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Hutchinson JM, Raffoul A, Pepetone A, Andrade L, Williams TE, McNaughton SA, Leech RM, Reedy J, Shams-White MM, Vena JE, Dodd KW, Bodnar LM, Lamarche B, Wallace MP, Deitchler M, Hussain S, Kirkpatrick SI. Advances in methods for characterizing dietary patterns: A scoping review. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.20.24309251. [PMID: 38947003 PMCID: PMC11213084 DOI: 10.1101/2024.06.20.24309251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
There is a growing focus on better understanding the complexity of dietary patterns and how they relate to health and other factors. Approaches that have not traditionally been applied to characterize dietary patterns, such as machine learning algorithms and latent class analysis methods, may offer opportunities to measure and characterize dietary patterns in greater depth than previously considered. However, there has not been a formal examination of how this wide range of approaches has been applied to characterize dietary patterns. This scoping review synthesized literature from 2005-2022 applying methods not traditionally used to characterize dietary patterns, referred to as novel methods. MEDLINE, CINAHL, and Scopus were searched using keywords including machine learning, latent class analysis, and least absolute shrinkage and selection operator (LASSO). Of 5274 records identified, 24 met the inclusion criteria. Twelve of 24 articles were published since 2020. Studies were conducted across 17 countries. Nine studies used approaches that have applications in machine learning to identify dietary patterns. Fourteen studies assessed associations between dietary patterns that were characterized using novel methods and health outcomes, including cancer, cardiovascular disease, and asthma. There was wide variation in the methods applied to characterize dietary patterns and in how these methods were described. The extension of reporting guidelines and quality appraisal tools relevant to nutrition research to consider specific features of novel methods may facilitate complete and consistent reporting and enable evidence synthesis to inform policies and programs aimed at supporting healthy dietary patterns.
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Affiliation(s)
- Joy M Hutchinson
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Amanda Raffoul
- Department of Nutritional Sciences, University of Toronto, Toronto, ON, Canada
| | - Alexandra Pepetone
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Lesley Andrade
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Tabitha E Williams
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Sarah A McNaughton
- Health and Well-Being Centre for Research Innovation, School of Human Movement and Nutrition Sciences, University of Queensland, St. Lucia, QLD, Australia
| | - Rebecca M Leech
- Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Victoria, Geelong, Australia
| | - Jill Reedy
- National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Marissa M Shams-White
- Population Science Department, American Cancer Society, Washington DC, USA
- Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA
| | - Jennifer E Vena
- Alberta's Tomorrow Project, Alberta Health Services, Edmonton, AB, Canada
| | - Kevin W Dodd
- Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, USA
| | - Lisa M Bodnar
- School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Benoît Lamarche
- Centre Nutrition, santé et société (NUTRISS), Institut sur la nutrition et les aliments fonctionnels (INAF), Université Laval, Québec City, QC, Canada
| | - Michael P Wallace
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada
| | - Megan Deitchler
- Intake - Center for Dietary Assessment, FHI Solutions, Washington, DC, USA
| | - Sanaa Hussain
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
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Sosa-Holwerda A, Park OH, Albracht-Schulte K, Niraula S, Thompson L, Oldewage-Theron W. The Role of Artificial Intelligence in Nutrition Research: A Scoping Review. Nutrients 2024; 16:2066. [PMID: 38999814 PMCID: PMC11243505 DOI: 10.3390/nu16132066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 06/20/2024] [Accepted: 06/24/2024] [Indexed: 07/14/2024] Open
Abstract
Artificial intelligence (AI) refers to computer systems doing tasks that usually need human intelligence. AI is constantly changing and is revolutionizing the healthcare field, including nutrition. This review's purpose is four-fold: (i) to investigate AI's role in nutrition research; (ii) to identify areas in nutrition using AI; (iii) to understand AI's future potential impact; (iv) to investigate possible concerns about AI's use in nutrition research. Eight databases were searched: PubMed, Web of Science, EBSCO, Agricola, Scopus, IEEE Explore, Google Scholar and Cochrane. A total of 1737 articles were retrieved, of which 22 were included in the review. Article screening phases included duplicates elimination, title-abstract selection, full-text review, and quality assessment. The key findings indicated AI's role in nutrition is at a developmental stage, focusing mainly on dietary assessment and less on malnutrition prediction, lifestyle interventions, and diet-related diseases comprehension. Clinical research is needed to determine AI's intervention efficacy. The ethics of AI use, a main concern, remains unresolved and needs to be considered for collateral damage prevention to certain populations. The studies' heterogeneity in this review limited the focus on specific nutritional areas. Future research should prioritize specialized reviews in nutrition and dieting for a deeper understanding of AI's potential in human nutrition.
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Affiliation(s)
- Andrea Sosa-Holwerda
- Department of Nutritional Sciences, Texas Tech University, Lubbock, TX 79409, USA
| | - Oak-Hee Park
- College of Health & Human Sciences, Texas Tech University, Lubbock, TX 79409, USA
| | | | - Surya Niraula
- Department of Nutritional Sciences, Texas Tech University, Lubbock, TX 79409, USA
| | - Leslie Thompson
- Department of Animal and Food Sciences, Texas Tech University, Lubbock, TX 79409, USA
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An R, Perez-Cruet J, Wang J. We got nuts! use deep neural networks to classify images of common edible nuts. Nutr Health 2024; 30:301-307. [PMID: 35861193 DOI: 10.1177/02601060221113928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Nuts are nutrient-dense foods that contribute to healthier eating. Food image datasets enable artificial intelligence (AI) powered diet-tracking apps to help people monitor daily eating patterns. AIM This study aimed to create an image dataset of commonly consumed nut types and use it to build an AI computer vision model to automate nut type classification tasks. METHODS iPhone 11 was used to take photos of 11 nut types-almond, brazil nut, cashew, chestnut, hazelnut, macadamia, peanut, pecan, pine nut, pistachio, and walnut. The dataset contains 2200 images, 200 per nut type. The dataset was randomly split into the training (60% or 1320 images), validation (20% or 440 images), and test sets (20% or 440 images). A neural network model was constructed and trained using transfer learning and other computer vision techniques-data augmentation, mixup, normalization, label smoothing, and learning rate optimization. RESULTS The trained neural network model correctly predicted 338 out of 440 images (40 per nut type) in the validation set, achieving 99.55% accuracy. Moreover, the model classified the 440 images in the test set with 100% accuracy. CONCLUSION This study built a nut image dataset and used it to train a neural network model to classify images by nut type. The model achieved near-perfect accuracy on the validation and test sets, demonstrating the feasibility of automating nut type classification using smartphone photos. Being made open-source, the dataset and model can assist the development of diet-tracking apps that facilitate users' adoption and adherence to a healthy diet.
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Affiliation(s)
- Ruopeng An
- Brown School, Washington University, St Louis, MO, USA
| | | | - Junjie Wang
- Department of Kinesiology, Dalian University of Technology, Dalian, China
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Choi I, Kim J, Kim WC. An Explainable Prediction for Dietary-Related Diseases via Language Models. Nutrients 2024; 16:686. [PMID: 38474813 DOI: 10.3390/nu16050686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024] Open
Abstract
Our study harnesses the power of natural language processing (NLP) to explore the relationship between dietary patterns and metabolic health outcomes among Korean adults using data from the Seventh Korea National Health and Nutrition Examination Survey (KNHANES VII). Using Latent Dirichlet Allocation (LDA) analysis, we identified three distinct dietary patterns: "Traditional and Staple", "Communal and Festive", and "Westernized and Convenience-Oriented". These patterns reflect the diversity of dietary preferences in Korea and reveal the cultural and social dimensions influencing eating habits and their potential implications for public health, particularly concerning obesity and metabolic disorders. Integrating NLP-based indices, including sentiment scores and the identified dietary patterns, into our predictive models significantly enhanced the accuracy of obesity and dyslipidemia predictions. This improvement was consistent across various machine learning techniques-XGBoost, LightGBM, and CatBoost-demonstrating the efficacy of NLP methodologies in refining disease prediction models. Our findings underscore the critical role of dietary patterns as indicators of metabolic diseases. The successful application of NLP techniques offers a novel approach to public health and nutritional epidemiology, providing a deeper understanding of the diet-disease nexus. This study contributes to the evolving field of personalized nutrition and emphasizes the potential of leveraging advanced computational tools to inform targeted nutritional interventions and public health strategies aimed at mitigating the prevalence of metabolic disorders in the Korean population.
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Affiliation(s)
- Insu Choi
- Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| | - Jihye Kim
- Department of Genetics and Biotechnology, College of Life Sciences, Kyung Hee University, Yongin 17104, Republic of Korea
| | - Woo Chang Kim
- Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
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Shonkoff E, Cara KC, Pei X(A, Chung M, Kamath S, Panetta K, Hennessy E. AI-based digital image dietary assessment methods compared to humans and ground truth: a systematic review. Ann Med 2023; 55:2273497. [PMID: 38060823 PMCID: PMC10836267 DOI: 10.1080/07853890.2023.2273497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 10/16/2023] [Indexed: 12/18/2023] Open
Abstract
OBJECTIVE Human error estimating food intake is a major source of bias in nutrition research. Artificial intelligence (AI) methods may reduce bias, but the overall accuracy of AI estimates is unknown. This study was a systematic review of peer-reviewed journal articles comparing fully automated AI-based (e.g. deep learning) methods of dietary assessment from digital images to human assessors and ground truth (e.g. doubly labelled water). MATERIALS AND METHODS Literature was searched through May 2023 in four electronic databases plus reference mining. Eligible articles reported AI estimated volume, energy, or nutrients. Independent investigators screened articles and extracted data. Potential sources of bias were documented in absence of an applicable risk of bias assessment tool. RESULTS Database and hand searches identified 14,059 unique publications; fifty-two papers (studies) published from 2010 to 2023 were retained. For food detection and classification, 79% of papers used a convolutional neural network. Common ground truth sources were calculation using nutrient tables (51%) and weighed food (27%). Included papers varied widely in food image databases and results reported, so meta-analytic synthesis could not be conducted. Relative errors were extracted or calculated from 69% of papers. Average overall relative errors (AI vs. ground truth) ranged from 0.10% to 38.3% for calories and 0.09% to 33% for volume, suggesting similar performance. Ranges of relative error were lower when images had single/simple foods. CONCLUSIONS Relative errors for volume and calorie estimations suggest that AI methods align with - and have the potential to exceed - accuracy of human estimations. However, variability in food image databases and results reported prevented meta-analytic synthesis. The field can advance by testing AI architectures on a limited number of large-scale food image and nutrition databases that the field determines to be adequate for training and testing and by reporting accuracy of at least absolute and relative error for volume or calorie estimations.
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Affiliation(s)
- Eleanor Shonkoff
- School of Health Sciences, Merrimack College, North Andover, MA, USA
| | - Kelly Copeland Cara
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| | - Xuechen (Anna) Pei
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| | - Mei Chung
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| | - Shreyas Kamath
- School of Engineering, Tufts University, Medford, MA, USA
| | - Karen Panetta
- School of Engineering, Tufts University, Medford, MA, USA
| | - Erin Hennessy
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
- ChildObesity180, Tufts University, Boston, MA, USA
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Namkhah Z, Fatemi SF, Mansoori A, Nosratabadi S, Ghayour-Mobarhan M, Sobhani SR. Advancing sustainability in the food and nutrition system: a review of artificial intelligence applications. Front Nutr 2023; 10:1295241. [PMID: 38035357 PMCID: PMC10687214 DOI: 10.3389/fnut.2023.1295241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 11/02/2023] [Indexed: 12/02/2023] Open
Abstract
Promoting sustainability in food and nutrition systems is essential to address the various challenges and trade-offs within the current food system. This imperative is guided by key principles and actionable steps, including enhancing productivity and efficiency, reducing waste, adopting sustainable agricultural practices, improving economic growth and livelihoods, and enhancing resilience at various levels. However, in order to change the current food consumption patterns of the world and move toward sustainable diets, as well as increase productivity in the food production chain, it is necessary to employ the findings and achievements of other sciences. These include the use of artificial intelligence-based technologies. Presented here is a narrative review of possible applications of artificial intelligence in the food production chain that could increase productivity and sustainability. In this study, the most significant roles that artificial intelligence can play in enhancing the productivity and sustainability of the food and nutrition system have been examined in terms of production, processing, distribution, and food consumption. The research revealed that artificial intelligence, a branch of computer science that uses intelligent machines to perform tasks that require human intelligence, can significantly contribute to sustainable food security. Patterns of production, transportation, supply chain, marketing, and food-related applications can all benefit from artificial intelligence. As this review of successful experiences indicates, artificial intelligence, machine learning, and big data are a boon to the goal of sustainable food security as they enable us to achieve our goals more efficiently.
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Affiliation(s)
- Zahra Namkhah
- Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Seyedeh Fatemeh Fatemi
- Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Amin Mansoori
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
- International UNESCO Center for Health Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Saeid Nosratabadi
- Department of Nutrition, Electronic Health and Statistics Surveillance Research Center, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Majid Ghayour-Mobarhan
- International UNESCO Center for Health Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Seyyed Reza Sobhani
- Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
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Townsend JR, Kirby TO, Sapp PA, Gonzalez AM, Marshall TM, Esposito R. Nutrient synergy: definition, evidence, and future directions. Front Nutr 2023; 10:1279925. [PMID: 37899823 PMCID: PMC10600480 DOI: 10.3389/fnut.2023.1279925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 09/28/2023] [Indexed: 10/31/2023] Open
Abstract
Nutrient synergy refers to the concept that the combined effects of two or more nutrients working together have a greater physiological impact on the body than when each nutrient is consumed individually. While nutrition science traditionally focuses on isolating single nutrients to study their effects, it is recognized that nutrients interact in complex ways, and their combined consumption can lead to additive effects. Additionally, the Dietary Reference Intakes (DRIs) provide guidelines to prevent nutrient deficiencies and excessive intake but are not designed to assess the potential synergistic effects of consuming nutrients together. Even the term synergy is often applied in different manners depending on the scientific discipline. Considering these issues, the aim of this narrative review is to investigate the potential health benefits of consuming different nutrients and nutrient supplements in combination, a concept we define as nutrient synergy, which has gained considerable attention for its impact on overall well-being. We will examine how nutrient synergy affects major bodily systems, influencing systemic health. Additionally, we will address the challenges associated with promoting and conducting research on this topic, while proposing potential solutions to enhance the quality and quantity of scientific literature on nutrient synergy.
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Affiliation(s)
- Jeremy R. Townsend
- Research, Nutrition, and Innovation, Athletic Greens International, Carson City, NV, United States
- Health & Human Performance, Concordia University Chicago, River Forest, IL, United States
| | - Trevor O. Kirby
- Research, Nutrition, and Innovation, Athletic Greens International, Carson City, NV, United States
| | - Philip A. Sapp
- Research, Nutrition, and Innovation, Athletic Greens International, Carson City, NV, United States
| | - Adam M. Gonzalez
- Department of Allied Health and Kinesiology, Hofstra University, Hempstead, NY, United States
| | - Tess M. Marshall
- Research, Nutrition, and Innovation, Athletic Greens International, Carson City, NV, United States
| | - Ralph Esposito
- Research, Nutrition, and Innovation, Athletic Greens International, Carson City, NV, United States
- Department of Nutrition, Food Studies, and Public Health, New York University-Steinhardt, New York, NY, United States
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Shankar K, Chandrasekaran R, Jeripity Venkata P, Miketinas D. Investigating the Role of Nutrition in Enhancing Immunity During the COVID-19 Pandemic: Twitter Text-Mining Analysis. J Med Internet Res 2023; 25:e47328. [PMID: 37428522 PMCID: PMC10366666 DOI: 10.2196/47328] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/09/2023] [Accepted: 05/09/2023] [Indexed: 07/11/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic has brought to the spotlight the critical role played by a balanced and healthy diet in bolstering the human immune system. There is burgeoning interest in nutrition-related information on social media platforms like Twitter. There is a critical need to assess and understand public opinion, attitudes, and sentiments toward nutrition-related information shared on Twitter. OBJECTIVE This study uses text mining to analyze nutrition-related messages on Twitter to identify and analyze how the general public perceives various food groups and diets for improving immunity to the SARS-CoV-2 virus. METHODS We gathered 71,178 nutrition-related tweets that were posted between January 01, 2020, and September 30, 2020. The Correlated Explanation text mining algorithm was used to identify frequently discussed topics that users mentioned as contributing to immunity building against SARS-CoV-2. We assessed the relative importance of these topics and performed a sentiment analysis. We also qualitatively examined the tweets to gain a closer understanding of nutrition-related topics and food groups. RESULTS Text-mining yielded 10 topics that users discussed frequently on Twitter, viz proteins, whole grains, fruits, vegetables, dairy-related, spices and herbs, fluids, supplements, avoidable foods, and specialty diets. Supplements were the most frequently discussed topic (23,913/71,178, 33.6%) with a higher proportion (20,935/23,913, 87.75%) exhibiting a positive sentiment with a score of 0.41. Consuming fluids (17,685/71,178, 24.85%) and fruits (14,807/71,178, 20.80%) were the second and third most frequent topics with favorable, positive sentiments. Spices and herbs (8719/71,178, 12.25%) and avoidable foods (8619/71,178, 12.11%) were also frequently discussed. Negative sentiments were observed for a higher proportion of avoidable foods (7627/8619, 84.31%) with a sentiment score of -0.39. CONCLUSIONS This study identified 10 important food groups and associated sentiments that users discussed as a means to improve immunity. Our findings can help dieticians and nutritionists to frame appropriate interventions and diet programs.
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Affiliation(s)
- Kavitha Shankar
- Department of Nutrition and Food Sciences, Texas Woman's University Institute for Health Sciences, Houston, TX, United States
| | - Ranganathan Chandrasekaran
- Department of Information and Decision Sciences, University of Illinois at Chicago, Chicago, IL, United States
| | | | - Derek Miketinas
- Department of Nutrition and Food Sciences, Texas Woman's University Institute for Health Sciences, Houston, TX, United States
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Han R, Todd A, Wardak S, Partridge SR, Raeside R. Feasibility and Acceptability of Chatbots for Nutrition and Physical Activity Health Promotion Among Adolescents: Systematic Scoping Review With Adolescent Consultation. JMIR Hum Factors 2023; 10:e43227. [PMID: 37145858 PMCID: PMC10199392 DOI: 10.2196/43227] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 03/15/2023] [Accepted: 04/13/2023] [Indexed: 05/06/2023] Open
Abstract
BACKGROUND Reducing lifestyle risk behaviors among adolescents depends on access to age-appropriate health promotion information. Chatbots-computer programs designed to simulate conversations with human users-have the potential to deliver health information to adolescents to improve their lifestyle behaviors and support behavior change, but research on the feasibility and acceptability of chatbots in the adolescent population is unknown. OBJECTIVE This systematic scoping review aims to evaluate the feasibility and acceptability of chatbots in nutrition and physical activity interventions among adolescents. A secondary aim is to consult adolescents to identify features of chatbots that are acceptable and feasible. METHODS We searched 6 electronic databases from March to April 2022 (MEDLINE, Embase, Joanna Briggs Institute, the Cumulative Index to Nursing and Allied Health, the Association for Computing Machinery library, and the IT database Institute of Electrical and Electronics Engineers). Peer-reviewed studies were included that were conducted in the adolescent population (10-19 years old) without any chronic disease, except obesity or type 2 diabetes, and assessed chatbots used nutrition or physical activity interventions or both that encouraged individuals to meet dietary or physical activity guidelines and support positive behavior change. Studies were screened by 2 independent reviewers, with any queries resolved by a third reviewer. Data were extracted into tables and collated in a narrative summary. Gray literature searches were also undertaken. Results of the scoping review were presented to a diverse youth advisory group (N=16, 13-18 years old) to gain insights into this topic beyond what is published in the literature. RESULTS The search identified 5558 papers, with 5 (0.1%) studies describing 5 chatbots meeting the inclusion criteria. The 5 chatbots were supported by mobile apps using a combination of the following features: personalized feedback, conversational agents, gamification, and monitoring of behavior change. Of the 5 studies, 2 (40.0%) studies focused on nutrition, 2 (40.0%) studies focused on physical activity, and 1 (20.0%) focused on both nutrition and physical activity. Feasibility and acceptability varied across the 5 studies, with usage rates above 50% in 3 (60.0%) studies. In addition, 3 (60.0%) studies reported health-related outcomes, with only 1 (20.0%) study showing promising effects of the intervention. Adolescents presented novel concerns around the use of chatbots in nutrition and physical activity interventions, including ethical concerns and the use of false or misleading information. CONCLUSIONS Limited research is available on chatbots in adolescent nutrition and physical activity interventions, finding insufficient evidence on the acceptability and feasibility of chatbots in the adolescent population. Similarly, adolescent consultation identified issues in the design features that have not been mentioned in the published literature. Therefore, chatbot codesign with adolescents may help ensure that such technology is feasible and acceptable to an adolescent population.
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Affiliation(s)
- Rui Han
- Engagement and Co-Design Research Hub, School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Westmead, Australia
| | - Allyson Todd
- Engagement and Co-Design Research Hub, School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Westmead, Australia
| | - Sara Wardak
- Engagement and Co-Design Research Hub, School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Westmead, Australia
| | - Stephanie R Partridge
- Engagement and Co-Design Research Hub, School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Westmead, Australia
| | - Rebecca Raeside
- Engagement and Co-Design Research Hub, School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Westmead, Australia
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10
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Ferraz de Arruda H, Aleta A, Moreno Y. Food composition databases in the era of Big Data: Vegetable oils as a case study. Front Nutr 2023; 9:1052934. [PMID: 36687693 PMCID: PMC9851468 DOI: 10.3389/fnut.2022.1052934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 12/07/2022] [Indexed: 01/07/2023] Open
Abstract
Understanding the population's dietary patterns and their impacts on health requires many different sources of information. The development of reliable food composition databases is a key step in this pursuit. With them, nutrition and health care professionals can provide better public health advice and guide society toward achieving a better and healthier life. Unfortunately, these databases are full of caveats. Focusing on the specific case of vegetable oils, we analyzed the possible obsolescence of the information and the differences or inconsistencies among databases. We show that in many cases, the information is limited, incompletely documented, old or unreliable. More importantly, despite the many efforts carried out in the last decades, there is still much work to be done. As such, institutions should develop long-standing programs that can ensure the quality of the information on what we eat in the long term. In the face of climate change and complex societal challenges in an interconnected world, the full diversity of the food system needs to be recognized and more efforts should be put toward achieving a data-driven food system.
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Affiliation(s)
- Henrique Ferraz de Arruda
- ISI Foundation, Turin, Italy,CENTAI Institute, Turin, Italy,*Correspondence: Henrique Ferraz de Arruda ✉
| | - Alberto Aleta
- ISI Foundation, Turin, Italy,Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza, Spain,Department of Theoretical Physics, Faculty of Sciences, University of Zaragoza, Zaragoza, Spain
| | - Yamir Moreno
- ISI Foundation, Turin, Italy,CENTAI Institute, Turin, Italy,Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza, Spain,Department of Theoretical Physics, Faculty of Sciences, University of Zaragoza, Zaragoza, Spain
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11
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Cahoon DS, Shertukde SP, Nirmala N, Lau J, Lichtenstein AH. Perspective: Appraisal of the Evidence Base to Update DRI Values-Lessons from the Past, Thoughts for the Future. Adv Nutr 2022; 13:975-981. [PMID: 35404437 PMCID: PMC9340966 DOI: 10.1093/advances/nmac041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 02/10/2022] [Accepted: 04/08/2022] [Indexed: 11/12/2022] Open
Abstract
Updating evidence-based nutrient guidance is challenging. One set of recommendations for which a robust evidence base is essential is the DRIs. In the past 10 y, DRI values for 4 essential nutrients have been re-evaluated in 2 groups: vitamin D and calcium, and sodium and potassium. To support the work of the committees tasked with evaluating the available evidence, the federal agencies that sponsor the DRI reviews contracted with the Agency for Healthcare Research and Quality to perform systematic reviews on predefined questions for these nutrient groups. Our aims were to tabulate the studies included in these systematic reviews and then, within the context of prespecified outcomes, summarize the totality of the available evidence and identify areas for consideration to maximize the value of the end products for future DRI committees. For the outcomes of interest, the available studies did not tend to report age data consistent with the current DRI categories. For some life stage categories, particularly pregnancy and lactation, there is a dearth of data. A wide range of study interventions were used, making it challenging to combine data to accurately derive or re-evaluate DRI values. There is also an under-representation of data on race/ethnicity and overweight/obesity, which is of concern, given the shifting demographic in the US and Canadian populations. Moving forward, it may be advantageous to develop a process to prospectively target research funding for studies designed to generate data that will most closely support re-evaluation of DRI values.
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Affiliation(s)
- Danielle S Cahoon
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA,Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA
| | - Shruti P Shertukde
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| | - Nanguneri Nirmala
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA
| | - Joseph Lau
- Center for Evidence Synthesis in Health, Brown University, Providence, RI, USA
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