This report by Hall et al. presents a mathematical modeling approach to simulate energy expenditure adaptations during weight loss in adults. The authors develop a web-based simulator to predict weight change dynamics, highlighting that the body's response to changes in energy intake is slow, with half-lives of about 1 year. They find that adults with greater adiposity require a larger energy deficit to achieve the same weight loss and reach steady-state weight more slowly. Using a population-averaged model, they calculate the energy-balance dynamics underlying the US adult obesity epidemic, showing a small but persistent daily energy imbalance gap of about 30 kJ per day. However, to maintain increased weight, energy intake must increase by about 0-9 MJ per day, quantifying the public health challenge to reverse the obesity epidemic. The report also discusses the limitations of static weight-loss rules and the importance of considering dynamic physiological adaptations. It concludes by emphasizing the need for accurate mathematical models to assess the quantitative effects of interventions at both individual and population levels.This report by Hall et al. presents a mathematical modeling approach to simulate energy expenditure adaptations during weight loss in adults. The authors develop a web-based simulator to predict weight change dynamics, highlighting that the body's response to changes in energy intake is slow, with half-lives of about 1 year. They find that adults with greater adiposity require a larger energy deficit to achieve the same weight loss and reach steady-state weight more slowly. Using a population-averaged model, they calculate the energy-balance dynamics underlying the US adult obesity epidemic, showing a small but persistent daily energy imbalance gap of about 30 kJ per day. However, to maintain increased weight, energy intake must increase by about 0-9 MJ per day, quantifying the public health challenge to reverse the obesity epidemic. The report also discusses the limitations of static weight-loss rules and the importance of considering dynamic physiological adaptations. It concludes by emphasizing the need for accurate mathematical models to assess the quantitative effects of interventions at both individual and population levels.