Personalization Paradox in Behavior Change Apps: Lessons from a Social Comparison-Based Personalized App for Physical Activity

TL;DR

This study explores how AI-driven personalization of social comparison targets in a physical activity app can boost motivation, revealing a paradox between user modeling and adaptation that impacts behavior change effectiveness.

Contribution

It introduces an AI-based method for personalizing social comparison in health apps and discusses the personalization paradox as a key design challenge.

Findings

AI personalization increased motivation with small-to-moderate effect sizes

User study with 53 participants showed positive impact on physical activity

Identified the personalization paradox as a critical issue 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.