Measurement error correction approaches to wearable device-based measures of physical activity and self-reported measures of dietary intake in obesity and type 2 diabetes research

肥胖和 2 型糖尿病研究中基于可穿戴设备的体力活动测量和自我报告饮食摄入测量的测量误差校正方法

• 批准号: 10609059
• 负责人: Carmen D. Tekwe
• 金额: $ 58.76万
• 依托单位: TRUSTEES OF INDIANA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-15 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10609059
• 关键词: Accelerometer; Address; Adult; Affect; Age; Behavior; Biological Markers; Biomedical Research; Body mass index; Characteristics; Chronic; Classification; Clinical; Complex; Complex Mixtures; Computer software; Data; Data Set; Databases; Demographic Factors; Devices; Dietary intake; Energy Intake; Ensure; Ethnic Origin; Evaluation; Failure; Fatty acid glycerol esters; Food; Frequencies; Health; Intake; Intervention; Life Style; Linear Regressions; Measurement; Measures; Methodology; Methods; Modeling; Monitor; National Health and Nutrition Examination Survey; Noise; Non-Insulin-Dependent Diabetes Mellitus; Nutrient; Obesity; Outcome; Patient Self-Report; Pattern; Performance; Physical activity; Population Heterogeneity; Precision Health; Process; Property; Public Health; Questionnaires; Race; Recommendation; Regression Analysis; Research; Research Personnel; Role; Sampling; Sensitivity and Specificity; Statistical Data Interpretation; Statistical Methods; Statistical Models; Subgroup; Testing; Validation; Variant; Vulnerable Populations; Work; analytical method; design; discrete time; effective intervention; health outcome disparity; high dimensionality; improved; indexing; insight; modifiable behavior; novel; risk minimization; semiparametric; sex; simulation; software development; time interval; wearable device

Many current recommendations for dietary intake (DI) and physical activity (PA) to maintain optimal health and minimize risks for chronic health conditions, such as obesity and type 2 diabetes (T2D), are based on statistical analyses of data prone to measurement error, including those collected from self-reported questionnaires and wearable devices. Self-reported measures based on food frequency questionnaires are often used in DI assessments, however, they are prone to recall bias. Wearable devices enable the continuous monitoring of PA but generate complex functional data with poorly characterized systematic errors. Our work and that of others established that failure to account for measurement errors associated with scalar-valued covariates can lead to severely biased estimates, the impacts of function-valued covariates prone to complex heteroscedastic errors or mixtures of error-prone functional and scalar covariates are not well understood. Most work on functional data views the data as smooth, latent curves obtained at discrete time intervals with some random noise that is often regarded as a random process with mean zero and constant variance. By viewing this noise as homoscedastic and independent, potential serial correlations are ignored. However, our preliminary studies indicate that failure to account for these serial correlations in error-prone function-valued covariates can severely bias estimations. Additionally, while classification methods of PA patterns using device-based PA data have been proposed, there is limited work to correct for heteroscedastic measurement errors when classifying error- prone function-valued covariates, such as device-based PA data. With the increased availability of complex, massive high-dimensional function- and scalar-valued biomedical data, the need to correct for measurement error biases within these datasets to permit their accurate evaluation in various regression settings is critical. This project will address these current data limitations by developing novel statistical methods that correct for the complex mixtures of measurement errors associated with device-based PA and self-reported measures of DI applied to obesity and T2D research. Our primary objective is to investigate health outcome-related complex covariate relationships in various U.S. subpopulations by designing and applying statistical models that correct for error-prone DI and PA data biases. Aim 1: Identify latent groups of PA patterns based on device-based functional curves prone to heteroscedastic measurement errors and determine the association between identified PA patterns and T2D status, adjusting for PA biomarkers, age, sex, and race. Aim 2: Assess impacts of measurement error in DI and PA data on the quantile functions of FMI and BMI, adjusting T2D status, age, race, and sex. Aim 3: Construct generalized functional linear regression models with error-prone function- and scalar-valued covariates to evaluate the influence of PA and DI on T2D status. This project will overcome current analytic barriers to accurately evaluating the effects of DI and PA on obesity-related health outcomes.

目前许多关于饮食摄入量(DI)和体力活动(PA)的建议，以保持最佳的健康和 将肥胖和2型糖尿病(T2D)等慢性健康状况的风险降至最低是基于统计数据 分析容易出现测量误差的数据，包括从自填问卷收集的数据和 可穿戴设备。DI中经常使用基于食物频率问卷的自我报告测量 然而，在评估中，它们容易出现回忆偏差。可穿戴设备可持续监控 但会产生复杂的函数数据，而系统误差的表征很差。我们的工作和 其他人证实，未能解释与标量值协变量相关的测量误差可能会 导致估计严重偏倚，影响函数值协变量易出现复数异方差 错误或容易出错的泛函协变量和标量协变量的混合并不是很好的理解。大多数工作都是在 泛函数据将数据视为在离散时间间隔内获得的具有一定随机性的平滑、潜伏的曲线 通常被认为是均值为零且方差不变的随机过程的噪声。通过查看此噪声 由于同方差和独立，潜在的序列相关性被忽略。然而，我们的初步研究 这表明在容易出错函数值协变量中未能解释这些序列相关性可能会严重 有偏差的估计。此外，虽然使用基于设备的PA数据的PA模式的分类方法具有 已经提出，在对误差进行分类时，对异方差测量误差的校正工作有限。 倾向函数值协变量，例如基于设备的PA数据。随着Complex的可用性增加， 海量高维函数和标量值生物医学数据，需要进行测量校正 这些数据集中的误差偏差使其能够在各种回归设置中进行准确评估，这一点至关重要。 该项目将通过开发新的统计方法来解决这些当前数据的限制，以纠正 与基于设备的功放和自我报告的测量相关的测量误差的复杂混合 DI应用于肥胖和T2D研究。我们的主要目标是调查与健康结果相关的复合体 通过设计和应用统计模型来修正美国不同亚群中的协变量关系 针对易出错的DI和PA数据偏差。目标1：基于设备识别潜在的PA模式组 函数曲线容易出现异方差测量误差，并确定两者之间的关联 确定PA模式和T2D状态，根据PA生物标志物、年龄、性别和种族进行调整。目标2：评估影响 关于FMI和BMI分位数函数的DI和PA数据的测量误差，调整T2D状态、年龄、 种族，还有性爱。目标3：构造具有易错函数的广义泛函线性回归模型--和 标量值协变量来评估PA和DI对T2D状态的影响。这个项目将克服目前的 准确评估DI和PA对肥胖相关健康结果的影响的分析障碍。