Kei Long Cheung1, Dilara Durusu2, Xincheng Sui3, Hein de Vries1. 1. Department of Health Promotion, CAPHRI Research School for Public Health and Primary Care, Maastricht University, the Netherlands. 2. Department of Health Services Research, CAPHRI Research School for Public Health and Primary Care, Maastricht University, the Netherlands. 3. Department of Work and Social Psychology, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands.
Abstract
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.
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.
Entities:
Keywords:
Adoption; computer tailoring; digital health; eHealth; hybrid; recommender system; usage; user experience
Authors: Wendy Brouwer; Anke Oenema; Hein Raat; Rik Crutzen; Jascha de Nooijer; Nanne K de Vries; Johannes Brug Journal: Health Educ Res Date: 2009-11-06
Authors: Adrian Aguilera; Caroline A Figueroa; Rosa Hernandez-Ramos; Urmimala Sarkar; Anupama Cemballi; Laura Gomez-Pathak; Jose Miramontes; Elad Yom-Tov; Bibhas Chakraborty; Xiaoxi Yan; Jing Xu; Arghavan Modiri; Jai Aggarwal; Joseph Jay Williams; Courtney R Lyles Journal: BMJ Open Date: 2020-08-20 Impact factor: 2.692