{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T06:01:29Z","timestamp":1789970489069,"version":"4.0.1"},"reference-count":47,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Nutr."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>General-purpose large language models (LLMs) are increasingly used for health and nutrition information. We evaluated the quality and practical limitations of LLM-generated sports nutrition advice for a standardized recreational female marathon runner, focusing on numerical recommendations, safety boundaries, and female-specific considerations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a time-stamped cross-sectional descriptive audit of five LLM configurations using 15 consumer-style prompts covering pre-race nutrition, in-race fueling and hydration, unexpected race situations, supplements, and female-specific nutritional risks. Each prompt was repeated 10 times per configuration, yielding 750 responses. The standardized profile was a 39-year-old, 58-kg recreational female marathon runner targeting a 4-h finish during the luteal phase. Responses were evaluated for scientific accuracy, numerical appropriateness and stability, safety-boundary handling, female-specific applicability, and readability. The coding framework was calibrated on 50 responses and finalized before blinded full-corpus evaluation by two author-raters and two independent external raters.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>All configurations produced structured advice, but performance and female-specific integration varied by configuration, prompt, and domain. In the directly prompted Q15 scenario, ChatGPT covered all seven indicators in all 10 runs. Gemini's Q5 recommendations ranged from 5\u20136 gels and 50\u201360 g\/h carbohydrate. DeepSeek and Doubao Q5 carbohydrate recommendations ranged from 15\u2013120 g\/h and 5\u2013145 g\/h, respectively, crossing both limits of the 30\u201390 g\/h comparison range. One Doubao response stated a 115\u2013145 g\/h target while its gel schedule provided 37.5\u201345 g\/h. Configurations differed in response stance toward strong pre-race coffee and in maintaining the training-first recommendation for new supplements (5\/10\u201310\/10). Model-level mean Flesch Reading Ease scores ranged from 45.5 to 57.8. Raw agreement between the independent experts was 91.24% for binary judgments (\u03ba = 0.780); agreement with final author ratings was 87.76%\u221289.98% (\u03ba = 0.703\u20130.763); exact numeric agreement was 75.0%; indicator-specific ICCs were 0.264\u20130.928.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>General-purpose LLMs can provide accessible and structured sports nutrition information for recreational female marathon runners, but their advice remains uneven in female-specific integration, numerical consistency, and safety-boundary communication. Apparent strengths were configuration-, prompt-, and context-specific and do not support a stable overall ranking of model families. LLM outputs may therefore be useful as supplementary information but should not replace individualized guidance from a sports dietitian or medical professional.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnut.2026.1902377","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T05:34:40Z","timestamp":1789968880000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating sports nutrition advice provided by general-purpose large language models for recreational female marathon runners: a multidimensional descriptive audit"],"prefix":"10.3389","volume":"13","author":[{"given":"Hao","family":"Sun","sequence":"first","affiliation":[{"name":"School of Sports Training, Wuhan Sports University","place":["Wuhan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinshuo","family":"Chu","sequence":"additional","affiliation":[{"name":"Jincheng College, Nanjing University of Aeronautics and Astronautics","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Sports Training, Wuhan Sports University","place":["Wuhan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Wang","sequence":"additional","affiliation":[{"name":"Physical Education Teaching Department, Xuanhua Vocational College of Science and Technology","place":["Zhangjiakou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,21]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/s40798-025-00810-3","article-title":"Physiology and pathophysiology of marathon running: a narrative review","volume":"11","author":"Braschler","year":"2025","journal-title":"Sports Med Open."},{"key":"B2","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1136\/bjsports-2018-100493","article-title":"Is running associated with a lower risk of all-cause, cardiovascular and cancer mortality, and is the more the better? 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