Title : Supported behavioural interventions in diabetes
Abstract:
Behavioural interventions remain central to effective diabetes care, but long-term adherence to lifestyle and treatment recommendations is low. Traditional approaches often fail to account for the cognitive, emotional, and social drivers of patient decisions. This presentation explores how behavioural science, combined with artificial intelligence, can address these gaps. AI systems now allow for personalised behavioural insights, real-time monitoring, and adaptive interventions. When designed with ethical and clinical safeguards, they support more accurate risk profiling, timely feedback, and tailored nudges. These tools can help shift patient behaviour in areas such as medication adherence, diet, exercise, and glucose monitoring. The presentation will outline evidence from applied behavioural economics, case studies using AI-driven platforms, and the limits of automation in health behaviour change. It will also address ethical concerns, including algorithmic bias, transparency, and patient autonomy. This approach reframes diabetes management as a dynamic interaction between human decision-making and technological support. The goal is not to replace clinical judgement but to complement it, improving outcomes by understanding and influencing what people actually do.
