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.

摘要 久坐时间和衰老研究(STAR)计划的项目3将调查坐着的时间， 站立时间、坐立转换和体力活动时间与健康衰老的生物标记物有关， 身体功能和5-7年死亡风险。该项目将应用新的、准确的、机器学习的 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行为将与健康老化生物标记物相关(横断面)进行研究，以及 在一个大型(6000人)、多样化(50%的少数民族)队列中的死亡率和2020年前身体机能的变化 来自妇女健康倡议(WHI)的绝经后女性在2012年佩戴加速计- 2014年作为“客观体力活动与心血管健康”(OPACH)辅助研究的一部分(PI：A。 Lacroix)。在大型队列研究中，有越来越多的证据表明久坐行为与 和健康，根据体力活动进行调整。此外，这些研究还表明，老年人在 不活动的风险。许多研究是横断面的，大多数采用了自我报告的方法。 衡量坐着的时间，侧重于有限范围的结果。很少有研究关注于 与身体功能和保持机动性的预期关系，这是非常显著的 老年人保持独立性的结果。当研究包括目标时 加速计测量久坐时间和体力活动，他们采用了基于强度的 切割点。我们的研究表明，这些切点错误地分类了包括坐在车里的行为， 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，包括坐着的时间、站立的时间、从坐到站的转变，以及走路/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 学习的分类器(根据老年妇女的数据进行培训，并根据已知事实执行 准确率为85%)来评估每种行为的总分钟数。此外，我们还将完善我们现有的 通过观察项目1中的转换并验证新的 针对项目2中的大腿磨损测斜仪数据的算法。然后，这些算法将应用于 项目3中现有的加速度计数据。项目3将评估与现有 葡萄糖调节、内皮功能(炎症和血压)和血脂的生物标志物。 死亡率和自我报告的身体功能将在2020年前每年进行评估。模特们将探索 跨部门和随时间推移的关系的强度、形状和独立性。生物统计学的核心将 在这些分析的基础上，探索长时间的坐姿，以及一天中发生坐姿的时间。我们 将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以供 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。