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

Santiago Hors-Fraile, Hein De Vries, Shwetambara Malwade, Francisco Luna-Perejon, Claudio Amaya, Antón Civit, Francine Schneider, Panagiotis Bamidis, Shabbir Syed-Abdul, Yu Chuan Li

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Article number8922703
Pages (from-to)176525-176540
Number of pages16
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - Jan 1 2019

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Recommender systems
Health
Application programs
Systems analysis
Feedback
Communication

Keywords

  • Artificial intelligence
  • behavioral sciences
  • context awareness
  • filtering algorithms
  • mobile applications
  • recommender systems
  • smoking cessation

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

Cite this

Opening the Black Box : Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change Model. / Hors-Fraile, Santiago; De Vries, Hein; Malwade, Shwetambara; Luna-Perejon, Francisco; Amaya, Claudio; Civit, Antón; Schneider, Francine; Bamidis, Panagiotis; Syed-Abdul, Shabbir; Li, Yu Chuan.

In: IEEE Access, Vol. 7, 8922703, 01.01.2019, p. 176525-176540.

Research output: Contribution to journalArticle

Hors-Fraile, S, De Vries, H, Malwade, S, Luna-Perejon, F, Amaya, C, Civit, A, Schneider, F, Bamidis, P, Syed-Abdul, S & Li, YC 2019, 'Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change Model', IEEE Access, vol. 7, 8922703, pp. 176525-176540. https://doi.org/10.1109/ACCESS.2019.2957696
Hors-Fraile S, De Vries H, Malwade S, Luna-Perejon F, Amaya C, Civit A et al. Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change Model. IEEE Access. 2019 Jan 1;7:176525-176540. 8922703. https://doi.org/10.1109/ACCESS.2019.2957696
Hors-Fraile, Santiago ; De Vries, Hein ; Malwade, Shwetambara ; Luna-Perejon, Francisco ; Amaya, Claudio ; Civit, Antón ; Schneider, Francine ; Bamidis, Panagiotis ; Syed-Abdul, Shabbir ; Li, Yu Chuan. / Opening the Black Box : Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change Model. In: IEEE Access. 2019 ; Vol. 7. pp. 176525-176540.
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