Behrouz Madahian

Lisa Klesges

Ramin Homayouni

Robert C Klesges

PubMed

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MEETING ABSTRACT Open Access
System dynamics modeling of childhood obesity
Behrouz Madahian1, Robert C Klesges2, Lisa Klesges3, Ramin Homayouni1,4*
From 11th Annual UT-ORNL-KBRIN Bioinformatics Summit 2012
Louisville, KY, USA. 30 March - 1 April 2012
Background
Effective strategies for prevention of obesity have been elu-
sive since the recognition of obesity as a major public
health issue [1]. Obesity is a result of chronic, quantitative
imbalance between energy intake and energy expenditure,
influenced by a combination of genetic, environmental,
psychological and social factors. Therefore, a systems per-
spective is needed to examine effective obesity prevention
strategies [2]. In this study, a system dynamics model was
developed using the data from the Girls health Enrichment
* Correspondence: rhomayon@memphis.edu
1Bioinformatics Program, University of Memphis, Memphis, TN 38152, USA
Full list of author information is available at the end of the article
Figure 1 The complete system dynamic body weight model included sugar and fat intake variables (open circles) as well as environmental and
behavioral subsystems (green circles).
Madahian et al. BMC Bioinformatics 2012, 13(Suppl 12):A13
http://www.biomedcentral.com/1471-2105/13/S12/A13
© 2012 Madahian et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
Multi-site Studies (GEMS). GEMS tested the efficacy of a
2-year family-based intervention to reduce excessive
increase in body mass index in 8-10 year old African-
American girls.
Methods
System dynamics models were built using Vensim soft-
ware (Ventana Systems, Inc) [3]. First, a core model was
built which contained energy intake, energy expenditure
and energy surplus subsystems contributing to the total
body weight [4,5]. Then, an optimum model was built by
systematically adding variables (from among 313 collected
in GEMS) to fit the observed data by regression analysis
for 50 randomly selected individuals from the cohort. The
final model included nutrition, physical activity, and sev-
eral environmental and behavioral factors (Figure 1).
Results and discussion
We evaluated the performance of a large number of mod-
els containing different food intake, energy expenditure
and environmental variables collected in GEMS. The BMI
simulated by our final complete model showed a correla-
tion coefficient of 0.83 with the observed data, whereas the
initial core model showed a correlation of 0.55. The model
accuracy was within 10% for 84% of the individuals in the
study. For a few individuals, the simulated BMI and
observed BMI values were greatly (> 35%) different. It is
possible that the data collected from these individuals
were inaccurate or, alternatively, it is possible that these
individuals had other confounding factors (e.g. genetic
variability) that were not captured in the study. Consistent
with previous observations, we found that the two inter-
vention strategies in the GEMS study did not affect the
BMI increases observed in the cohort [1]. Interestingly
however, using our model, we were able to compare var-
ious intervention strategies and found that 10 min exercise
plus a reduction of intake by 100 calories produced the
same effect on BMI change as 30 min of exercise per day
(Figure 2). Our work suggests that system dynamics mod-
eling may be useful for testing potential intervention stra-
tegies in complex disorders such as obesity.
Acknowledgements
The GEMS study was supported by cooperative agreements HL62662 and
HL62663 from the National Heart, Lung, and Blood Institute, National
Institutes of Health. This work was supported in part by The University of
Memphis Bioinformatics Program, The University of Memphis School of
Public Health, and the Memphis Research Consortium.
Author details
1Bioinformatics Program, University of Memphis, Memphis, TN 38152, USA.
2Department of Preventive Medicine, University of Tennessee Health Science
Center, Memphis, TN 38163, USA. 3Department of Epidemiology and Social
and Behavioral Sciences, University of Memphis, Memphis, TN 38152, USA.
4Department of Biology, University of Memphis, Memphis, TN 38152, USA.
Published: 31 July 2012
References
1. Klesges RC, Obarzanek E, Kumanyika S, Murray DM, Klesges LM, Relyea GE,
Stockton MB, Lanctot JQ, Beech BM, McClanahan BS, Sherrill-Mittleman D,
Slawson DL: The Memphis Girls’ health Enrichment Multi-site Studies
(GEMS): an evaluation of the efficacy of a 2-year obesity prevention
program in African American girls. Arch Pediatr Adolesc Med 2010,
164(11):1007-1114.
2. Huang TT, Drewnowski A, Kumanyika SK, Glass TA: A systems-oriented
multilevel framework for addressing obesity in the 21st century. Prev
Chronic Dis 2009, 6(3):A82.
3. Eberlein RL, Peterson DW: Understanding models with Vensim(TM).
European Journal of Operational Research, Elsevier 1992, 59(1):216-219.
4. Hammond RA: Complex systems modeling for obesity research. Prev
Chronic Dis 2009, 6(3):A97.
5. Abdel-hamid T: Modeling the dynamics of human energy regulation and
its implications for obesity treatment. System Dynamics Rev 2002,
18:431-471.
doi:10.1186/1471-2105-13-S12-A13
Cite this article as: Madahian et al.: System dynamics modeling of
childhood obesity. BMC Bioinformatics 2012 13(Suppl 12):A13.
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Figure 2 Comparison of various intervention schemes on simulated
BMI change. The GEMS cohort consists of an intervention group (I)
and an alternative group (A). Different durations (0, 15, 30 and 45
min) of exercise were simulated for both groups as denoted on the
x-axis. In addition, a ‘mixed’ intervention was simulated which
included 10 min of exercise with a 100 cal reduction in food intake.
Madahian et al. BMC Bioinformatics 2012, 13(Suppl 12):A13
http://www.biomedcentral.com/1471-2105/13/S12/A13
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System dynamics modeling of childhood obesity

Complex systems modeling for obesity research. Prev Chronic Dis

DL: The Memphis Girls’ health Enrichment Multi-site Studies (GEMS): an evaluation of the efficacy of a 2-year obesity prevention program in African American girls. Arch Pediatr Adolesc Med

Modeling the dynamics of human energy regulation and its implications for obesity treatment. System Dynamics Rev 2002, 18:431-471. doi:10.1186/1471-2105-13-S12-A13 Cite this article as: Madahian et al.: System dynamics modeling of childhood obesity.

Peterson DW: Understanding models with Vensim(TM).

TA: A systems-oriented multilevel framework for addressing obesity in the 21st century. Prev Chronic Dis

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System dynamics modeling of childhood obesity

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