JMIR Publications

ABSTRACT

Background:

Gamification and persuasive games are effective tools to motivate behavior change, particularly to promote daily physical activities. Studies have suggested that “one-size-fits-all” approach does not work well for persuasive game design. On the other hand, player modeling and recommender systems are increasingly used for personalizing contents. However, there are few existing works on how to build comprehensive player models for personalizing gamified systems and recommending daily physical activities and on the long-term effectiveness of such gamified exercise-promoting system.

Objective:

To bridge the gaps introduced above, this paper introduces a gamified 24/7 fitness assistant system that provides personalized recommendations and generates gamified contents targeted at individual user. This research aims to investigate how to design gamified physical activity interventions to achieve long-term engagement.

Methods:

we propose a comprehensive model for gamified fitness recommender systems that uses detailed and dynamic player modeling and wearable-based tracking to provide personalized game features and activity recommendations. We also conduct a long-term investigation on the effectiveness of our recommender system that gradually establishes and updates an individual player model (for each unique user) over a relatively long period (60-days).

Results:

Our 60-day long-term study shows the feasibility and effectiveness of the proposed system, particularly generating personalized exercise recommendations using player modeling.

Conclusions:

Based on these results and drawing from the gamer modeling literature, we conclude that personalizing recommendations using player modeling and gamification can improve participants’ engagement and motivation towards fitness activities over time.