Project 3- EPI

项目3-EPI

• 批准号: 9278025
• 负责人: ANDREA Z. LACROIX
• 金额: $ 12.81万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9278025
• 关键词: Accelerometer; Activities of Daily Living; Acute; Adult; Aerobic Exercise; Age; Aging; Algorithms; American; Ancillary Study; Behavior; Behavioral; Biological Markers; Biostatistics Core; Blood Pressure; Cessation of life; Chronic; Classification; Cohort Studies; Computational Technique; Confidence Intervals; Data; Elderly; Etiology; Grant; Guidelines; Health; Health Expenditures; Health Status; High Prevalence; Hospitalization; Hour; Inflammation; Interruption; Light Exercise; Lipids; Long-Term Care; Long-Term Effects; Longevity; Maps; Measures; Meta-Analysis; Methods; Minority; Modeling; Movement; National Health and Nutrition Examination Survey; Outcome; Outpatients; Patient Self-Report; Pattern; Physical Function; Physical activity; Postmenopause; Prospective Studies; Public Health; Recommendation; Relative Risks; Reporting; Research; Risk; Running; Services; Shapes; Specificity; Thigh structure; Time; Training; Visit; Walking; Woman; Women' s Health; age effect; age group; base; behavior measurement; blood glucose regulation; cardiovascular health; cohort; disability; epidemiology study; follow-up; healthy aging; improved; learned behavior; men; mortality; novel; older women; programs; prospective; sedentary; sedentary lifestyle

Abstract Project 3 of the Sedentary Time and Aging Research (STAR) Program will investigate how sitting time, standing time, sit-to-stand transitions and physical activity time are related to biomarkers of healthy aging, physical functioning and 5-7 year mortality risk. This Project will apply novel, accurate, machine-learned, behavior-based classification methods to define new SB variables using existing accelerometer data. These SB behaviors will be studied in relation to healthy aging biomarkers (cross-sectionally), and mortality and changes in physical function through 2020 in a large (>6000), diverse (50% minority) cohort of postmenopausal women from the Women's Health Initiative (WHI) who wore accelerometers in 2012- 2014 as part of the “Objective Physical Activity and Cardiovascular Health” (OPACH) ancillary study (PI: A. LaCroix). There is growing evidence in large cohort studies for a relationship between sedentary behavior and health, adjusting for physical activity. Further, these studies have shown that older adults are most at risk for inactivity. Many studies have been cross-sectional and most have employed self-reported measures of sitting time and focused on a limited range of outcomes. Few studies have focused on prospective relationships with physical functioning and maintaining mobility, which are highly salient outcomes for preserving independence in older adults. When studies have included objective accelerometer measures of sedentary time and physical activity, they have employed intensity-based cutpoints. Our research shows that these cutpoints misclassify behaviors including sitting in vehicle, standing and walking. In Project 3, we propose to accurately classify specific behaviors being studied in Projects 1 and 2, including sitting time, standing time, sit-to-stand transitions, and walking/running by employing new computational techniques with the existing accelerometer data. We will employ machine learned classifiers (that have been trained on data from older women and perform against known truths with 85% accuracy) to assess total minutes in each behavior. In addition, we will refine our existing classifiers for sit-to-stand transitions by observing transitions in Project 1 and validating the new algorithms against thigh worn inclinometer data in Project 2. The algorithms will then be applied to the existing accelerometer data in Project 3. Project 3 will assess cross-sectional relationships with existing biomarkers of glucose regulation, endothelial functioning (inflammation and blood pressure) and lipids. Mortality and self-reported physical functioning will be assessed annually through 2020. Models will explore the strength, shape and independence of relationships cross-sectionally and over time. The Biostatistics Core will build upon these analyses by exploring prolonged bouts of sitting, and the time of day when bouts occur. We will map these temporal patterns to Project 2 outcomes. Project 3 will provide valuable new evidence to inform public health guidelines on how best to interrupt sitting to improve prospects for healthy aging.

抽象的 久坐时间与衰老研究 (STAR) 计划的项目 3 将调查久坐时间如何 站立时间、从坐到站的转换和体力活动时间与健康衰老的生物标志物相关， 身体机能和 5-7 年死亡风险。该项目将应用新颖、准确、机器学习、 基于行为的分类方法，使用现有的加速度计数据定义新的 SB 变量。 这些 SB 行为将结合健康衰老生物标志物（横断面）进行研究，并且 到 2020 年，大型（>6000）、多样化（50% 少数族裔）队列的死亡率和身体功能变化 2012 年，来自妇女健康倡议 (WHI) 的绝经后妇女中佩戴加速度计的人数 2014 年，作为“客观身体活动和心血管健康”(OPACH) 辅助研究的一部分（PI：A. 拉克鲁瓦）。大型队列研究中有越来越多的证据表明久坐行为之间存在关系 和健康，调整体力活动。此外，这些研究表明，老年人最容易 不活动的风险。许多研究都是横断面的，大多数都采用自我报告的方法 测量坐姿时间并关注有限范围的结果。很少有研究关注 与身体机能和保持活动能力的预期关系，这是非常重要的 保持老年人独立性的结果。当研究包含客观 加速度计测量久坐时间和身体活动，他们采用了基于强度的 切点。我们的研究表明，这些切点对行为进行了错误分类，包括坐在车内、 站立和行走。在项目 3 中，我们建议对正在研究的特定行为进行准确分类 项目 1 和 2，包括坐着时间、站立时间、从坐到站的转换以及步行/跑步 利用现有的加速度计数据采用新的计算技术。我们将使用机器 学习分类器（已接受老年妇女数据的训练并针对已知事实进行执行 准确度为 85%）来评估每种行为的总分钟数。此外，我们还将完善现有的 通过观察项目 1 中的转换并验证新的分类器来进行从坐到站的转换 针对项目 2 中大腿佩戴的倾斜计数据的算法。然后该算法将应用于 项目 3 中现有的加速度计数据。项目 3 将评估与现有加速度计的横截面关系 葡萄糖调节、内皮功能（炎症和血压）和血脂的生物标志物。 到 2020 年，每年都会评估死亡率和自我报告的身体机能。模型将探讨 横截面和时间关系的强度、形状和独立性。生物统计学核心将 在这些分析的基础上，探索久坐的时间以及一天中久坐发生的时间。我们 将把这些时间模式映射到项目 2 的结果。项目 3 将提供有价值的新证据来告知 关于如何最好地中断久坐以改善健康老龄化前景的公共卫生指南。