Novel Statistical Models for EMA Studies of Physical Activity

描述（由申请人提供）：本提案响应PAR-12-198（“改善饮食和身体活动评估”）。它将开发和测试新的多层次统计方法，以检查身体活动的生态瞬时评估（EMA）研究中时变变量的受试者水平参数（方差和斜率）的影响。身体活动水平低会增加一生中罹患许多致命疾病（如心脏病、中风、癌症、糖尿病）的风险。EMA在体育活动研究中的应用正在迅速增长，因为实时数据捕获方法为这种行为的决定因素提供了新的见解。在EMA研究中，通常每个受试者有多达30或40个观察值，这使我们能够对受试者水平参数进行建模，如方差（例如，受试者的情绪有多不稳定？）和斜率（例如，受试者的情绪在不同环境下变化多少？）例如，在我们最近的EMA工作中，我们发现身体活动较多的儿童比身体活动较少的儿童具有更大的积极和消极情绪稳定性。然而，目前的多层次建模策略仅限于将受试者水平的方差和斜率作为结果。因此，统计模型没有能力检验受试者水平方差和斜率参数是否对身体和久坐活动具有预测、中介和调节作用。例如，我们无法提出重要的研究问题，如不稳定的情绪是否介导了抑郁对身体活动的影响，或者生活在高度适宜步行的社区对身体活动的影响是否对自我效能感不稳定的个体有所减弱。这种建模限制严重限制了我们利用EMA数据时变特性的全部潜力来加强体育活动研究的能力。为了解决这一关键的方法差距，我们建议开发多层次模型、软件和策略来测试EMA研究中这些参数的影响。我们将这些建模策略应用于5项联邦政府和基金会支持的EMA体育活动研究的汇总数据的二次分析，这些研究的总样本量为N = 553名参与者（包括儿童和成人）。主要目标是：(1)开发新的多层次建模策略和软件，以测试受试者水平方差和斜率参数是否对受试者水平的身体和久坐活动结果具有预测、中介和调节作用；(2)将这些新的建模策略和软件应用于现有EMA数据集的二次分析，以检查受试者水平方差和斜率对时变变量的影响，如安全、压力、疲劳、身体和久坐活动的自我效能感。这项研究有可能为体育活动研究中的EMA数据分析做出新的方法和实质性贡献。待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。

DESCRIPTION (provided by applicant): 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 capitaliz 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.