How recommender systems could support and enhance computer-tailored digital health programs: A scoping review

Abstract

ObjectiveTailored 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.MethodsA 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.ResultsA 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.ConclusionsIncorporating 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.

mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE Study

“…This can include feedback, goal setting or user targeting (ie, conveying that communication is designed specifically for the user). 21 However, more complex forms of personalisation might be needed to increase and maintain engagement with PA interventions, 22 a requirement for a digital intervention to be effective.…”

Tailored health communication: Opportunities and challenges in the digital era

“…More opportunities for improvement result from the technological advancements our society is experiencing. Within this rapidly changing context, Lutkenhaus et al 21 and Cheung et al 22 suggest the need to move towards alternative approaches to tailoring. By leveraging the potential of influencer marketing, Lutkenhaus et al 21 combined network analysis and text mining techniques to identify online communities and map their health-related and cultural beliefs, and to identify appropriate social influencers as channels to more effectively convey tailored health-related messages to these online communities.…”

“…By leveraging the potential of influencer marketing, Lutkenhaus et al 21 combined network analysis and text mining techniques to identify online communities and map their health-related and cultural beliefs, and to identify appropriate social influencers as channels to more effectively convey tailored health-related messages to these online communities. In their scoping review, Cheung et al 22 discussed another alternative approach to optimize tailored health interventions, i.e., by linking them to recommender systems. Recommender systems can help select messages that are most relevant to users, either based on their past choices or by having the user express their preferences through a rating system.…”

“… 21 and Cheung et al. 22 suggest the need to move towards alternative approaches to tailoring. By leveraging the potential of influencer marketing, Lutkenhaus et al.…”

“…In their scoping review, Cheung et al. 22 discussed another alternative approach to optimize tailored health interventions, i.e., by linking them to recommender systems. Recommender systems can help select messages that are most relevant to users, either based on their past choices or by having the user express their preferences through a rating system.…”