Novel Statistical Models for EMA Studies of Physical Activity

身体活动 EMA 研究的新统计模型

• 批准号: 8615553
• 负责人: Genevieve Fridlund Dunton
• 金额: $ 4.14万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-05-01 至 2014-08-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8615553
• 关键词: Address; Adult; Affect; Affective; Area; Asthma; Attenuated; Behavior; Belief; Child; Chronic Disease; Cognition; Cognitive; Complex; Computer software; Coupling; Data; Data Analyses; Data Set; Development; Diabetes Mellitus; Diet; Disease; Emotional; Event; Exercise; Fatigue; Feeling; Foundations; Gender; Health; Heart Diseases; High School Student; Hour; Individual; Life; Life Cycle Stages; Linear Models; Malignant Neoplasms; Measures; Mediating; Memory; Mental Depression; Meta-Analysis; Methods; Modeling; Moods; Nature; Neighborhoods; Outcome; Parents; Participant; Persons; Physical activity; Public Health; Research; Risk; Safety; Sample Size; Secondary to; Self Efficacy; Smoking; Statistical Methods; Statistical Models; Stress; Stroke; Testing; Time; Work; base; cost; improved; insight; medication compliance; middle age; multilevel analysis; novel; programs; psychologic; public health relevance; response; sedentary; simulation; social; trafficking

PROJECT SUMMARY This proposal responds to PAR-12-198 ("Improving Diet and Physical Activity Assessment"). It will develop and test novel multilevel statistical methods to examine the effects of subject-level parameters (variance and slope) of time-varying variables in ecological momentary assessment (EMA) studies of physical activity. Low level of physical activity heightens the risk of numerous deadly diseases (e.g., heart disease, stroke, cancer, diabetes) throughout the life course. The use of EMA in physical activity research is growing rapidly because real-time data capture methods supply novel insights into determinants of this behavior. In EMA studies, it is common to have up to thirty or forty observations per subject, and this allows us to model subject-level parameters such as variances (e.g., how erratic is a subject's mood?) and slopes (e.g., how much does a subject's mood change across contexts?) of time-varying variables. For example, in our recent EMA work, we have found that more physically active children have greater positive and negative emotional stability than children who are less physically active. However, current multilevel modeling strategies are restricted to treating subject-level variances and slopes as outcomes. As a consequence, statistical models do not have the ability to test whether subject-level variance and slope parameters have predictive, mediating, and moderating effects on physical and sedentary activity. For example, we are unable to ask important research questions such as whether erratic mood mediates the effects of depression on physical activity, or whether the effects of living in a highly walkable neighborhood on physical activity are attenuated for individuals with unstable self- efficacy beliefs. This modeling restriction severely limits our ability to capitalize on the full potential of the time- varying nature of EMA data to enhance physical activity research. To address this critical methodological gap, we propose to develop multilevel models, software, and strategies to test for the effects of these parameters in EMA studies. We will apply these modeling strategies to secondary analyses of pooled data from five federally- and foundation-supported EMA studies of physical activity with a combined sample size of N = 553 participants (including children and adults). The primary aims are (1) to develop novel multilevel modeling strategies and software to test whether subject-level variance and slope parameters have predictive, mediating, and moderating effects on subject-level physical and sedentary activity outcomes and (2) to apply these novel modeling strategies and software in secondary analyses of existing EMA datasets to examine the effects of subject-level variance and slopes of time-varying variables such as safety, stress, fatigue, and self-efficacy on physical and sedentary activity. This study has the potential to make novel methodological and substantive contributions for analysis of EMA data in physical activity research. The methods to be developed can easily generalize to a variety of chronic disease-relevant research areas.

项目摘要 本提案响应PAR-12-198（“改善饮食和体力活动评估”）。经济社会发展呈现 并测试新的多层次统计方法，以检查受试者水平参数（方差和 斜率）的时变变量的生态瞬时评估（EMA）研究的身体活动。低 身体活动的水平增加了许多致命疾病的风险（例如，心脏病中风癌症 糖尿病，贯穿整个生命过程。EMA在身体活动研究中的使用正在迅速增长， 实时数据捕获方法提供了对这种行为的决定因素的新的见解。在EMA研究中， 通常每个受试者有多达30或40个观察结果，这使我们能够模拟受试者级别 诸如方差的参数（例如，受试者的情绪有多不稳定？）和斜率（例如，多少钱 主题的情绪变化在上下文中？）时变的变量。例如，在我们最近的EMA工作中，我们 他们发现，更多的身体活动的儿童有更大的积极和消极的情绪稳定性比 身体活动较少的儿童。然而，目前的多级建模策略仅限于 将受试者水平的方差和斜率视为结果。因此，统计模型不具有 检验受试者水平方差和斜率参数是否具有预测、中介和调节作用的能力 对身体和久坐活动的影响。例如，我们无法提出重要的研究问题， 例如，不稳定的情绪是否介导了抑郁对身体活动的影响，或者 生活在一个高度步行的社区对身体活动的衰减与不稳定的自我， 效能信念这种建模限制严重限制了我们充分利用时代潜力的能力- 不同性质的EMA数据，以加强身体活动的研究。为了弥补这一关键的方法差距， 我们建议开发多层次模型、软件和策略，以测试这些参数对 EMA研究。我们将应用这些建模策略，从五个联邦政府的汇总数据的二次分析- 和基金会支持的EMA体力活动研究，合并样本量为N = 553例受试者 （包括儿童和成人）。主要目的是（1）开发新的多层次建模策略， 测试受试者水平方差和斜率参数是否具有预测、中介和 调节受试者水平的身体和久坐活动的结果和（2）应用这些新的 现有EMA数据集二次分析中的建模策略和软件，以检查 受试者水平的方差和时变变量的斜率，如安全性、压力、疲劳和自我效能， 身体和久坐不动的活动。这项研究有可能使新的方法和实质性的 为身体活动研究中的EMA数据分析做出了贡献。要开发的方法可以很容易地 推广到各种慢性病相关的研究领域。