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

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

359
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...
359
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Modeling with Differential Equations01:25

Modeling with Differential Equations

334
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
334
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.3K
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.3K
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

119
Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
119
Mass and Weight01:19

Mass and Weight

12.5K
Mass and weight are often used interchangeably in everyday conversation. For example,  medical records often show our weight in kilograms, but never in the correct units of newtons. In physics, however, there is an important distinction. Weight is the pull of the Earth on an object. It depends on the distance from the center of the Earth. Weight dramatically varies if we leave the Earth's surface, unlike mass, which does not vary with location. On the Moon, for example, the...
12.5K

You might also read

Related Articles

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

Sort by
Same author

Predicting curbside food waste generation from responses to Self-Administered Surveys: An Application of Machine learning to improve household estimates.

Waste management (New York, N.Y.)·2026
Same author

Unpacking weight management interventions measuring eating disorder risk in adults: coding of components of interventions in a systematic review.

Journal of eating disorders·2026
Same author

Self-selected dietary intake and association with achieved caloric restriction in CALERIE<sup>™</sup> 2.

Critical reviews in food science and nutrition·2026
Same author

A hierarchical network model for the estimate of the energy expenditure in individuals with type 1 diabetes.

Engineering applications of artificial intelligence·2026
Same author

Disseminating and Implementing the Science of Pediatric Obesity Treatment and Prevention.

Obesity (Silver Spring, Md.)·2026
Same author

Lunches in UK early years education and care settings: cross-sectional analysis of food processing, provision and intake.

Research square·2026

Related Experiment Video

Updated: May 1, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

2.9K

A Simple Model Predicting Individual Weight Change in Humans.

Diana M Thomas1, Corby K Martin2, Steven Heymsfield3

  • 1Department of Mathematical Sciences, Montclair State University, Montclair, NJ.

Journal of Biological Dynamics
|April 8, 2014
PubMed
Summary

A new mathematical model predicts individual weight change and maintenance, crucial for understanding obesity and improving dietary adherence. This tool aids in personalized weight management strategies for adults.

Keywords:
dietary adherenceenergy balance equationmetabolic adaptationnon-exercise activity thermogenesis

More Related Videos

A Simple and Inexpensive Running Wheel Model for Progressive Resistance Training in Mice
06:59

A Simple and Inexpensive Running Wheel Model for Progressive Resistance Training in Mice

Published on: April 28, 2022

4.3K
Control of Eating Behavior Using a Novel Feedback System
04:48

Control of Eating Behavior Using a Novel Feedback System

Published on: May 8, 2018

11.9K

Related Experiment Videos

Last Updated: May 1, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

2.9K
A Simple and Inexpensive Running Wheel Model for Progressive Resistance Training in Mice
06:59

A Simple and Inexpensive Running Wheel Model for Progressive Resistance Training in Mice

Published on: April 28, 2022

4.3K
Control of Eating Behavior Using a Novel Feedback System
04:48

Control of Eating Behavior Using a Novel Feedback System

Published on: May 8, 2018

11.9K

Area of Science:

  • Obesity and metabolic health research
  • Mathematical modeling in biology
  • Public health and nutrition science

Background:

  • Over two-thirds of US adults are overweight, posing significant health risks like heart disease and type 2 diabetes.
  • Understanding weight change and maintenance mechanisms is critical for public health interventions.
  • Accurate predictive models can enhance patient adherence to dietary plans in clinical settings.

Purpose of the Study:

  • To develop a simple, accurate mathematical model for predicting individual adult weight change.
  • To validate the model's predictive capabilities using data from underfeeding and overfeeding studies.
  • To assess the model's performance against existing one-dimensional weight change models.

Main Methods:

  • Developed a one-dimensional differential equation model based on the energy balance equation.
  • Integrated an algebraic relationship between fat-free mass and fat mass using CDC data.
  • Validated predictions against final weight data from two underfeeding and one overfeeding study.

Main Results:

  • Model predictions showed low mean absolute errors (underfeeding: <1.8 ± 1.3 kg; overfeeding: <2.5 ± 1.6 kg).
  • The model demonstrated improved accuracy compared to other one-dimensional models.
  • Individual weight change predictions showed a 60% decrease in maximum absolute error, indicating high reliability.

Conclusions:

  • The developed model offers a reliable method for estimating individual weight change due to dietary modifications.
  • This tool can be used to assess dietary adherence in weight management and research settings.
  • The model supports personalized approaches to weight loss and maintenance strategies.