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Welcome to Nutrition & Diabetes
A peer-reviewed online open access journal bringing to the fore outstanding research in the areas of nutrition and chronic disease, including diabetes, from the molecular to the population level.
Adherence to Mediterranean dietary pattern and the risk of gestational diabetes mellitus: a systematic review and meta-analysis of observational studies
- Saeede Jafari Nasab
- Matin Ghanavati
- Maryam Nasirian
Joint association of sleep quality and physical activity with metabolic dysfunction-associated fatty liver disease: a population-based cross-sectional study in Western China
- Yining Yang
Imaging-based body fat distribution and diabetic retinopathy in general US population with diabetes: an NHANES analysis (2003–2006 and 2011–2018)
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The need for a prognostic biomarker and challenge test for phenotypic flexibility.
- Sai Krupa Das
- Theodore K. Kyle
- Leah D. Whigham
Hemoglobin glycation index and cardiovascular outcomes in patients with diabetes and coronary artery disease: insights from a large cohort study
- Zhangyu Lin
Dietary iron intake predicts all-cause and cardiovascular mortality in patients with diabetes
- Chenchen Yang
- Tingting Hu
- Aifeng Gong
A potential therapeutic strategy of an innovative probiotic formulation toward topical treatment of diabetic ulcer: an in vivo study
- Farkhonde Karimi
- Nima Montazeri-Najafabady
- Ahmad Gholami
Ginger essential oil prevents NASH progression by blocking the NLRP3 inflammasome and remodeling the gut microbiota-LPS-TLR4 pathway in mice
- Suraphan Panyod
- Lee-Yan Sheen
Loss of Cldn5 -and increase in Irf7-in the hippocampus and cerebral cortex of diabetic mice at the early symptomatic stage
- Marta Carús-Cadavieco
- Sandra González de la Fuente
- Carlos G. Dotti
Optimization of diabetes prediction methods based on combinatorial balancing algorithm
- HuiZhi Shao
- QingJun Song
Identification of interactions between genetic risk scores and dietary patterns for personalized prevention of kidney dysfunction in a population-based cohort
- Min-Jae Jang
- Li-Juan Tan
Association between fast eating speed and metabolic dysfunction-associated steatotic liver disease: a multicenter cross-sectional study and meta-analysis
- Xiaoyang Sun
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Nutrition for Diabetes: Nutritional Approaches for the Treatment of Diabetes
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Adherence to a Mediterranean diet and long-term changes in weight and waist circumference in the EPIC-Italy cohort
A plant-based diet in overweight individuals in a 16-week randomized clinical trial: metabolic benefits of plant protein
Association between muscle mass and diabetes prevalence independent of body fat distribution in adults under 50 years old
Taurine reduces the risk for metabolic syndrome: a systematic review and meta-analysis of randomized controlled trials
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Nutrition Journals
Elsevier's nutrition journals cover a wide range of topics across the field of nutrition from education, health, diet, and research to disease prevention and management. Whether you are looking to enhance your knowledge, subscribe to a journal in print or online, or submit an article or case study, this page is your starting point to finding a journal, locating existing research, or having your research published.
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Advances in Nutrition
An Open Access International Review Journal of the American Society for Nutrition opens in new tab/window
Advances in Nutrition is ranked 2nd in the field of Nutrition and Dietetics and publishes reviews spanning basic, translational, clinical, and population research. Articles focus on recent advancements in contemporary topics and innovative methods to broaden knowledge in nutrition and related biomedical sciences.
ISSN: 2161-8313
Clinical Nutrition
An Official Journal of the European Society for Clinical Nutrition and Metabolism (ESPEN) opens in new tab/window
Clinical Nutrition publishes on nutritional and metabolic care and the relationship between nutrition and disease in basic science and clinical practice with a focus on those factors in acute and chronic diseases which have metabolic and nutritional implications. The Journal also publishes scientific works related to the development of new techniques and their application in the field of clinical nutrition.
ISSN: 0261-5614
Clinical Nutrition ESPEN
Clinical Nutrition ESPEN publishes peer-reviewed articles covering the relationship between nutrition and disease in the setting of basic science and clinical practice.
ISSN: 2405-4577
Clinical Nutrition Open Science
With an aim to stimulate experimental research activities in the field of clinical nutrition, the journal welcomes the latest and most exciting experimental and clinical articles on methodology, nutrient physiology and metabolism, nutritional neurosciences, toxicities, immunology, nutrigenomics, molecular nutrition, vitamins, trace elements, antioxidants, genetics and microbiota.
ISSN: 2667-2685
Current Developments in Nutrition
An Open Access Journal of the American Society for Nutrition opens in new tab/window
Current Developments in Nutrition briskly reviews, publishes, and disseminates high-quality research encompassing the breadth of nutrition science. Its scope ranges from mechanistic studies at molecular, biochemical, and cellular levels to whole-body metabolism, fitness, and clinical and population health. New areas include food and nutrition policy; eating behavior; implementation science; food systems; animal nutrition; exercise physiology and nutrition; and food and nutrition of Indigenous people.
ISSN: 2475-2991
Human Nutrition & Metabolism
Human Nutrition & Metabolism is a new journal dedicated to research focusing on the physiology and athophysiology of nutrition and metabolism in humans. The journal publishes original papers, reviews, commentaries and opinion pieces in human nutritional and metabolic science to bridge the gap between preclinical and clinical work. Papers can focus on areas such as lifestyle, diabetes, insulin resistance, metabolic diseases, undernutrition, in-hospital nutrition, and disease specific nutrition.
ISSN: 2666-1497
Journal of Renal Nutrition
Official Journal of the Council on Renal Nutrition of the National Kidney Foundation opens in new tab/window and the International Society of Renal Nutrition and Metabolism opens in new tab/window The Journal of Renal Nutrition is devoted exclusively to renal nutrition science and renal dietetics. Its content is appropriate for nutritionists, physicians and researchers working in nephrology. Each issue contains a state-of-the-art review, original research, articles on the clinical management and education of patients, a current literature review, and nutritional analysis of food products that have clinical relevance.
ISSN: 1051-2276
Journal of Nutrition Education and Behavior
Official Journal of the Society for Nutrition Education and Behavior opens in new tab/window
The Journal of Nutrition Education and Behavior serves as a global resource to advance nutrition education and behavior related to research, practice, and policy. JNEB publishes original research, as well as papers focused on emerging issues, policies and practices broadly related to nutrition education and behavior. These topics include, but are not limited to, nutrition education interventions; theoretical interpretation of behavior; epidemiology of nutrition and health; food systems; food assistance programs; nutrition and behavior assessment; and public health nutrition. Strategies to implement nutrition education, such as policy, systems, and environmental approaches or technological advances are also considered. Skill development within interventions, such as food procurement and culinary expertise, physical activity partnered with nutrition education, and strategies to reduce food insecurity are valued.
ISSN: 1499-4046
Journal of the Academy of Nutrition and Dietetics
The Official Journal of the Academy of Nutrition and Dietetics opens in new tab/window
The Journal of the Academy of Nutrition and Dietetics is the premier source for the practice and science of food, nutrition, and dietetics. The Journal publishes on a range of research and practice issues such as: nutritional science, medical nutrition therapy, public health nutrition, food science and biotechnology, foodservice systems, leadership and management, and dietetics education.
ISSN: 2212-2672
Journal of Trace Elements and Minerals
The Journal of Trace Elements and Minerals serves as a broader scope, companion journal to the Journal of Trace Elements in Medicine and Biology by publishing on all aspects of trace elements, and trace element species and minerals in the widest sense, including analytical methods, biochemistry, pathobiochemistry of metabolic processes, molecular biology, biogeochemistry, nutrition, toxicology, environmental toxicology, epidemiology, clinical applications in diagnosis, therapy, food chain and veterinary medicine. The journal is interdisciplinary and seeks to bring together different approaches for a diverse readership.
ISSN: 2773-0506
Journal of Trace Elements in Medicine and Biology
Official Journal of the Federation of European Societies on Trace Elements and Minerals (FESTEM) opens in new tab/window and the International Society of Zinc Biology (ISZB) opens in new tab/window
The Journal of Trace Elements in Medicine and Biology publishes on the theoretical and applied aspects of trace elements in medicine and biology and is devoted to the advancement of scientific knowledge about trace elements and trace element species. The journal focuses on the biological role and action of trace elements including different fields such as analytical methods, biochemistry, patho-biochemistry of metabolic processes, molecular biology, nutrition, toxicology, environmental toxicology, epidemiology, clinical applications in diagnosis, therapy, food chain and veterinary medicine.
ISSN: 0946-672X
The International Journal of Applied and Basic Nutritional Sciences
Nutrition publishes advances in nutrition research and science; informs readers on new and advancing technologies and data in clinical nutrition practice; encourages the application of the techniques of outcomes research and meta-analyses to problems in patient-related nutrition; and seeks to help clarify and set the research, policy and practice agenda for nutrition science.
ISSN: 0899-9007
Nutrition, Metabolism and Cardiovascular Diseases
Official Journal of the Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis and the Italian Society of Human Nutrition
Nutrition, Metabolism & Cardiovascular Diseases publishes on the interplay between nutritional and metabolic alterations with cardiovascular disorders. The Journal serves to help refine strategies against the nutrition-related epidemics of metabolic and cardiovascular diseases by publishing on clinical and preventive medicine; vascular biology; and the origins, mechanisms and means to prevent and control diabetes, atherosclerosis, hypertension, and other nutrition-related diseases.
ISSN: 0939-4753
Nutrition Research
An International Publication for Nutrition to Advance Food and Life Science Research
Nutrition Research publishes a broad range of nutrition sciences research that advances human health and promotes clinical applications through a comprehensive understanding of dietary patterns, foods, nutrients, and bioactive food components. This understanding includes exploring diet-related interactions with environmental exposures, human behavior, genetics, and underlying health status.
ISSN: 0271-5317
The American Journal of Clinical Nutrition
An Official Journal of the American Society for Nutrition opens in new tab/window
The American Journal of Clinical Nutrition , one of the most influential journals in biology and medicine, publishes research in human nutrition and fosters the application of science into clinical practice. Topics include high impact clinical, observational, public health, and epidemiologic studies in all areas of nutrition including obesity and metabolism; micronutrients; body composition; and nutrition in clinical settings. Genetic and novel precision nutrition approaches are featured.
ISSN: 0002-9165
The Journal of Nutrition
The Journal of Nutrition is the oldest journal devoted to nutrition research in the world, and publishes top-quality original research articles, reviews, and perspectives of experimental nutrition across various species including humans and laboratory, food, and companion animals. The Journal emphasizes novel mechanism, technology innovation, and significant real world impact of nutrition at molecular, cellular, tissue, organism, community, and population levels. The Journal promotes integrations and convergences of nutrition science with -omics, microbiome, brain function, aging, One Health, climate change, agri-food system transformation, food as medicine, and digital/data/AI technology. Prominent nutritionists and nutrition history are featured. A series of prestigious awards are established to recognize best papers.
ISSN: 0022-3166
The Journal of nutrition, health and aging
Publishes on the relationship between nutrition and health as part of the aging process from birth to old age. The journal has a particular interest in the role of nutrition on healthy longevity, functional capacity, intrinsic capacity, resilience, geroscience, and geriatric syndromes, such as frailty and sarcopenia; studies related to the role of nutrition and exercise on a life course perspective; randomized clinical trials on healthy longevity; and studies investigating the role of specific nutrients as well as diets and dietary patterns on functional capacity and healthy longevity - especially when using a longitudinal design.
ISSN: 1279-7707
The Journal of Nutritional Biochemistry
The Journal of Nutritional Biochemistry presents experimental nutrition research as it relates to: biochemistry, molecular biology, toxicology, or physiology. The Journal focuses on nutrition research being conducted at the cellular, animal and human level.
ISSN: 0955-2863
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The journals of the european society for clinical nutrition and metabolism (espen), nutrition: calls for papers.
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As one of the first Open Access journals in its field, Food & Nutrition Research (FNR) offers an important forum for researchers to exchange the latest results from research on human nutrition broadly and food-related nutrition in particular. Learn more about the journal's Aims & Scope . FNR is widely indexed by relevant services and databases, including PubMed Central/PubMed, Scopus, Science Citation Index, with an Impact Factor of 3.5 (2023) and a 5-year Impact Factor of 5.0 (2022).
- Iron – a background article for the Nordic Nutrition Recommendations 2023 381
- A combination of Citrus aurantifolia fruit rind and Theobroma cacao seed extracts supplementation enhances metabolic rates in overweight subjects: a randomized, placebo-controlled, cross-over study 377
- Social and economic dimensions of food sustainability – a background paper for the Nordic Nutrition Recommendations 348
- Natural antagonistic flavones for AhR inhibit indoxyl sulfate-induced inflammatory gene expression in vitro and renal pathological damages in vivo 335
- Potassium – a scoping review for Nordic Nutrition Recommendations 2023 329
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Impact Factor – The 2022 Impact Factor as announced by Clarivate Analytics, is 3.3 . The 5-year Impact Factor is 5.0 (2022). Open Access – Food & Nutrition Research is free from all access barriers, allowing for the widest possible global dissemination of your work, leading to more citations. Automatic deposit in PubMed Central – all articles published in Food & Nutrition Research are automatically deposited in PubMed and PMC, in compliance with NIH policies. Comply with archiving policies – authors can deposit any version of their manuscript in any required repository or archive, or post articles to their personal or institutional website. Retain copyright – authors retain the copyright to their own article; you are free to disseminate your work, make unlimited copies, and more. Personal service – Food & Nutrition Research is published in partnership with Open Academia , a Publishing Partner dedicated to giving you excellent service all the way from submission to publication.
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A Systematic Review of Nutrition Recommendation Systems: With Focus on Technical Aspects
1 PhD Candidate, Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran;
2 PhD, Professor, Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran;
L. Azadbakht
3 PhD, Professor, Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran;
K. B. Lankarani
4 MD, Professor, Health Policy Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran;
Sh. R. Niakan Kalhori
5 PhD, Associate Professor, Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran;
B. Honarvar
6 MD, Associate Professor, Health Policy Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran;
7 PhD, Department of Food Science and Technology, National Nutrition and Food Technology Research Institute, Faculty of Nutrition Sciences and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
S. M. Ayyoubzadeh
S. zakerabasali, y. jalilpiran.
8 PhD student, Department of Clinical Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran;
Background:
Nutrition informatics has become a novel approach for registered dietitians to practice in this field and make a profit for health care. Recommendation systems considered as an effective technology into aid users to adjust their eating behavior and achieve the goal of healthier food and diet. The purpose of this study is to review nutrition recommendation systems (NRS) and their characteristics for the first time.
Material and Methods:
The systematic review was conducted using a comprehensive selection of scientific databases as reference sources, allowing access to diverse publications in the field. The process of articles selection was based on the PRISMA strategy. We identified keywords from our initial research, MeSH database and expert’s opinion. Databases of PubMed, Web of Sciences, Scopus, Embase, and IEEE were searched. After evaluating, they obtained records from databases by two independent reviewers and inclusion and exclusion criteria were applied to each retrieved work to select those of interest. Finally, 25 studies were included.
Hybrid recommender systems and knowledge-based recommender systems with 40% and 32%, respectively, were the mostly recommender types used in NRS. In NRS, rule-based and ontology techniques were used frequently. The frequented platform that applied in NRS was a mobile application with 28%.
Conclusion:
If NRS was properly designed, implemented and finally evaluated, it could be used as an effective tool to improve nutrition and promote a healthy lifestyle. This study can help to inform specialists in the nutrition informatics domain, which was necessary to design and develop NRS.
Introduction
Nutrition is the essential basis for health and development of human life from the earliest stage of fetal development into old age. Healthy food and proper nutrition are indubitable requirements for survival, physical development, mental growth, performance and productivity, health, and well-being [ 1 - 5 ].
The combination of information technology (IT), information sciences and nutrition has generated the concept of nutrition informatics. This branch of health informatics has become a novel approach for registered dietitians and dietetic technicians to practice in this field and make a profit for health care. The first scientific use of term ‘nutrition informatics’ referred to 1996; however, the dietitians have been applying the nutrition information and assuming technology for decades. The first paper exhibiting the potential usage of the computer to analyze dietary intake was published in 1962. Health care has included IT to establish patient care by the use of electronic health records, gather population data through the collection of personal health records and support clinical research via the reuse of data. Moreover, dietitians have involved particular electronic tools developed for managing patient tray service, indexing, and evaluation of nutrients [ 6 - 17 ].
Though most people have been informed about the significance of healthy eating habits, they incline to neglect suitable behaviors because of urban lifestyle and/or unwillingness to spend cognitive attempt on food provision. Those barriers prevent people from healthy food intake [ 18 - 20 ]. Nutrition recommendation systems (NRS) are one of the main technologies applyed in nutrition informatics scope. They are explored as an effective tool in order to support users for changing their eating behavior and reaching the goal of healthier food choices. NRS not only recommends users’ preferences for food, but also recommends healthy diet choices; in addition, it can advise appropriate diet and keep pursue of eating behavior, realize health problems, and lead into alteration user behavior [ 21 - 24 ].
Generally, recommendation systems have developed an efficient and effective technology to extract valuable information and then use it in an effectual way. A recommender system can predict the preferences of users for unrated items and recommend new items to users. The capability of these systems depends on technical requirements and proper design based on system types and functionality. Several various techniques have been proposed for developing personalized recommendations; these varieties in applied techniques and design may create various types of recommendation systems, including collaborative filtering recommender systems (CF), content-based recommender systems (CB), knowledge-based recommender systems (KBS) and hybrid recommender systems (HRS) [ 21 , 24 - 28 ].
There has been no detailed investigation to review NRS and their characteristics. The purpose of this study is to review recommendation systems in the nutrition field with focus on characteristics, types and evaluation methods. Moreover, this study uses a systematic review approach based on PRISMA guideline.
This systematic review responds to the following research questions:
- RQ1: Which types of recommendation systems are mostly used in the field of nutrition?
- RQ2: Which Artificial Intelligence (AI) techniques or intelligent methods are applied in NRS?
- RQ3: What are the main modules of these NRS?
- RQ4: Which platforms design NRS?
Material and Methods
This systematic review was conducted based on the preferred reporting items for systematic reviews and meta-analysis (PRISMA). Figure 1 displays the process of PRISMA for data collection and analysis.
Process of PRISMA for data collection and analysis
Search strategy
The papers from PubMed, Scopus, Embase, Web of Science, IEEE (Institute of Electrical and Electronics Engineers) and Google Scholar databases were searched without time limitation (up 10 January 2019).
The PICO (population, intervention, comparison, and outcome) criteria were used to define the search string: population (P), intervention (I), comparison (C), and outcome (O). The population was defined based on whom systems were designed for. The intervention was the usage of recommendation systems in the nutrition field. The comparison was excluded and the outcomes were the characteristics and types of developed NRS as well as their methods of evaluation.
We identified keywords from our initial research and matched them with those existing on the database of Medical Subject Headings (MeSH), which is managed by the US National Library of Medicine. In addition, we used an expert working in the medical branch libraries to improve the quality of searches. The search strings based on MeSH and title, abstract and keywords were as follows: (nutrition* OR diet OR food* OR meal) and (recommend* system*).
Inclusion and exclusion criteria
The inclusion criteria were original articles and proceedings. Moreover, just articles which focus on NRS and have health-promotion goals, and papers up 10 January 2019 were included. Exclusion criteria were Non-English papers, any types of publication other than journal articles and proceedings (review papers, letters, etc.), and non-human study papers. Furthermore, papers focusing on systems other than recommendation systems such as expert, clinical decision support systems and those only working with business and marketing purposes were excluded.
Selection process
In the primary screening, two individual reviewers screened titles and abstracts of the articles based on the research and PICO questions and the papers were classified into three sets completely. The first set (number 1) contained the papers with certainly inclusion criteria. The next set (number 2) contained the papers that their inclusion criteria were not clear for reviewers. Lastly, set (number 0) referred to the papers with no inclusion criteria, which were ineligible and excluded from the screening. The reviewers assessed the papers with different assigned numbers and discussed them. All the papers with similar numbers (one or two) were involved for the next stage. Following, the full text of the involved papers was achieved for the second-stage of screening and then evaluated by two reviewers. Finally, we were certain about 25 papers, then summarized and reported them in tables and charts according to the aims of the study.
Data extraction
In this phase, 6 variables were extracted in order to response to the research questions. These variables contained the name of the system, type of recommendation system, artificial intelligence (AI) or intelligent techniques applied in recommendation system, system modules and type of system’s platform.
Table 1 shows data extracted from the 25 articles reviewed [ 23 , 29 - 52 ]. The sixth variables of the selected papers are presented in Table 1 .
A brief Summary of Evaluated Technical Result for each Unit
| Number | Title , authors name, year and country | Name of the system | Type of recommendation system | Type of AI techniques applied in recommendation system | System modules | System Platform |
|---|---|---|---|---|---|---|
| 1 | A Mobile Application for Managing Diabetic Patients’ Nutrition: A Food Recommender System Norouzi et al. [ ] 2018 Iran | Iranian Snack Recommender System | Knowledge-based recommender systems (KBS) | Rule base | Patient's profile, recording physical activity, recording users favorites, periodic reports, setting for reminder, recording lab results, requesting for snack | Mobile Application |
| 2 | PERSON-Personalized Expert Recommendation System for Optimized Nutrition Chen et al. [ ] 2018 UK | PERSON | Hybrid recommender systems (HRS) | Deep learning neural network, Genetic Algorithm (GA) | Word Embedding & Padding model , a DNN model for product categorization, decision recommendation model, an operational state machine | - |
| 3 | A Chronic Disease Diet Recommendation System Based on Domain Ontology and Decision Tree Chen et al. [ ] 2017 Taiwan | - | Knowledge-based recommender systems (KBS) | Rule base, Decision tree, Domain Ontology | - | Web base application |
| 4 | Diet-Right: A Smart Food Recommendation System Rehman et al. [ ] 2017 Pakistan | Diet-Right | Knowledge-based recommender systems (KBS) | Rule baseAnt Colony Optimization (ACO) | - | - |
| 5 | Yum-Me: A personalized nutrient-based meal recommender system Yang et al. [ ] 2017 USA | Yum-Me | Hybrid recommender systems (HRS) | Food Preference Elicitation Algorithm. User State Update Algorithm k-means++ Algorithm for Exploration Images Selection Algorithm | - | Web browser, Mobile application, smart watch |
| 6 | PREFer: A prescription-based food recommender system Bianchini et al. [ ] 2017 Italy | PREFer | Hybrid recommender systems (HRS) | Ontology | Recipes, menus and prescriptions, users’ profiles | Web application |
| 7 | DFRS: Diet Food Recommendation System for Diabetic Patients based on Ontology Kumar and Latha [ ] 2015 India | DFRS | Hybrid recommender systems (HRS) | Ontology, K-Means clustering algorithms, rule-base, SelfOrganizing Map (SOM) | Data Processing Module, Diet Planning Module, Food Ontology Construction Module, Food Recommendation Module | - |
| 8 | Nutrition for Elder Care: a nutritional semantic recommender system for the elderly Espin et al. [ ] 2015 Spain | NutElCare | Hybrid recommender systems (HRS) | Rule base, Domain Ontology | Knowledge base and items representation, User profiling and learning techniques of user interests, Obtaining and providing recommendations about items in the knowledge base through semantic similarity measures | - |
| 9 | u-BabSang: a context-aware food recommendation system Oh et al. [ ] 2010 South Korea | U-BabSang | Content-based recommender systems (CBR) | Multi agent system | - | Windows base software |
| 10 | A Disease-driven Nutrition Recommender System based on a Multi-agent Architecture Ivaşcu et al. [ ] 2018 Romania | - | Hybrid recommender systems (HRS) | Multi-agent system Rule base Ontology | Hospital Agent, User InfoKDA, GUIAgent, HealthKDA, FoodKDA, UserProfileAgent, RecommenderAgent | mobile application |
| 11 | Nutrilize a Personalized Nutrition Recommender System: an enable study Leipold et al. [ , ] 2018 Germany | Nutrilizesystem | Knowledge-based recommender systems (KBS) | - | An accurate nutritional food database, a user nutrition profile, a recipe database, and a knowledge-based utility function for each nutrient. | mobile application |
| 12 | A hybrid framework for a comprehensive physical activity and diet recommendation system Ali et al. [ ] 2018 South Korea | - | Hybrid recommender systems (HRS) | Rule base | Modules are divided into two categories i.e. main module and supporting modules. Main module represents the main working engine of the framework while the supporting modules provide services to the main module. These modules are: Data Acquisition and Processing, Context Generation,Expert Knowledge Repository,Presentation | - |
| 13 | A personalized diet and exercise recommender system in minimizing clinical risk for type 1 diabetes: An in silico study Xie and Wang [ ] 2017 USA | - | Knowledge-based recommender systems (KBS) | A Nonlinear Autoregressive Moving Average with Exogenous Inputs (NARMAX) Model | - | - |
| 14 | The Research and Design of Recommendation System for Nutritional Combo Li and Yang [ ] 2016 China | - | Hybrid recommender systems (HRS) | - | This system has 2 main modules. Web Module(Log in module, Registering module, Saving module, Singular screening module, Recommendation module) and Back-end recommendation module | - |
| 15 | Online Recommender System for Personalized Nutrition Advice Franco [ ] 2017 UK | Nutri | Knowledge-based recommender systems (KBS) | Rule base | - | mobile application |
| 16 | A healthy food recommendation system by combining clustering technology with the Weighted slope one Predictor Bundasak [ ] 2017 Thailand | - | Hybrid recommender systems (HRS) | Self-Organizing Map (SOM), K-mean clustering analysis | - | - |
| 17 | DIETOS: a recommender system for health profiling and diet management in chronic diseases Agapito et al. [ ] 2017 Italy | DIETOS | Content-based recommender systems (CB) | - | DIETOS User Profiler, DIETOS Reminder, DIETOS History, CKD Calculator, DIETOS Foods Filter and DIETOS Security | Web application |
| 18 | Health-aware Food Recommender System Mouzhi Ge et al. [ ] 2015 Italy | - | Collaborative filtering recommender systems (CFR) | - | - | mobile application |
| 19 | Mobile Nutrition Recommendation System for 0-2 Year Infant Anggraini et al. [ ] 2014 Indonesia | Nutrisi | Knowledge-based recommender systems (KBS) | Rule base forward and backward chaining method | - | mobile application |
| 20 | Profiling basic health information of touriststowards a recommendation system for the adaptive delivery of medical certified nutrition contents Giuseppe et al. [ ] 2014 Italy | - | Content-based recommender systems (CB) | - | Users Data Manager module, Users Profile Creation module, Recommendation System module, Adaptive Food Selection module, Security Layer module | Web application |
| 21 | The runner - Recommender system of workout and nutrition for runners Donciu et al. [ ] 2011 Romania | The Runner | Hybrid recommender systems (HRS) | semantic web and ontologies | web application | |
| 22 | A recipe recommendation system based on automatic nutrition information extraction UETA et al. [ ] 2011 Japan | - | Collaborative filtering recommender systems (CF) | NLP | - | mobile application |
| 23 | Application of Data Mining Techniques in a Personalized Diet Recommendation System for Cancer Patients Husain et al. [ ] 2011 Malaysia | - | Knowledge-based recommender systems (KBS) | Data mining techniques of Case-based Reasoning, Rule-based Reasoning and Genetic Algorithm | User management module, diet planning module, menu construction module and menu adaptation module. | - |
| 24 | Food recommendation system using clustering analysis for diabetic patients Phanich et al. [ ] 2010 Thailand | Collaborative filtering recommender systems (CF) | Self-Organizing Map (SOM) K-mean clustering | - | - | |
| 25 | Design of Diet Recommendation System for Healthcare Service Based on User Information Kim et al. [ ] 2009 South Korea | Content-based recommender systems (CB) | Multi agent system | Nutrient Extraction Module, Preference Configuration Module | - | |
Type of recommendation system
As presented in the fourth column of Table 1 , the types of recommendation system were studied. The frequency (%) of recommendation system types is presented in Figure 2 .
The frequency (%) of recommendation system types
AI or intelligent techniques apply in recommendation systems
As shown in the fifth column of Table 1 , various AI or intelligent techniques have been applied in recommendation systems. The types of these techniques with their frequency in the researches are stated in Figure 3 .
Frequncy of AI or intelligent techniques applied in recommendation systems
The seventh column of Table 1 illustrates the type of system’s platform. Figure 4 demonstrates the frequency of the type of system’s platform. Based on the chart, the frequented platform applied in NRS was a mobile application with 28%.
Frequncy of type of system’s platform
Despite the various application of recommendation systems in many domains, there is not much evidence for using recommendation systems in health informatics or medicine [ 53 ]. According to the Valdez framework [ 53 ], if we want to design or assess the health recommender system, it will be necessary to consider 3 main aspects. First of all, we should understand the health domain. In this aspect, answering these two questions is very important: 1) which one of following items is recommended (nutrition, medicine, sport)? In addition, 2) who is it recommended to (the patient, the doctor, the nurse, policy maker)?
Another aspect that should be considered is the understanding of technical specifications and data analysis methods used in health recommendation system. The third aspect that needs to be addressed in the analysis of health recommendation system is the understanding of the methods and techniques of evaluating recommender systems. Besides, this paper for the first time reviewed the technical specifications of NRS.
The items in the results have been discussed in the following:
Type of recommendation systems
Hybrid recommender systems (HRS) are the mostly used recommender type in nutrition recommendation systems ( Figure 2 ). As these systems use a combination of other systems type, they could compensate disadvantages of one type of recommendation system by another type. Moreover, hybrid types are applied in order to increase the recommender’s performance [ 21 , 25 ].
AI or intelligent techniques applied in recommendation systems
In NRS, rule-based techniques are frequently used ( Figure 3 ). The reason behind that might be the simplicity, transparency, and consistency of rule-based techniques with nutrition knowledge. Ontology techniques also have been used for the same reasons to show the knowledge and concepts of relations in a clear manner.
Cold start, which is one of the classic problems in recommendation systems, happens when a new user uses the system with no preferences [ 54 ]. Although this situation seems to not occur frequently in NRS, recommendation of collaborative filtering, which is content-based, is made by [ 41 ] to overcome this issue.
Type of system’s platform and their structure
Owing to the penetration of smartphones, their processing, connectivity capabilities and their accessibility [ 55 , 56 ], mobile applications had a growth in the medical field [ 56 ]. As it has been shown that mobile apps have potential effects on health behavior change [ 57 ] and chronic disease management [ 58 ], they have been used in the most of reviewed systems for NRS as shown in Figure 4 .
In addition, web technology, due to its cross-platform specificity leading into the use of multiple clients with different hardware and operating system, is popular for these recommendation systems.
This is the first systematically review of the recommendation systems in the nutrition field with focus on characteristics and types. The study offers some important insights into the condition of recommendation systems in the nutrition field with focus on technical aspects. This study can help to inform specialists in the nutrition informatics domain, which was necessary to design and develop NRS.
However, there are many mobile applications in the nutrition field useing “recommendation system” technology which have not been mentioned in the design methodology [ 57 ]. This study concentrates only on research, which explicitly discusses the use of the recommendation system.
In this paper, we systematically reviewed the recommender systems in nutrition scope with a focus on characteristics and types. The results of this investigation show that hybrid recommender systems (HRS) are mostly used recommender type in NRS. Many AI or intelligent techniques applied in recommendation systems, which are one of the most frequented techniques, are systems based on rules. Furthermore, the frequented platform applied in NRS was a mobile application. If NRS is properly designed, implemented, and finally evaluated, it can be used as an effective tool to improve nutrition and promote a healthy lifestyle.
Acknowledgement
The authors would like to gratefully acknowledge the contribution department of health information management and department of community nutrition in Tehran University of Medical Sciences. This paper was developed as part of a Ph.D. thesis that is funded and supported jointly by Tehran University of Medical Sciences and Shiraz University of Medical Sciences.
Conflict of Interest: The authors declare that there is no conflict of interest regarding the publication of this manuscript.
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