Novel Analytic Techniques to Assess Physical Activity

评估身体活动的新颖分析技术

• 批准号: 7148449
• 负责人: Patty S. Freedson
• 金额: $ 25.33万
• 依托单位: UNIVERSITY OF MASSACHUSETTS AMHERST
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-20 至 2011-05-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7148449
• 关键词: classification; clinical research; measurement

DESCRIPTION (provided by applicant): 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.

描述（申请人提供）：在开发和使用基于加速度计的运动传感器进行身体活动研究方面取得了进展。然而，处理活动监视器数据的传统方法不能提供足够的准确性以满足在研究竞技场中使用客观身体活动数据的当前趋势。本提案旨在解决基于加速度计的PA评估方法的这一弱点：具体目标是：1）开发和验证处理Actigraph加速度计数据的新方法，以使用强大的现代分类方法来改善PA的估计（分类树、判别分析、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控制环境中评估PA：（3）比较分类方法和传统方法在自由生活PA条件下量化PA并选择推荐的方法;（4）对传统的PA定量方法和新的PA分类方法在习惯性PA的汇总估计中的测量误差进行校正。我们独特的多学科研究小组将通过首先开发创新的分类方法来确定实验室环境中的特定活动，然后使用从受控实验室环境和自由生活情况下进行的已知活动收集的数据来验证模型，从而实现这些目标。基于这些研究的结果，将改进分类方法，并使用统计测量误差方法调整PA行为的估计值，以获得更准确的PA估计值。我们选择的分类方法包括其他人可以很容易使用的公开可用的“现成”分类方法。由此产生的数据处理程序将在流行的商业软件包中实施，并免费提供。拟议的调查结果将推动PA评估领域的前进，提供创新的方法，以获得更准确和详细的估计PA使用流行的基于加速度计的PA监视器。这种系统的方法将提供信息，导致更清楚地了解PA和健康之间的剂量反应关系和这种关系的生理基础。