Advancing Assessment of Energy Expenditure in Women with Increased Cancer Risk

能量消耗是能量学的关键组成部分，而体力活动是能量消耗中最大的可改变的组成部分。能量消耗和体力活动与胰岛素抵抗和其他对癌症风险至关重要的血糖控制标记物密切相关。久坐的行为最近也成为代谢风险的独立预测因子，并对客观的时间分析 久坐行为数据表明，久坐时间的休息可能是一种关键的干预策略，以补充适度到剧烈体力活动的改善。在过去的十年里，人们还从体力活动、久坐行为和体重状况方面评估了建筑环境的影响。然而，这项研究关注的是对居民区的静态看法，这可能会混淆健康和地点之间的关系。我们建议推动能源领域的发展 在我们的TREC项目#2和#3中，通过提高与能量消耗相关的评估的准确性，我们将使用具有同步心率记录的最先进的加速度计来提高测量体力活动、久坐行为和能量消耗的准确性。除了分支方程建模技术外，我们还将使用新的计算方法来分析这些数据流 设备，包括人工神经网络，允许组合这些数据，以破译体力活动和久坐行为的频率、强度、持续时间和类型，从而优化研究参与者的行为特征，并减少在这些行为与血糖控制标志物之间观察到的关系中的测量噪音。最后，来自全球定位系统设备的跟踪参与者的时间和空间移动的数据将与圣地亚哥县现有的地理信息系统数据相结合，使我们能够开发出致病环境暴露估计，并将这些估计与代谢风险因素联系起来。这些数据将通过我们的团队在NIH基因和环境倡议下开发的软件进行处理。 这将使我们能够使用新的计算技术来评估能量消耗、体力活动和久坐行为与项目2和3中测量的与乳腺癌相关的代谢风险因素之间的关系，以及暴露在肥胖环境中的缓和效果。

Energy expenditure is a key component of energetics, and physical activity comprises the largest modifiable component of energy expenditure. Energy expenditure and physical activity are strongly related to insulin resistance and other markers of glycemic control important for cancer risk. Sedentary behavior has also recently emerged as an independent predictor of metabolic risk, and temporal analyses of objective sedentary behavior data have indicated that breaks in sitting time may be a critical intervention strategy to complement improvements in moderate to vigorous physical activity. In the last decade, the impact of the built environment has also been assessed in relation to physical activity, sedentary behavior and weight status. This research, however, has focused on a static view of residential neighborhood which may be confounding the relationship between health and place. We propose to advance the field of energy expenditure, physical activity, and sedentary behavior assessment across the cancer continuum by improving the accuracy of energy expenditure-related assessments in our TREC projects #2 and #3. We will use state ofthe art acceierometers with simultaneous heart rate recording to improve the accuracy of measuring physical activity, sedentary behavior, and energy expenditure. In addition to branched equation modeling techniques we will also use new computational approaches for analyzing data streams from these devices, including artificial neural networks that allow comtjining these data to decipher the frequency,intensity, duration, and type of physical activity and sedentary behavior so as to optimally characterize behaviors of study participants and reduce the measurement noise in observed relationships between these behaviors and markers of glycemic control. Finally, data from Global Positioning System devices that track the temporal and spatial movements of participants will be combined with existing Geographic Information Systems data for San Diego County to allow us to develop obesogenic environmental exposure estimates and relate these to the metabolic risk factors. These data will be processed through software developed by our group under the NIH Gene & Environment Initiative. This will enable us to use novel computational techniques to assess the relationships over time and across the study arms between energy expenditure, physical activity and sedentary behavior and metabolic risk factors related to breast cancer measured in Projects #2 & #3 as well as the moderating effect of exposure to obesogenic environments.