Energy metabolism modelling with sensitivity to activity thermogenesis tracking data

这项研究计划旨在开发更准确的人体能量代谢数学模型。这些模型对行业合作伙伴Fitmylife Health Analytics具有重要价值。该公司的目标是利用这些模型来帮助客户建立行为模式，以实现健康目标。这些模型确保客户的决策植根于对自己身体新陈代谢独特方面的准确理解。最准确的人体能量代谢模型预测身体成分变化的基础上，数学模型的过程，支配身体的加工消耗的宏量营养素：蛋白质，脂肪和碳水化合物。然而，这些模型存在重要的缺陷，限制了它们预测任何给定个体的身体成分变化的能力。这些缺陷的实例包括：（a）归因于身体活动水平的单一参数，（B）对建模受试者进行的身体活动类型不敏感，（c）没有关于常量营养素的卡路里摄入的信息，（d）稳态初始条件的假设，以及（e）对卡路里摄入的一天中的时间不敏感。事实上，可以避免所有这些缺陷，并为模型提供精确的数据。通常可用的健身跟踪设备提供由于活动沿着活动类型而导致的每分钟心率的准确信息。饮食跟踪应用程序提供按常量营养素和一天中的时间分解卡路里摄入量。利用建模对象的历史数据，可以避免对稳态初始条件的假设。最后，现在可以用六种成分而不仅仅是两种成分来精确地模拟身体组成。通过这个项目，研究小组的目标是开发代谢模型，这些模型可以从更精确的热量摄入和每日总能量消耗信息中受益。最终，我们计划评估这些新模型相对于国家卫生研究所建立的当前领先模型所建立的基线的预测能力。

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. 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. 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.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.