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Development and Evaluation of Health Recommender Systems: Systematic Scoping Review and Evidence Mapping
BACKGROUND: Health recommender systems (HRSs) are information retrieval systems that provide users with relevant items according to the users’ needs, which can motivate and engage users to change their behavior. OBJECTIVE: This study aimed to identify the development and evaluation of HRSs and creat...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9896351/ https://www.ncbi.nlm.nih.gov/pubmed/36656630 http://dx.doi.org/10.2196/38184 |
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author | Sun, Yue Zhou, Jia Ji, Mengmeng Pei, Lusi Wang, Zhiwen |
author_facet | Sun, Yue Zhou, Jia Ji, Mengmeng Pei, Lusi Wang, Zhiwen |
author_sort | Sun, Yue |
collection | PubMed |
description | BACKGROUND: Health recommender systems (HRSs) are information retrieval systems that provide users with relevant items according to the users’ needs, which can motivate and engage users to change their behavior. OBJECTIVE: This study aimed to identify the development and evaluation of HRSs and create an evidence map. METHODS: A total of 6 databases were searched to identify HRSs reported in studies from inception up to June 30, 2022, followed by forward citation and grey literature searches. Titles, abstracts, and full texts were screened independently by 2 reviewers, with discrepancies resolved by a third reviewer, when necessary. Data extraction was performed by one reviewer and checked by a second reviewer. This review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) statement. RESULTS: A total of 51 studies were included for data extraction. Recommender systems were used across different health domains, such as general health promotion, lifestyle, and generic health service. A total of 23 studies had reported the use of a combination of recommender techniques, classified as hybrid recommender systems, which are the most commonly used recommender techniques in HRSs. In the HRS design stage, only 10 of 51 (19.6%) recommender systems considered personal preferences of end users in the design or development of the system; a total of 29 studies reported the user interface of HRSs, and most HRSs worked on users’ mobile interfaces, usually a mobile app. Two categories of HRS evaluations were used, and evaluations of HRSs varied greatly; 62.7% (32/51) of the studies used the offline evaluations using computational methods (no user), and 33.3% (17/51) of the studies included end users in their HRS evaluation. CONCLUSIONS: Through this scoping review, nonmedical professionals and policy makers can visualize and better understand HRSs for future studies. The health care professionals and the end users should be encouraged to participate in the future design and development of HRSs to optimize their utility and successful implementation. Detailed evaluations of HRSs in a user-centered approach are needed in future studies. |
format | Online Article Text |
id | pubmed-9896351 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-98963512023-02-04 Development and Evaluation of Health Recommender Systems: Systematic Scoping Review and Evidence Mapping Sun, Yue Zhou, Jia Ji, Mengmeng Pei, Lusi Wang, Zhiwen J Med Internet Res Review BACKGROUND: Health recommender systems (HRSs) are information retrieval systems that provide users with relevant items according to the users’ needs, which can motivate and engage users to change their behavior. OBJECTIVE: This study aimed to identify the development and evaluation of HRSs and create an evidence map. METHODS: A total of 6 databases were searched to identify HRSs reported in studies from inception up to June 30, 2022, followed by forward citation and grey literature searches. Titles, abstracts, and full texts were screened independently by 2 reviewers, with discrepancies resolved by a third reviewer, when necessary. Data extraction was performed by one reviewer and checked by a second reviewer. This review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) statement. RESULTS: A total of 51 studies were included for data extraction. Recommender systems were used across different health domains, such as general health promotion, lifestyle, and generic health service. A total of 23 studies had reported the use of a combination of recommender techniques, classified as hybrid recommender systems, which are the most commonly used recommender techniques in HRSs. In the HRS design stage, only 10 of 51 (19.6%) recommender systems considered personal preferences of end users in the design or development of the system; a total of 29 studies reported the user interface of HRSs, and most HRSs worked on users’ mobile interfaces, usually a mobile app. Two categories of HRS evaluations were used, and evaluations of HRSs varied greatly; 62.7% (32/51) of the studies used the offline evaluations using computational methods (no user), and 33.3% (17/51) of the studies included end users in their HRS evaluation. CONCLUSIONS: Through this scoping review, nonmedical professionals and policy makers can visualize and better understand HRSs for future studies. The health care professionals and the end users should be encouraged to participate in the future design and development of HRSs to optimize their utility and successful implementation. Detailed evaluations of HRSs in a user-centered approach are needed in future studies. JMIR Publications 2023-01-19 /pmc/articles/PMC9896351/ /pubmed/36656630 http://dx.doi.org/10.2196/38184 Text en ©Yue Sun, Jia Zhou, Mengmeng Ji, Lusi Pei, Zhiwen Wang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 19.01.2023. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Review Sun, Yue Zhou, Jia Ji, Mengmeng Pei, Lusi Wang, Zhiwen Development and Evaluation of Health Recommender Systems: Systematic Scoping Review and Evidence Mapping |
title | Development and Evaluation of Health Recommender Systems: Systematic Scoping Review and Evidence Mapping |
title_full | Development and Evaluation of Health Recommender Systems: Systematic Scoping Review and Evidence Mapping |
title_fullStr | Development and Evaluation of Health Recommender Systems: Systematic Scoping Review and Evidence Mapping |
title_full_unstemmed | Development and Evaluation of Health Recommender Systems: Systematic Scoping Review and Evidence Mapping |
title_short | Development and Evaluation of Health Recommender Systems: Systematic Scoping Review and Evidence Mapping |
title_sort | development and evaluation of health recommender systems: systematic scoping review and evidence mapping |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9896351/ https://www.ncbi.nlm.nih.gov/pubmed/36656630 http://dx.doi.org/10.2196/38184 |
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