Effects of a Personalized Fitness Recommender System Using Gamification and Continuous Player Modeling: System Design...

Area of Science:

• Human-Computer Interaction
• Behavioral Science
• Health Informatics

Background:

• Gamification and persuasive games motivate physical activity, but a one-size-fits-all approach is ineffective.
• Player modeling and recommender systems offer personalization, yet comprehensive models for gamified fitness are lacking.
• Long-term effectiveness of gamified exercise systems requires further investigation.

Purpose of the Study:

• Introduce a gamified fitness assistant for personalized recommendations and content generation.
• Investigate designing gamified physical activity interventions for sustained user engagement.
• Address gaps in personalized gamified system design and long-term behavioral impact.

Main Methods:

• Developed a comprehensive model for gamified fitness recommender systems.
• Utilized dynamic player modeling and wearable-based tracking for personalized features and recommendations.
• Conducted a 60-day study with 40 participants to establish and update individual player models.

Main Results:

• The system demonstrated feasibility and effectiveness in generating personalized exercise recommendations.
• Statistically significant improvements in motivation, satisfaction, and preference were observed across groups.
• Qualitative feedback highlighted demand for customized storylines, multiplayer modes, and goal-tracking features.

Conclusions:

• Personalized recommendations via player modeling and gamification significantly boost long-term engagement and motivation in fitness activities.
• The study validates the approach for creating effective, engaging, and personalized gamified fitness interventions.
• Findings contribute to understanding long-term adherence in gamified health behavior change.