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有关身体活动的普遍性以及与各种健康相关结果的关联的重要问题。本申请的目的是开发和评估统计程序，以在常用和接受的身体活动回忆工具（24小时身体活动回忆24hPAR）中对测量误差进行建模、量化和调整。为了开发适当的统计模型，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）的代表性样本中获取回忆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 PA 和能量消耗的准确估计。每个监测日结束后，参与者将完成电话管理的 24hPAR 评估。获得SP3和24hPAR的重复测量值后，将获得自我报告的身体活动倾向数据，为估计日常身体活动的模型和分布提供辅助信息。一系列具体目标将解决有关身体活动测量误差的独特问题，并导致程序和方法的开发，以促进这些方法在未来研究中的应用。在目标 1 中，我们将开发可在大规模调查环境中进行的自我管理身体活动倾向问卷 (PAPQ) 和 24 小时 PA 回忆 (24hPAR) 电话访谈。在目标 2 中，将为召回和参考测量估计测量误差模型，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。在目标 3 中，24PAR 将根据时间匹配的 SP3 数据进行校准，以便测量结果基本上表现得就像是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群体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，以建模和纠正身体活动测量误差。这项研究意义重大，因为它将直接解决身体活动领域中一个复杂且长期存在的测量问题（即获得日常身体活动的准确指标）。这些信息将有助于改进身体活动流行病学研究，并促进更有效的公共卫生监测研究的发展。这项研究得出的身体活动测量模型也将有助于未来旨在联合建模能量摄入和能量消耗误差的研究。该项目由强大的研究团队指导，该团队在研究的所有必要方面（体育活动评估、调查设计和管理以及测量误差建模）拥有专业知识。公共卫生相关性：拟议的研究将开发和评估统计程序，以对常用和公认的身体活动回忆工具（24 小时身体活动回忆）中的测量误差进行建模、量化和调整。该研究的数据将通过多成分活动监测方案（体力活动测量调查）获得，该方案将从居住在爱荷华州 3 个不同种族的 1200 名成年人（21-70 岁）的代表性样本中收集回忆和客观体力活动数据县。数据分析将涉及统计程序的开发和评估，根据客观身体数据校准自我报告测量，以获得对人群“通常”身体活动的准确估计。