Development and Application of Measurement Error Models for Physical Activity

体力活动测量误差模型的开发与应用

• 批准号: 7641417
• 负责人: JANET A. TOOZE
• 金额: $ 19.54万
• 依托单位: WAKE FOREST UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7641417
• 关键词: Adult; Age; Algorithms; Animals; Biological Markers; Blood Pressure; Body mass index; Calibration; Characteristics; Chronic Disease; Classification; Data; Data Analyses; Development; Diabetes Mellitus; Disease; Disease Progression; Energy Metabolism; Epidemiologic Studies; Equation; Ethnic Origin; Gender; Gold; Health; Hypertension; Individual; Label; Malignant Neoplasms; Measurement; Measures; Methods; Modeling; National Health and Nutrition Examination Survey; Participant; Patient Self-Report; Physical activity; Physical assessment; Proteins; Questionnaires; Race; Recommendation; Relative Risks; Research Personnel; Statistical Methods; Statistical Models; Structure; Subgroup; Surveys; Validation; Water; Weight; Woman; attenuation; base; cohort; energy balance; innovation; insight; instrument; men; middle age; nutrition; public health relevance; research study; response; surveillance study

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.

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可能对估计身体活动与疾病之间的关系产生很大影响。通过使用对身体活动进行无偏测量的校准方法，可以对有偏仪器的观测值获得对真实值（不可观测值）的预测。当使用有偏差的身体活动测量来预测疾病状态时，可以估计一个校正因子来校正发生疾病的相对风险。这种方法的实用之处在于，预测真实身体活动的方程式可能只适用于一小部分个体，而不是整个群体。显然，评估一个测量是否无偏是很重要的，因为无偏测量既可以用于验证和校准研究，也可以用于监测研究。观察蛋白质和能量营养（OPEN）研究探索了自我报告饮食工具的测量误差结构，使用无偏倚的生物标志物，双标记水（DLW）， 451名40-69岁的男性和女性。OPEN参与者也接受了一份身体活动问卷（PAQ），与2003-06年国家健康和营养检查调查（NHANES）中使用的PAQ相同。我们预计，从2003-06年的NHANES中，大约有3,670名年龄在40-69岁之间的参与者完成了PAQ并佩戴了Actigraph加速计。虽然Actigraph是一种客观测量，但尚不清楚它是否公正，或者它是否对不同性别、年龄、种族/民族或体重状况组的活动进行了同样的测量。本研究将利用OPEN研究和NHANES的数据来开发一个用于身体活动评估的测量误差框架，并将该框架应用于中年人的OPEN和NHANES数据。本研究的具体目的是：(1)使用DLW作为能量消耗的无偏测量来验证和校准OPEN研究中的PAQ；(2)确定NHANES中体力活动的Actigraph测量是否无偏；(3)根据个人特征比较测量误差模型的估计值。本研究将为纠正流行病学研究中测量误差的体育活动数据提供统计学方法。它将提供关于Actigraph是否可以用作无偏测量的见解，并为评估其他研究中的其他加速度计提供统计建模方法。公共卫生相关性：调查和其他测量身体活动的方法容易产生测量误差。由于这个错误，研究人员很难就真正的体育活动与疾病的发展或进展之间的关系得出正确的结论，也很难就人们需要做多少体育活动来保持健康提出建议。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。