Digital twin frameworks for smoking and nicotine dependence: toward predictive and personalised smoking cessation

Despite decades of public health interventions, tobacco use remains one of the leading preventable causes of disease worldwide. While cessation programmes and pharmacotherapies have improved outcomes, relapse rates remain high because nicotine addiction is dynamic, context-dependent, and strongly modulated by individual physiology, psychology, and social environment. Digital twin technology is an adaptive computational model that continuously mirrors the state of a real person, thus offering a new approach to understand, predict, and manage smoking behaviour and its health consequences. By integrating physiological, behavioural, environmental, and molecular data, digital twins could provide real-time insight into craving cycles, stress triggers, and therapeutic response. This article proposes a conceptual framework for constructing digital twins for smokers, discusses potential applications in prevention, cessation, and harm reduction, and highlights ethical and technical challenges. The approach could transform smoking cessation from a reactive, population-based model to a proactive, personalised, and data-driven process.