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

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. This study aims to develop a knowledge graph-based personalized meal recommendation system for community-dwelling older adults and to conduct preliminary effectiveness testing. 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. 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. 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.

Xu Z ，Gu Y ，Xu X ，Topaz M ，Guo Z ，Kang H ，Sun L ，Li J ... -  《-》

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

Inappropriate eating behaviors, particularly a lack of food diversity and poor diet quality, have a significant impact on the prognosis of certain chronic conditions and exacerbate these conditions in the community-dwelling elderly population. Current dietary interventions for the elderly have not adequately considered the nutritional needs associated with multiple chronic conditions and personal dietary preferences of elderly individuals. A personalized recommendation system has been recognized as a promising approach to address this gap. However, its effectiveness as a component of an elderly-targeted dietary intervention in real-world settings remains unknown. Additionally, it is unclear whether this intervention approach will be user-friendly for the elderly. Therefore, this study aims to examine the effectiveness of a personalized meal recommendation system designed to improve dietary behavior in community-dwelling elders. The implementation process in terms of System usability and satisfaction will also be assessed. The trial has been designed as a 6-month, non-blinded, parallel two-arm trial. One hundred fifty community-dwelling elders who meet the eligibility criteria will be enrolled. Subjects will be allocated to either the intervention group, receiving personalized meal recommendations and access to corresponding food provided as one component of the intervention, as well as health education on elder nutrition topics, or the control group, which will receive nutritional health education lectures. Outcomes will be measured at three time points: baseline at 0 months, 3 months, and 6 months. The primary outcomes will include dietary diversity (DDS) and diet quality (CDGI-E) of enrolled community-dwelling elders, representing their dietary behavior improvement, along with dietary behavior adherence to recommended meals. Secondary outcomes will measure the perceived acceptability and usability of the personalized meal recommendation system for the intervention group. Exploratory outcomes will include changes in the nutritional status and anthropometric measurements of the community-dwelling elders. This study aims to examine the effectiveness, acceptability, and usability of a personalized meal recommendation system as a data-driven dietary intervention to benefit community-dwelling elders. The successful implementation will inform the future development and integration of digital health strategies in daily nutrition support for the elderly. Chinese Clinical Trial Registry ChiCTR2300074912. Registered on August 20, 2023, https://www.chictr.org.cn/showproj.html?proj=127583.

Xu Z ，Xu X ，Sun L ，Guo Z ，Lai J ，Kang L ，Li J ... -  《Trials》

Personalized Flexible Meal Planning for Individuals With Diet-Related Health Concerns: System Design and Feasibility Validation Study.

Chronic diseases such as heart disease, stroke, diabetes, and hypertension are major global health challenges. Healthy eating can help people with chronic diseases manage their condition and prevent complications. However, making healthy meal plans is not easy, as it requires the consideration of various factors such as health concerns, nutritional requirements, tastes, economic status, and time limits. Therefore, there is a need for effective, affordable, and personalized meal planning that can assist people in choosing food that suits their individual needs and preferences. This study aimed to design an artificial intelligence (AI)-powered meal planner that can generate personalized healthy meal plans based on the user's specific health conditions, personal preferences, and status. We proposed a system that integrates semantic reasoning, fuzzy logic, heuristic search, and multicriteria analysis to produce flexible, optimized meal plans based on the user's health concerns, nutrition needs, as well as food restrictions or constraints, along with other personal preferences. Specifically, we constructed an ontology-based knowledge base to model knowledge about food and nutrition. We defined semantic rules to represent dietary guidelines for different health concerns and built a fuzzy membership of food nutrition based on the experience of experts to handle vague and uncertain nutritional data. We applied a semantic rule-based filtering mechanism to filter out food that violate mandatory health guidelines and constraints, such as allergies and religion. We designed a novel, heuristic search method that identifies the best meals among several candidates and evaluates them based on their fuzzy nutritional score. To select nutritious meals that also satisfy the user's other preferences, we proposed a multicriteria decision-making approach. We implemented a mobile app prototype system and evaluated its effectiveness through a use case study and user study. The results showed that the system generated healthy and personalized meal plans that considered the user's health concerns, optimized nutrition values, respected dietary restrictions and constraints, and met the user's preferences. The users were generally satisfied with the system and its features. We designed an AI-powered meal planner that helps people create healthy and personalized meal plans based on their health conditions, preferences, and status. Our system uses multiple techniques to create optimized meal plans that consider multiple factors that affect food choice. Our evaluation tests confirmed the usability and feasibility of the proposed system. However, some limitations such as the lack of dynamic and real-time updates should be addressed in future studies. This study contributes to the development of AI-powered personalized meal planning systems that can support people's health and nutrition goals.

Amiri M ，Li J ，Hasan W 《-》

Nutrition-Related Knowledge Graph Neural Network for Food Recommendation.

Ma W ，Li M ，Dai J ，Ding J ，Chu Z ，Chen H ... -  《Foods》

Quality of nutritional uptake of community-dwelling, care-dependent older adults in Zurich.

This study evaluated the quality of nutritional uptake of the care-dependent, community-dwelling older adults. Community-dwelling care-dependent elders were recruited in this study. The food items along with their nutritional content were extracted from the participants' refrigerators and categorized according to the NOVA classification (G1: unprocessed/minimally processed; G2: processed culinary ingredients; G3: processed; G4: ultra-processed). The nutritional information of the food items was entered into a dietary analysis software that analyzed the content. Data was verified for a normal distribution and non-parametric tests were applied for statistical analysis (p < 0.05). 100 subjects (mean age = 81.1±9.5 y; mean MMSE: 26.6±6.8) participated in this study. The participants had significantly more G1 than G2, G3 or G4 (p < 0.001) foods. Women had more G1 items in their refrigerators than men (rs = 0.372, p < 0.001). Higher socio-economic status indicated a presence of more G1 (rs = 0.313, p = 0.002), G2 (rs = 0.342, p < 0.001) and G4 (rs = 0.237, p = 0.024) foods. Higher cognitive scores revealed an increase in presence of G4 (rs = 0.238, p = 0.023) items. Participants with an increased need for assistance had less G2 (rs = -0.332, p = 0.001), and G4 (rs = -0.215, p = 0.041) foods; age had no influence. The findings of this study confirm that the majority of care-dependent, community-dwelling adults procured healthy dietary aliments for their daily living; however, whether this correlated to the actual nutritional state of these older adults needs to be further investigated. This highlights the need for further investigation and tailored interventions to ensure good nutrition, emphasizing regular assessments and comprehensive support beyond just food access. Clinicians must recognize that access to healthy food alone may not ensure good nutrition in older adults. Regular nutritional assessments, personalized dietary interventions, and additional support services like meal preparation assistance, nutritional counseling, and tailored programs are essential to address specific dietary needs and preferences.

Lourenço PDF ，Angst L ，Srinivasan M 《-》