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Accepted for/Published in: JMIR Formative Research

Date Submitted: Aug 25, 2023
Open Peer Review Period: Aug 24, 2023 - Oct 19, 2023
Date Accepted: Mar 22, 2024
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Developing a Personalized Meal Recommendation System for Chinese Older Adults: Observational Cohort Study

Xu Z, Gu Y, Xu X, Topaz M, Guo Z, Kang H, Sun L, Li J

Developing a Personalized Meal Recommendation System for Chinese Older Adults: Observational Cohort Study

JMIR Form Res 2024;8:e52170

DOI: 10.2196/52170

PMID: 38814702

PMCID: 11176883

Developing a personalized meal recommendation system for Chinese elderly population: a knowledge-driven and community-based approach

  • Zidu Xu; 
  • Yaowen Gu; 
  • Xiaowei Xu; 
  • Maxim Topaz; 
  • Zhen Guo; 
  • Hongyu Kang; 
  • Lianglong Sun; 
  • Jiao Li

ABSTRACT

Background:

Knowledge graph-based food recommendations are critical for the nutritional support of older adults. However, the integration of disease-based nutritional principles and preference-related requirements needs to be optimized in current recommendation processes.

Objective:

This study aims to design a knowledge graph-based personalized meal recommendation system for community-dwelling elders, and to conduct the preliminary effectiveness testing.

Methods:

ElCombo, a personalized meal recommendation system, was developed driven by user profiles and food knowledge graph. User profiles were established from a survey of 96 community-dwelling elders. Food knowledge graphs were supported by data from websites of Chinese cuisine recipes and eating history, consisting of five entity classes (dishes, ingredients, category of ingredients, nutrients, and diseases), corresponding attributes, and relations between entities. A personalized meal recommendation algorithm was then developed to generate packaged meals as outputs, considering disease-related nutritional constraints and personal dietary preferences.

Results:

Our recommendation system, ElCombo, was evaluated by comparing the dietary diversity and diet quality of its recommended meals with those of the autonomous choices of community-dwelling elders. Simulation experiments based on retrospective data of 96 community-dwelling elders revealed that recommended meals had significantly higher diet quality and dietary diversity (P<0.001). Two representative cases involving community-dwelling elders with and without eating history demonstrated the recommendation system’s potential to fulfill complex nutritional requirements associated with multiple morbidities.

Conclusions:

ElCombo proved superior performance in simulations compared to autonomous choices, implying the potential for improving dietary practices for community-dwelling elders. Future studies are needed to optimize its real-world application and refine data handling abilities.


 Citation

Please cite as:

Xu Z, Gu Y, Xu X, Topaz M, Guo Z, Kang H, Sun L, Li J

Developing a Personalized Meal Recommendation System for Chinese Older Adults: Observational Cohort Study

JMIR Form Res 2024;8:e52170

DOI: 10.2196/52170

PMID: 38814702

PMCID: 11176883

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