Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: a study protocol
|Author||Hors Fraile, Santiago
Fernández Luque, Luis
Luna Perejón, Francisco
Civit Balcells, Antón
Bamidis, Panagiotis D.
Vries, Hein de
|Department||Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores|
|Abstract||Background: Smoking is one of the most avoidable health risk factors, and yet the quitting success rates are low.
The usage of tailored health messages to support quitting has been proved to increase quitting success ...
Background: Smoking is one of the most avoidable health risk factors, and yet the quitting success rates are low. The usage of tailored health messages to support quitting has been proved to increase quitting success rates. Technology can provide convenient means to deliver tailored health messages. Health recommender systems are information-filtering algorithms that can choose the most relevant health-related items—for instance, motivational messages aimed at smoking cessation—for each user based on his or her profile. The goals of this study are to analyze the perceived quality of an mHealth recommender system aimed at smoking cessation, and to assess the level of engagement with the messages delivered to users via this medium. Methods: Patients participating in a smoking cessation program will be provided with a mobile app to receive tailored motivational health messages selected by a health recommender system, based on their profile retrieved from an electronic health record as the initial knowledge source. Patients’ feedback on the messages and their interactions with the app will be analyzed and evaluated following an observational prospective methodology to a) assess the perceived quality of the mobile-based health recommender system and the messages, using the precision and time-to-read metrics and an 18-item questionnaire delivered to all patients who complete the program, and b) measure patient engagement with the mobile-based health recommender system using aggregated data analytic metrics like session frequency and, to determine the individual-level engagement, the rate of read messages for each user. This paper details the implementation and evaluation protocol that will be followed. Discussion: This study will explore whether a health recommender system algorithm integrated with an electronic health record can predict which tailored motivational health messages patients would prefer and consider to be of a good quality, encouraging them to engage with the system. The outcomes of this study will help future researchers design better tailored motivational message-sending recommender systems for smoking cessation to increase patient engagement, reduce attrition, and, as a result, increase the rates of smoking cessation.