Advancing Physical Activity Measurement Using Pattern Recognition Techniques

使用模式识别技术推进身体活动测量

• 批准号: 7937759
• 负责人: Patty S. Freedson
• 金额: $ 49.06万
• 依托单位: UNIVERSITY OF MASSACHUSETTS AMHERST
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7937759
• 关键词: Address; Advisory Committees; Advocate; Affect; American; Area; Behavior; Behavioral; Biological Markers; Biological Neural Networks; Blood Pressure; Cardiovascular Diseases; Cardiovascular system; Cholesterol; Clinical; Data; Detection; Development; Dose; Energy Metabolism; Ensure; Environment; Environmental Exposure; Exercise; Feedback; Funding; Genetic; Guidelines; Health; Heart; Hematological Disease; Indirect Calorimetry; Individual; Insulin; Intervention; Journals; Knowledge; Laboratories; Life; Light; Literature; Lung; Machine Learning; Measurement; Measures; Mediating; Metabolic; Metabolic syndrome; Methodology; Methods; Moderate Exercise; Modification; Monitor; National Heart, Lung, and Blood Institute; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Onset of illness; Outcome; Paper; Participant; Pathway Analysis; Pattern; Pattern Recognition; Persons; Physical activity; Physiology; Population Surveillance; Positioning Attribute; Prevention; Process; Public Health; Publishing; Reporting; Research; Research Personnel; Research Proposals; Risk Factors; Risk Reduction; Role; Structure; Techniques; Technology; Time; Training; Training Programs; Triglycerides; United States Dept. of Health and Human Services; base; blood lipid; cardiovascular disorder risk; computerized data processing; design; fitness; improved; innovation; innovative technologies; mortality; novel; programs; response; sedentary; tool

DESCRIPTION (provided by applicant): This application addresses the broad Challenge Area (01) Behavior, Behavioral Change, and Prevention and specific Challenge Area 01-HL-101: Develop innovative technologies and measurements to assess and provide real-time feedback on behavioral and environmental exposures for disease onset and progression for heart, lung, and blood disease. In October, 2008 the US Department of Health and Human Services issued the first-ever federally mandated Physical Activity Guidelines for Americans. The Guidelines reflect the view of the Physical Activity Guidelines Advisory Committee (PAGAC) and are based on an extensive review of the scientific literature on physical activity (PA) and health. In their report, the PAGAC points out the limited knowledge of the dose-response relationship between PA and health, and identifies poor measures of PA exposure as a major contributing factor to this gap in knowledge. Our application directly addresses this issue by applying innovative technologies to measure PA dose in a free- living environment. We will use these technologies to examine if habitual PA performed outside of purposeful exercise influences biomarkers of cardiovascular health. Although insufficient PA clearly correlates with an increased risk for cardiovascular disease (CVD), research evidence is equivocal regarding the effects of training on CVD risk factors (e.g. insulin action, triglycerides, blood pressure, and cholesterol). Research suggests increases in sedentary behavior may negate the benefits of training however this idea has not been explored experimentally. Our application will consider habitual free-living PA as a possible mechanism mediating the relationship between training and risk factors for cardiovascular disease. In order to elucidate the relationship between PA and biomarkers of cardiovascular disease risk, it is critical that valid, objective measures are used to quantify PA. We propose to use novel analytic techniques known as artificial neural networks (ANN) to process accelerometer-based measurements of PA. The first part of this project (Aim 1) will examine the ANN's sensitivity to change in PA dose by applying the ANN technique to distinguish three distinct patterns of habitual PA - Sedentary, Moderately Active, and Very Active. These three conditions represent common activity patterns that impact health. Accurately assessing changes to habitual PA levels that are relevant to public health will advance the field by further establishing a technique for application in population surveillance research and detection of changes in PA consequent to an intervention. The second part of this project (Aim 2) will apply the ANN methodology to examine the effect of free-living activity and inactivity levels, performed outside of training, on insulin action, blood pressure, triglycerides, cholesterol, and cardiorespiratory fitness following a 12-week exercise training trial in previously sedentary individuals with an elevated risk for CVD. Results from this study have the potential to impact how clinical exercise trials are conducted (e.g. require objective monitoring of PA outside of an exercise training trial) and how exercise is prescribed (e.g. reducing sedentary time AND maintaining sufficient PA). The Physical Activity Guidelines Advisory Committee advocates improved measures of physical activity exposure in order to elucidate the relationship between physical activity dose and health. To address this challenge we will apply and validate innovative accelerometer-based technologies for measuring physical activity to assess its sensitivity to detecting changes in dose of physical activity and to monitor activity outside of a training program designed to improve cardiorespiratory fitness and biomarkers of cardiovascular disease risk. Through improved measures of physical activity this project will promote a better understanding of how the dose of physical activity affects selected health outcomes.

描述（由申请人提供）：本申请涉及广泛的挑战领域（01）行为、行为改变和预防，以及特定的挑战领域01-HL-101：开发创新技术和测量方法，以评估心脏、肺和血液疾病发病和进展的行为和环境暴露并提供实时反馈。2008年10月，美国卫生与公众服务部发布了有史以来第一份联邦强制规定的美国人身体活动指南。该指南反映了身体活动指南咨询委员会（PAGAC）的观点，并基于对身体活动（PA）和健康科学文献的广泛审查。PAGAC在其报告中指出，对PA与健康之间的剂量-反应关系的了解有限，并确定PA暴露的不良措施是造成这一知识差距的主要因素。我们的应用程序通过应用创新技术来测量自由生活环境中的PA剂量，直接解决了这个问题。我们将使用这些技术来检查在有目的的运动之外进行的习惯性PA是否会影响心血管健康的生物标志物。尽管PA不足与心血管疾病（CVD）风险增加明显相关，但关于训练对CVD风险因素（例如胰岛素作用，甘油三酯，血压和胆固醇）的影响，研究证据是模棱两可的。研究表明，久坐行为的增加可能会抵消训练的好处，但这种想法还没有经过实验研究。我们的申请将考虑习惯性自由生活PA作为一种可能的机制，调解训练和心血管疾病的危险因素之间的关系。为了阐明PA和心血管疾病风险生物标志物之间的关系，使用有效、客观的措施来量化PA至关重要。我们建议使用新的分析技术，称为人工神经网络（ANN）处理基于加速度计的测量PA。本项目的第一部分（目标1）将通过应用ANN技术来区分习惯性PA的三种不同模式-久坐、中度活跃和非常活跃，来检查ANN对PA剂量变化的敏感性。这三种情况代表了影响健康的常见活动模式。准确评估与公共卫生相关的习惯性PA水平的变化将通过进一步建立一种应用于人群监测研究和检测干预后PA变化的技术来推进该领域。该项目的第二部分（目标2）将应用ANN方法，在先前久坐不动的CVD风险升高的个体中进行12周的运动训练试验后，检查在训练之外进行的自由生活活动和不活动水平对胰岛素作用、血压、甘油三酯、胆固醇和心肺功能的影响。这项研究的结果可能会影响临床运动试验的进行方式（例如，需要在运动训练试验之外对PA进行客观监测）以及如何规定运动（例如，减少久坐时间并保持足够的PA）。身体活动指南咨询委员会主张改进身体活动暴露的措施，以阐明身体活动剂量与健康之间的关系。为了应对这一挑战，我们将应用并验证创新的基于加速度计的技术来测量身体活动，以评估其检测身体活动剂量变化的灵敏度，并监测旨在改善心肺健康和心血管疾病风险生物标志物的训练计划之外的活动。通过改进体力活动的措施，该项目将促进更好地了解体力活动的剂量如何影响选定的健康结果。