{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T01:08:13Z","timestamp":1792804093258,"version":"4.5.0"},"reference-count":172,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T00:00:00Z","timestamp":1785801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012314","name":"Applied Physics Laboratory, Johns Hopkins University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012314","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:p>\n                    Health misinformation and disinformation (mis-disinformation) on social media presents a growing threat to individual and population health, societal resilience, and national security. While social media enables the rapid dissemination of health information, it also facilitates the spread of health mis-disinformation, a challenge further compounded by foreign influence campaigns, AI-generated content, and divergent regulatory environments. Effective interventions require tailoring to local socio-cultural and geo-political contexts. This article proposes a lifecycle model for health professionals that conceptualizes how content creation, dissemination, exposure, belief formation, and behavioral outcomes interact and can be targeted through strategic interventions to improve health outcomes and mitigate adverse behavioral effects. To achieve this, the article characterizes the challenges posed by the current and emerging health information environment; identifies and evaluates mis-disinformation lifecycle models in order to strengthen the existing knowledge base; and assesses the current state of knowledge and gaps on comparing intervention strategies to elicit desired behavioral responses, with an emphasis on individual approaches (e.g., debunking, media literacy, fact checking). A review of literature (2020\u20132025) identified 13 cross-comparative intervention studies which focused on key findings. Four lifecycle models were identified and assessed against six criteria derived from the lifecycle literature to identify the most suitable framework for adaptation in the public health domain. Kruijver et al.'s C5 Interaction Model emerged as the framework that satisfied the greatest number of criteria and was selected for adaptation. The model was extended to account for diverse socio-political and information environments, emerging technological interventions, and the distinct challenges posed by both mis-disinformation. Adaptation involved integrating concepts from risk perception, the Social Amplification of Risk Framework, and Social Judgment Theory, alongside health-specific examples to enhance relevance and practical applicability. To help translate the insights gained to strategy, we also convey the information in an\n                    <jats:italic>Integrated Framework for Managing Health Mis-disinformation<\/jats:italic>\n                    . By linking the evolution of health mis-disinformation to targeted interventions, the model and framework provide a foundation for promoting healthier behaviors and mitigating the adverse effects of misleading health information across diverse socio-demographic and cultural settings to improve health outcomes.\n                  <\/jats:p>","DOI":"10.3389\/fmed.2026.1814102","type":"journal-article","created":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T04:27:55Z","timestamp":1785817675000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["From emergence to amplification: an analysis of lifecycle models to address health mis-disinformation in the digital environment"],"prefix":"10.3389","volume":"13","author":[{"given":"Ben","family":"Sheppard","sequence":"first","affiliation":[{"name":"Applied Physics Laboratory, Johns Hopkins University","place":["Laurel, MD, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linda","family":"Durnell","sequence":"additional","affiliation":[{"name":"Whiting School of Engineering and Johns Hopkins School of Medicine, Johns Hopkins University","place":["Baltimore, MD, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amy J.","family":"Haufler","sequence":"additional","affiliation":[{"name":"Applied Physics Laboratory, Johns Hopkins University","place":["Laurel, MD, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,4]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"105","DOI":"10.34172\/hpp.2023.13","article-title":"Social media for public health: reaping the benefits, mitigating the harms","volume":"13","author":"Jafar","year":"2023","journal-title":"Health Promot Perspect."},{"key":"B2","doi-asserted-by":"publisher","first-page":"561","DOI":"10.31083\/j.rcm.2020.04.195","article-title":"Positive and negative impact of social media in the COVID-19 era","volume":"21","author":"Venegas-Vera","year":"2020","journal-title":"Rev Cardiovasc Med."},{"key":"B3","doi-asserted-by":"publisher","first-page":"12","DOI":"10.34172\/hpp.2021.03","article-title":"Rumor surveillance methods in outbreaks: a systematic literature review","volume":"11","author":"Salehinejad","year":"2021","journal-title":"Health Promot Perspect."},{"key":"B4","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1186\/s13104-021-05736-z","article-title":"Socio-economic and demographic impacts on the full awareness of the methods for controlling\/preventing the spread of COVID-19 among social media users in some African countries at the onset of the pandemic","volume":"14","author":"Okorie","year":"2021","journal-title":"BMC Res Notes."},{"key":"B5","doi-asserted-by":"crossref","unstructured":"Petkovic\n              J\n            \n            \n              Duench\n              S\n            \n            \n              Trawin\n              J\n            \n            \n              Dewidar\n              O\n            \n            \n              Pardo\n              JP\n            \n            \n              Simeon\n              R\n            \n          \n          Behavioural interventions delivered through interactive social media for health behaviour change, health outcomes, and health equity in the adult population\n          \n          2021","DOI":"10.1002\/14651858.CD012932.pub2"},{"key":"B6","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1136\/amiajnl-2014-002841","article-title":"The influence of social networking sites on health behavior change: a systematic review and meta-analysis","volume":"22","author":"Laranjo","year":"2015","journal-title":"J Am Med Inform Assoc."},{"key":"B7","doi-asserted-by":"publisher","first-page":"e17187","DOI":"10.2196\/17187","article-title":"Prevalence of health misinformation on social media: systematic review","volume":"23","author":"Suarez-Lledo","year":"2021","journal-title":"J Med Internet Res"},{"key":"B8","doi-asserted-by":"publisher","first-page":"2474830","DOI":"10.1080\/19424396.2025.2474830","article-title":"Tobacco and oral health misinformation on Twitter (X): implications for dental professionals","volume":"53","author":"Han","year":"2025","journal-title":"J Calif Dent Assoc"},{"key":"B9","doi-asserted-by":"publisher","first-page":"819","DOI":"10.1007\/978-981-19-2350-0_77","article-title":"Vaccine hesitancy to vaccine hope: comparison of MR vaccine and COVID vaccine trends in India","author":"Vasudevan","year":"2022","journal-title":"Proceedings of the International Conference on Cognitive and Intelligent Computing. 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