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Health recommender systems (HRSs) offer personalized recommendations to encourage behavior change. This review maps their development and evaluation, highlighting the need for user-centered design and evaluation in future health technology.

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Area of Science:

  • Health Informatics
  • Human-Computer Interaction
  • Information Science

Background:

  • Health recommender systems (HRSs) are information retrieval tools designed to motivate users towards healthier behaviors.
  • These systems provide personalized recommendations based on individual user needs.

Purpose of the Study:

  • To systematically map the development and evaluation of Health recommender systems (HRSs).
  • To create an evidence map of existing HRS research.

Main Methods:

  • A comprehensive search of 6 databases was conducted up to June 30, 2022.
  • Forward citation and grey literature searches were performed, adhering to PRISMA-ScR guidelines.
  • Two independent reviewers screened titles, abstracts, and full texts, with a third reviewer resolving discrepancies.

Main Results:

  • 51 studies were included, utilizing HRSs across various health domains like lifestyle and general health promotion.
  • Hybrid recommender systems, combining multiple techniques, were most common (23 studies).
  • Only 19.6% of systems incorporated end-user preferences in design; most utilized mobile app interfaces, with evaluations predominantly offline (62.7%) rather than user-centered (33.3%).

Conclusions:

  • Findings provide a visual overview of HRS development and evaluation for nonmedical professionals and policymakers.
  • Increased participation of healthcare professionals and end-users in HRS design and development is crucial for optimizing utility and implementation.
  • Future research should prioritize user-centered evaluation methods for HRS.