This innovation involves a metabolic health monitor (MHM) app using self-adaptive individualized stochastic mathematical models to capture dynamic slow changes and trajectories of the metabolism with the goal of observing and controlling fat weight, lean body mass, and insulin resistance. Notably, a canonical representation of the metabolism was created expressed by the daily energy density of the lean body mass change, the daily energy density of the fat mass change, and the ratio of lean body mass change velocity and fat mass change velocity or R ratio. These three parameters can serve as metrics for dynamic changes such as trend prediction for fat mass, lean body mass, and insulin resistance changes, allow for intra-and interindividual comparisons, and make possible self-directed therapy and optional web based feedback by a healthy lifestyle team. The real time feedback of predicted dynamic changes of metabolic parameters allow for the user to make appropriate modifications of diet and physical activity realizing adaptive self-control of dietary and exercise regime for lifestyle change to fight obesity and insulin resistance. It is shown here that from control engineering point of view our canonical representation assures full observability and controllability. Proof of concept for clinical observability and controllability is provided with simulation studies using clinical trial data. For the first time the otherwise difficult to measure parameters of daily utilized macronutrient energy intake, macronutrient oxidation rate, daily changes of fat weight, lean body mass, and insulin resistance is realized with minimum error variance supported by the Kalman filter.