A Health Recommeder System to Tailor Message Preferences in a Smoking Cessation Programme

Study Overview
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Brief Summary
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Patients attending the smoking cessation programme at the Virgen del Rocío University Hospital under the SoLoMo clinical trial of the SmokeFreeBrain project and provided with the SoLoMo mobile app will be observed for one year. This mobile app which sends the patients tailored health motivational messages selected by a health recommender system, and based on their user profile retrieved from an electronic health record. Patients' messages feedback and interactions with the app will be analyzed and evaluated following an observational prospective methodology to see whether patients like the messages, and measure the patient engagement with the health recommender system.

Official Title
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Study on Patient Acceptance and Engagement of Timely Tailored Motivational Messages Sent by a Health Recommender System and Delivered Via a Mobile App for Smoking Cessation

Conditions
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Smoking Cessation, Health Behavior, Smoking, Tobacco, Smoking

Intervention / Treatment
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* Other: Social, Local and Mobile


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Patients must be taken from the the smoking cessation unit of the Virgen del Rocio University Hospital (Seville, Spain) Patients must be willing to start the treatment to quit smoking Patients must own an Android smartphone and be able to interact with it Patients must be willing to and install the Libre de Humos smoking cessation app recommended by the doctor in the SmokeFreeBrain study. Patients must sign an informed consent Exclusion Criteria: Patients have known previous adverse effects on the pharmacological treatment included in the study (Bupropion & Varenicline)

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Social, Local and Mobile<br>Patients having smoking cessation standard care (behavioral and pharmacological therapy) and using the SoLoMo mobile app that sends them motivational messages. | Other: Social, Local and Mobile<br>* Patients in a smoking cessation program will use a mobile application to receive messages and rate them from their smartphones. The messages patient will receive belong to one of the following five topics: general motivation, diet tips, physical exercise tips, personal performance, and the benefits of being a non-smoker. For each one of these topics, there will be a pool of 150 different messages with tailored information for the patient. Topics and messages were approved by a smoking cessation psychologist and a pulmonologist. The selection of the time the messages have to be sent, and the message topic is selected by an health recommender system algorithm based on the patients' user profile (demographic information, message rating information, and app interaction information).<br>* Other names: SoLoMo;|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Objective quality of the health recommender system | The objective quality of the health recommender system is calculated by measuring its precision. The precision of the health recommender system is the relation between the number of messages patients have rated with positive and/or positive and neutral feedback the first month (baseline), and all subsequent months. | Up to 18 months | 
| Subjective quality of the health recommender system | The subjective quality of the health recommender systems is determined by eighteen questions which will be answered using a 1 to 5 point Likert scale by all patients finishing their smoking cessation programme. | Up to 18 months | 
| Engagement at aggregated level - Mobile application rolling retention | The percentage of users still active N days after installation. This is a ratio of the number of users whose last day of activity is past day N to the number of users who could have been active on day N. This metric will be assessed throughout the observation until its end. | Up to 18 months | 
| Engagement at aggregated level - Mobile application session length distribution | The session length is defined as the length of time between the start of the application event and the end of the application event. The session length determines the engagement as it is relevant to know how much time patients spend in the app per session. This metric will be assessed throughout the observation until its end. | Up to 18 months | 
| Engagement at aggregated level - Mobile application usage frequency | The frequency of use is a measure of how often each unique patient used the app within a given time interval. This metric will be assessed throughout the observation until its end. | Up to 18 months | 
| Engagement at aggregated level - Number of sessions per user | A session is one use of the mobile application by a patient. This begins when the application is launched and ends when the application is terminated. This metric will be assessed throughout the observation until its end. | Up to 18 months | 
| Engagement at aggregated level - Return rate | Return rate measures the percentage of patients who return to the app on a specific time after installation. It is measured by cohort group - that is, based on when patients first opened the app. It is calculated as the ratio of the number of users active on a given period to the size of the cohort. This metric will be assessed throughout the observation until its end. | Up to 18 months | 
| Engagement at individual level | Engagement at individual level will be assessed based on the rate of read messages by the patients. This is calculated as the quotient between the messages the patients have read, and the total number of messages the system has sent to the patients. This metric will be assessed throughout the observation until its end. | Up to 18 months | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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smoking, habit, preference, behavior, tailoring, cessation, engagement, digital, mobile app, recommender system