# Real-Time Model Predictive Control of Human Bodyweight Based on Energy Intake
## 3 Results and Discussion
### Current Limitations and Future Perspectives  
The Minnesota starvation experiment was performed in 1944 to investigate the psychological and physiological effects of severe dietary restriction [53]. The experiment was designed to mimic the dietary conditions of prisoners during World War II. Therefore, due to both the period in which the experiment was performed and the focus, only male participants were selected. This restriction in energy intake is not a recommended method to lose weight, as the energy intake is lower than the minimum requirements. However, this abrupt change in energy intake presents the perfect conditions to perform an analysis of the system's dynamics and the modelling of body weight in response to energy intake. Unfortunately, the experiment has some limitations with respect to age, gender, size of the study population, and BMI category. The population of the Minnesota starvation experiment counted 32 male participants. The size of the population was small and the population did not represent differences in gender, BMI category and age. A study, performed on male and female mice, showed that the metabolic processes during bodyweight dynamics are gender-dependent [54]. Therefore, it is necessary to verify and model the body weight dynamics of both males and females. The age of the participants varied between a limited range of 20 and 33 years. Metabolic processes are not only dependent on the gender but also on the age. To estimate the basal metabolic rate (BMR), the equations in [49] are often used and are different depending on gender and age. Calculation of BMR is based on height and weight. In the Minnesota starvation experiment, the average body weight and BMI of the participants prior to the starvation phase were 69.36 kg and 21.7, respectively. All participants apart from one had a BMI in the normal category. Only one subject was slightly overweight with a BMI of 25.4. The aim of this study is to make a first step towards designing a controller to help overweight or obese people lose body weight. However, the constructed controller is based on data of 32 male participants within the normal BMI category. Regardless of the limited subject variation, the controller should eventually depend on time-variant parameters opposed to time-invariant parameters. The benefit of using time-variant parameters is that if the underlying metabolic processes are changed due to unknown factors, the parameters are updated in order to predict future body weight more accurately. The modelling framework also has certain limitations. It is known that the weight loss intervention strategies will be affected by external factors [55], such as psychological [56; 57; 58; 59], occupational [60; 61; 62; 63], and metabolic factors [64; 65], besides their combined effects [66; 67]. These external factors will affect the weight loss intervention, as well as its maintenance in the long term [68; 69]. Besides, there is a high degree of heterogeneity in the impact of these factors both, during and after a weight intervention [70]. In principle, the modelling framework developed in this study cannot provide directly any information about this matter during its application throughout the weight intervention. However, there is an aspect in the proposed modelling approach, which can actually allow getting some insight in this regard. One of the reasons for allowing the parameters to vary with time is to capture the contribution of external factors, which impact the process under study but which are not taken explicitly into account in the model. Therefore, the time evolution of the model parameters can be understood as a summary of the contribution of each individual external factor, besides the inherent time evolution related to the process input (energy intake in this study). This is the reason why, although we use a simple compact model based solely on energy intake and bodyweight, this allows describing accurately the weight loss intervention.