arXiv subjects

Chongzhi Di

Publications and source records attributed to Chongzhi Di.

7 recordsLinked to original sources

arXiv2026

A functional joint model with baseline functional covariates: linking sitting accumulation patterns to physical function and mortality among older women

In large-scale epidemiological studies, it is often of interest to investigate joint relationships between longitudinal and time-to-event outcomes with exposures that are trajectories or functions. Our motivation study is the Objective Physical Activity and Cardiovascular Health (OPACH) Study, which collected accelerometry-measured physical activity in 6,489 older women. One of the scientific aims is to understand sedentary behavior accumulation patterns and its association with physical function (longitudinal) and mortality (time-to-event). We propose a novel approach that first converts raw accelerometry data into daily sitting bout accumulation profiles, which are treated as functional covariates, and then develop a functional joint model for longitudinal and time-to-event outcomes that incorporates a baseline functional covariate. The longitudinal process is modeled using functional data methods and linked to the survival process through functional principal component scores. Both sub-models include interpretable linear scalar-on-function regression coefficients to capture flexible dose-response associations between sitting accumulated across varying bout durations and health outcomes. Estimation is carried out via an efficient expectation-maximization (EM) algorithm with penalized spline approximations. Simulation studies demonstrate accurate parameter estimation and reliable model selection. Application to the OPACH data reveals flexible and interpretable dose-response relationships between sitting bout durations, physical function, and mortality.

stat.AP↗

arXiv2026

MoCA: Multi-modal Cross-masked Autoencoder for Time Series in Digital Health

Wearable devices enable continuous multi-modal physiological and behavioral monitoring, yet analysis of these data streams faces fundamental challenges including the lack of gold-standard labels and incomplete sensor data. While self-supervised learning approaches have shown promise for addressing these issues, existing multi-modal extensions present opportunities to better leverage the rich temporal and cross-modal correlations inherent in simultaneously recorded wearable sensor data. We propose the Multi-modal Cross-masked Autoencoder (MoCA), a self-supervised learning framework that combines transformer architecture with masked autoencoder (MAE) methodology, using a principled cross-modality masking scheme that explicitly leverages correlation structures between sensor modalities. MoCA demonstrates strong performance boosts across reconstruction and downstream classification tasks on diverse benchmark datasets. We further establish theoretical guarantees by establishing a fundamental connection between multi-modal MAE loss and kernelized canonical correlation analysis through a Reproducing Kernel Hilbert Space framework, providing principled guidance for correlation-aware masking strategy design. Our approach offers a novel solution for leveraging unlabeled multi-modal wearable data while handling missing modalities, with broad applications across digital health domains.

stat.ML↗

arXiv2026

Modeling Physical Activity Change as Smooth Transformations: Temporal and Amplitude Patterns Associated with Physical Function in Older Women

Purpose: To investigate whether longitudinal changes in timing and magnitude of PA are associated with physical function (PF) in older women. Methods: Women from OPACH study with accelerometry at baseline and WHISH study W1 and W2 were included. Minute-level PA counts were averaged and smoothed as diurnal PA curves. Consecutive-visit change was modeled within periods (baseline--W1 and W1--W2) as a Riemannian deformation from earlier to later curves, with two-dimensional initial momenta characterizing timing and magnitude shifts. Multivariate functional principal component analysis (MFPCA) summarized coupled timing-magnitude patterns, and principal component (PC) scores and deformation energy were derived for each participant-period. Linear mixed-effects models related these features to RAND-36 PF, adjusting for baseline PF and covariates. Results: Mean PA deformation in both periods showed downward shifts in PA magnitude and temporal redistribution after 10:00. Top 15 PCs explained at least 90% of variability in both periods. PC1 captured diurnal PA increase/decrease, explaining 22.4% of variability for baseline--W1 and 20.8% for W1--W2. Among participants with complete PF scores and baseline covariates (N=1,157), higher PC1 scores, reflecting relative increase/maintenance of PA across day, were positively associated with PF (P<0.0001). Deformation energy, a metric for overall diurnal pattern change between visits, showed a significant interaction with period for PF (P=0.003), with a larger positive association during W1--W2 than during baseline--W1. Conclusions: In older women, longitudinal changes in diurnal PA accumulation were associated with PF. Riemannian deformation analysis identified clinically interpretable markers of PA pattern change that may capture functional-aging information not represented by conventional PA summaries.

stat.AP↗

arXiv2024

A linear mixed model approach for measurement error adjustment: applications to sedentary behavior assessment from wearable devices

In recent years, wearable devices have become more common to capture a wide range of health behaviors, especially for physical activity and sedentary behavior. These sensor-based measures are deemed to be objective and thus less prone to self-reported biases, inherent in questionnaire assessments. While this is undoubtedly a major advantage, there can still be measurement errors from the device recordings, which pose serious challenges for conducting statistical analysis and obtaining unbiased risk estimates. There is a vast literature proposing statistical methods for adjusting for measurement errors in self-reported behaviors, such as in dietary intake. However, there is much less research on error correction for sensor-based device measures, especially sedentary behavior. In this paper, we address this gap. Exploiting the excessive multiple-day assessments typically collected when sensor devices are deployed, we propose a two-stage linear mixed effect model (LME) based approach to correct bias caused by measurement errors. We provide theoretical proof of the debiasing process using the Best Linear Unbiased Predictors (BLUP), and use both simulation and real data from a cohort study to demonstrate the performance of the proposed approach while comparing to the naïve plug-in approach that directly uses device measures without appropriately adjusting measurement errors. Our results indicate that employing our easy-to-implement BLUP correction method can greatly reduce biases in disease risk estimates and thus enhance the validity of study findings.

stat.ME↗

arXiv2022

A Riemann Manifold Model Framework for Longitudinal Changes in Physical Activity Patterns

Physical activity (PA) is significantly associated with many health outcomes. The wide usage of wearable accelerometer-based activity trackers in recent years has provided a unique opportunity for in-depth research on PA and its relations with health outcomes and interventions. Past analysis of activity tracker data relies heavily on aggregating minute-level PA records into day-level summary statistics, in which important information of PA temporal/diurnal patterns is lost. In this paper we propose a novel functional data analysis approach based on Riemann manifolds for modeling PA and its longitudinal changes. We model smoothed minute-level PA of a day as one-dimensional Riemann manifolds and longitudinal changes in PA in different visits as deformations between manifolds. The variability in changes of PA among a cohort of subjects is characterized via variability in the deformation. Functional principal component analysis is further adopted to model the deformations and PC scores are used as a proxy in modeling the relation between changes in PA and health outcomes and/or interventions. We conduct comprehensive analyses on data from two clinical trials: Reach for Health (RfH) and Metabolism, Exercise and Nutrition at UCSD (MENU), focusing on the effect of interventions on longitudinal changes in PA patterns and how different modes of changes in PA influence weight loss, respectively. The proposed approach reveals unique modes of changes including overall enhanced PA, boosted morning PA, and shifts of active hours specific to each study cohort. The results bring new insights into the study of longitudinal changes in PA and health and have the potential to facilitate designing of effective health interventions and guidelines.

stat.AP↗

arXiv2022

Sedentary Behavior Estimation with Hip-worn Accelerometer Data: Segmentation, Classification and Thresholding

Cohort studies are increasingly using accelerometers for physical activity and sedentary behavior estimation. These devices tend to be less error-prone than self-report, can capture activity throughout the day, and are economical. However, previous methods for estimating sedentary behavior based on hip-worn data are often invalid or suboptimal under free-living situations and subject-to-subject variation. In this paper, we propose a local Markov switching model that takes this situation into account, and introduce a general procedure for posture classification and sedentary behavior analysis that fits the model naturally. Our method features changepoint detection methods in time series and also a two stage classification step that labels data into 3 classes(sitting, standing, stepping). Through a rigorous training-testing paradigm, we showed that our approach achieves > 80% accuracy. In addition, our method is robust and easy to interpret.

cs.LG↗

arXiv2021

Robust Functional Principal Component Analysis via Functional Pairwise Spatial Signs

Functional principal component analysis (FPCA) has been widely used to capture major modes of variation and reduce dimensions in functional data analysis. However, standard FPCA based on the sample covariance estimator does not work well in the presence of outliers. To address this challenge, a new robust functional principal component analysis approach based on the functional pairwise spatial sign (PASS) operator, termed PASS FPCA, is introduced where we propose estimation procedures for both eigenfunctions and eigenvalues with and without measurement error. Compared to existing robust FPCA methods, the proposed one requires weaker distributional assumptions to conserve the eigenspace of the covariance function. In particular, a class of distributions called the weakly functional coordinate symmetric (weakly FCS) is introduced that allows for severe asymmetry and is strictly larger than the functional elliptical distribution class, the latter of which has been well used in the robust statistics literature. The robustness of the PASS FPCA is demonstrated via simulation studies and analyses of accelerometry data from a large-scale epidemiological study of physical activity on older women that partly motivates this work.

stat.ME↗