자료유형  

 학위논문

Control Number  

0016935705

International Standard Book Number  

9798380374095

Dewey Decimal Classification Number  

574

Main Entry-Personal Name  

Banker, Margaret M.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of Michigan., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(131 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 85-03, Section: B.

General Note  

Advisor: Song, Peter X. K.

Dissertation Note  

Thesis (Ph.D.)--University of Michigan, 2023.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Restrictions on Access Note  

This item must not be added to any third party search indexes.

Summary, Etc.  

요약Accelerometry data collected by high-capacity sensors present a primary data type in smart mobile health. Such data enable scientists to extract personal digital features that are useful for precision health decision making. Existing methods in accelerometry data analysis typically begin with discretizing summary single-axis counts by certain fixed cutoffs into several activity categories, such as Vigorous, Moderate, Light, and Sedentary. One well-known limitation is that the chosen cutoffs have often been validated under restricted settings, and thus they cannot be generalizable across populations, devices, or studies. Motivated by the Early Life Exposure in Mexico to ENvironmental Toxicants (ELEMENT) research cohort, in this dissertation I develop data-driven approaches to overcome this bottleneck in the analysis of physical activity data.In Chapter 2, I propose to holistically summarize an individual subject's activity profile using Occupation Time curves (OTCs). Being a functional predictor, OTCs describe the percentage of time spent at or above a continuum of activity count levels. The resulting functional curve is informative to capture time-course individual variability of physical activities. I develop a multi-step adaptive learning algorithm, termed FRACT (Functional Regularized Adaptive Changepoint-detection Technique), to perform supervised learning via scalar-on-function regression modeling that involves OTC as the functional predictor of interest as well as other scalar covariates. This learning analytic first incorporates a hybrid approach of fused lasso for clustering and Hidden Markov Model for change-point detection, and then executes a few refinement procedures to determine activity windows of interest. Through extensive simulation experiments I show the proposed FRACT performs well in both changepoint detection and regression coefficient estimation. In application of this method on real world data, I analyze 354 adolescent subjects from the ELEMENT cohort to assess the influence of physical activity on two different biological aging outcomes. I find that the different biological aging outcomes are each associated with different activity window of interest, demonstrating the flexibility of the method to determine data-driven associations based both on the underlying functional variables of interest, as well as the specific health outcomes.In Chapter 3, I investigate functional analytics under an \uD835\uDC3F0 regularization approach that enables the handling of highly correlated micro-activity windows that serve as predictors in the scalar-on-function regression model proposed in Chapter 2. Relatively recent advances in \uD835\uDC3F0 regularization and discrete optimization have promoted this powerful optimization paradigm making it computationally viable. Utilizing such recent algorithmic and numeric capabilities, I develop a new one-step method that can simultaneously conduct fusion via change-point detection and parameter estimation through a new \uD835\uDC3F0 constraint formulation. This new approach is not only computationally efficient but also avoids propagation of subjective errors incurred in a multi-stage analytic. I implement a new algorithm via GUROBI, a modern optimization solver that provides a fast one-stage analytic for both parameter fusion and changepoint detection. I evaluate and illustrate the performance of the proposed learning analytics through simulation experiments and a reanalysis of the relationship between physical activity and biological aging.In Chapter 4, I extend the previous \uD835\uDC3F0 regularization framework of Chapter 3 to a longitudinal functional framework with repeated wearable data to understand the influence of serially measured functional accelerometer data on longitudinal health outcomes. The statistical methodological extension invokes the means of Quadratic Inference Functions (QIF), with an aim to detect physical activity intensity windows and assess their population-average effects on children health outcomes. I consider a population-average effects model, and develop a regularized QIF via mixed integer optimization to carry out longitudinal data analysis. In contrast to the previous chapters, which considered the physical activity data during a seven-day period, with the repeated measurements taken approximately two years after the first, I focus on a longitudinal study of physical activity patterns from late-adolescence into early adulthood on sub-scapular skin thickness (SSST). SSST is a measure of truncal fat distribution; changes in SSST diverge dramatically in boys and girls as they undergo puberty. SSST is among the measures of body composition that can be influenced by PA behaviors, which decline and vary in adolescents. To our knowledge, this is the first study to consider a longitudinal functional measure of PA in relation to changes in SSST in male and female adolescents.

Subject Added Entry-Topical Term  

Biostatistics.

Subject Added Entry-Topical Term  

Toxicology.

Subject Added Entry-Topical Term  

Environmental studies.

Subject Added Entry-Topical Term  

Medicine.

Index Term-Uncontrolled  

Functional data regression

Index Term-Uncontrolled  

Fusion regularization

Index Term-Uncontrolled  

Mixed integer optimization

Index Term-Uncontrolled  

Wearable devices

Index Term-Uncontrolled  

Actigraphy data

Added Entry-Corporate Name  

University of Michigan Biostatistics

Host Item Entry  

Dissertations Abstracts International. 85-03B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:641278