Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2018-2019)

This research initiative aims to develop more accurate mathematical models of human energy metabolism. These models are of great value to the industrial partner, Fitmylife Health Analytics. The company aims to use the models to help its customers establish behaviour patterns to achieve health goals. The models ensure that customers' decisions are rooted in an accurate understanding of the unique aspects of their own body's metabolism. x000D
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The most accurate models of human energy metabolism predict body composition change based on mathematical models of processes that govern the body's processing of consumed macronutrients: protein, fat, and carbohydrates. However, these models suffer important deficiencies that limit their power to predict body composition change for any given individual. Examples of these deficiencies include: (a) single parameter ascribed to physical activity level, (b) no sensitivity to the type of physical activity performed by subjects of the modelling, (c) no information on calorie intake by macronutrient, (d) assumption of steady state initial conditions, and (e) no sensitivity to time of day for calorie intake. x000D
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In fact, it is possible to avoid all of these deficiencies and inform models with precise data. Commonly available fitness tracking devices provide accurate information of minute-by-minute heart rate due to activity along with activity type. Diet tracking applications provide decomposition of calorie intake by macronutrient and time of day. With historical data on modelling subjects it is possible to avoid assumptions of steady state initial conditions. Finally, it is now possible to accurately model body composition with six components rather than just two.x000D
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Through this project the research team aims to develop models of metabolism that benefit from much more precise information regarding caloric intake and total daily energy expenditure. Ultimately, we plan to evaluate the predictive power of these new models relative to baselines established by the current leading model established by the National Institute of Health.x000D