Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: a study protocol

Santiago Hors-Fraile, Francine Schneider, Luis Fernandez-Luque, Francisco Luna-Perejon, Anton Civit, Dimitris Spachos, Panagiotis Bamidis, Hein de Vries, Santiago Hors-Fraile, Francine Schneider, Luis Fernandez-Luque, Francisco Luna-Perejon, Anton Civit, Dimitris Spachos, Panagiotis Bamidis, Hein de Vries

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 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.

Trial registration: The trial was registered at clinicaltrials.org under the ClinicalTrials.gov identifier NCT03206619 on July 2nd 2017. Retrospectively registered.

Keywords: Mobile app; Patient; Recommender system; Smoking cessation; Tailored messages; mHealth.

Conflict of interest statement

Ethics approval and consent to participate

The Ethical Review Board “CEI de los Hospitales Universitarios Virgen Macarena y Virgen del Rocío”, affiliated to the Virgen Macarena and Virgen del Rocío University Hospitals, approved the SoLoMo study with code SFB-APP_EC-2016-01. This Ethical Review Board also reviewed the informed consent, which all participants were given in written text, and had to read and sign to be included in the study.

Competing interests

LFL is the owner of Salumedia Tecnologías, SHF is its administrator. Salumedia Tecnologías is the company that developed the app used in this study. The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Concept diagram of a simple recommender system. In this example, the recommended items are books. The user receives suggestions on what to read next based on the genres and features of books he or she has liked in the
Fig. 2
Fig. 2
Screen captures of the message list and the rating mechanism of the “Libre de humos” smoking cessation app. Patients can provide feedback on the messages they receive via the app. They can provide feedback on each message by indicating “like”, “dislike”, or “indifferent”
Fig. 3
Fig. 3
Description of the mHealth Recommender System’s architecture and flow of data information

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