An international multi-stakeholder study on developing teacher capabilities and agency to implement and sustain physically active learning in schools.

Type 2 diabetes is a major public health challenge in the United States, affecting around 36 million adults and imposing major clinical and financial burdens (). This often preventable condition is associated with serious health complications, including cardiovascular disease, kidney disease, neuropathy, and vision loss, as well as high healthcare costs (). Evidence-based primary prevention strategies are aim to prevent disease onset of disease by targeting risk factors and promoting protective behaviors, such as healthy eating, physical activity, weight management, early identification of high-risk individuals, and sustainable behavior change (). Given the prevalence of type 2 diabetes and the effectiveness of prevention strategies, primary prevention represents an opportunity for innovation (). AI tools are increasingly available to support risk prediction, behavioral coaching, and personalized recommendations (). AI for primary prevention is appealing for its scalability, personalization, and capacity to deliver continuous, low-intensity interventions such as reminders and alerts (). AI has increasingly been applied to diabetes prevention through tools that support clinicians by identifying individuals at elevated risk. These tools can also be used directly by patients to support personalized prevention strategies (). For example, machine learning models can leverage electronic health records, demographic characteristics, laboratory values, and behavioral data to improve risk prediction. Machine learning-based and digital health interventions can provide tailored lifestyle recommendations and preventive support through chatbots or other interactive tools (, ). As AI technologies become more widely implemented, understanding public acceptance of AI-assisted prevention becomes important. Given that diabetes is a behavior-dependent chronic condition with strong evidence for lifestyle-based prevention and clear clinical guidelines (, , ), it provides a useful case study for studying AI integration in primary prevention. Trust in AI, trust in clinicians, and trust in health systems (), are likely to shape attitudes about specific uses of AI in healthcare. Understanding these factors is important for assessing patient comfort with AI tools for disease prevention (, ). Previous research suggests that comfort and trust in AI are influenced by multiple individual and contextual factors (, ) with trust and perceived value playing important roles. Drawing on technology acceptance and trust in AI frameworks, transparency may reduce uncertainty about AI-generated recommendations and thereby enhance perceived trustworthiness (, , ). Greater trust in health systems and AI tools has been associated with higher willingness to engage with such technologies (). In parallel, perceived benefits-such as improved access, safety and effectiveness-may increase acceptance and comfort with AI use (). Physician confidence in AI reliability can further increase patient comfort by signaling credibility and safety and influencing how AI-supported recommendations are communicated (, ). Finally, comfort with AI tools for preventive care may also vary across demographic and contextual factors (), reflecting differences in experiences, expectations, and perceptions across populations.