Digital interventions can promote positive health outcomes, but maintaining user engagement remains a challenge. A major issue is that, while these interventions primarily consist of essential health-related tasks, such content is perceived as unengaging, leading to high dropout rates. In contrast, game content is inherently engaging, but effectively balancing it with health-focused activities presents a design challenge. In this study, we explore the use of automated planning to structure digital intervention content by integrating unengaging health tasks with engaging game content while maintaining a balanced fun ratio across levels. Through experimental validation, we demonstrate that automated planning can effectively balance fun activities while ensuring progression aligns with defined pacing constraints. Our findings suggest that this approach helps generate balanced levels, though scalability remains a challenge due to the excessive state space. Future research should focus on improving the scalability of automated planning solutions to enhance their practicality in health interventions.

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Automated Planning of Entertainment Content for Digital Health Interventions: A Technical Feasibility Study on Balance VS Fun

  • L. J. James,
  • Emanuele De Pellegrin,
  • Laura Genga,
  • Barbara Montagne,
  • Pieter Van Gorp

摘要

Digital interventions can promote positive health outcomes, but maintaining user engagement remains a challenge. A major issue is that, while these interventions primarily consist of essential health-related tasks, such content is perceived as unengaging, leading to high dropout rates. In contrast, game content is inherently engaging, but effectively balancing it with health-focused activities presents a design challenge. In this study, we explore the use of automated planning to structure digital intervention content by integrating unengaging health tasks with engaging game content while maintaining a balanced fun ratio across levels. Through experimental validation, we demonstrate that automated planning can effectively balance fun activities while ensuring progression aligns with defined pacing constraints. Our findings suggest that this approach helps generate balanced levels, though scalability remains a challenge due to the excessive state space. Future research should focus on improving the scalability of automated planning solutions to enhance their practicality in health interventions.