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

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....

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Autores: Hors Fraile, Santiago, Schneider, Francine, Fernández Luque, Luis, Luna Perejón, Francisco, Civit Balcells, Antón, Spachos, Dimitris, Bamidis, Panagiotis D., Vries, Hein de
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2018
País:España
Recursos:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/98991
Acesso em linha:https://hdl.handle.net/11441/98991
https://doi.org/10.1186/s12889-018-5612-5
Access Level:acceso abierto
Palavra-chave:Recommender system
Tailored messages
Smoking cessation
Mobile app
Patient
mHealth
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spelling Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: a study protocolHors Fraile, SantiagoSchneider, FrancineFernández Luque, LuisLuna Perejón, FranciscoCivit Balcells, AntónSpachos, DimitrisBamidis, Panagiotis D.Vries, Hein deRecommender systemTailored messagesSmoking cessationMobile appPatientmHealthBackground: 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.BMCArquitectura y Tecnología de ComputadoresTEP108: Robótica y Tecnología de ComputadoresVirgen del Rocío University Hospital (Spain)Aristotle University of Thessaloniki (Greece)Northern Greece Neuroscience Centre2018info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/98991https://doi.org/10.1186/s12889-018-5612-5reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésBMC Public Health, 18 (698)https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-018-5612-5info:eu-repo/semantics/openAccessoai:idus.us.es:11441/989912026-06-17T12:51:07Z
dc.title.none.fl_str_mv Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: a study protocol
title Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: a study protocol
spellingShingle Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: a study protocol
Hors Fraile, Santiago
Recommender system
Tailored messages
Smoking cessation
Mobile app
Patient
mHealth
title_short Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: a study protocol
title_full Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: a study protocol
title_fullStr Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: a study protocol
title_full_unstemmed Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: a study protocol
title_sort Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: a study protocol
dc.creator.none.fl_str_mv Hors Fraile, Santiago
Schneider, Francine
Fernández Luque, Luis
Luna Perejón, Francisco
Civit Balcells, Antón
Spachos, Dimitris
Bamidis, Panagiotis D.
Vries, Hein de
author Hors Fraile, Santiago
author_facet Hors Fraile, Santiago
Schneider, Francine
Fernández Luque, Luis
Luna Perejón, Francisco
Civit Balcells, Antón
Spachos, Dimitris
Bamidis, Panagiotis D.
Vries, Hein de
author_role author
author2 Schneider, Francine
Fernández Luque, Luis
Luna Perejón, Francisco
Civit Balcells, Antón
Spachos, Dimitris
Bamidis, Panagiotis D.
Vries, Hein de
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Arquitectura y Tecnología de Computadores
TEP108: Robótica y Tecnología de Computadores
Virgen del Rocío University Hospital (Spain)
Aristotle University of Thessaloniki (Greece)
Northern Greece Neuroscience Centre
dc.subject.none.fl_str_mv Recommender system
Tailored messages
Smoking cessation
Mobile app
Patient
mHealth
topic Recommender system
Tailored messages
Smoking cessation
Mobile app
Patient
mHealth
description 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.
publishDate 2018
dc.date.none.fl_str_mv 2018
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/98991
https://doi.org/10.1186/s12889-018-5612-5
url https://hdl.handle.net/11441/98991
https://doi.org/10.1186/s12889-018-5612-5
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv BMC Public Health, 18 (698)
https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-018-5612-5
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv BMC
publisher.none.fl_str_mv BMC
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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