Cargando…
How recommender systems could support and enhance computer-tailored digital health programs: A scoping review
OBJECTIVE: Tailored digital health programs can promote positive health-related lifestyle changes and have been shown to be (cost) effective in trials. However, such programs are used suboptimally. New approaches are needed to optimise the use of these programs. This paper illustrates the potential...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6379797/ https://www.ncbi.nlm.nih.gov/pubmed/30800414 http://dx.doi.org/10.1177/2055207618824727 |
_version_ | 1783396186743373824 |
---|---|
author | Cheung, Kei Long Durusu, Dilara Sui, Xincheng de Vries, Hein |
author_facet | Cheung, Kei Long Durusu, Dilara Sui, Xincheng de Vries, Hein |
author_sort | Cheung, Kei Long |
collection | PubMed |
description | OBJECTIVE: Tailored digital health programs can promote positive health-related lifestyle changes and have been shown to be (cost) effective in trials. However, such programs are used suboptimally. New approaches are needed to optimise the use of these programs. This paper illustrates the potential of recommender systems to support and enhance computer-tailored digital health interventions. The aim is threefold, to explore: (1) how recommender systems provide health recommendations, (2) to what extent recommender systems incorporate theoretical models and (3) how the use of recommender systems may enhance the usage of computer-tailored interventions. METHODS: A scoping review was conducted, using MEDLINE and ScienceDirect, to identify health recommender systems reported in studies between January 2007 and December 2017. Information was subsequently extracted to understand the potential benefits of recommender systems for computer-tailored digital health programs. Titles and abstracts of 1184 studies were screened for the full-text screening, in which two reviewers independently selected articles and systematically extracted data using a predefined extraction form. RESULTS: A total of 26 articles were included for data extraction. General characteristics were reported, with eight studies reporting hybrid filtering. A description of how each recommender system provides a recommendation is described; the majority of recommender systems used messages as recommendation. We identified the potential effects of recommender systems on efficiency, effectiveness, trustworthiness and enjoyment of the digital health program. CONCLUSIONS: Incorporating a collaborative method with demographic filtering as a second step to knowledge-based filtering could potentially add value to traditional tailoring with regard to enhancing the user experience. This study illustrates how recommender systems, especially hybrid programs, may have the potential to bring tailored digital health forward. |
format | Online Article Text |
id | pubmed-6379797 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-63797972019-02-22 How recommender systems could support and enhance computer-tailored digital health programs: A scoping review Cheung, Kei Long Durusu, Dilara Sui, Xincheng de Vries, Hein Digit Health Tailored Health Communication OBJECTIVE: Tailored digital health programs can promote positive health-related lifestyle changes and have been shown to be (cost) effective in trials. However, such programs are used suboptimally. New approaches are needed to optimise the use of these programs. This paper illustrates the potential of recommender systems to support and enhance computer-tailored digital health interventions. The aim is threefold, to explore: (1) how recommender systems provide health recommendations, (2) to what extent recommender systems incorporate theoretical models and (3) how the use of recommender systems may enhance the usage of computer-tailored interventions. METHODS: A scoping review was conducted, using MEDLINE and ScienceDirect, to identify health recommender systems reported in studies between January 2007 and December 2017. Information was subsequently extracted to understand the potential benefits of recommender systems for computer-tailored digital health programs. Titles and abstracts of 1184 studies were screened for the full-text screening, in which two reviewers independently selected articles and systematically extracted data using a predefined extraction form. RESULTS: A total of 26 articles were included for data extraction. General characteristics were reported, with eight studies reporting hybrid filtering. A description of how each recommender system provides a recommendation is described; the majority of recommender systems used messages as recommendation. We identified the potential effects of recommender systems on efficiency, effectiveness, trustworthiness and enjoyment of the digital health program. CONCLUSIONS: Incorporating a collaborative method with demographic filtering as a second step to knowledge-based filtering could potentially add value to traditional tailoring with regard to enhancing the user experience. This study illustrates how recommender systems, especially hybrid programs, may have the potential to bring tailored digital health forward. SAGE Publications 2019-01-24 /pmc/articles/PMC6379797/ /pubmed/30800414 http://dx.doi.org/10.1177/2055207618824727 Text en © The Author(s) 2019 http://creativecommons.org/licenses/by-nc/4.0/ Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Tailored Health Communication Cheung, Kei Long Durusu, Dilara Sui, Xincheng de Vries, Hein How recommender systems could support and enhance computer-tailored digital health programs: A scoping review |
title | How recommender systems could support and enhance computer-tailored
digital health programs: A scoping review |
title_full | How recommender systems could support and enhance computer-tailored
digital health programs: A scoping review |
title_fullStr | How recommender systems could support and enhance computer-tailored
digital health programs: A scoping review |
title_full_unstemmed | How recommender systems could support and enhance computer-tailored
digital health programs: A scoping review |
title_short | How recommender systems could support and enhance computer-tailored
digital health programs: A scoping review |
title_sort | how recommender systems could support and enhance computer-tailored
digital health programs: a scoping review |
topic | Tailored Health Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6379797/ https://www.ncbi.nlm.nih.gov/pubmed/30800414 http://dx.doi.org/10.1177/2055207618824727 |
work_keys_str_mv | AT cheungkeilong howrecommendersystemscouldsupportandenhancecomputertailoreddigitalhealthprogramsascopingreview AT durusudilara howrecommendersystemscouldsupportandenhancecomputertailoreddigitalhealthprogramsascopingreview AT suixincheng howrecommendersystemscouldsupportandenhancecomputertailoreddigitalhealthprogramsascopingreview AT devrieshein howrecommendersystemscouldsupportandenhancecomputertailoreddigitalhealthprogramsascopingreview |