Recommender systems for obesity prevention: Scoping review of reviews.

Introduction: Recommender systems are technology-based systems that generate recommendations or guide users to relevant information. This study is a scoping review aiming to describe what is known about the recommender systems for obesity prevention according to systematic reviews on this topic.

Methods: This scoping review adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA ScR) guideline. Out of 148 records labeled as reviews in the database and online searches until October 2023, 10 reviews fulfilled the inclusion criteria according to the Population, Concept, and Context framework: Population (human), Concept (recommender systems), and Context (obesity prevention). Bibliographic, population, concept, and context characteristics, and topics addressed in reviews were charted and synthesized using relative frequencies or described narratively. An overlap that occurs when the same primary studies are included in multiple reviews was assessed as the overall Corrected Covered Area (CCA: 0%-5% low overlap to ⩾15% very high overlap).

Results: The reviews were published between 2017 and 2023 and included 308 primary studies. The overlap in primary studies among the 10 reviews was low (CCA = 1.29%). The reviews described the recommender system properties (n = 8) or their implementation (n = 2) in any (n = 6) or specific populations (e.g., elderly; n = 4) and focused on nutrition (n = 9) and physical activity (n = 4) within obesity prevention context. The topics addressed in reviews were recommendation generation (i.e., technical system properties; n = 9), health content (e.g., nutritional advice; n = 7), and implementation (i.e., system evaluation and user application; n = 5). The evidence gaps included the need for new system development and evaluation (n = 8) and a focus on diverse health contexts (n = 4).

Conclusion: Evidence from past reviews suggests that despite the existence of several technical solutions, there is yet no consensus on how to generate the most accurate nutrition recommendations in the obesity prevention context. Future studies addressing system and user outcome evaluation are needed to identify the optimal parameters for any long-term behavior change in recommender system users.