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

Abstract

BackgroundSmoking 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.MethodsPatients 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.DiscussionThis 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 registrationThe trial was registered at clinicaltrials.org under the ClinicalTrials.gov identifier NCT03206619 on July 2nd 2017. Retrospectively registered.

How recommender systems could support and enhance computer-tailored digital health programs: A scoping review

“…Several extracted articles outlined a hybrid RS that combined content- based and collaborative filtering techniques; a hybrid system has the potential to enhance efficiency, as only the top-ranked most applicable items as well as similar items that are matched on metadata of items preferred in the past are recommended to the target user. 19,26,27,32,36,42,43,45 For example, in the study by Esteban and colleagues, 36 the RS incorporated information from databases of exercise recommendations and patient pathology, as well as users’ ratings on the recommended exercises they completed, and was thus able to generate a limited but tailored number of exercises for the prevention of lower back pain problems. Similarly, in the study by Narducci et al., 43 the hybrid RS recommended only the top-ranked physicians and health facilities for a patient by integrating information from his or her personal health record and ratings of health facilities or doctors consulted by other patients with a similar health status.…”

“…Furthermore, the RSs that utilise different approaches such as combining content-based and collaborative filtering, 19,26,27,32,36,42,43,45 combining rule-, case-, and preference-based reasoning, 30 and combining knowledge-based and content-based filtering 35 may optimise recommendation accuracy by integrating different sources of information. 25,26,43 For instance, a hybrid (content-based and collaborative) RS for patients with lower back pain problems provided exercise recommendations that were appropriate for the patients by making predictions based on a community of users’ explicit ratings of the recommended exercises.…”

Using the Social-Local-Mobile App for Smoking Cessation in the SmokeFreeBrain Project: Protocol for a Randomized Controlled Trial

“…Furthermore, we are conducting a parallel study to assess the perceived quality of the health recommender system and the level of engagement with the motivational messages. The analysis of these technical aspects will provide a more comprehensive understanding of the So-Lo-Mo intervention [39].…”

A survey on semanticized and personalized health recommender systems

“…Hors‐Fraile et al () proposed an RS to generate motivational health messages for smoking cessation using m‐Health solution integrated with an electronic health record. In this study, researchers developed a m‐Health Recommender System (m‐HRS) that provides to send tailored motivational health messages selected by a health counsellor based on the current electronic health records and profiles of patients who are participating in a smoking cessation programme.…”