Novel Analytic Techniques to Assess Physical Activity

在开发和使用基于加速度计的运动传感器进行身体活动方面取得了进展 research.然而，处理活动监视器数据的传统方法不能提供足够的信息。 准确性，以满足当前在研究竞技场中使用客观身体活动数据的趋势。的 该提案的目的是解决基于加速计的PA评估方法的这一弱点： 具体目标是：1）开发和验证处理Activograph加速度计数据的新方法， 使用强大现代分类方法（分类树、判别式 分析、隐马尔可夫模型、神经网络、回归样条和支持向量机）; 2） 比较这些分类方法和传统方法在受控环境中评估PA; 3） 比较自由生活PA中PA的分类方法和传统方法 4）总结修正测量误差 从新的分类方法和传统的方法来量化PA的习惯性PA的估计。 我们独特的合格的多学科研究小组将通过首先开发创新的 分类方法，以确定实验室环境中的特定活动，然后验证模型 使用从受控实验室环境和自由环境中进行的已知活动中收集的数据， 生活状况。根据这些研究的结果，将改进分类方法， PA行为的估计将使用统计测量误差方法进行调整， 准确估计PA。我们选择的分类方法包括公开的“非官方” 其他人可以很容易地使用的“货架”分类方法。由此产生的数据处理程序将是 在流行的商业软件包中实现并免费提供。的结果 拟议的调查将通过提供创新的方法， 使用流行的基于加速度计的PA监视器获得更准确、更详细的PA估计。这 系统的方法将提供信息，从而更清楚地了解剂量反应 PA与健康的关系以及这种关系的生理基础。

Progress has been made in developing and using accelerometer-based motion sensors for physical activity research. However, traditional methods of processing activity monitor data do not provide sufficient accuracy to satisfy current trends in the use of objective physical activity data in the research arena. The aims of this proposal address this weakness in accelerometer- based PA assessment methodologies: The specific aims are: 1) To develop and validate novel methods to process Actigraph accelerometer data to improve estimates of PA using powerful modern classification methods (classification trees, discriminant analyses, hidden Markov models, neural networks, regression splines, and support vector machines); 2) To compare these classification methods and traditional approaches for assessing PA in a controlled setting; 3) To compare the classification methods and traditional approaches for quantifying PA in free living PA conditions and to select a recommended method; and 4) To correct for measurement error in summary estimates of habitual PA from the novel classification methods and traditional approaches for quantifying PA. Our uniquely qualified multidisciplinary research group will address these aims by first developing innovative classification methods to identify specific activities in a laboratory setting, and then validating the models using data collected from known activities performed in both controlled laboratory environments and free- living situations. Based on the results of these studies, the classification methods will be refined, and estimates of PA behavior will be adjusted using statistical measurement error methods to derive more accurate estimates of PA. We have chosen the classification methods to include publicly available "off-the shelf" classification methods that others can easily use. The resulting data processing programs will be implemented in popular commercial software packages and made freely available. The results of the proposed investigations will move the field of PA assessment forward by providing innovative approaches to derive more accurate and detailed estimates of PA using a popular accelerometer-based PA monitor. This systematic approach will provide information leading to a clearer understanding of the dose-response relationship between PA and health and the physiological basis of this relationship.