Related Experiment Video
Updated: Jul 12, 2025
06:55
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
14.5K
The association between reallocations of time and health using compositional data analysis: a systematic scoping
Aaron Miatke1,2, Tim Olds3,4, Carol Maher3
1Alliance for Research in Exercise, Nutrition and Activity, Allied Health and Human Performance, University of South Australia, GPO box, Adelaide, S.A, 2471, 5001, Australia. aaron.miatke@mymail.unisa.edu.au.
Summary
Reallocating time to moderate-to-vigorous physical activity (MVPA) benefits health, while moving time away from MVPA is detrimental. This review highlights the importance of MVPA in time-use and health associations.
Area of Science:
- Public Health
- Epidemiology
- Behavioral Science
Background:
- Time allocation significantly impacts health outcomes.
- Increasing time in one behavior necessitates decreasing time in others.
- Compositional data analysis (CoDA) is increasingly used to study health implications of time reallocations.
Purpose of the Study:
- To provide an overview of studies using CoDA to model associations between time reallocations and health.
- To synthesize findings on how shifting time between different daily activities affects health.
- To identify research gaps and suggest future directions in time-use and health research.
Main Methods:
- Systematic search of four electronic databases (MEDLINE, Embase, Scopus, SPORTDiscus) conducted in October 2022.
- Inclusion of studies utilizing CoDA to analyze associations between time reallocations and health outcomes.
- Consideration of reallocations among movement behaviors (sedentary behavior, light physical activity, MVPA) and other daily activities.
Main Results:
- 103 studies were included, with adiposity being the most frequent health outcome.
- Reallocating time to moderate-to-vigorous physical activity (MVPA) generally showed favorable health associations.
- Reallocating time away from MVPA was generally associated with unfavorable health outcomes.
- Longitudinal studies often found no significant associations, and the context of behavior (leisure vs. work) mattered.
Conclusions:
- Shifting time towards MVPA yields the strongest positive health benefits.
- Shifting time away from MVPA has the most significant negative health consequences.
- Future research should employ longitudinal and experimental designs to further explore these associations across diverse outcomes.
Related Concept Videos
01:18Introduction To Survival Analysis
251
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...
The primary goal of survival analysis is to estimate survival time—the time...
251
01:05Noncompartmental Analysis: Mean Residence Time
157
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...
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...
157
01:20Comparing the Survival Analysis of Two or More Groups
201
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
201
00:56Noncompartmental Analysis: Statistical Moment Theory
109
Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...
109
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
45
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...
45
01:25Statistical Methods for Analyzing Epidemiological Data
385
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:
385