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

基本信息

批准号：
8136073
负责人：
Greg J Welk
金额：
$ 32.28万
依托单位：
IOWA STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-09-15 至 2015-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8136073
关键词：
Accounting Address Adult African American Age Area Behavior Calibration Characteristics 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 Gender Goals Health Hispanic Americans 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 response tool

项目摘要

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）：体育活动评估中的测量错误使得很难回答有关体育活动的普遍性以及与各种与健康相关结果的关联的重要问题。本应用程序的目的是制定和评估统计程序，以在常用和接受的体育活动回忆器中对测量误差进行建模，量化和调整（24小时体育活动召回24HPAR）。为了开发适当的统计模型，我们将进行一项体育活动测量调查（PAM），以获取来自居住在爱荷华州农村和城市环境中的1200名成年人（19-70年）的代表性样本的召回和客观体育活动数据。参与者将使用BodyMedia Sensewear Pro III（SP3）完成两天的体育活动监测，这是一种多通道模式识别设备，可准确估算PA和能量消耗。在每个监视日之后，参与者将完成电话管理的24HPAR评估。在获得SP3和24HPAR的重复度量后，将获得自我报告的体育活动倾向数据，以提供辅助信息，以估算常规体育锻炼的模型和分布。一系列特定目的系列将解决有关体育活动中测量误差的独特问题，并导致开发程序和方法，以促进这些方法在未来的研究中的应用。在AIM 1中，我们将开发自我管理的体育活动倾向问卷（PAPQ）和24小时PA召回（24HPAR）电话采访，可以在大规模的调查环境中进行管理。在AIM 2中，将估算召回和参考度量的测量误差模型，以估算测量的偏差和随机测量误差结构。提出的建模的一个独特方面是，我们将利用新的基于倾向的方法来解决以下事实：许多人口中的许多成年人没有任何体育锻炼。在AIM 3中，将对与时间匹配的SP3数据进行校准24PAR，以使测量基本上表现得好像是使用参考仪器收集的。在AIM 4中，测量误差模型和校准程序将用于估计个体在亚群中的日常体育活动。这项研究中的方法是创新的，因为它利用了最先进的监测状态，并将导致开发新的统计技术来建模和纠正体育活动测量误差。拟议的研究很重要，因为它将直接解决体育活动领域中复杂而长期的测量问题（即获得通常的体育活动的准确指标）。该信息将有助于改善体育活动流行病学研究，并促进开发更有效的公共卫生监视研究。这项研究产生的体育活动测量模型还将促进旨在共同建模能量摄入和能量消耗的误差的未来研究。该项目由一个强大的研究团队指导，在研究的所有必要方面具有专业知识（体育活动评估，调查设计和管理以及测量误差建模）。公共卫生相关性：拟议的研究将制定和评估统计程序，以建模，量化和调整为常用和接受的体育锻炼召回工具中的测量误差（24小时体育活动回忆）。该研究的数据将通过多组分活动监测方案（体育活动测量调查）获得，该协议将从1200名成年人（21 - 70年）的代表性样本中收集召回和客观的体育活动数据，这些数据居住在3个种族多元化的爱荷华州县中。数据分析将涉及统计程序的开发和评估，这些程序校准了针对客观物理数据的自我报告措施，以获得对人群中“常规”体育活动的准确估计。

项目成果

期刊论文数量（12）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Context of Physical Activity in a Representative Sample of Adults.

成人代表性样本中身体活动的背景。

DOI：
10.1249/mss.0000000000000641
发表时间：
2015
期刊：
Medicine and science in sports and exercise
影响因子：
4.1
作者：
Welk,GregoryJ;Kim,Youngwon
通讯作者：
Kim,Youngwon

Association between usual sodium and potassium intake and blood pressure and hypertension among U.S. adults: NHANES 2005-2010.

DOI：
10.1371/journal.pone.0075289
发表时间：
2013
期刊：
PloS one
影响因子：
3.7
作者：
Zhang Z;Cogswell ME;Gillespie C;Fang J;Loustalot F;Dai S;Carriquiry AL;Kuklina EV;Hong Y;Merritt R;Yang Q
通讯作者：
Yang Q

Associations of movement behaviors and body mass index: comparison between a report-based and monitor-based method using Compositional Data Analysis.