Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change Model

• Published in: IEEE access Vol. 7; pp. 176525 - 176540
• Main Authors: Hors-Fraile, Santiago, Malwade, Shwetambara, Luna-Perejon, Francisco, Amaya, Claudio, Civit, Anton, Schneider, Francine, Bamidis, Panagiotis, Syed-Abdul, Shabbir, Li, Yu-Chuan, de Vries, Hein
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Analytical models; Applications programs; Artificial intelligence; behavioral sciences; Computational modeling; context awareness; Europe; filtering algorithms; Hybrid systems; Medical services; Messages; mobile applications; Mobile computing; Principles; Recommender systems; Smoking; Smoking cessation; User interfaces
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2957696

Recommender systems are gaining traction in healthcare because they can tailor recommendations based on users' feedback concerning their appreciation of previous health-related messages. However, recommender systems are often not grounded in behavioral change theories, which may further increase the effectiveness of their recommendations. This paper's objective is to describe principles for designing and developing a health recommender system grounded in the I-Change behavioral change model that shall be implemented through a mobile app for a smoking cessation support clinical trial. We built upon an existing smoking cessation health recommender system that delivered motivational messages through a mobile app. A group of experts assessed how the system may be improved to address the behavioral change determinants of the I-Change behavioral change model. The resulting system features a hybrid recommender algorithm for computer tailoring smoking cessation messages. A total of 331 different motivational messages were designed using 10 health communication methods. The algorithm was designed to match 58 message characteristics to each user profile by following the principles of the I-Change model and maintaining the benefits of the recommender system algorithms. The mobile app resulted in a streamlined version that aimed to improve the user experience, and this system's design bridges the gap between health recommender systems and the use of behavioral change theories. This article presents a novel approach integrating recommender system technology, health behavior technology, and computer-tailored technology. Future researchers will be able to build upon the principles applied in this case study.