New computational model enables personalized simulation of exercise's metabolic effects

A new mathematical approach incorporates personalized details to simulate the metabolic effects of exercise which can be adapted to different individual characteristics, such as age and weight, as well as different types and intensities of exercise.

Physical activity helps to prevent or treat metabolic diseases and to understand better of the molecular effects of exercise that could aid clinical efforts to address the disease. It is difficult to monitor in people the system-level effects of exercise, so the scientists have developed mathematical models to simulate them. However, previously developed models don’t provide key details such as exercise type and personal characteristics.

To address this challenge, the existing model was extended to make it more personalized which used known properties of different organs and tissues to simulate the effects of exercise on metabolic dynamics of glucose, hormones, and related substances in those tissues. However, the model only addressed one type of exercise (cycling) at a fixed intensity level for one type of person (a 70-kilogram male with no cycling training). Keeping intact the biological basis of the older model, the researchers mathematically extended it to incorporate a better, more personalized definition of physical exercise. The resulting new model accounts for a subject's gender, age, body weight, fitness level, exercise duration, and exercise intensity as measured in the context of personal fitness level.

The model was validated by showing that it accurately simulated results from previous, real-world studies in which blood samples were used to monitor metabolic effects in people with different individual characteristics who performed different forms of exercise.Modeling the influence of physical exercise on the control of glucose homeostasis is of primary importance in the understanding of how physical activities prevents disease and improves health outcomes, and consequently in the development of health monitoring devices for personalized medicine.

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