A5076145211- Statistical Methods and Inference
- Bayesian Methods and Mixture Models
- Statistical Methods and Bayesian Inference
- Physical Activity and Health
- Sleep and related disorders
- Sleep and Wakefulness Research
- Gene expression and cancer classification
- Mental Health Research Topics
- Advanced Statistical Methods and Models
- Obesity, Physical Activity, Diet
- Obstructive Sleep Apnea Research
- Statistical Distribution Estimation and Applications
- Machine Learning in Healthcare
- Heart Rate Variability and Autonomic Control
- Health disparities and outcomes
- Dementia and Cognitive Impairment Research
- Balance, Gait, and Falls Prevention
- Air Quality and Health Impacts
- Fault Detection and Control Systems
- Artificial Intelligence in Healthcare
- Innovation Diffusion and Forecasting
- Cognitive Science and Mapping
- Ergonomics and Musculoskeletal Disorders
- Non-Invasive Vital Sign Monitoring
- Nutritional Studies and Diet
University of South Carolina
2022-2025
Harvard University Press
2023
Johns Hopkins University
2021-2022
North Carolina State University
2018-2020
Abstract Introduction Few studies have explored whether gait measured continuously within a community setting can identify individuals with Alzheimer's disease (AD). This study tests the feasibility of this method to at earliest stage AD. Methods Mild AD (n = 38) and cognitively normal control (CNC; n 48) participants from University Kansas Disease Center Registry wore GT3x+ accelerometer for 7 days assess gait. Penalized logistic regression repeated five‐fold cross‐validation followed by...
The purpose of this study was to evaluate the reliability and validity raw accelerometry output from research-grade consumer wearable devices compared accelerations produced by a mechanical shaker table. Raw data total 40 (i.e., n = 10 ActiGraph wGT3X-BT, Apple Watch Series 7, Garmin Vivoactive 4S, Fitbit Sense) were reference an orbital table at speeds ranging 0.6 Hz (4.4 milligravity-mg) 3.2 (124.7mg). Two-way random effects absolute intraclass correlation coefficients (ICC) tested...
Summary With the advent of continuous health monitoring with wearable devices, users now generate their unique streams data such as minute-level step counts or heartbeats. Summarizing these via scalar summaries often ignores distributional nature and almost unavoidably leads to loss critical information. We propose capture user-specific quantile functions (QF) use QFs predictors in scalar-on-quantile-function-regression (SOQFR). As an alternative approach, we also represent L-moments, robust...
ABSTRACT Introduction This study examined the potential of a device agnostic approach for predicting physical activity energy expenditure (PAEE) from research grade and consumer wearable accelerometry heartrate (HR) raw data compared to indirect calorimetry in children. Methods Two-hundred thirty-one 5–12-year-olds (52.4% male) diverse skin tone body weights participated 60-minute protocol with multiple activities at varying intensities. Children wore two three wearables (Apple Watch Series...
Conventional clinical assessments in multiple sclerosis (MS), such as the Expanded Disability Status Scale (EDSS), often miss subtle functional changes. While accelerometry provides an objective measure of real-world motor activity, most daily summaries focus on average values, neglecting both peak performance and its variability throughout day. This diurnal may reflect a person's capacity to sustain high-effort activity despite fatigue, phenomenon we term observable movement reserve. To...
Abstract Wearable data is a rich source of information that can provide deeper understanding links between human behaviors and health. Existing modelling approaches use wearable summarized at subject level via scalar summaries in regression, temporal (time-of-day) curves functional analysis (FDA), distributions distributional (DDA). We propose to capture temporally local using subject-specific time-by-distribution (TD) objects. Specifically, we develop on regression (SOTDR) model...
The challenge of handling missing data is widespread in modern analysis, particularly during the preprocessing phase and various inferential modeling tasks. Although numerous algorithms exist for imputing data, assessment imputation quality at patient level often lacks personalized statistical approaches. Moreover, there a scarcity methods metric space based objects. aim this paper to introduce novel two-step framework that comprises: (i) objects taking values metrics spaces, (ii) criterion...
Abstract Study Objectives Evaluate wrist-placed accelerometry predicted heartrate compared to electrocardiogram (ECG) in children during sleep. Methods Children (n = 82, 61% male, 43.9% black) wore a Apple Watch Series 7 (AWS7) and ActiGraph GT9X polysomnogram. Three-Axis data was extracted from AWS7 the GT9X. Accelerometry estimates were derived jerk (the rate of acceleration change), computed using peak magnitude frequency short time Fourier Transforms Hilbert transformed magnitude....
Abstract Study Objectives Evaluate the performance of actigraphy-based open-source and proprietary sleep algorithms compared to polysomnography in children with suspected disorders. Methods In a clinic, 110 (5-12 years, 54% female, 50% Black, 82% disorders) wore wrist-placed ActiGraph GT9X during overnight polysomnography. Actigraphy data were scored as or wake using GGIR ActiLife software. Discrepancy epoch-by-epoch analyses conducted assess agreement between polysomnography, along...
Background and Objectives Hearing aids may reduce the risk of dementia among individuals with hearing loss. However, no evidence is available from randomized controlled trials (RCTs) on effectiveness use in reducing incident dementia. Using target trial emulation, we leveraged an existing longitudinal cohort study to estimate association between initiation Research Design Methods The Health Retirement Study was used emulate non-institutionalized participants aged ≥50 years self-reported...
Summary We propose a novel method for variable selection in functional linear concurrent regression. Our research is motivated by fisheries footprint study where the goal to identify important time-varying sociostructural drivers influencing patterns of seafood consumption, and hence footprint, over time, as well estimating their dynamic effects. develop variable-selection regression extending classically used scalar-on-scalar methods like lasso, smoothly clipped absolute deviation (SCAD)...
We develop a functional proportional hazards mixture cure model with scalar and covariates measured at the baseline. The model, useful in studying populations fraction of particular event interest is extended to data. employ expectation–maximization algorithm semiparametric penalized spline-based approach estimate dynamic coefficients incidence latency part. proposed method computationally efficient simultaneously incorporates smoothness estimated via roughness penalty. Simulation studies...
Depth measures have gained popularity in the statistical literature for defining level sets complex data structures like multivariate data, functional and graphs. Despite their versatility, integrating depth into regression modeling establishing prediction regions remains underexplored. To address this gap, we propose a novel method utilizing model-free uncertainty quantification algorithm based on conditional kernel mean embeddings. This enables creation of tailored tolerance models...
ABSTRACT Introduction This study examined the potential of a device agnostic approach for predicting physical activity from consumer wearable accelerometry compared with research-grade accelerometry. Methods Seventy-five 5- to 12-year-olds (58% male, 63% White) participated in 60-min protocol. Children wore wrist-placed wearables (Apple Watch Series 7 and Garmin Vivoactive 4) (ActiGraph GT9X) concurrently an indirect calorimeter (COSMED K5). Activity intensities (i.e., inactive, light,...
We develop a new method for variable selection in nonlinear additive function-on-scalar regression (FOSR) model. Existing methods FOSR have focused on the linear effects of scalar predictors, which can be restrictive assumption presence multiple continuously measured covariates. propose computationally efficient approach existing using functional principal component scores response and extend this framework to The proposed provides unified flexible FOSR, allowing Numerical analysis...
Abstract The purpose of this study was to evaluate the reliability and validity raw accelerometry output from research-grade consumer wearable devices compared accelerations produced by a mechanical shaker table. Raw data total 40 (i.e., n=10 ActiGraph wGT3X-BT, Apple Watch Series 7, Garmin Vivoactive 4S, Fitbit Sense) were criterion an orbital table at speeds ranging 0.6 Hz (4.4 milligravity-mg) 3.2 (124.7mg). For testing, identical oscillated for 5 trials that lasted 2 minutes each. 1...
We develop a new method for variable selection in nonparametric functional concurrent regression. The commonly used linear model (FLCM) is far too restrictive assuming linearity of the covariate effects, which not necessarily true many real‐world applications. (NPFCM), on other hand, much more flexible and can capture complex dynamic relationships present between response covariates. extend classically methods, e.g., group LASSO, SCAD MCP, to perform NPFCM. show via numerical simulations...