Effectiveness of personalized meal recommendation in improving dietary behaviors of Chinese community-dwelling elders: study protocol for a cluster randomized controlled trial

Xu, Zidu; Xu, Xiaowei; Sun, Lianglong; Guo, Zhen; Lai, Jianqiang; Kang, Lin; Li, Jiao

doi:10.1186/s13063-023-07865-1

Cited by 2 publications

References 39 publications

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

Xu,

Gu,

et al. 2024

JMIR Form Res

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Background China’s older population is facing serious health challenges, including malnutrition and multiple chronic conditions. There is a critical need for tailored food recommendation systems. Knowledge graph–based food recommendations offer considerable promise in delivering personalized nutritional support. 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 develop a knowledge graph–based personalized meal recommendation system for community-dwelling older adults and to conduct preliminary effectiveness testing. Methods We developed ElCombo, a personalized meal recommendation system driven by user profiles and food knowledge graphs. User profiles were established from a survey of 96 community-dwelling older adults. Food knowledge graphs were supported by data from websites of Chinese cuisine recipes and eating history, consisting of 5 entity classes: dishes, ingredients, category of ingredients, nutrients, and diseases, along with their attributes and interrelations. A personalized meal recommendation algorithm was then developed to synthesize this information to generate packaged meals as outputs, considering disease-related nutritional constraints and personal dietary preferences. Furthermore, a validation study using a real-world data set collected from 96 community-dwelling older adults was conducted to assess ElCombo’s effectiveness in modifying their dietary habits over a 1-month intervention, using simulated data for impact analysis. 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 96 eligible community-dwelling older adults. Participants were grouped based on whether they had a recorded eating history, with 34 (35%) having and 62 (65%) lacking such data. Simulation experiments based on retrospective data over a 30-day evaluation revealed that ElCombo’s meal recommendations consistently had significantly higher diet quality and dietary diversity compared to the older adults’ own selections (P<.001). In addition, case studies of 2 older adults, 1 with and 1 without prior eating records, showcased ElCombo’s ability to fulfill complex nutritional requirements associated with multiple morbidities, personalized to each individual’s health profile and dietary requirements. Conclusions ElCombo has shown enhanced potential for improving dietary quality and diversity among community-dwelling older adults in simulation tests. The evaluation metrics suggest that the food choices supported by the personalized meal recommendation system surpass autonomous selections. Future research will focus on validating and refining ElCombo’s performance in real-world settings, emphasizing the robust management of complex health data. The system’s scalability and adaptability pinpoint its potential for making a meaningful impact on the nutritional health of older adults.

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

Xu,

Gu,

et al. 2024

JMIR Form Res

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Recommendation Systems in Nutrition Field: A Scoping Review (Preprint)

Zhao,

Xue,

Chen

et al. 2024

Preprint

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BACKGROUND Recommendation systems (RS) have been widely used in the field of nutrition to promote the nutritional self-management, but few NRSs have been widely adopted due to various reasons. Limited studies have reviewed the RSs in food, with some methodological flaws including limited databases searches, high heterogeneity among included studies and rapidly evolving nature of evidence. OBJECTIVE We conducted a scoping review to summarize currently available recommendation systems applied in nutrition (NRS) including published articles, patents and application software and explore the potential gaps between development and implementation. METHODS We conducted a comprehensive search of seven bibliographic databases, two patent databases, four mobile apps store and three websites engines for this scoping reviews. Data extraction was conducted by four reviewers, a pilot study was performed before formal extraction, and the interrater agreement percentage needed to be >75%. Discrepancies were resolved by consensus or the involvement of a third reviewer. Frequency count and narrative summaries were performed for each study. RESULTS A total of 877 NRSs were included and half of them were released after 2022 (n=423, 48.2%) and 155 (17.7%) were from China. The most users were overweight or obese population (n=152, 17.3%), the primary inputs being self-reported data on nutritional status, diet, and exercise (the same n=157, 17.9% for every one), the primary output being nutrition plans (n=254, 29.0%), and the main audience being general population (n=244, 27.8%). Of 49 studies published in journals or essays, a few researchers from the nutrition filed were reported (n=4, 3.6%), the primary data were from public survey (n=22, 46.8%). Forty studies reported the evaluation stage, with incomplete processes and the lack of nutritional outcome. Of the 18 artificial intelligence technologies used in the studies, four could automatically update systems by themselves, and two were technologies proposed in the last decade. In addition, three recommendation algorithms were identified, only one was the latest knowledge-based algorithm that can improve precise matching. CONCLUSIONS While NRSs has primarily focused on the general population, there is a growing demand for professional NRSs tailored to special populations that incorporate dynamic updates and enhanced individual identification. Standardized evaluation of NRSs based on their technical performance and clinical impact can effectively support their public application in future. CLINICALTRIAL The protocol was registered on the Open Science Framework (https://doi.org/10.17605/OSF.IO/VF7NB)

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Effectiveness of personalized meal recommendation in improving dietary behaviors of Chinese community-dwelling elders: study protocol for a cluster randomized controlled trial

Cited by 2 publications

References 39 publications

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

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

Recommendation Systems in Nutrition Field: A Scoping Review (Preprint)

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