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A Dynamic Systems Approach to Weight Related Health Problems Author Details Author 1 Name: Yahia Zare Mehrjerdi Department: Industrial Engineering University/Institution: Yazd University Town/City: Yazd State (US only): Yazd Country: Iran Corresponding author: Yahia Zare Mehrjerdi Corresponding Authors Email: yazm2000@yahoo.com

Please check this box if you do not wish your email address to be published Acknowledgments (if applicable): Biographical Details (if applicable):

Yahia Zare Mehrjerdi, PhD, is associate professor at Yazd University, department of industrial engineering

where he teaches at the graduate and undergraduate levels. His research areas are: dynamic systems, multi

criteria decision making, Health economics, and supply chain management with RFID technology

integration. Yahia enjoys teaching theory of decision making, dynamic systems, simulation-optimization, and

advanced engineering economics at the graduate levels. He is the author of six books in Farsi and one

unpublished book in English. He has published in scientific journals of: European Journal of Operational

Research, Applied Soft Computing, International Journal of Assembly Automation, International Journal of

Quality and Reliability Management, The Electronic Library, journal of Performance Measurement and

Metrics to mention a few. He has presented many articles at the national and international conference levels.

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Keywords: Systems Thinking, System Dynamics, Complex systems, health issues, hearth attack, and

Weights. Article Classification: Research paper For internal production use only

Running Heads: the dynamics of weight related health problems

Structured Abstract:

Purpose of this paper: the purpose of this article is to present a system dynamic model for studying the interconnections between human being weight and the health problems causing various problems

all over the life. To do so, this author has reviewed key points about the system thinking, its theories,

and the system dynamics. Next, models in the form of causal loops presenting the interconnections

between weight factor and health problems are developed and discussed. Thereafter, a flow model of

the problem is constructed and the heart attack deaths are studied under two situations of regular and

taught cases.

Design/methodology/approach: Identifies key health problems related to weight by using causal loops that demonstrate the whole picture of the situation.

Findings: With the aid of systems thinking and dynamic modeling researchers can study the impacts of weights on the generation of various health problems as such as heart disease, high blood pressure,

blood sugar, knee problem and more. This study shows that teaching people about their health will

have a significant impact on the number of deaths related to heart attack.

Practical implications (if applicable): With the model proposed here various studies can be carried on that relates weight to health issues. A sample situation is presented where deaths related to

heart attack is simulated.

What is original/value of paper: This article makes a significant contribution to the health study issues due to the fact that it shows how a factor such as weight can have impacts on hearth attack,

blood pressure, and blood sugar, to mention a few. Since, to the best of this author's knowledge, this is

the first study that relates weight to health problems using systems thinking concepts and system

dynamic it make a significant contribution to the health literature.

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A Dynamic Systems Approach to

Weight Related Health Problems

Purpose of this paper: the purpose of this article is to present a new system dynamic model for studying the interconnections between human being weight and the health problems causing various

problems all over the life. To do so, this author has studies key points about the system thinking, its

theories, and the system dynamics. Next, models in the form of causal loops presenting the

interconnections between weight factor and health problems are developed and discussed. Thereafter,

a flow model of the problem is constructed and the heart attack deaths are studied under two situations

of regular and taught cases.

Design/methodology/approach: Identifies key health problems related to weight by using causal loops that demonstrate the whole picture of the situation.

Findings: With the aid of this new systems thinking and dynamic modeling researchers can study the impacts of weights on the generation of various health problems as such as heart disease, high

blood pressure, blood sugar, knee problem and more. This study shows that teaching people about

their health will have a significant impact on the number of deaths related to heart attack.

Practical implications (if applicable): With the model proposed here various studies can be carried on that relates weight to health issues. A sample situation is presented where deaths related to

heart attack is simulated.

What is original/value of paper: This article makes a significant contribution to the health study issues due to the fact that it shows how a factor such as weight can have impacts on heart attack,

blood pressure, and blood sugar, to mention a few. Since, to the best of this author's knowledge, this is

the first study that relates weight to health problems using systems thinking concepts and system

dynamic it make a significant contribution to the health literature.

Key Words: Systems Thinking, System Dynamics, Complex systems, health issues, heart attack, and Weights.

2

1. Introduction The health care system is large and complex, one that does not naturally lend itself to

easy analysis, design, or even understanding. The complexity and critical nature of the

system beg for the development and use of good, representative models (Keolling and

Schwandt, 2005). For middle aged people and up, health related issues are of highly

important. Due to the fact that as people gets aged their immune system as well as

their muscular body structure starts to weaken more if sport and movement is not

apart of their daily program for living. Those who work in the office and have about

minimal exercise per day are of highly potential group that gets sick sooner rather

than later. The complexity of the health system is obvious to every one these days.

This is why modeling health systems with newly developed techniques or the ones

capable of taking many key variables into consideration for modeling, quantifying,

and analysis are of very high concern.

Keolling and Schwandt (2005) in their article entitled "Health Systems: A dynamic

system-benefits from system dynamics" have classified general health systems

framework into: Health Systems, Systems, Clinical, Delivery, Prevention, and

Epidemiology. The very best way for representing the health system architectures is

through the use of causal loop diagrams that are considered a powerful way for

representation of complex systems, in general. For example Hirsch et al. (2005)

explore the health system from the perspective of population health. The authors

expand the basic model to consider the effects of things such as high tech medicine,

fragmentation of services, cost containment, living conditions, and patient

involvement. Their proposed model integrates all of these effects rather than

considering them individually, demonstrating a strength of SD modeling.

Grossman (1972a) developed a dynamic model for health and then a solution for the

dynamic optimization problem that leads to the optimal life-cycle health paths, gross

investment in each period, consumption of medical care (which is seen as a derived

demand) and time inputs in the gross investment function in each period. Using the

original study and US data, Grossman found positive effects of education and wages

on the demand for health. It also was shown that age had a positive effect on health

demand and a negative effect on medical care (Grossman, 1972a, 1972b, 2000, and

Jones et al., 2003).

Health care is complex system because of the great number of interconnections within

and among small care systems (Institute of Medicine, 2001). A health care system can

be defined as a set of connected or interdependent parts or agents including

caregivers and patientsbound by a common purpose and acting on their knowledge.

Research has proven both the general potential of systems thinking (Plsek and

Greenhalgh, 2001) and applications in specific areas. While health care systems are

complex and include many interconnected elements, the rules and heuristics

generating managerial decisions are too simplistic to cope with the complexity

involved in such systems. The result is that the well-intentioned decisions, which aim

to improve the performance of these systems, lead generally to completely opposite

results, a syndrome known as policy resistance (Lebcir, R.M., 2006). Systems

thinking combine an array of methods and techniques drawn from various fields such

as engineering, computing, cybernetics, and cognitive psychology. It allows managers

to overcome the feeling of helplessness and having necessary tools to analyze,

understand, and influence the functioning of the systems they are trying to improve

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The rest of this paper is organized as follows: section 2 described the methodology

that this researcher has followed to conduct this research. Section 3 describes systems

thinking while systems thinking diagram is discussed in section 4. Dynamic thinking

diagram is discussed in section 5. Systems thinking patterns of the sample situation is

elaborated in section 6. A dynamic study of heart attack related deaths is the topic of

section 7. Future Research and work extensions are discussed in section 8. The topic

of section 9 is about the limitations and the scope of this research for future working

on. Our discussion and conclusion is given in section 10.

2.Methodology The purpose of this article is two folds: (1) to show how systems thinking can be used

for the study of basic health problems and the way of tackling some health models for

discussion purposes; and (2) how a stock and flow can be employed to develop a

dynamic model for calculating the heart attack related death patterns over time. In

addition to that, author demonstrates the use of systems thinking and dynamic systems

in the area of health care management for better analysis of the systems and

productivity enhancements. Such a research is needed to determine the capability of

these tools in complex model building and analysis. A brief description of the systems

thinking and dynamic systems are provided for introducing the topic to a group of

new readers, however. More specifically, we can identify the following list of

objectives for achieving the overall goal of this study:

1. Identifying system variables within the human-being-body-system taking the

principles of Cybernetics into consideration.

2. Generating the causal loop diagram for the human-being-body-system using

the identified variables from 1.

3. Flow diagrams construction.

4. Developing the mathematical formulation of the problem.

5. Model simulation using the predefined values.

6. Conclusion and propositions.

3. Systems Thinking We all are familiar with the systems in such a way that we mindfully and without

realizing it regularly attribute and credit events to all kinds of systems. That usually is

done without paying attention to the type of the system that it is a real one or an

imagined one. System thinking studies the whole as a result of the connections

between its parts. System thinking is the opposite of reductionism, the idea that

something is simply the sum of its parts (OConnor and McDermott, 1997). Most

systems are comprised of smaller systems that we call them the subsystems. The

definition of system must hold for any subsystems of a system as well. Systems

thinking allow consideration of the whole rather than individual elements and

representation of time related behavior of systems rather than static snapshots

(Senge 1990). Some of the topics associated with systems thinking are: causal

feedback (Richardson, 1991); stockflow structures and open and closed systems

(Sterman, 2000); nonlinear systems and chaos (Strogatz, 1994); cybernetics (Francois,

2004); and system dynamics (Forrester, 1961, 1994, 2003, and Richardson, 1996).

System thinking is a conceptual framework for problem-solving that considers

problems in their entirety. Problem-solving in this way involves pattern finding to

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enhance understanding of, and responsiveness to, the problem. Outcomes from

systems thinking depend heavily on how a system is defined because systems thinking

examine relationships between the various parts of the system. Boundaries must be set

to distinguish what parts of the world are contained inside the system and what parts

are considered the environment of the system. The environment of the system will

influence problem-solving because it influences the system, but it is not part of the

system. The concept of system thinking is derived from a computer simulation model,

created in 1956 by Forrester of MIT to deal with management problems in enterprises.

Then, Senge and Lannon (1990) applied this concept to organization research, and

advocated that for effective application of system thinking, researchers have to pay

attention to the four tiers/levels in the system: event, behavior pattern, structure and

mental pattern.

System thinking is important because the society is full of dynamic complexity. The

term dynamic complexity was coined by Senge and Lannon (1990) to indicate that the

real world we live-in are actually composed of numerous causes and effects. People

often concentrate on individual events and forget to consider the entire environment,

and thus confine themselves to thinking in parts rather than whole. Therefore, to solve

dynamic complexity, we need the assistance of system thinking to clearly see the

relation between all problems and prevent the phenomenon that a change in one part

affects the whole. The operation of an enterprise is just like a small society. We start

system thinking by realizing a simple concept, feedback, which explains how

actions intensify or offset each other, and whose ultimate aim is to clearly see the

simple structure behind the complicated events so as to simplify social problems.

4. Systems Thinking Diagrams In systems thinking diagrams we use two components of elements and influences. An

influence also has a direction, indicated by an arrow, and an indicator as to whether

the influenced element is changed in the same (with a + sign) or opposite (with a -

sign) direction as the influencing element. A simplest diagram (Figure 1) is comprised

of two elements of Pain and Medicine can be constructed as follow:

Pain Medicine+

Figure 1: Pain and Medicine relationship

From this diagram there are only a couple things which are implied:

Pain and Medicine are elements of the model.

Pain influences Medicine in the same (+) direction as Pain. This means that as

Pain increases the use of Medication by the patient also increases.

Due to the fact that reinforcing and balancing loops build the foundation for other

loops, in the sections that followed first the reinforcing loop and then balancing loop

are discussed briefly.

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4.1 Reinforcing feedbacks are the engines of growth. Whenever you are in a situation where things are growing, you can be sure that reinforcing feedback is at

work. Reinforcing feedback can also generate accelerating decline a pattern of

decline where small drops amplify themselves into larger and larger drops, such as

the decline in bank assets when there is a financial panic. Figure 2 depicts a typical

reinforcing feedback or loop. In this loop, when physician pays attention to patient

the result is satisfied patients who would have positive view of MD and hence the

positive word of mouths. That would cause having more patients for the MD and the

MD would care even more for the patients.

Physician (MD)

Satisfied Patient

Positive word of

mouth

+

+

+

Reinforcing Loop

Figure 2: A reinforcing feedback

4.2 Balancing feedback operates whenever there is a goal-oriented behaviour. If the goal is to be not moving, then balancing feedback will act the way the brakes in a

car do. If the goal is to be moving at hundred kilometres per hour, then balancing

feedback will cause you to accelerate to hundred but no faster. What makes balancing

processes so difficult in management is that the goals are often implicit, and no one

recognizes that the balancing process exists at all. And often this has something to do

with corporate culture. But identifying these balancing processes is crucial for system

dynamics modelling. Figure 3a shows a balancing feedback loop with a goal set as

"Patient Health" or (Goal) representing the ultimate "Patient's Health". Between the

"current level of patient's health" and the "the real health of patient" is a gap shown

by variable "Gap". This "Gap" level gives the physician a good reason for taking

action in order to increase the dosage of the medication, if necessary, to improve the

health of his/her patient. The behavior of balancing feedback loop is shown by Figure

3b.

Many feedback processes contain delays, interruptions in the flow of influence

which make the consequences of actions occur gradually. Delays are interruptions

between actions and their consequences. Delays can make you badly overshoot your

mark, or they can have a positive effect if you recognize them and work with them.

Delays exist everywhere in business systems. According to system dynamics,

researchers can model a complete dynamic system by combining these different

elements, like reinforcing feedbacks, balancing feedbacks, and delays. For example a

model could be built to analyze a business system with limits to growth.

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Patient's Health

(Goal)

Gap

Action (Increase level

of Medication)

Current level of

Health

+

+

-

+

Balancing Loop

Figure 3(a): A balancing feedback loop and goal seeking

Figure 3(b): A balancing feedback loop and goal seeking

5. Dynamic Thinking Diagrams Systems archetype is composed of many circulations formed as a result of all kinds of

problems that affect one another in society. Senge and Lannon (1990) classified these

circulations into nine major systems archetypes: (1) Delayed balancing process; (2)

Limitation to goals; (3) Shifting the burden; (4) Temporary solution; (5) Escalation;

(6) Success; (7) Common tragedy; (8) Failure; (9) Growth and underachievement;

Fixes that Fail; and (11) Accidental Adversaries. Systems Dynamic (SD) modeling offers a unique opportunity to improve decision-

makers understanding of the sources of their systems under-performance as it allows

both qualitative and quantitative analysis, which lead more easily to consensus

building, improved shared understanding, and enhanced organizational learning

(Wolstenholme 1993). With regard to a model proposed, once the researcher is able to

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identify the qualitative structure describing the problem situation, which are being

used in the Causal Loop Diagrams (CLDs), the next step is to build a computer-based

behavioral model reflecting the qualitative structure. The stocks (variables subject to

accumulation and depletion processes over time) and the flows (which determine the

time related movement of units from one stock to the others) are determined and the

relationships between them defined. In this phase, a link is established between the

variables and their dynamic behavior. The quantitative nature of this phase makes it

the most important one in terms of generating insights about the situation. It is

important to notice here that many specialist software programs have been written for

SD modeling (Richmond 1987, Richardson and Pugh 1981) to make the process easy

and accessible to people even without strong computational background.

In his pioneering book on the subject, Forrester (1961) presented the dynamic analysis

of a business problem through a model of a production-distribution system that shows

oscillatory behavior. Policies to improve system performance were discussed, and

numerous policy experiments were demonstrated. Since then, system dynamic

modeling approach has become a powerful tool for analyzing complex systems.

Lyneis (2000) highlight three important advantages of system dynamic modeling

approach:

1. System dynamics models can provide more reliable forecasts than statistical

(non-structural) models;

2. System dynamics models provide a means of understanding the causes of

industry behavior; and

3. System dynamics models allow the determination of reasonable scenarios as

inputs to decisions and policies.

System dynamic is typically used for models that represent relationships between

system variables, rates of change over time, and explicit feedback. Rather than

focusing on individual transactions in the system, the models focus more on the levels

of variable stocks and the flows between variable states. As a result, SD models are

more often associated with higher level types of problems, especially consideration of

the impact of policy and strategy decisions.

Dynamic Systems Diagrams are composed of four different components: Levels,

Rates, Auxiliary variables, and Connectors. The labels may vary slightly in different

arenas. In relation to Dynamic Systems diagrams, the following points are correct:

Rates influence Levels (state variables)

Levels can influence rates or auxiliary variables

Auxiliary variables can influence Flows or other Converters

Auxiliaries cannot influence Levels

levels cannot influence other Levels

A diagram containing all of these elements is shown by Figure 4:

8

Levels+

Aux variables+

+

Rates

Figure 4: a general flow diagram

Systems Dynamic modeling has been applied, in specific health care management

issues such as health care work-force planning and emergency health care provision

(Royston et al 1999, Lane et al 2000), effect of joint health care provision by different

sectors (Wolstenholme 1999), and the effect of a shift from the free-to- service to self-

paying service (Hirsch and Immediato 1999). These models demonstrate the rich

variety of areas in which SD may play a significant role in health policy design.

6. Systems Thinking Patterns of the Sample Situations On the basis of our observations as well as many opinions gathered by the field

specialists and people in general, this study focuses on how human-being body

operates, and some parts perform and interact with the other parts of the body. Figures

5through 9 discuss various health related problems and then figure 10 concentrates on

the flow diagram for the dynamic study of the heart attack problem.

6.1 Reciprocal effect between loop 1 and loop 2

In Figure 5, the left loop is an indication of that as pain decreases and happiness get in

its place the body will act in the way that the soul and the body come to a start of

healthy situation. As a result of that, persons can have higher levels of movements

that would cause to have stronger muscles and bones and hence lesser pains and

therefore to be happier. Loop 1 and 2 are of reinforcing and balancing loops,

respectively. As the theorem of "Limits to Growth" indicates the growth would not

lasts forever and we know that loop 2 tries to bring that into balancing situation. So,

"no pain" and "happiness" would not last forever because loop 2 has the "movement"

of the person under the control.

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Soul and Body

Health

Happiness

Pain Pressure on

Knee

Muscle

strenthening

Movement

+

+

+

-

+

-

Knee Swelling

Knee Watering

Bone Building

Knee

Correct cell

reconstructionKnee Robbing

-

-

+

+

+-

Loop 1Loop 2Reinforcing

Balancing

Figure 5: Movement, Knee problems and health

6.2 Movement, weight, and knee problems

Figure 6 is comprised of four loops which are named loops 3, 4, 5, and 6 and with the

polar natures of +, +, +, and signs, respectively. Loop 3 relates weight and

movement where weight is under the influence of outside factors such as "unhealthy

food" and "good eating planning" and "bad eating habit". Loop 4 studies the impacts

of weight on the knee pain and person's movement. Loops 5 and 6 are both under the

influence of movement which is under the impact of weight. Loop 6 points to this fact

that weight is not the only factor that bring human being movement to a stationary

stage that could be its least knee problems as well as knee pain can have a big impact

on the movement. The lesser the movement the lesser is the happiness, and the higher

the weight the lesser is the movement.

Good eating plan

Bad eating habit

Weight

+

-

Movement

Muscles

bone Robbing

Knee robbing

Pressure on Knee

Knee Pain

-

+ +

+-

+

-

+

+

-Loop 3

Loop 4

Loop 5

Loop 6

Figure 6: Movement, Weight, and Knee problems

6.3 Weight, Movement, Knee and Vascular problems According to the Centers for Disease Control, heart disease is the leading cause of

death in the United States and is a major cause of disability. In 2002, almost 700,000

people died of heart disease, just over half of which were women. These statistics

mean that nearly 30% all U.S. deaths were due to heart disease. According to the

American Heart Association, heart disease has been the leading killer of adult females

10

since 1908. Overweight is considered a major risk factor for both coronary heart

disease and heart attack. Being 20% overweight or more significantly increases

person risk for developing heart disease, especially if that person has a lot of

abdominal fat. The American Heart Association has found that even if a person has no

other related health conditions, obesity itself increases risk of heart disease (Plsek and

Greenhalgh, 2001).

A healthy diet is also an important part of lowering the risk of heart disease. The

American Heart Association recommends a diet that contains no more than 30% of

daily calories from fat. For example, if one eats a diet of 2,000 calories per day, no

more than 600 calories should come from fat. The American Heart Association

recommends a diet rich in fruits, vegetables, healthful fatty acids and limited saturated

fat (Walker and Reamy, 2009). The American heart Association

(http://www.heart.org) has identified the health diet goals claims that: heart disease is

the No. 1 killer of Americans. We can reduce heart disease by promoting a healthy

diet and lifestyle. Getting information from credible sources can help us make smart

choices that will benefit your long-term heart health. In a study conducted by

Johannes A.N. Dorresteijn, et al. (2012) it is stated that high blood pressure has since

long been recognized as a strong and modifiable risk factor for cardiovascular disease

and mortality.

Figure 7 is comprised of two balancing loops and one reinforcing loop. Loops 8 and 9

that are balancing and reinforcing, respectively, look into the impacts of weight on

heart problem as well as the bones and pains associated with that. This group of three

loops also have a deep look at the weight and the negative impacts that it can have on

the heart and movement. Although, loop 9 points to this fact that as a result of high

weight and less movement pain can be high and increase over time but in loop 8 as a

result of fighting with heart problem and its related impacts on body performance

some improvement will be done which result in bringing the weight down to an

acceptable level.

Good eating habit

Bad eating habit

Weight

+-

Movement

Pressure on Bones

Robbing

Pain

+

+

-

Proactive actions

Body Fat

Muscle Laziness

+

+

+

-

-

Heart Muscle

Laziness

Heart Operation

disorder

Vascular Problesm

Death related to

heart problems

+

++

+

+

+ Loop 7

(Balancing)

Loop 8

(Balancing)

Loop 9

(Reinforcing)

Figure 7: Weight, Movement, Knee and Vascular problems

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6.4 Weight, exercise and total health (About Loop 4) Figure 8 is comprised of two reinforcing loops of 10 and 11. Loop 10 relates weight,

exercise, movement, and health with the good eating while loop 11 takes all these

elements and see their impacts on the health of soul. This is the reason why a large

number of people not happy with their extra weight. So, taking figures 6 and 7 and 8

into consideration, we can notice the impact of extra weight on the movement, heart

problem, joint problem, and now on the health of the soul.

Weight

Excercise

Movement

Body health

Total health

Healthy eating

Soul health

-

-

++

+

+

+

+Loop 10

Loop 11

Figure 8: Weight, exercise and total health

6. 5 Weight, health, diabetic, blood pressure, and over eating (Obesity)

problems Julia Steinberger and Stephen R. Daniels (2003) claim that obesity increases the risk

of cardiovascular disease in adults and has been strongly associated with insulin

resistance in normoglycemic persons and in individuals with type II diabetes. An

association between obesity and insulin resistance has been reported in the young, as

has the link between insulin resistance, hypertension, and abnormal lipid profile.

There is an increasing amount of data showing that being overweight during

childhood and adolescence is significantly associated with insulin resistance,

dyslipidemia, and elevated blood pressure in young adulthood. Weight loss by obese

youngsters results in a decrease in insulin concentration and improvement in insulin

sensitivity.

Figure 9 is comprised of 8 loops. Loops 12, 13, 14, and 15 are sharing the weight as

the main cause. We see that also bad eating habit and less movement and exercise can

cause the extra weights the weight by itself can be the source of another problem of

diabetics. Loop 13 which is a balancing loop relates weight with blood sugar,

diabetics and action to control the diet and eating habit. This all together would cause

the weight lost. However, loop 14 relates bad eating habit to extra eating and many

times eating and then the body laziness with the less movement and hence the extra

weight. Loop 15 sees the impacts of blood sugar on diabetic problem, on eye sight

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and hence on the eye problem as such as blindness. Loop 12 through 15 sees extra

weight and bad eating habit as the main cause of problem.

Loops 17, 18, and 19 share extra eating as the main source of problem and the only

cause for appearing problems as such as blood pressure, blood cholesterol, and

stomach discomfort and reflux problem. These loops all requires some sort of action

(probably a serious one) for dealing with the extra eating.

Bad eating

Weight+ Movement

Consecutive eating

Over eating

Lozziness

+

+

-

- -

-

Bad habit eating

+

+

Preventive actions

Death due to heart

attack

Heart Attack

Hearth Disease

Vein ClosingCholestrole

Blood pressure

Stomach Problems+

+

+

+

+ +

+

+-

+

Blood Sugure

DiebeticProactive

preventive actions

+

+

+

+

+

Loop 12

Loop 13

Loop 14

Sight

Sight Problems-+

+

Loop 15

Loop 16

Loop 17

Loop 18

Loop 19

Figure 9: Weight, health, diabetic, blood pressure, and over eating problems

7. A Dynamic Study of Heart Attach related Deaths The model proposed in figure 9 is being simplified and shown by flow diagram in

Figure 10. By finding the values of heart attack death rate factor of R1 and action

related impact factor of R2 we can determine the pattern of heart attack death

occurring over the time. For comparison purposes, the model is broken into two cases

where in the first case only heart attack death pattern is determined (without

considering the action impact- learning) while in the second case the action impact

(learning) is included.

7.1 Mathematical Model 1 (With no learning impact factor) To perform a study of the situation, we need to convert the flow diagram model 14

into mathematical model suitable for simulation purposes. In order to quantitatively

simulate the dynamic behaviors of a system, modelers need to write a set of equations

to describe each relationship, and then operate them (Chen, et al., 2004). Causal loop

diagrams help to qualitatively realize the fundamental structure of the system under

study. In order to further describe all relationships among system elements in detail,

VENSIM PLE provides modelers a series of convenient tools to construct a stock-

flow diagram.

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7.2 Model Verification

Vensim PLE software has capability for verification purposes. For this purpose, the

structure check which includes formulas check and units check, is used to find

whether there are formulas or units errors in the model of the problem. After

successful completion of checking the formulas and units loaded into the software the

model of choice is simulated.

7.3 Model Validation

Vensim PLE allows model validation using the reality check, the option that the

system provides. One can use that for comparing simulation results with perceived

reality. The smaller difference between them can guide us that model is adequately

addressing the problem to which it is being applied. In this study, we first execute the

model using the regular data without considering the learning rates that would impact

the system to reduce weight, to control blood pressure, to control the amount of sugar

and salt that would, as a result, reduce their chance of suffering heart attack. Then the

system was run using the data with its learning impact.

7.4 Level and Rate Dynamic systems deal with two types of variables known as level and rate. The

Level refers to a given element within a specific time interval. In dynamic systems,

level type variable is the one that accumulation occurs in that. Meanwhile, the rate

variable causes the increase or decrease in the accumulation, the level variable (Zare

Mehrjerdi, 2012). The level is calculated from the difference between a rate variable

that increases the level and a rate variable that reduces the level. Specifically

speaking, the level deals with rates related to input and output. Therefore, the value of

level can be identified easily. Determination of rate is not a simple task and requires a

great deal of effort in almost all the cases for about every problem. Most of the time a

rate is calculated by finding the average value of the accumulated level over the total

time taken to get that (Zare Mehrjerdi, 2012). In some cases rates are defined

according to following formula:

Rate (t) = Const * Level (t-1)

Const= A predetermined value

Using the Formula for the rate given above one can determine the Level variable at

time t as a function of the Level variable at time (t-1) as shown below:

Level (t) = Level (t-1) + dt * Rate

The above formula can also be written as follow:

d(Level) / dt = Rate

In above formulas dt stands for a time period that we are looking into the changes.

This time period is defined by the researcher as a minute, day, week, month, year, or

others.

7.5 Simulation and Analysis

14

After the models evaluation, we ran it to simulate the dynamic behaviors of

Total_HA_Death, and rate changes over a period of 60 month. Simulation parameters

were defined as follows:

Total_HA_Death (t+1) = Total_HA_Death (t) + DT * RT (t, t+1)

RT (t, t+1) = Const * Total_HA_Death (t)

Const = a pre determined amount

DT = 1 month

Where, the variables used are:

Total_HA_Death = total number of deaths from heart attack situation.

RT (t, t+1) = the rate of occurring heart attack in the time space between t and t+1.

DT= time distance that is considered to be equal to 1time period.

Const=a pre determined amount

t= period t

Total Death (heart attackrelated)

Heart Attack Death

rate factor =R1

Action related

impact factor =R2

Population

Death rate

Figure 10: A flow diagram for total heart attack death counts and population

7.6 Model 2 (With learning impact factor) Now the mathematics used in the first model can be presented as below:

Total_HA_Death (t+1) = Total_HA_Death (t) + DT * [RT1 (t, t+1) RT2 (t, t+1)]

RT1 (t, t+1) = Const * Total_HA_Death (t)

RT2 (t, t+1) = Rand (.) * Total_HA_Death (t)

Const = a pre determined amount

DT = 1

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Rand (.) = Represents the random rate produced as a result of teaching people to

exercise, to reduce weight, to control blood pressure, to control the amount of sugar

and salt that would, as a result, reduce their chance of suffering the heart attack.

Figure 11: Heart Attack Deaths Related before Learning

Figure 11 shows the heart attack deaths by periods without learning factors applied.

On the other hand, figure 12 shows the random learning rates that could obtain as a

result of teaching people about their heart functioning, heart attack, heart problems,

eating habits, blood pressure, sugar blood, and what their impacts are on the heart.

Using the proposed model we were able to determine the heart attack death right after

the learning that has been occurred, with the random learning rates given. As can be

seen from figure 14, the new heart attack related deaths are lower as well as its related

overall deaths.

Figure 13 shows the overall death reduction as a result of the learning that happened.

Since death rates are considered to be random variables related death counts are

showing reduction in death amounts which also behaves as random variable. The new

heart related deaths as well as the total deaths are shown by figure 14.

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Figure 12: Random learning rates

Figure 13: Death reduction as a results of learning rates

Figure 14: New heart attack deaths

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8.Future Research and extensions Currently, one of the central issues of health systems and health providers is to

research on the problems that can be used for showing the insured that taking care of

their personal body is a good business for them. This, in turn, would be very good for

insurance companies too. So it seems a harmonious way with insurance companies to

use SD in health problems and related system modeling. With this taught in mind, this

author proposes the following areas of concern for future researches:

Proposition 1: Obesity, blood pressure and heart attack are closely related causes.

Proposition 2: Obesity, blood pressure, blood sugar, and heart problem are closely

related causes.

Proposition 3: Exercise and heart problem are closely related causes.

Proposition 4: Weight, diabetes, and heart attack are closely related causes.

As can be seen from loops 12 and 13 one can study the impacts that bad eating habit

can have on the weight gain and on the blood sugar, and then identify a pattern for the

type of the food eaten, and that snacks are not good for the kids as parents often tell

them.

9.Limitations and scope for future research As it is stated by Sterman (2000) All models are wrong, so no models are valid or

verifiable in the sense of establishing their truth. The question facing client and model

builder is never whether a model is true but whether it is a useful one. We can only

say dynamic system is a good tool for studying complex systems. Specially, for a

system as such as human being body with all the complexities that it has. To do a

deep study on that, we are in need of trustful data. Having good data for data analysis

is a big limitation of the proposed model.

10. Discussion and Conclusion In this article, author has discussed the fundamentals on systems thinking, and system

dynamics. In addition to that five health related models are developed that are highly

beneficial for studying the impacts of weight on different health problems such as

movement (knee problems), blood pressure, blood sugar, heart disease, and heart

attack. To study heart attack problem and its related causes and the death counts a

flow diagram with two models are proposed. The first model just takes in the rates for

heart attack problem while the second model takes in the action related impacts factor

relating to the teaching of people, causing to reduce death due to heart attack. Figure

14 shows the action related impact factors that are generated randomly, assuming that

this factor is not a fixed number and changes over time due to learning more and

implementing that. The pattern shown by figure 14 are the through representation of

the fact that heart attack deaths will reduce once people are taught well and they do

implement that knowledge accordingly.

The main message of this research is that for every human being regardless of their

sex, race, age, and the country that he/she is living in, it is the weight that we carry on

a daily basis. There are some factors that contribute to body mass accumulation as we

get older. We have noticed that bad eating habits and less movements and exercises

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can cause the extra weights where weight by itself is the main source of problem for

the known diabetic disease. This study showed that there is a balancing loop that

relates weight with blood sugar, diabetics and action to control the diet and eating

habit. However, bad eating habit may be the cause of extra eating or many times

eating and hence laziness and less movement. This study also points to the impacts of

blood sugar on diabetic problem, on eye sight, and hence on the eye problem as such

as blindness.

American Heart Association (http://www.heart.org) has defined what it means to have

ideal cardiovascular health, identifying seven health and behavior factors that impact

health and quality of life. We know that even simple, small changes can make a big

difference in living a better life. Known as Lifes Simple 7, these steps can help add

years to your life: (1) dont smoke; (2) maintain a healthy weight; (3) engage in

regular physical activity; (4) eat a healthy diet; (5) manage blood pressure; (6) take

charge of cholesterol; and (7) keep blood sugar, or glucose, at healthy levels.

The results of this problem also clearly shows that our random learning on heart

disease and its related problems can contribute to lower the overall death which is a

good news for all. This article makes a significant contribution to the health study

issues due to the fact that it shows how a factor such as weight can have impacts on

heart attack, blood pressure, and blood sugar, to mention a few. Since, to the best of

this author's knowledge, this is the first study that relates weight to health problems

using systems thinking concepts and system dynamic it make a significant

contribution to the health literature.

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