MyBehavior: automatic personalized health feedback from user behaviors and preferences using smartphones

Mashfiqui Rabbi, Min Hane Aung, Mi Zhang, Tanzeem Choudhury

Research output: Contribution to conferencePaperpeer-review

165 Citations (Scopus)


Mobile sensing systems have made significant advances in tracking human behavior. However, the development of personalized mobile health feedback systems is still in its infancy. This paper introduces MyBehavior, a smartphone application that takes a novel approach to generate deeply personalized health feedback. It combines state-of-the-art behavior tracking with algorithms that are used in recommendation systems. MyBehavior automatically learns a user's physical activity and dietary behavior and strategically suggests changes to those behaviors for a healthier lifestyle. The system uses a sequential decision making algorithm, Multi-armed Bandit, to generate suggestions that maximize calorie loss and are easy for the user to adopt. In addition, the system takes into account user's preferences to encourage adoption using the pareto-frontier algorithm. In a 14-week study, results show statistically significant increases in physical activity and decreases in food calorie when using MyBehavior compared to a control condition.
Original languageEnglish
Publication statusPublished - 2015
Externally publishedYes
Event2015 ACM International Joint Conference - Osaka, Japan
Duration: 7 Sep 201511 Sep 2015


Conference2015 ACM International Joint Conference

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