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.
Highlights
Smoking is one of the most avoidable health risk factors, and yet the quitting success rates are low
One of the ways in which technology is used to do so is by designing tailored health messages targeted at patients
A recommender system is a piece of software that learns to predict the best item for each user from a set of items [12]
Summary
Smoking is one of the most avoidable health risk factors, and yet the quitting success rates are low. 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 New technologies such as smartphones and wearables can be used to support behavior change among patients, as many studies have already shown [1,2,3,4,5,6,7]. One of the ways in which technology is used to do so is by designing tailored health messages targeted at patients Some such platforms use expert systems [8] that use the rules of human expert reasoning and infer results based on people’s answers to questions about behavior knowledge and motivational aspects like attitude and self-efficacy. This can be done using different techniques like comparing the features of the books you have liked with the features of other books you have not read, as shown in Fig. 1, or by considering books that people with similar tastes as you have liked
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