…”

“…An important feature of mHealth apps is that they allow users to compare themselves with others (Gupta et al, 2020;Arigo et al, 2020). Part of the appeal, particularly of fitness and dietary apps, lies in the fact that they provide features such as leader boards where individual performances are evaluated and compared with others as well as features that allow users to directly compete against and/or compare themselves with friends (Li et al, 2019;Zhu et al, 2021). The Fitbit app, for instance, allows users to compare their weekly exercise with friends, running apps such as Runtastic compare individual performance with an undisclosed comparison group, and the Chinese Mi Fit app compares sleep, exercise, and weight profiles with other users in the same region.…”