Development and Application of Measurement Error Models for Physical Activity

描述(由申请人提供)：需要更好地了解当前身体评估措施的局限性。评估方法中的随机误差和系统误差都会对估计体力活动和疾病之间的关系产生很大影响。通过使用具有无偏的体力活动测量的校准方法，可以对有偏的仪器的观测值获得真理的预测(这是不可观察的)。当使用有偏见的体力活动测量来预测疾病状态时，可以估计校正系数以校正发生疾病的相对风险。这种方法的实用之处在于，预测真正的体力活动的方程可能是在个人的子集上开发的，而不是整个队列。显然，评估一项措施是否公正很重要，因为在验证和校准研究以及监督研究中都可以使用公正的措施。观察蛋白质和能量营养(OPEN)研究使用总能量消耗的无偏生物标志物--双标记水(DLW)，对451名年龄在40-69岁的男性和女性进行了自我报告膳食工具的测量误差结构的研究。开放参与者还接受了体力活动问卷(PAQ)，与2003-06年国家健康与营养检查调查(NHANES)中使用的PAQ相同。我们预计，在2003-06年的NHANES期间，将有大约3,670名年龄在40-69岁、完成PAQ并佩戴Actigraph加速度计的参与者提供数据。尽管Actigraph是一项客观的衡量标准，但尚不清楚它是否没有偏见，或者它是否同样能很好地衡量不同性别、年龄、种族/民族或体重状况群体的活动。这项研究将利用开放式研究和NHANES的数据来开发一个体力活动评估的测量误差框架，并将该框架应用于OPEN和NHANES中年人的数据。这项研究的具体目的是：(1)验证和校准开放研究中的PAQ，使用DLW作为能量消耗的无偏见测量；(2)确定NHANES中体力活动的Actigraph测量是否没有偏见；以及(3)比较测量误差模型对个人特征的估计。这项研究将提供在流行病学研究中纠正测量误差的体力活动数据的统计方法。它将为Actigraph是否可以作为一种公正的测量方法提供洞察力，并为在其他研究中评估其他加速度计提供统计建模方法。公共卫生相关性：调查和其他测量体力活动的方法容易出现测量误差。由于这个错误，研究人员很难就真正的体育锻炼与疾病的发展或进展之间的关系做出正确的结论，也很难就人们需要进行多少体育锻炼才能保持健康提出建议。需要统计方法来描述存在的错误类型，检查它们对研究研究中得出的结论的影响，并开发更好的方法来分析这些研究的数据。

DESCRIPTION (provided by applicant): A better understanding of the limitations of current physical assessment measures is needed. Both random and systematic errors in an assessment method can have a large impact on estimating the relationship between physical activity and disease. By using a calibration method with an unbiased measure of physical activity, a prediction of truth (which is unobservable) may be obtained for an observed value of a biased instrument. A correction factor can be estimated to correct the relative risk of developing disease when a biased measure of physical activity is used to predict disease status. The utility of this method is that the equation predicting true physical activity may be developed on a subset of individuals, rather than an entire cohort. Clearly, assessing whether a measure is unbiased is important, as unbiased measures may be used in both validation and calibration studies, as well as in surveillance studies. The Observing Protein and Energy Nutrition (OPEN) Study explored the measurement error structure of self-report dietary instruments using an unbiased biomarker for total energy expenditure, doubly labeled water (DLW), for 451 men and women ages 40-69 y. A physical activity questionnaire (PAQ) was also administered to the OPEN participants, the same PAQ that was used in National Health and Nutrition Examination Survey (NHANES) 2003-06. We anticipate data from approximately 3,670 participants ages 40-69 y who completed the PAQ and wore Actigraph accelerometers will be available from 2003-06 NHANES. Although the Actigraph is an objective measure, it is not clear if it is unbiased, or if it measures activity equally well for different gender, age, race/ethnicity, or weight status groups. This study will utilize data from the OPEN study and NHANES to develop a measurement error framework for physical activity assessment, and to apply this framework to OPEN and NHANES data for middle-aged adults. The specific aims of this study are: (1) To validate and calibrate the PAQ in the OPEN Study using DLW as an unbiased measure of energy expenditure; (2) To determine whether the Actigraph measure of physical activity in NHANES is unbiased; and (3) to compare estimates from the measurement error models by personal characteristics. This study will provide statistical methods for correcting physical activity data for measurement error in an epidemiologic study. It will provide insight as to whether the Actigraph may be used as an unbiased measure, and provide a statistical modeling approach for assessing other accelerometers in other studies. PUBLIC HEALTH RELEVANCE: Surveys and other methods of measuring physical activity are prone to measurement error. Due to this error, it is difficult for researchers to make the correct conclusions about the relationship between true physical activity and the development or progression of disease, or to make recommendations about how much physical activity people need to do to stay healthy. Statistical methods are needed to describe the types of errors that exist, to examine their impact on conclusions made in research studies, and to develop better ways of analyzing data from these studies.