{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T15:55:02Z","timestamp":1792252502190,"version":"4.4.0"},"reference-count":70,"publisher":"Elsevier BV","issue":"9","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"funder":[{"DOI":"10.13039\/100011539","name":"University of Minnesota Masonic Cancer Center","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100011539","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["The Journal of Nutrition"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.tjnut.2026.101712","type":"journal-article","created":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T06:39:38Z","timestamp":1783147178000},"page":"101712","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"title":["Evaluation of Energy and Nutrient Estimates from Large Language Models Using Text-Based Queries"],"prefix":"10.1016","volume":"156","author":[{"given":"Razi","family":"Lawabni","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olivia R","family":"Solano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leo L","family":"Kampen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Kurasch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nabil","family":"Alshurafa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jung-Su","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Evans","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongji","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0045-8826","authenticated-orcid":false,"given":"Annie W","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.tjnut.2026.101712_bib2","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1007\/s12160-016-9830-8","article-title":"Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support","volume":"52","author":"Nahum-Shani","year":"2018","journal-title":"Ann. Behav. Med."},{"key":"10.1016\/j.tjnut.2026.101712_bib3","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1093\/advances\/nmz086","article-title":"Perspective: guiding principles for the implementation of personalized nutrition approaches that benefit health and function","volume":"11","author":"Adams","year":"2020","journal-title":"Adv. Nutr."},{"key":"10.1016\/j.tjnut.2026.101712_bib4","doi-asserted-by":"crossref","DOI":"10.1016\/j.cct.2025.107808","article-title":"Optimizing a mobile just-in-time adaptive intervention (JITAI) for weight loss in young adults: rationale and design of the AGILE factorial randomized trial","volume":"150","author":"Nezami","year":"2025","journal-title":"Contemp. Clin. Trials."},{"key":"10.1016\/j.tjnut.2026.101712_bib5","doi-asserted-by":"crossref","DOI":"10.1161\/JAHA.123.031234","article-title":"A physical activity and diet just-in-time adaptive intervention to reduce blood pressure: the myBPmyLife study rationale and design","volume":"13","author":"Golbus","year":"2024","journal-title":"J. Am. Heart Assoc."},{"key":"10.1016\/j.tjnut.2026.101712_bib6","doi-asserted-by":"crossref","DOI":"10.2196\/33568","article-title":"Optimizing a just-in-time adaptive intervention to improve dietary adherence in behavioral obesity treatment: protocol for a microrandomized trial","volume":"10","author":"Goldstein","year":"2021","journal-title":"JMIR Res. Protoc."},{"key":"10.1016\/j.tjnut.2026.101712_bib7","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1186\/s12966-021-01224-6","article-title":"A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss","volume":"18","author":"Oh","year":"2021","journal-title":"Int. J. Behav. Nutr. Phys. Act."},{"key":"10.1016\/j.tjnut.2026.101712_bib8","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1042\/BIO04105020","article-title":"Artificial intelligence decision-making in mobile health","volume":"41","author":"Menictas","year":"2019","journal-title":"Biochem (Lond)."},{"key":"10.1016\/j.tjnut.2026.101712_bib9","article-title":"The use of artificial intelligence\u2013based conversational agents (chatbots) for weight loss: scoping review and practical recommendations, JMIR Med","volume":"10","author":"Chew","year":"2022","journal-title":"Inform"},{"key":"10.1016\/j.tjnut.2026.101712_bib10","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.copbio.2021.02.007","article-title":"Overview of dietary assessment methods for measuring intakes of foods, beverages, and dietary supplements in research studies","volume":"70","author":"Bailey","year":"2021","journal-title":"Curr. Opin. Biotechnol."},{"key":"10.1016\/j.tjnut.2026.101712_bib11","series-title":"Dietary Assessment in Adults - UpToDate","author":"Olendzki","year":"2025"},{"key":"10.1016\/j.tjnut.2026.101712_bib12","series-title":"The Nutrition Care Process (NCP). Academy of Nutrition and Dietetics","year":"2026"},{"key":"10.1016\/j.tjnut.2026.101712_bib13","doi-asserted-by":"crossref","first-page":"1977","DOI":"10.3390\/nu12071977","article-title":"Effect of behavioral weight management interventions using lifestyle mHealth self-monitoring on weight loss: a systematic review and meta-analysis","volume":"12","author":"Cavero-Redondo","year":"2020","journal-title":"Nutrients"},{"key":"10.1016\/j.tjnut.2026.101712_bib14","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1093\/ajcn\/nqx048","article-title":"Systematic review and meta-analysis of remotely delivered interventions using self-monitoring or tailored feedback to change dietary behavior","volume":"107","author":"Teasdale","year":"2018","journal-title":"Am. J. Clin. Nutr."},{"key":"10.1016\/j.tjnut.2026.101712_bib15","doi-asserted-by":"crossref","first-page":"1268","DOI":"10.1016\/S0002-8223(21)07997-9","article-title":"Sources of data for developing and maintaining a nutrient database","volume":"88","author":"Schakel","year":"1988","journal-title":"J. Am. Diet. Assoc."},{"key":"10.1016\/j.tjnut.2026.101712_bib16","doi-asserted-by":"crossref","DOI":"10.2196\/54509","article-title":"Reliability issues of mobile nutrition apps for cardiovascular disease prevention: comparative study","volume":"12","author":"Ho","year":"2024","journal-title":"JMIR Mhealth Uhealth"},{"key":"10.1016\/j.tjnut.2026.101712_bib17","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1016\/j.jand.2021.10.015","article-title":"Comparative validity of mostly unprocessed and minimally processed food items differs among popular commercial nutrition apps compared with a research food database","volume":"122","author":"Lin","year":"2022","journal-title":"J. Acad. Nutr. Diet."},{"key":"10.1016\/j.tjnut.2026.101712_bib18","doi-asserted-by":"crossref","first-page":"1495","DOI":"10.1017\/S1368980018000393","article-title":"Assessment of the accuracy of nutrient calculations of five popular nutrition tracking applications","volume":"21","author":"Griffiths","year":"2018","journal-title":"Public Health Nutr"},{"key":"10.1016\/j.tjnut.2026.101712_bib19","first-page":"1170","article-title":"Analyzing description, user understanding and expectations of AI in mobile health applications","volume":"2020","author":"Su","year":"2021","journal-title":"AMIA Annu. Symp. Proc."},{"key":"10.1016\/j.tjnut.2026.101712_bib20","doi-asserted-by":"crossref","DOI":"10.2196\/44030","article-title":"Quality, usability, and effectiveness of mHealth apps and the role of artificial intelligence: current scenario and challenges","volume":"25","author":"Deniz-Garcia","year":"2023","journal-title":"J. Med. Internet Res."},{"key":"10.1016\/j.tjnut.2026.101712_bib21","doi-asserted-by":"crossref","first-page":"2579","DOI":"10.3390\/healthcare13202579","article-title":"Artificial intelligence in nutrition and dietetics: a comprehensive review of current research","volume":"13","author":"Panayotova","year":"2025","journal-title":"Healthcare (Basel)"},{"key":"10.1016\/j.tjnut.2026.101712_bib22","article-title":"Language models in nutrition and dietetics: a scoping review","volume":"123","author":"O\u2019Hara","year":"2025","journal-title":"Am. J. Clin. Nutr."},{"key":"10.1016\/j.tjnut.2026.101712_bib23","doi-asserted-by":"crossref","first-page":"1676","DOI":"10.3390\/healthcare9121676","article-title":"A comprehensive survey of image-based food recognition and volume estimation methods for dietary assessment","volume":"9","author":"Tahir","year":"2021","journal-title":"Healthcare (Basel)"},{"key":"10.1016\/j.tjnut.2026.101712_bib24","first-page":"1","article-title":"NutriMatch: harmonizing food composition databases with large language models for enhanced nutritional prediction, NPJ Digit","volume":"1","author":"Jankelow","year":"2026","journal-title":"Public Health"},{"key":"10.1016\/j.tjnut.2026.101712_bib25","author":"Lambert"},{"key":"10.1016\/j.tjnut.2026.101712_bib26","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1038\/s43856-025-01159-0","article-title":"DietAI24 as a framework for comprehensive nutrition estimation using multimodal large language models","volume":"5","author":"Yan","year":"2025","journal-title":"Commun. Med. (Lond.)"},{"key":"10.1016\/j.tjnut.2026.101712_bib27","doi-asserted-by":"crossref","first-page":"4283","DOI":"10.3390\/s20154283","article-title":"goFOODTM: An artificial intelligence system for dietary assessment","volume":"20","author":"Lu","year":"2020","journal-title":"Sensors (Basel)"},{"key":"10.1016\/j.tjnut.2026.101712_bib28","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1093\/jamia\/ocag003","article-title":"NutriRAG: unleashing the power of large language models for food identification and classification through retrieval methods","volume":"33","author":"Zhou","year":"2026","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.tjnut.2026.101712_bib29","doi-asserted-by":"crossref","first-page":"607","DOI":"10.3390\/nu17040607","article-title":"An evaluation of ChatGPT for nutrient content estimation from meal photographs","volume":"17","author":"O\u2019Hara","year":"2025","journal-title":"Nutrients"},{"key":"10.1016\/j.tjnut.2026.101712_bib30","doi-asserted-by":"crossref","DOI":"10.1016\/j.cdnut.2025.107556","article-title":"Performance evaluation of 3 large language models for nutritional content estimation from food images","volume":"9","author":"Fridolfsson","year":"2025","journal-title":"Curr. Dev. Nutr."},{"key":"10.1016\/j.tjnut.2026.101712_bib31","doi-asserted-by":"crossref","first-page":"3044","DOI":"10.3390\/nu17193044","article-title":"Comparison of accuracy in the evaluation of nutritional labels on commercial ready-to-eat meal boxes between professional nutritionists and chatbots","volume":"17","author":"Hsuan","year":"2025","journal-title":"Nutrients"},{"key":"10.1016\/j.tjnut.2026.101712_bib32","doi-asserted-by":"crossref","DOI":"10.1016\/j.nut.2023.112325","article-title":"AI dietician: unveiling the accuracy of ChatGPT\u2019s nutritional estimations","volume":"119","author":"Haman","year":"2024","journal-title":"Nutrition"},{"key":"10.1016\/j.tjnut.2026.101712_bib33","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2023.50367","article-title":"Consistency and accuracy of artificial intelligence for providing nutritional information","volume":"6","author":"Hoang","year":"2023","journal-title":"JAMA Netw. Open"},{"key":"10.1016\/j.tjnut.2026.101712_bib34","doi-asserted-by":"crossref","first-page":"1930","DOI":"10.1038\/s41591-023-02448-8","article-title":"Large language models in medicine","volume":"29","author":"Thirunavukarasu","year":"2023","journal-title":"Nat. Med."},{"key":"10.1016\/j.tjnut.2026.101712_bib35","series-title":"Introducing GPT-5.2","year":"2025"},{"key":"10.1016\/j.tjnut.2026.101712_bib36","series-title":"Google DeepMind","year":"2026"},{"key":"10.1016\/j.tjnut.2026.101712_bib37","series-title":"Introducing Claude Opus 4.5","year":"2025"},{"key":"10.1016\/j.tjnut.2026.101712_bib38","series-title":"How people use ChatGPT","author":"Chatterji","year":"2025"},{"key":"10.1016\/j.tjnut.2026.101712_bib39","series-title":"Q4 earnings call: remarks from our CEO","author":"Pichai","year":"2026"},{"key":"10.1016\/j.tjnut.2026.101712_bib40","series-title":"Anthropic economic index report: economic primitives","author":"Appel","year":"2026"},{"key":"10.1016\/j.tjnut.2026.101712_bib41","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1016\/S0002-8223(21)01429-2","article-title":"Food group intake patterns and associated nutrient profiles of the US population","volume":"91","author":"Kant","year":"1991","journal-title":"J. Am. Diet. Assoc."},{"key":"10.1016\/j.tjnut.2026.101712_bib42","series-title":"Agricultural Research Service. WWEIA\/NHANES Overview : USDA ARS","year":"2025"},{"key":"10.1016\/j.tjnut.2026.101712_bib43","series-title":"2017-March 2020 Pre-Pandemic Dietary Data - Continuous NHANES","year":"2026"},{"key":"10.1016\/j.tjnut.2026.101712_bib44","series-title":"FPED databases: USDA ARS","year":"2026"},{"key":"10.1016\/j.tjnut.2026.101712_bib45","series-title":"Nutrition Coordinating Center. Foods, nutrients and food groups","year":"2025"},{"key":"10.1016\/j.tjnut.2026.101712_bib47","series-title":"NCC nutrient information sources","year":"2026"},{"key":"10.1016\/j.tjnut.2026.101712_bib48","series-title":"Trans fat","year":"2024"},{"key":"10.1016\/j.tjnut.2026.101712_bib50","series-title":"The Llama 4 herd: The beginning of a new era of natively multimodal AI innovation. Meta AI","author":"Meta","year":"2025"},{"key":"10.1016\/j.tjnut.2026.101712_bib51","article-title":"TELeR: a general taxonomy of LLM prompts for benchmarking complex tasks","author":"Santu","year":"2023","journal-title":"arXiv"},{"key":"10.1016\/j.tjnut.2026.101712_bib52","unstructured":"R Project Developers. R: the R project for statistical computing [Internet]. R Foundation for Statistical Computing; 2025. Available from: https:\/\/www.r-project.org\/."},{"key":"10.1016\/j.tjnut.2026.101712_bib53","article-title":"Read excel files","author":"Wickham","year":"2026","journal-title":"Posit"},{"key":"10.1016\/j.tjnut.2026.101712_bib54","series-title":"dplyr: a grammar of data manipulation","author":"Wickham","year":"2026"},{"key":"10.1016\/j.tjnut.2026.101712_bib55","article-title":"Tidy messy data","author":"Wickham","year":"2026","journal-title":"Posit"},{"key":"10.1016\/j.tjnut.2026.101712_bib56","author":"Wickham"},{"key":"10.1016\/j.tjnut.2026.101712_bib57","series-title":"irr: Various coefficients of interrater reliability and agreement","author":"Gamer","year":"2005"},{"key":"10.1016\/j.tjnut.2026.101712_bib58","article-title":"Create elegant data visualisations using the grammar of graphics","author":"Wickham","year":"2025","journal-title":"Posit"},{"key":"10.1016\/j.tjnut.2026.101712_bib59","author":"Sievert"},{"key":"10.1016\/j.tjnut.2026.101712_bib60","series-title":"ramnathv\/htmlwidgets","author":"Vaidyanathan","year":"2026"},{"key":"10.1016\/j.tjnut.2026.101712_bib61","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","article-title":"A guideline of selecting and reporting intraclass correlation coefficients for reliability research","volume":"15","author":"Koo","year":"2016","journal-title":"J. Chiropr. Med."},{"key":"10.1016\/j.tjnut.2026.101712_bib62","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1037\/0033-2909.86.2.420","article-title":"Intraclass correlations: Uses in assessing rater reliability","volume":"86","author":"Shrout","year":"1979","journal-title":"Psychol Bull"},{"key":"10.1016\/j.tjnut.2026.101712_bib63","first-page":"30","volume":"1","author":"McGraw","year":"1996"},{"key":"10.1016\/j.tjnut.2026.101712_bib64","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/S0140-6736(86)90837-8","article-title":"Statistical methods for assessing agreement between two methods of clinical measurement","volume":"327","author":"Bland","year":"1986","journal-title":"Lancet"},{"key":"10.1016\/j.tjnut.2026.101712_bib65","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1177\/096228029900800204","article-title":"Measuring agreement in method comparison studies","volume":"8","author":"Bland","year":"1999","journal-title":"Stat. Methods Med. Res."},{"issue":"Supplement 1","key":"10.1016\/j.tjnut.2026.101712_bib66","doi-asserted-by":"crossref","first-page":"3S","DOI":"10.1093\/jn\/nxaa175","article-title":"Iron fortification practices and implications for iron addition to salt","volume":"151","author":"Hurrell","year":"2021","journal-title":"J. Nutr."},{"issue":"Supplement","key":"10.1016\/j.tjnut.2026.101712_bib67","doi-asserted-by":"crossref","first-page":"1710S","DOI":"10.1093\/ajcn\/80.6.1710S","article-title":"Vitamin D fortification in the United States and Canada: current status and data needs","volume":"80","author":"Calvo","year":"2004","journal-title":"Am. J. Clin. Nutr."},{"key":"10.1016\/j.tjnut.2026.101712_bib68","doi-asserted-by":"crossref","first-page":"2792","DOI":"10.1093\/jn\/132.9.2792","article-title":"Folic acid intake from fortification in United States exceeds predictions","volume":"132","author":"Choumenkovitch","year":"2002","journal-title":"J. Nutr."},{"key":"10.1016\/j.tjnut.2026.101712_bib69","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1016\/j.jand.2023.08.001","article-title":"ChatGPT and future artificial intelligence chatbots: what may be the influence on credentialed nutrition and dietetics practitioners?","volume":"123","author":"Chatelan","year":"2023","journal-title":"J. Acad. Nutr. Diet."},{"key":"10.1016\/j.tjnut.2026.101712_bib70","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.appet.2010.10.011","article-title":"Comparable increases in energy, protein and fat intakes following the addition of seasonings and sauces to an older person\u2019s meal","volume":"56","author":"Best","year":"2011","journal-title":"Appetite"},{"key":"10.1016\/j.tjnut.2026.101712_bib71","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1111\/jhn.13096","article-title":"Do sauces, condiments and seasonings contribute important amounts of nutrients to Australian dietary intakes?","volume":"36","author":"Whatnall","year":"2023","journal-title":"J. Hum. Nutr. Diet."},{"key":"10.1016\/j.tjnut.2026.101712_bib72","doi-asserted-by":"crossref","first-page":"1836","DOI":"10.1016\/j.ajcnut.2025.10.013","article-title":"Customized multimodal Diabot-GPT-4o enhances accuracy of image-based dietary assessments in dietetic trainees in Taiwan: validation against weighed food records","volume":"122","author":"Chen","year":"2025","journal-title":"Am. J. Clin. Nutr."},{"key":"10.1016\/j.tjnut.2026.101712_bib73","doi-asserted-by":"crossref","first-page":"7810","DOI":"10.3390\/jcm13247810","article-title":"Comparison of the accuracy, completeness, reproducibility, and consistency of different AI chatbots in providing nutritional advice: an exploratory study","volume":"13","author":"Ponzo","year":"2024","journal-title":"J. Clin. Med."}],"container-title":["The Journal of Nutrition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0022316626003615?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0022316626003615?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T10:43:30Z","timestamp":1788518610000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0022316626003615"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":70,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2026,9]]}},"alternative-id":["S0022316626003615"],"URL":"https:\/\/doi.org\/10.1016\/j.tjnut.2026.101712","relation":{},"ISSN":["0022-3166"],"issn-type":[{"value":"0022-3166","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Evaluation of Energy and Nutrient Estimates from Large Language Models Using Text-Based Queries","name":"articletitle","label":"Article Title"},{"value":"The Journal of Nutrition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.tjnut.2026.101712","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 American Society for Nutrition. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"101712"}}