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

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

doi:10.1109/access.2019.2957696

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.

Highlights

Traditional computer-tailored interventions based on behavioral change models can yield highly personalized motivational messages to help an individual adopt and maintain healthy habits [1]–[4]
2) META-FEATURES ADAPTATION Resulting from the message design process and the health recommender system (HRS) definition, we divided the meta-features into two groups: 7 basic meta-features, and 51 extended meta-features
The basic meta-features included the most essential demographic information, and five other smoking-cessation indicators typically required in smoking cessation interventions to determine the patients’ smoking habits. These 7 variables contain the minimum information required for them to assess a smoking cessation patient. These variables were previously used in the Social Local and Mobile (SoLoMo) intervention, they were validated by Taiwanese smoking cessation experts coming from Taipei Medical University Hospital, and Wellcome Clinic in Taipei

Summary

Introduction

Traditional computer-tailored interventions based on behavioral change models can yield highly personalized motivational messages to help an individual adopt and maintain healthy habits [1]–[4]. Provide feedback on ‘static’ scores for each individual’s answers. A health recommender system (HRS) can dynamically compute a list of recommended items for each user using artificial intelligence (AI). An HRS is a type of machine learning system that provides users with relevant items (i.e., messages) based on, for instance, their past behavior or similarities they share with other users. Combining HRSs with behavioral change models can yield an innovative.

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