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 Concept Videos

Pulse rhythm01:30

Pulse rhythm

754
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
754
Time-Series Graph00:54

Time-Series Graph

4.3K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.3K
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

3.2K
The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
3.2K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

80
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
80
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

59
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
59
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

577
The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
577

You might also read

Related Articles

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

Sort by
Same author

Reciprocal relationships between personality traits and job satisfaction? A continuous time approach with two investigations.

The Journal of applied psychology·2026
Same author

Toward a dynamic understanding of work-family boundary management: A control theory perspective.

Journal of occupational health psychology·2025
Same author

Psychosocial safety climate (PSC) and working conditions, predictors of mental health and antidepressant and opioid use in Australia: a study protocol for longitudinal data linkage.

BMJ open·2023
Same author

Revisit the causal inference between organizational commitment and job satisfaction: A meta-analysis disentangling its sources of inconsistencies.

The Journal of applied psychology·2023
Same author

Start even Smaller, and then more Random. Comment on "Start Small, not Random: Why does Justifying your Time-Lag Matter?" by Yannick Griep, Ivana Vranjes, Johannes M. Kraak, Leonie Dudda, & Yingjie Li.

The Spanish journal of psychology·2022
Same author

Predicting new major depression symptoms from long working hours, psychosocial safety climate and work engagement: a population-based cohort study.

BMJ open·2021

Related Experiment Video

Updated: May 27, 2025

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
05:51

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

Published on: February 21, 2025

354

I watch SEM: continuous time dynamic models with N≥1 smart watch data.

Christian Dormann1,2, Olga Diener1

  • 1Johannes Gutenberg University of Mainz, Germany.

Industrial Health
|February 16, 2025
PubMed
Summary
This summary is machine-generated.

This study explains how to extract intensive longitudinal data (ILD) from smartwatches for continuous time structural equation modeling (CTSEM). It details data preparation, model specification in R, and result interpretation for health monitoring.

Keywords:
Continuous timeDynamic panel modelsLongitudinalStructural equation modelling

More Related Videos

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
15:00

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Published on: February 3, 2023

2.4K
Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.2K

Related Experiment Videos

Last Updated: May 27, 2025

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
05:51

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

Published on: February 21, 2025

354
Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
15:00

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Published on: February 3, 2023

2.4K
Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.2K

Area of Science:

  • * Psychometrics and Quantitative Psychology
  • * Health Informatics
  • * Data Science

Background:

  • * Smart devices are increasingly used for health monitoring, generating intensive longitudinal data (ILD).
  • * Analyzing repeatedly measured data requires understanding different designs and modeling approaches, particularly dynamic models.
  • * Distinctions between within-person and between-person effects, and static versus dynamic models are crucial.

Purpose of the Study:

  • * To provide a practical guide for retrieving and preparing N=1 bivariate intensive longitudinal data (ILD) from smartwatches for continuous time structural equation modeling (CTSEM).
  • * To demonstrate the specification, fitting, and interpretation of cross-lagged panel CTSEM using the R package `ctsem` for N>1 multivariate extensions.
  • * To offer a theoretical introduction to repeated measure designs, continuous time modeling, and the mathematical underpinnings of CTSEM.

Main Methods:

  • * Data retrieval from popular smartwatches for N=1 bivariate ILD.
  • * Data preparation techniques for CTSEM, including multivariate extensions (N>1).
  • * Model specification, fitting, and interpretation using the `ctsem` R package for cross-lagged panel models.

Main Results:

  • * Demonstration of a feasible workflow for extracting smartwatch data and applying CTSEM.
  • * Guidance on specifying and interpreting complex continuous time models for longitudinal health data.
  • * Discussion of the limitations inherent in CTSEM.

Conclusions:

  • * Smartwatch data can be effectively utilized for advanced longitudinal health analysis using CTSEM.
  • * The `ctsem` R package provides a powerful tool for analyzing intensive longitudinal data.
  • * Understanding continuous time modeling is essential for accurate health monitoring and forecasting.