Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Individualized dynamic latent factor model for multi-resolutional data with application to mobile health.

J Zhang1, F Xue2, Q Xu1

  • 1Department of Statistics, University of California, Irvine, Donald Bren Hall 2219, Irvine, California, 92697, U.S.A.

Biometrika
|June 29, 2026
PubMed
Summary

Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Dimensions of Health and Illness01:21

Dimensions of Health and Illness

The factors influencing the health-illness continuum can be internal or external and may or may not be under conscious control. They are related to the following eight human dimensions, and each dimension is interrelated to one other.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Physical activity buffers physiological stress during high emotional distress: a wearable-derived prospective cohort study.

medRxiv : the preprint server for health sciences·2026
Same author

Heterogeneous effects of physical activity on physiological stress during pregnancy.

PLOS digital health·2025
Same author

Optimal Transport based Cross-Domain Integration for Heterogeneous Data.

Journal of the American Statistical Association·2025
Same author

Association of Tooth Loss With Stress and Depressive Symptoms in Korean Adults: An Analysis Using Korean National Health and Nutrition Examination Survey (KNHANES VI) (2013, 2015).

Gerodontology·2025
Same author

Heterogeneous effects of physical activity on physiological stress during pregnancy.

medRxiv : the preprint server for health sciences·2025
Same author

Sleep Quality of Persons with Dementia and Family Caregivers in Korean Americans: Wearable Technology to Study the Dyadic Association.

Western journal of nursing research·2025
This summary is machine-generated.

This study introduces a new model to accurately fill in missing health data from smartphones and wearables. The individualized dynamic latent factor model improves tracking of individual health status using irregular time series data.

Area of Science:

  • Biomedical Informatics
  • Data Science
  • Time Series Analysis

Background:

  • Mobile health (mHealth) utilizes smartphones and wearables for health tracking.
  • mHealth generates heterogeneous, multi-resolution, and irregular time series data.
  • Existing methods struggle with integrating and interpolating such complex data.

Purpose of the Study:

  • To propose an individualized dynamic latent factor model.
  • To address challenges in handling irregular multi-resolution time series data in mHealth.
  • To enable accurate interpolation of unsampled measurements.

Main Methods:

  • Developed an individualized dynamic latent factor model.
  • Mapped multi-resolution data to a latent space for integration.
Keywords:
Data integrationInterpolationMobile healthNonparametric approximationWearable device data

Related Experiment Videos

  • Utilized B-spline approximation methods for interpolation.

    • Main Results:

    • The proposed model effectively integrates multiple irregular time series and subjects.
    • Demonstrated superior performance in interpolating unsampled measurements compared to existing methods.
    • Validated through simulation studies and real-world smartwatch data.

    Conclusions:

    • The individualized dynamic latent factor model offers a robust solution for mHealth data.
    • Enables accurate analysis of heterogeneous longitudinal health information.
    • Advances the field of mHealth data processing and analysis.