Energy metabolism modelling with sensitivity to activity thermogenesis tracking data

对活动生热跟踪数据敏感的能量代谢建模

• 批准号: 508657-2017
• 负责人: Hillen, Thomas
• 金额: $ 1.81万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=640699
• 关键词: Energy; metabolism; modelling; sensitivity; activity

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.

该研究计划旨在开发人类能量代谢的更准确的数学模型。这些模型对工业合作伙伴Fitmylife Health Analytics具有很高的价值。该公司旨在使用这些模型来帮助其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的独特方面的准确理解。人类能量代谢的最准确模型预测了人体组成的变化，这些过程的数学模型控制了人体对消耗的大量营养素的处理：蛋白质，脂肪和碳水化合物。但是，这些模型遭受了重要的缺陷，这些缺陷限制了其能力预测任何给定个人的身体成分变化。这些缺陷的示例包括：（a）归因于体育活动级别的单个参数，（b）对建模受试者进行的体育活动类型没有敏感性，（c）没有关于大量摄入的卡路里摄入量的信息，（d）假设稳态初始条件，以及（e）对卡路里摄入的一天中的时间敏感。实际上，有可能避免所有这些缺陷，并使用精确的数据告知模型。通常可用的健身跟踪设备提供了由于活动以及活动类型而导致的一分钟心率的准确信息。饮食跟踪应用可通过大量营养素和一天中的时间来分解卡路里摄入量。借助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，现在可以将身体组成准确地用六个组件建模，而不仅仅是两个组件。研究小组旨在开发新陈代谢模型，这些模型受益于有关热量摄入和每日能量总消耗的更精确信息。最终，我们计划评估这些新模型相对于由国家卫生研究院建立的当前领先模型建立的基线的预测能力。