{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T12:14:58Z","timestamp":1781871298523,"version":"3.54.5"},"reference-count":19,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T00:00:00Z","timestamp":1728259200000},"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. Digit. Health"],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Digital therapeutics (DTx) in the form of mobile health (mHealth) self-management programs have demonstrated effectiveness in reducing disease activity across various diseases, including fibromyalgia and arthritis. However, the content of online self-management programs varies widely, making them difficult to compare.<\/jats:p><\/jats:sec><jats:sec><jats:title>Aim<\/jats:title><jats:p>This study aims to employ generative artificial intelligence (AI)-based knowledge graphs and network analysis to categorize and structure mHealth content at the example of a fibromyalgia self-management program.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A multimodal mHealth online self-management program targeting fibromyalgia and post-viral fibromyalgia-like syndromes was developed. In addition to general content, the program was customized to address specific features and digital personas identified through hierarchical agglomerative clustering applied to a cohort of 202 patients with chronic musculoskeletal pain syndromes undergoing multimodal assessment. Text files consisting of 22,150 words divided into 24 modules were used as the input data. Two generative AI web applications, ChatGPT-4 (OpenAI) and Infranodus (Nodus Labs), were used to create knowledge graphs and perform text network analysis, including 3D visualization. A sentiment analysis of 129 patient feedback entries was performed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The ChatGPT-generated knowledge graph model provided a simple visual overview with five primary edges: \u201cMental health challenges\u201d, \u201cStress and its impact\u201d, \u201cImmune system function\u201d, \u201cLong COVID and fibromyalgia\u201d and \u201cPain management and therapeutic approaches\u201d. The 3D visualization provided a more complex knowledge graph, with the term \u201cpain\u201d appearing as the central edge, closely connecting with \u201csleep\u201d, \u201cbody\u201d, and \u201cstress\u201d. Topical cluster analysis identified categories such as \u201cchronic pain management\u201d, \u201csleep hygiene\u201d, \u201cimmune system function\u201d, \u201ccognitive therapy\u201d, \u201chealthy eating\u201d, \u201cemotional development\u201d, \u201cfibromyalgia causes\u201d, and \u201cdeep relaxation\u201d. Gap analysis highlighted missing links, such as between \u201cnegative behavior\u201d and \u201csystemic inflammation\u201d. Retro-engineering of the self-management program showed significant conceptual similarities between the knowledge graph and the original text analysis. Sentiment analysis of free text patient comments revealed that most relevant topics were addressed by the online program, with the exception of social contacts.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Generative AI tools for text network analysis can effectively structure and illustrate DTx content. Knowledge graphs are valuable for increasing the transparency of self-management programs, developing new conceptual frameworks, and incorporating feedback loops.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fdgth.2024.1466211","type":"journal-article","created":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T05:10:50Z","timestamp":1728277850000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Generative AI-based knowledge graphs for the illustration and development of mHealth self-management content"],"prefix":"10.3389","volume":"6","author":[{"given":"Marc","family":"Blanchard","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincenzo","family":"Venerito","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pedro","family":"Ming 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