382 research outputs found
Health Recommender Systems for Behavior Change: Exploring their Potential for Smoking Cessation
Analyzing recommender systems for health promotion using a multidisciplinary taxonomy: A scoping review
Background: Recommender systems are information retrieval systems that provide users with relevant items
(e.g., through messages). Despite their extensive use in the e-commerce and leisure domains, their application in
healthcare is still in its infancy. These systems may be used to create tailored health interventions, thus reducing
the cost of healthcare and fostering a healthier lifestyle in the population.
Objective: This paper identifies, categorizes, and analyzes the existing knowledge in terms of the literature
published over the past 10 years on the use of health recommender systems for patient interventions. The aim of
this study is to understand the scientific evidence generated about health recommender systems, to identify any
gaps in this field to achieve the United Nations Sustainable Development Goal 3 (SDG3) (namely, “Ensure healthy
lives and promote well-being for all at all ages”), and to suggest possible reasons for these gaps as well as to
propose some solutions.
Methods: We conducted a scoping review, which consisted of a keyword search of the literature related to health
recommender systems for patients in the following databases: ScienceDirect, PsycInfo, Association for Computing
Machinery, IEEExplore, and Pubmed. Further, we limited our search to consider only English-lan-guage journal
articles published in the last 10 years. The reviewing process comprised three researchers who filtered the results
simultaneously. The quantitative synthesis was conducted in parallel by two researchers, who classified each
paper in terms of four aspects—the domain, the methodological and procedural aspects, the health promotion
theoretical factors and behavior change theories, and the technical aspects—using a new multidisciplinary
taxonomy.
Results: Nineteen papers met the inclusion criteria and were included in the data analysis, for which thirty-three
features were assessed. The nine features associated with the health promotion theoretical factors and behavior
change theories were not observed in any of the selected studies, did not use principles of tailoring, and did not
assess (cost)-effectiveness.
Discussion: Health recommender systems may be further improved by using relevant behavior change strategies
and by implementing essential characteristics of tailored interventions. In addition, many of the features required
to assess each of the domain aspects, the methodological and procedural aspects, and technical aspects
were not reported in the studies.
Conclusions: The studies analyzed presented few evidence in support of the positive effects of using health recommender
systems in terms of cost-effectiveness and patient health outcomes. This is why future studies should
ensure that all the proposed features are covered in our multidisciplinary taxonomy, including integration with
electronic health records and the incorporation of health promotion theoretical factors and behavior change
theories. This will render those studies more useful for policymakers since they will cover all aspects needed to
determine their impact toward meeting SDG3.European Union's Horizon 2020 No 68112
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