A Measurement Error Approach to Estimating Usual Daily Physical Activity Distribu

估计日常体力活动分布的测量误差方法

• 批准号: 7870899
• 负责人: Greg J Welk
• 金额: $ 7.59万
• 依托单位: IOWA STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-15 至 2012-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7870899
• 关键词: Accounting; Address; Adult; African American; Age; Area; Arts; Behavior; Calibration; Characteristics; Classification; Cognitive; Complex; County; Data; Data Analyses; Development; Devices; Diagnostic Sensitivity; Diet; Dietary Assessment; Dietary History; Dietary intake; Eating; Energy Intake; Energy Metabolism; Environment; Epidemic; Epidemiologic Studies; Ethnic Origin; Evaluation; Expenditure; Food; Foundations; Frequencies; Future; Gender; Goals; Health; Hispanics; Hour; Household; Individual; Intake; Interest Group; Interview; Investigation; Iowa; Lead; Light; Literature; Measurement; Measures; Methodology; Methods; Modeling; Monitor; National Health and Nutrition Examination Survey; Nature; Nutrient; Obesity; Occupational; Outcome; Outcome Measure; Participant; Patient Self-Report; Pattern; Pattern Recognition; Persons; Physical activity; Pilot Projects; Population; Population Surveillance; Prevalence; Procedures; Process; Property; Protocols documentation; Public Health; Qualifying; Questionnaires; Race; Recruitment Activity; Reporting; Research; Research Personnel; Rural; Sampling; Self-Administered; Series; Social Desirability; Solutions; Statistical Models; Structure; Study models; Surveys; Techniques; Telephone; Telephone Interviews; Testing; Time; Work; base; cost; cost effective; data modeling; design; experience; food consumption; improved; innovation; instrument; nutrition; public health priorities; public health relevance; response; tool

DESCRIPTION (provided by applicant): Measurement error in physical activity assessment has made it difficult to answer important questions about the prevalence of physical activity and associations with various health-related outcomes. The objective in the present application is to develop and evaluate statistical procedures to model, quantify and adjust for measurement error in a commonly used and accepted physical activity recall instrument (24 hour physical activity recall 24hPAR). To develop appropriate statistical models, we will conduct a Physical Activity Measurement Survey (PAMS) to obtain recall and objective physical activity data from a representative sample of 1200 adults (19- 70 yrs) who reside in rural and urban environments in Iowa. Participants will complete two days of physical activity monitoring with the BodyMedia Sensewear Pro III (SP3), a multi-channel pattern recognition device that provides accurate estimates of PA and energy expenditure. After each monitoring day, participants will complete a telephone-administered 24hPAR assessment. After replicate measures of the SP3 and 24hPAR are obtained, self-reported physical activity propensity data will be obtained to provide auxiliary information for estimating models and distributions of usual physical activity. The sequential series of Specific Aims will address unique questions about measurement error in physical activity and lead to the development of procedures and methodologies to facilitate the application of these methods in future research. In Aim 1, we will develop a self- administered physical activity propensity questionnaire (PAPQ) and a 24 hr PA recall (24hPAR) telephone interview that can be administered in a large-scale survey setting. In Aim 2, measurement error models will be estimated for the recall and reference measures in order to estimate the bias and random measurement error structure of measurements. A unique aspect of the proposed modeling is that we will utilize new propensity-based approaches to address the fact that many adults in the population report no physical activity. In Aim 3, the 24PAR will be calibrated against the temporally matched SP3 data so that the measurements essentially behave as if they had been collected using a reference instrument. In Aim 4, the measurement error model and calibration procedures will be used to estimate usual daily physical activity of individuals in subpopulations. The approach in this research is innovative, because it utilizes state of the art monitors and will lead to the development of new statistical techniques to model and correct physical activity measurement error. The proposed research is significant, because it will directly address a complex and long-standing measurement problem in the physical activity field (i.e. obtaining accurate indicators of usual physical activity). This information will help to improve physical activity epidemiology research and facilitate the development of more effective public health surveillance research. The resulting physical activity measurement model from this study will also facilitate future research aimed at jointly modeling error in energy intake and energy expenditure. The project is guided by a strong research team with expertise in all necessary facets of the study (physical activity assessment, survey design and administration, and measurement error modeling). PUBLIC HEALTH RELEVANCE: The proposed study will develop and evaluate statistical procedures to model, quantify and adjust for measurement error in a commonly used and accepted physical activity recall instrument (24 hour physical activity recall). Data for the study will be obtained through a multi-component activity monitoring protocol (Physical Activity Measurement Survey) that will collect recall and objective physical activity data from a representative sample of 1200 adults (21-70 yrs) who reside in 3 ethnically diverse Iowa counties. Data analyses will involve the development and evaluation of statistical procedures that calibrate the self-report measure against objective physical data to obtain accurate estimates of "usual" physical activity in the population.

描述（由申请人提供）：身体活动评估中的测量误差使得很难回答关于身体活动的流行程度及其与各种健康相关结果的关联的重要问题。在本应用程序的目的是开发和评估统计程序，以模拟，量化和调整测量误差在一个常用的和公认的体育活动召回仪器（24小时体育活动召回24hPAR）。为了建立适当的统计模型，我们将进行身体活动测量调查（PAMS），从居住在爱荷华州农村和城市环境的1200名成年人（19- 70岁）的代表性样本中获得召回和客观的身体活动数据。参与者将使用BodyMedia Sensewear Pro III （SP3）完成为期两天的身体活动监测，这是一种多通道模式识别设备，可提供准确的PA和能量消耗估计。在每个监测日之后，参与者将完成电话管理的24hPAR评估。在获得SP3和24hPAR的重复测量后，将获得自我报告的体育活动倾向数据，为估计通常体育活动的模型和分布提供辅助信息。一系列的具体目标将解决有关体育活动测量误差的独特问题，并导致程序和方法的发展，以促进这些方法在未来研究中的应用。在目标1中，我们将开发一个自我管理的身体活动倾向问卷（PAPQ）和一个24小时的PA回忆（24hPAR）电话访谈，可以在一个大规模的调查设置中进行。在Aim 2中，将估计召回和参考测量的测量误差模型，以估计测量的偏差和随机测量误差结构。所提出的模型的一个独特方面是，我们将利用新的基于倾向的方法来解决人口中许多成年人报告没有体育活动的事实。在Aim 3中，24PAR将根据临时匹配的SP3数据进行校准，这样测量结果基本上就像使用参考仪器收集的一样。在目标4中，测量误差模型和校准程序将用于估计亚种群中个体的日常身体活动。这项研究的方法是创新的，因为它利用了最先进的监测器，并将导致新的统计技术的发展，以模拟和纠正体育活动测量误差。本研究具有重要意义，因为它将直接解决体育活动领域一个复杂而长期存在的测量问题（即获得日常体育活动的准确指标）。这些信息将有助于改进体育活动流行病学研究，并促进开展更有效的公共卫生监测研究。本研究得出的体力活动测量模型也将有助于未来针对能量摄入和能量消耗联合建模误差的研究。该项目由一个强大的研究团队指导，他们在研究的所有必要方面（体育活动评估、调查设计和管理、测量误差建模）都具有专业知识。公共卫生相关性：拟议的研究将开发和评估统计程序，以模拟、量化和调整常用和公认的身体活动回忆工具（24小时身体活动回忆）的测量误差。本研究的数据将通过多组分活动监测方案（身体活动测量调查）获得，该方案将从居住在爱荷华州3个不同种族的县的1200名成年人（21-70岁）的代表性样本中收集回忆和客观的身体活动数据。数据分析将涉及统计程序的开发和评估，这些程序将自我报告测量与客观身体数据进行校准，以获得对人口“通常”身体活动的准确估计。