With the global expansion of financial incentives to promote behavior change, they are being considered in One Health contexts characterized by the interdependence of human, animal, and ecosystem health. Little specific evidence exists about how to maximize the effectiveness of financial incentives and minimize negative outcomes in these complex settings. We review over two decades of research on financial incentives for behavior change to examine their potential effects on improved practices related to water, sanitation and hygiene, food safety, and animal and ecosystem health. We apply lessons from the literature to the case of Guinea worm disease, a neglected tropical disease for which financial incentives have been utilized or considered to motivate the uptake of various preventive behaviors. Guinea worm disease prevention is a useful example for considering the advantages, disadvantages, and unknowns of incorporating financial incentives into behavior change interventions because such an approach calls for changes in multiple behaviors with diverse attributes, and the use of incentives may have implications for other disease control, elimination, and eradication programs. Further, Guinea worm disease represents a classic One Health challenge with interventions necessary in human, animal, and ecological systems to reduce disease incidence. We present evidence-based recommendations for the design and use of incentives that may further facilitate effective behavior change. We also discuss potential negative outcomes from incentives, and critical unknowns such as how to design incentives for collective behavior and what happens when incentives end. We stress that financial incentives are not always appropriate; they must be considered carefully and coupled with proper communication efforts that are driven by a deep understanding of community social norms, motivations, and practices.

Worcester Polytechnic Institute (WPI) researchers have identified a promising tool for early detection of Alzheimer's disease using machine learning and brain scans to predict the disease with nearly 93% accuracy. Published in Neuroscience, the study found that patterns of brain volume loss linked to Alzheimer’s differ by age and sex, an insight that could help clinicians move toward more personalized diagnosis and treatment strategies.