Food Recommendations Research Articles

Environmental and public health co-benefits of consumer switches to immunity-supporting food

During COVID-19, there has been a surge in public interest for information on immunity-boosting foods. There is little scientific support for immunity-supporting properties of specific foods, but strong evidence for food choice impacts on other health outcomes (e.g. risk of non-communicable disease) and environmental sustainability. Here, we relate online recommendations for “immunity-boosting” foods across five continents to their environmental and human health impacts. More frequently recommended food items and groups are plant based and have lower land use and greenhouse gas emission impacts plus more positive health outcomes (reducing relative risks of mortality or chronic diet-related diseases) per serving of food. We identify trade-offs between environmental outcomes of increasing consumption of recommended food items, with aquatic environment impacts increasing with food recommendation frequency. People’s reliance on the Internet for health information creates an opportunity to consolidate behaviour change towards consuming foods with multiple co-benefits. Our study identifies win–win options for nudging online information-seeking behaviour towards more sustainable choices for terrestrial biodiversity conservation and human health.

Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process

Hybrid data mining processes are employed in recent days on several applications to achieve a better prediction and classification rate along with customer satisfaction. Hybrid data mining processes are the combination of different form of data considered for a neural network decision. In some cases, the different form of data represents image along with numerical data. In the proposed work, a food recommendation system is developed with respect to the flavour taste of the customer and considering the review comments of previous customers. The suggestions given by the users are taken into account as a feedback layer in the neural network for fine tuning the accuracy of the prediction process. The architectural design of the proposed model is employed with an ADNet (Adaptively Dense Convolutional Neural Network) algorithm to enable the usage of low range features in an efficient way. To verify the performance of the developed model, a pizza flavour recommender dataset is employed in the work for analysis. The experimental work analysis indicates that the ADNet algorithm works in a better way on a hybrid data analysis than the traditional DenseNet and ResNet algorithms.

Recipe Recommendation System Using TF-IDF

A Recipe Recommendation System is being proposed in this following paper. Food recommendation is a new area, with few systems that are focus on analysing and user preferences and constraints such as ingredients available at their side being deployed in real settings in the form of web application or mobile application [4]. The proposed model is a mobile application which allows users to search recipes using ingredients available at them including vegetables. For this work we have find a dataset which is a collection of Indian cuisines recipes and apply the content-based recommendation using Term Frequency – Inverse Document Frequency (TF-IDF) and Cosine Similarity [1]. This application gives the recommendation of Indian recipes based on ingredients available at them and allows users to filter out the recipes on course type, diet type, etc.

A review of natural foods consumed during the COVID-19 pandemic life

Abstract Coronavirus 2019 (COVID-19) is an infectious disease that has brought life to a standstill around the world. Until a vaccine was found to combat COVID-19, the world conducted research and made recommendations for nutritional natural foods. Considering the risks incurred by contracting the disease, even though the production of various vaccines and vaccination of healthy people has started in some countries, individuals need useful foods to be ready for the COVID-19 pandemic. Recently, nutrient contents such as antioxidant compounds, vitamins, minerals, and probiotics that contribute to the immune system have been investigated. This paper attempts to determine the role of these dietary supplements in reducing the risk of COVID-19 and/or changing the course of the disease in COVID-19 patients and their effects on mortality. Supplements used and recommended for the COVID-19 pandemic life were investigated. In conclusion, more research is needed to determine the effectiveness of nutrients, vitamins, minerals, probiotics, prebiotics, and antioxidants used during the COVID-19 pandemic to inhibit the effect of SARS-CoV-2. In order to overcome the new global crisis, nutritional cures and treatments should be upgraded. However, additional research on the subject is needed.

Effects of the new fasting standard on sedation in the pediatric emergency room.

This study compared adherence to the fasting-time guidelines of the International Committee for the Advancement of Procedural Sedation (ICAPS) and the North American Society of Anesthesiologists (ASA) and complication rates in pediatric patients requiring procedure sedation and analgesia during treatment in the emergency room (ER). This retrospective, single-center study was performed between 2016 and 2020, and enrolled patients who received procedural sedation and analgesia in the ER with the fasting times recommended in the ICAPS and ASA guidelines. In total, 857 patients were included. The most frequent indication for procedural sedation and analgesia was fracture reduction in 420 patients (49.0%). Ketamine, the most commonly administered drug, was given to 710 patients (82.8%). Adherence to the ICAPS guidelines was higher (P < 0.01), with 772 (95.7%) and 351 (41.0%) patients, respectively, adhering to the ICAPS and ASA recommendations for food and drink fasting times. Complications occurred in 130 patients (15.2%), including SpO2 <90% in 75 patients (8.7%) and vomiting in 20 patients (2.3%). No serious complications, such as aspiration, cardiac arrest, or death, occurred. The complication rate between the two groups did not differ significantly, with 50 (14.2%) and 127 (15.5%) patients experiencing complications according to the ICAPS and ASA guidelines, respectively (P = 0.586). The fasting recommendations of the ICAPS guidelines, which propose risk stratification to determine the appropriate fasting time for procedural sedation and analgesia, are more tolerable to patients and the rate of adverse events did not appear to be different from that experienced when following the ASA guidelines.

Food4healthKG: Knowledge Graphs for Food Recommendations Based on Gut Microbiota and Mental Health

Food4healthKG: Knowledge Graphs for Food Recommendations Based on Gut Microbiota and Mental Health

Smart QR-based Restaurant Dine-in System with Sales Analysis

The modern-day customer has new priorities when it comes to dine-out and the top of them is safety. Most of the restaurant’s meal orders rely on the interaction with waiters to place orders into the kitchen. Due to the pandemic situation, there happens to be discomfort interacting with waiters and other staff members of the restaurant. To solve such problems this system is developed. This system covers the whole order process of a restaurant including the interaction between the customer, the waiter, the kitchen, and the cashier through a web application. Additionally, restaurant owners will be able to enhance sales by using this system to analyze data gathered. This system will attempt to replace the traditional manual ordering process.A better user experience that includes food recommendations can indirectly boost customer loyalty to the restaurant. It is a complete product for managing restaurant services with minimal human interaction while providing maximum contactless service.

Prevalensi dan Faktor Risiko Anemia pada Anak di Negara Maju

Background: Anemia is a global public health problem that occurs in developed countries or in developing countries. The prevalence of anemia in children aged 1-5 years in the United States is 3.9% and iron anemia is 1.1%. This review discussed prevalence and the risk factors and anemia in children in developed countries. Method: The method used is a literature review, the data source in the form of research articles is obtained from online databases, namely Google Scholar and Science Direct. The articles reviewed were 10 articles that met the inclusion criteria. Result: Based on several studies, iron deficiency or deficiency is the most common cause in children in developed countries. 8% of children under five in the United States have iron deficiency, and 2-3% of them are iron deficiency anemia. Risk factors for anemia include lack of serum zinc, low levels of 25 (OH) D, exclusive breastfeeding without iron fortification, Low Birth Weight (LBW), food intake factors, history of asthma and eczema, and maternal education. Iron deficiency anemia in childhood has been shown to have a negative effect on cognitive and psychomotor development. Prevention of anemia that can be done include giving iron supplements and food recommendations. Conclusion: Risk factors for anemia include lack of serum zinc, low 25 (OH) D levels, exclusive breastfeeding without iron fortification, Low Birth Weight (LBW), food intake factors, history of asthma and eczema, and maternal education.

Exploring the Relationship Between Nutrition and Academic Learning among Students of Samtse College of Education

The study was conducted to explore the existence of relationship between the academic learning of college students and their nutritional diets receive from the college mess. The data was collected through a Student Food Survey (SFS) (N =135) and structured interview (N = 6) among college students who had the lived experience of having meals from the college mess. The survey data was analyzed for descriptive and correlational analysis using SPSS. Similarly, the interviews were transcribed and analyzed into three major themes: food quality, eating practices, and recommendations. The data revealed that food quality and nutritional diets served in the mess affect students’ eating habits and their academic learning. Further, the findings from survey indicate a significant positive correlation between food quality and academic learning of students. The study concluded by stating that nutritional deficiencies contribute to lack of academic concentration and therefore, recommends the college management devise strategic interventions to improve food quality in the mess.

High Adherence to the Food Pyramid’s Recommendations Avoids the Risk of Insufficient Nutrient Intake among Farmers in Peri-Urban Kenya

This study aimed to investigate whether the Kenyan Food Pyramid (FP) can evaluate excess or insufficient nutrient intake. Participants were farmers (56 men and 64 women, aged 18–60 years) in Wangige Village, Kiambu County—a peri-urban area of Kenya. Cross-sectional data were collected for demographic characteristics, physical measurements, and 2-day and 24-h dietary recalls. The average adherence level to the FP (hereafter, “FP score”) was 25.0 out of 50.0, with a minimum and maximum of 14.1 and 41.5, respectively. Energy and protein % energy ratio were significantly higher (p for trend < 0.05) in the higher FP score group. A higher FP score was also associated with a higher energy-adjusted micronutrient intake, and it was more likely to meet nutrient requirements. However, the higher FP score group had a higher risk of excess sodium intake (p for trend < 0.001). The Kenyan FP could be a useful tool for avoiding the risk of insufficient nutrient intake, but not for avoiding high energy and sodium intake. It is necessary to include appropriate evaluations to limit energy, sugar, and salt. Food groups and recommendations of the FP should be optimised according to the dietary environment of the target population so as to promote their health.

Food system development pathways for healthy, nature-positive and inclusive food systems.

Sustainable food systems require the integration of and alignment between recommendations for food and land use practices, as well as an understanding of the political economy context and identification of entry points for change. We propose a food systems transformation framework that takes these elements into account and links long-term goals with short-term measures and policies, ultimately guiding the decomposition of transformation pathways into concrete steps. Taking the transition to healthier and more sustainable diets as an example, we underscore the centrality of social inclusion to the food systems transformation debate.

Food recommendation with graph convolutional network

Food recommendation has attracted increasing attentions to various food-related applications and services. The food recommender models aim to match users’ preferences with recipes, where the key lies in the representation learning of users and recipes. However, ranging from early content-based filtering and collaborative filtering methods to recent hybrid methods, the existing work overlooks the various food-related relations, especially the ingredient-ingredient relations, leading to incomprehensive representations. To bridge this gap, we propose a novel model Food recommendation with Graph Convolutional Network (FGCN), which exploits ingredient-ingredient, ingredient-recipe, and recipe-user relations deeply. FGCN employs the information propagation mechanism and adopts multiple embedding propagation layers to model high-order connectivity across different food-related relations and enhance the representations. Specifically, we develop three types of information propagation: (1) ingredient-ingredient information propagation, (2) ingredient-recipe information propagation, and (3) recipe-user information propagation. To validate the effectiveness and rationality of FGCN, we conduct extensive experiments on a real-world dataset. The results show that the proposed FGCN outperforms the state-of-the-art baselines. Further in-depth analyses reveal that FGCN could alleviate the sparsity issue in food recommendation.

Effects and challenges of using a nutrition assistance system: results of a long-term mixed-method study

Healthy nutrition contributes to preventing non-communicable and diet-related diseases. Recommender systems, as an integral part of mHealth technologies, address this task by supporting users with healthy food recommendations. However, knowledge about the effects of the long-term provision of health-aware recommendations in real-life situations is limited. This study investigates the impact of a mobile, personalized recommender system named Nutrilize. Our system offers automated personalized visual feedback and recommendations based on individual dietary behaviour, phenotype, and preferences. By using quantitative and qualitative measures of 34 participants during a study of 2–3 months, we provide a deeper understanding of how our nutrition application affects the users’ physique, nutrition behaviour, system interactions and system perception. Our results show that Nutrilize positively affects nutritional behaviour (conditional R^2=.342) measured by the optimal intake of each nutrient. The analysis of different application features shows that reflective visual feedback has a more substantial impact on healthy behaviour than the recommender (conditional R^2=.354). We further identify system limitations influencing this result, such as a lack of diversity, mistrust in healthiness and personalization, real-life contexts, and personal user characteristics with a qualitative analysis of semi-structured in-depth interviews. Finally, we discuss general knowledge acquired on the design of personalized mobile nutrition recommendations by identifying important factors, such as the users’ acceptance of the recommender’s taste, health, and personalization.

SISTEM PENDUKUNG KEPUTUSAN MENENTUKAN MENU MAKANAN SEHAT MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING

Food is one of the important elements that affect health. Eating healthy foods will have a positive impact on the body, one of which can avoid various health problems. People's incomprehension about healthy food menus resulted in the decision-making process of choosing healthy food menus rarely noticed. Decision Support System (SPK) can help the community in making decisions, so they do not feel hesitant in choosing a menu, and the information is also more accurate. In the process of data processing, this system uses the Simple Additive Weighting (SAW) method. The SAW method is used to determine the exact healthy food menu based on the criteria and weights that have been determined. The decision support system to determine healthy food menus using the Simple Additive Weighting (SAW) method helps the community in determining a good food menu by providing healthy food recommendations for the community in the form of food rankings based on predetermined weights.

The Impact of Restaurant Recommendation Information and Recommendation Agent in the Tourism Website on the Satisfaction, Continuous Usage, and Destination Visit Intention

Enjoying local food could be one of the motives for tourism, and local food and restaurant recommendation information would be important for tourists to decide their destination. Recommendation agents are the sorting and searching function to find the best local food and restaurant among the complexity of information, and they could also be helpful for the tourist to decide their destination. Online tourism websites (e.g., Ctrip.com ) started to provide restaurant recommendations containing food-related information and recommendation agents to attract tourists. However, few studies have investigated their impact on the destination visit intention of potential Chinese tourists. This study aims to empirically validate how restaurant recommendation information, including food-related information and recommendation agents, could impact online tourists’ reactions, such as satisfaction, continuous website usage, and destination visits. We developed our hypothesis based on the information system (IS) success model. We gathered 202 data points from potential tourists using quasi-experimental methods, and these data were analyzed by the PLS algorithm. The results indicate that restaurant recommendation information and recommendation agents significantly increase the perceived information quality and perceived system quality. Increased perceived information quality and system quality could significantly increase potential tourists’ satisfaction, website continuous usage intention, and destination visit intention. The results of this study could contribute to making tourism websites more attractive by using local food and restaurant information and recommendation agents.

Recommendations for the care of patients with diabetes mellitus with risk factors or established cardiovascular disease and SARS-CoV-2mellitus con factores de riesgo o enfermedad cardiovascular establecida y SARS-CoV-2.

A review is carried out to examine the risk of patients suffering from diabetes mellitus in the context of general morbidity and mortality and related to infection by SARS-CoV-2. Likewise, the general recommendations for food and the prevention of comorbidities that most these patients suffer most frequently are also studied. Finally, a review of the pharmacological recommendations on both oral and parenteral treatment in the outpatient, in hospitalization and in critical states infected with SARS-CoV-2 is made.

Affordability of Healthy and Sustainable Diets in Nigeria

This study examines the cost and affordability of healthy diets in Nigeria. Using the 2015/16 and 2018/19 waves of Nigeria General Household Surveys, we find that, generally, the least-cost options to meet dietary recommendations for vegetables, dairy, and protein-rich foods are more expensive to meet than that of other food groups. Despite improvements during the survey years, the challenges of affordability of healthy diets appear more pronounced in rural than urban, among poorest household groups, and in northern than southern Nigeria. Results suggest that it will be more expensive to meet the dietary recommendation for dairy foods if priority will be given for food systems sustainability over concerns for food preferences of the households. It will however be cheaper to achieve dietary recommendations for vegetables, fruits and starchy staples even when households give more consideration to food systems sustainability than tastes and preferences in their choice of healthy foods. Relative affordability of protein-rich foods are less affected by whether (or not) tastes/preferences or food systems sustainability drives healthy food choices. Key implication is that interventions targeting on affordability of healthy diets should give greater attention to the poorest of the poor, to rural than urban, and to northern than southern Nigeria. Implications relevant for research, policy, and other actors that focus on transformation of the food systems toward achieving healthier diets in a sustainable food systems are discussed.

Food Menu Recommendations Based on Recommended Dietary Allowances using Genetic Algorithm

Nowadays, there are still often unbalanced nutritional problems such as overnutrition or malnutrition. Many factors can affect it, one of which is an unbalanced diet. One solution that can be done is a system for optimizing nutritional needs. In this study, the method used for optimization is genetic algorithms. Genetic algorithms are one of the metaheuristic methods that are often used for optimization problems. A particular chromosome representation is designed to provide suitable solutions. The system can provide food recommendations with nutrients close to a person's nutritional needs by using the genetic algorithm. Based on the test results obtained, the difference in nutrition from food recommendations with nutritional needs is below 5 percent.

Combining Fuzzy Logic and k-Nearest Neighbor Algorithm for Recommendation Systems

Recommendation systems are a type of systems that are able to help users finding relevant and personalized content in a wide variety of possibilities. To help computers perform recommendations, there are several approaches used nowadays such as the Content-based approach, the Collaborative filtering approach and the Hybrid recommendation approach. However, these approaches are sometimes inappropriate for use cases where there is no prior large datasets of users’ feedbacks or ratings needed for training Machine Learning models. Thus, in this work, we proposed a novel approach based on the combination of Fuzzy Logic and the k-Nearest neighbor algorithm (KNN). The proposed approach can be applied without any prior collected feedbacks of users and performs good recommendations. Moreover, our proposal uses Fuzzy Logic to infer values based on inputs and a set of rules. Furthermore, the KNN uses the output values of the Fuzzy Logic system to do some retrieval tasks based on existing distance measures. In order to evaluate our approach, we considered an expert system of food recommendation for people suffering from the two deadliest diseases in Cameroon: HIV/AIDS and Malaria. The obtained results are closed to the recommendation made by nutritionists. These results demonstrate how effective our approach can be used to solve a real nutrition problem for people suffering from Malaria or HIV/AIDS. Furthermore, this approach can be extended to other fields and even be used to perform any recommendation task where there is no prior collected user’s feedback or ratings by using the proposed approach as a framework.

Understanding needs and expectations of heart failure patients and their caregivers regarding digital health - the PASSION-HF project

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): INTERREG-NWE Background/Introduction Current heart failure (HF) healthcare provision is not sufficient. Due to demographic changes and subsequent increases in comorbidities, along with unequal distribution of medical care in rural areas, alternative approaches need be sought. The use of eHealth applications has potential to enable patients to become more self-sufficient. The "PASSION-HF" project aims to develop an interactive decision-making system – a virtual doctor – that provides solutions based on current guidelines and artificial intelligence. Patient independence is maximized through 24/7 access to personalized HF-management. Furthermore, the application defines decision points, where medical professionals need to be included. Purpose To understand needs and expectations of HF patients and their informal caregivers in regard to a virtual doctor. Methods We conducted an exploratory mixed-methods study within the Netherlands, UK, Ireland and Germany. Semi-structured qualitative interviews were supplemented by a standardized questionnaire. The interviews focused on i) acceptance and motivation to use a virtual doctor and ii) experience and perception of current health care provision. The interviews were analysed using the content analysis according to Mayring (2010) with the help of "ATLAS.TI" software. Additional information about the role of informal caregivers, technology acceptance and decision-making processes was collected via questionnaires. Results A total of 49 patients and 33 informal caregivers were interviewed. Most patients were male (76%), aged between 60 and 69 years (43%). Three key themes were identified in regard to an interactive decision-making system: 1) Reassurance, because patients felt uncertain about their condition and their symptoms, they had a strong desire for an application that could monitor their health 24/7, was able to spot deteriorations, before they occured and gave them instant feedback about their current health status; 2) Personalized advice, e.g. patients wanted the virtual doctor to adapt medication, sport activities and food recommendations to their current health status; and 3) Transparency, e.g. patients wanted to know, where the recommendations are coming from and justifications for management modifications. Interview findings also identified that the HF-nurses play a significant role in the care and management of the condition. Across all countries with HF-nurses, they were the primary point of contact, when patients had any HF related concerns. Conclusion The findings provide valuable information for the development and implementation of eHealth solutions. Patients want reassurance, independently of the availability of healthcare services, combined with personalized advice regarding day-to-day management of their HF. For the next step, we are planning a multicentre clinical trial to test the prototype of the application. Here all decisions are examined by a clinical committee and benefits are evaluated.