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

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

823
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
823
Introduction to Epidemiology01:26

Introduction to Epidemiology

1.5K
Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
1.5K
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

664
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
664
Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

498
According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...
498
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

202
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...
202
Data Collection by Observations01:08

Data Collection by Observations

14.3K
Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
14.3K

You might also read

Related Articles

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

Sort by
Same author

Optimal composition of movement behaviours for healthy weight across the lifespan: a one-stage individual participant data meta-analysis of 11 studies.

Research square·2026
Same author

Economic cost of movement behaviours: a systematic review.

Journal of activity, sedentary and sleep behaviors·2026
Same author

Comment on 'Asian Reference Values for Handgrip Strength, Gait Speed, Five-Times-Sit-to-Stand Test, Muscle Mass and Calf Circumference' by Grgic et al.-The Authors' Reply.

Journal of cachexia, sarcopenia and muscle·2026
Same author

Physical Activity Does Not Fully Offset the Health Risks of Sedentary Behaviour.

Medicine and science in sports and exercise·2026
Same author

Physical Activity Does Not Fully Offset the Health Risks of Sedentary Behaviour: Response to Ekelund and Colleagues.

Medicine and science in sports and exercise·2026
Same author

Latent profiles of movement behaviour compositions and their associations with adiposity and health-related quality of life in Australian children: a cross-sectional and 12-month longitudinal study.

BMJ open·2026

Related Experiment Video

Updated: Dec 25, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.0K

Compositional Data Analysis in Time-Use Epidemiology: What, Why, How.

Dorothea Dumuid1, Željko Pedišić2, Javier Palarea-Albaladejo3

  • 1Alliance for Research in Exercise, Nutrition and Activity (ARENA), University of South Australia, Adelaide 5001, Australia.

International Journal of Environmental Research and Public Health
|April 1, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces compositional data analysis (CoDA) for understanding 24-hour activity behaviors. CoDA reveals how daily time use, including sleep, sedentary behavior, and physical activity, relates to health outcomes like adiposity.

Keywords:
compositional dataphysical activitysedentary behaviorsleep

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.9K
Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions
05:45

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

Published on: January 7, 2019

11.9K

Related Experiment Videos

Last Updated: Dec 25, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.0K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.9K
Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions
05:45

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

Published on: January 7, 2019

11.9K

Area of Science:

  • Epidemiology
  • Behavioral Science
  • Biostatistics

Background:

  • Traditional research often examines physical activity, sedentary behavior, and sleep in isolation.
  • A shift towards a 24-hour time-use paradigm integrates all daily behaviors, analyzing them in relation to each other.
  • Compositional data analysis (CoDA) is a statistical method well-suited for analyzing relative time-use data.

Purpose of the Study:

  • To provide an overview of CoDA for time-use data in health research.
  • To summarize current research applying CoDA in time-use epidemiology.
  • To discuss challenges and future directions for CoDA in analyzing daily activity behaviors and health outcomes.

Main Methods:

  • Utilized 24-hour time-use diary data from the Longitudinal Study of Australian Children (n=3228).
  • Demonstrated descriptive analyses of time-use compositions.
  • Illustrated the exploration of relationships between daily time allocation (sleep, sedentary behavior, physical activity) and adiposity using CoDA.

Main Results:

  • CoDA enables a more nuanced understanding of how the composition of daily activities influences health.
  • Descriptive and inferential analyses using CoDA were demonstrated with real-world data.
  • The study highlights the importance of interpreting CoDA findings comprehensively for meaningful health insights.

Conclusions:

  • CoDA offers a powerful framework for analyzing complex 24-hour activity behavior patterns.
  • This approach enhances our understanding of the interplay between sleep, sedentary behavior, physical activity, and health outcomes.
  • Further application and methodological refinement of CoDA are encouraged for advancing time-use epidemiology.