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Dynamic model predicting overweight, obesity, and extreme obesity prevalence trends.

Thomas DM, Weedermann M, Fuemmeler BF, Martin CK, Dhurandhar NV, Bredlau C, Heymsfield SB, Ravussin E, Bouchard C - Obesity (Silver Spring) (2013)

Bottom Line: Mechanistic insights can be provided from a mathematical model.The model considers both social and nonsocial influences on weight gain, incorporates other known parameters affecting obesity trends, and allows for country specific population growth.This trend has important implications in accurately evaluating the impact of various anti-obesity strategies aimed at reducing obesity prevalence.

View Article: PubMed Central - PubMed

Affiliation: Center for Quantitative Obesity Research, Montclair State University, Montclair, New Jersey, USA.

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Related in: MedlinePlus

Diagram describing flow from each compartment formulated in the dynamic model.
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Figure 4: Diagram describing flow from each compartment formulated in the dynamic model.

Mentions: Modeling this flow requires identification of the mechanisms which increase or decrease the population within each BMI classification. The mechanisms considered in the developed model appear as a flowchart in Figure 1. Each compartment depicted in Figure 1 represents a state variable or more importantly, a variable we desire a prediction for over time. Table 1 lists each term of the differential equation model that describes the flow from and to each compartment as depicted in Figure 1.


Dynamic model predicting overweight, obesity, and extreme obesity prevalence trends.

Thomas DM, Weedermann M, Fuemmeler BF, Martin CK, Dhurandhar NV, Bredlau C, Heymsfield SB, Ravussin E, Bouchard C - Obesity (Silver Spring) (2013)

Diagram describing flow from each compartment formulated in the dynamic model.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3842399&req=5

Figure 4: Diagram describing flow from each compartment formulated in the dynamic model.
Mentions: Modeling this flow requires identification of the mechanisms which increase or decrease the population within each BMI classification. The mechanisms considered in the developed model appear as a flowchart in Figure 1. Each compartment depicted in Figure 1 represents a state variable or more importantly, a variable we desire a prediction for over time. Table 1 lists each term of the differential equation model that describes the flow from and to each compartment as depicted in Figure 1.

Bottom Line: Mechanistic insights can be provided from a mathematical model.The model considers both social and nonsocial influences on weight gain, incorporates other known parameters affecting obesity trends, and allows for country specific population growth.This trend has important implications in accurately evaluating the impact of various anti-obesity strategies aimed at reducing obesity prevalence.

View Article: PubMed Central - PubMed

Affiliation: Center for Quantitative Obesity Research, Montclair State University, Montclair, New Jersey, USA.

Show MeSH
Related in: MedlinePlus