From emergence to amplification: an analysis of lifecycle models to address health mis-disinformation in the digital environment.

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 (2020-2025) 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 Integrated Framework for Managing Health Mis-disinformation. 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.