Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change Model

Recommender systems are gaining traction in healthcare because they can tailor recommendations based on users' feedback concerning their appreciation of previous health-related messages. However, recommender systems are often not grounded in behavioral change theories, which may further increas...

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Autores: Hors Fraile, Santiago, Malwade, Shwetambara, Luna Perejón, Francisco, Amaya Rodríguez, Claudio Antonio, Civit Balcells, Antón, Schneider, Francine, Bamidis, Panagiotis D., Syed-Abdul, Shabbir, Li, Yuchuan, Vries, Hein de
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/94951
Acceso en línea:https://hdl.handle.net/11441/94951
https://doi.org/10.1109/ACCESS.2019.2957696
Access Level:acceso abierto
Palabra clave:Artificial intelligence
Behavioral sciences
Context awareness
Filtering algorithms
Mobile applications
Recommender systems
Smoking cessation
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spelling Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change ModelHors Fraile, SantiagoMalwade, ShwetambaraLuna Perejón, FranciscoAmaya Rodríguez, Claudio AntonioCivit Balcells, AntónSchneider, FrancineBamidis, Panagiotis D.Syed-Abdul, ShabbirLi, YuchuanVries, Hein deArtificial intelligenceBehavioral sciencesContext awarenessFiltering algorithmsMobile applicationsRecommender systemsSmoking cessationRecommender systems are gaining traction in healthcare because they can tailor recommendations based on users' feedback concerning their appreciation of previous health-related messages. However, recommender systems are often not grounded in behavioral change theories, which may further increase the effectiveness of their recommendations. This paper's objective is to describe principles for designing and developing a health recommender system grounded in the I-Change behavioral change model that shall be implemented through a mobile app for a smoking cessation support clinical trial. We built upon an existing smoking cessation health recommender system that delivered motivational messages through a mobile app. A group of experts assessed how the system may be improved to address the behavioral change determinants of the I-Change behavioral change model. The resulting system features a hybrid recommender algorithm for computer tailoring smoking cessation messages. A total of 331 different motivational messages were designed using 10 health communication methods. The algorithm was designed to match 58 message characteristics to each user pro le by following the principles of the I-Change model and maintaining the bene ts of the recommender system algorithms. The mobile app resulted in a streamlined version that aimed to improve the user experience, and this system's design bridges the gap between health recommender systems and the use of behavioral change theories. This article presents a novel approach integrating recommender system technology, health behavior technology, and computer-tailored technology. Future researchers will be able to build upon the principles applied in this case study.European Union's Horizon 2020 Research and Innovation Programme under Grant 681120IEEE Computer SocietyArquitectura y Tecnología de ComputadoresEuropean Union (UE)2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/94951https://doi.org/10.1109/ACCESS.2019.2957696reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésIEEE Access, 7, 176525-176540.681120https://ieeexplore.ieee.org/document/8922703info:eu-repo/semantics/openAccessoai:idus.us.es:11441/949512026-06-17T12:51:07Z
dc.title.none.fl_str_mv Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change Model
title Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change Model
spellingShingle Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change Model
Hors Fraile, Santiago
Artificial intelligence
Behavioral sciences
Context awareness
Filtering algorithms
Mobile applications
Recommender systems
Smoking cessation
title_short Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change Model
title_full Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change Model
title_fullStr Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change Model
title_full_unstemmed Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change Model
title_sort Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change Model
dc.creator.none.fl_str_mv Hors Fraile, Santiago
Malwade, Shwetambara
Luna Perejón, Francisco
Amaya Rodríguez, Claudio Antonio
Civit Balcells, Antón
Schneider, Francine
Bamidis, Panagiotis D.
Syed-Abdul, Shabbir
Li, Yuchuan
Vries, Hein de
author Hors Fraile, Santiago
author_facet Hors Fraile, Santiago
Malwade, Shwetambara
Luna Perejón, Francisco
Amaya Rodríguez, Claudio Antonio
Civit Balcells, Antón
Schneider, Francine
Bamidis, Panagiotis D.
Syed-Abdul, Shabbir
Li, Yuchuan
Vries, Hein de
author_role author
author2 Malwade, Shwetambara
Luna Perejón, Francisco
Amaya Rodríguez, Claudio Antonio
Civit Balcells, Antón
Schneider, Francine
Bamidis, Panagiotis D.
Syed-Abdul, Shabbir
Li, Yuchuan
Vries, Hein de
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Arquitectura y Tecnología de Computadores
European Union (UE)
dc.subject.none.fl_str_mv Artificial intelligence
Behavioral sciences
Context awareness
Filtering algorithms
Mobile applications
Recommender systems
Smoking cessation
topic Artificial intelligence
Behavioral sciences
Context awareness
Filtering algorithms
Mobile applications
Recommender systems
Smoking cessation
description Recommender systems are gaining traction in healthcare because they can tailor recommendations based on users' feedback concerning their appreciation of previous health-related messages. However, recommender systems are often not grounded in behavioral change theories, which may further increase the effectiveness of their recommendations. This paper's objective is to describe principles for designing and developing a health recommender system grounded in the I-Change behavioral change model that shall be implemented through a mobile app for a smoking cessation support clinical trial. We built upon an existing smoking cessation health recommender system that delivered motivational messages through a mobile app. A group of experts assessed how the system may be improved to address the behavioral change determinants of the I-Change behavioral change model. The resulting system features a hybrid recommender algorithm for computer tailoring smoking cessation messages. A total of 331 different motivational messages were designed using 10 health communication methods. The algorithm was designed to match 58 message characteristics to each user pro le by following the principles of the I-Change model and maintaining the bene ts of the recommender system algorithms. The mobile app resulted in a streamlined version that aimed to improve the user experience, and this system's design bridges the gap between health recommender systems and the use of behavioral change theories. This article presents a novel approach integrating recommender system technology, health behavior technology, and computer-tailored technology. Future researchers will be able to build upon the principles applied in this case study.
publishDate 2019
dc.date.none.fl_str_mv 2019
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/11441/94951
https://doi.org/10.1109/ACCESS.2019.2957696
url https://hdl.handle.net/11441/94951
https://doi.org/10.1109/ACCESS.2019.2957696
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IEEE Access, 7, 176525-176540.
681120
https://ieeexplore.ieee.org/document/8922703
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 IEEE Computer Society
publisher.none.fl_str_mv IEEE Computer Society
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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