View of Community Based Study to Assess the Prevalence of Risk Factors for Non-Communicable Diseases in Amarpur Village Up, India- Using Who Step 1 Approach

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Community Based Study to Assess the Prevalence of Risk Factors for Non- Communicable Diseases in Amarpur Village Up, India- Using Who Step 1

Approach

Prof. Dr. Ajoke Akinola^1*, Dr. Shaheen Khanum²Chomba Kabwe³

1Public Health Depart. School of Health Sciences Noida International University, Gautam Budha UP-India 2Physiotherapy Depart. School of Health Sciences Noida International University, Gautam Budha UP-India

3Public Health Depart. School of Health Sciences Noida International University, Gautam Budha UP-India

*[email protected](Affiliation email)

ABSTRACT

Background: Non-communicable diseases has led to several deaths worldwide especially in lower income countries.

The aim was to assess the prevalence of risk factors for NCDs and find out the association between demographics and behavioral risk factors.

Methods: Descriptive cross-sectional study, multi-stage random sampling technique, sample size 159 and WHO standardized structured questionnaire was used. SPSS version 21 software to calculate percentages, frequencies and chi-square test to assess the association between behavioral risk factors and socio-demographic variables.

Results: Prevalence of tobacco use 37.1%, alcohol consumption 33.3%, unhealthy diet 41% and physical inactivity 37.1%. The demographic variables were associated with the behavioral risk factors at P<0.05.

Conclusion: NCD risk factor prevalence is high due to Low level of education and poor background. However, adopting various health care modifications and change lifestyle choices is necessary to prevent the increasing burden of diseases.

Keywords: Alcohol consumption, Non-communicable diseases, Physical inactivity, Unhealthy diet.

Introduction Background

Non-communicable diseases (NCDs) are the leading cause of death and are responsible for 70%

of deaths worldwide and they affect both women and men almost equally. According to World Health Organisation status, 56.9 million global deaths that occurred in 2016, 40.5 million, or 71%, were due to non-communicable diseases (NCDs). The main four diseases associated with NCDs are cardiovascular diseases, cancers, diabetes and chronic lung diseases and they account to about 36 million out of 57 million of deaths worldwide [1].

The burden of these diseases is increasing at a faster rate especially in lower income country populations. In 2016, over 3 quarters of NCD deaths occurred in low- and middle-income countries with forty sixth of deaths occurring before the age of seventy in these countries. The major disease which accounts for most NCD deaths is cardiovascular disease with about 17.9 million people annually, followed by cancers (9.0 million), respiratory diseases (3.9million), and diabetes (1.6 million) [1].

In India, a total number of 58,17,000 deaths were recorded from diseases like cancer, diabetes and heart problems in 2016. Although the percentage of deaths from NCDs in India is lower compared to many other countries across the world, experts are concerned that the burden is rapidly rising because of change in lifestyle and environmental factors like pollution [2].

The main four risk factors responsible for NCDs are tobacco, unhealthy diet, physical inactivity and harmful use of alcohol. Other major metabolic factors associated with the risk factors are

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obesity, and raised blood pressure, blood glucose and blood cholesterol levels. Cardiovascular diseases like (coronary heart disease, stroke, and hypertension) contribute to about 45% of all NCD deaths, followed by chronic respiratory disease (22

%), cancer (12 %) and diabetes (3%). Even though, despite India having a lower percentage of deaths from NCDs, the level at which premature deaths occur due to such diseases is very high.

Cancer, diabetes and cardiovascular diseases alone account for 55% of the premature mortality in India in the age group of 30 -69 years annually.

Recent data from WHO shows that almost 23% are at risk of premature death due to NCDs[2]

Although NCDs are the major killer diseases in the world, they can also be preventable through eliminating the major risk factors and implementing effective interventions that address shared risk factors like tobacco use, unhealthy diet, physical inactivity and harmful use of alcohol. NCDs push many people into poverty due to catastrophic expenses for treatment and they have a huge impact on reducing productivity. Non-communicable diseases do not only pose as a health problem but as well as a development challenge. The WHO has warned countries, including India, against premature deaths due to NCDs and recommends governments to step up efforts immediately. [3]

NCDs contribute to around thirty-eight million (68%) of all the deaths globally and to about 5.87 million (60%) deaths in India. Uttar Pradesh has India’s second-highest mortality rates that contributes to the largest share of almost all communicable and non-communicable disease deaths [10]. Recent studies show evidence that there is need to put the approach in practice in a rural area setting like Amarpur village since there is no available data/study that has been done in that area regarding prevalence of non-communicable diseases and their risk factors. The information can be used to plan for and implement currently available interventions to address the disease patterns caused by these risk factors. The objectives of the study were to assess the prevalence of risk factors for non-communicable diseases and to find out the association between the demographic variables and behavioral risk factors.

Estimates projects that mortality rates for non-communicable diseases (NCDs) by the year 2020 will account for about 73% of deaths and 60% of disease burden globally. India admits the epidemiological transition which is leading to increase in the prevalence of NCDs. Resuscitating the risk factors for NCDs is recognized as an important preventive strategy. [5]

OperationalDefinitions

World Health Organisation Surveillance Step 1 approach

The World Health Organisation Surveillance Step 1 approach is a system for NCD risk factors which is set up to assess demographic variables and behavioral risk factors such as tobacco use, alcohol consumption, unhealthy diet and physical inactivity. The Steps Approach focuses on obtaining core data or information at each level on the established risk factors that determine the major disease burden. [6]

Non-communicable diseases (NCDs)

NCDS also known as chronic diseases, tend to be of long duration and are the result of a

combination of genetic, physiological, environmental and behaviour factors. The four main types of non-communicable diseases are cardiovascular diseases (like heart attacks and stroke), cancer, chronic respiratory diseases (such as chronic obstructed pulmonary disease and asthma) and

diabetes. [8,9]

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Behavioural risk factors

 Tobacco users- Using of smokeless tobacco like chewing tobacco and smoking like cigarettes etc.

 Alcohol consumption–Consuming any form of alcohol like beer, wine, spirits, whiskey and local alcohols

 Unhealthy diet -A diet with low or no consumption of fruits and vegetables; eating less than 4 servings of fruits and vegetables in a week.

 Physical inactivity -Low physical activity with moderate less than 150 minutes of physical activity per week

MATERIAL&METHODS Study Design

The research study was based on a descriptive cross-sectional study.

Duration of Study

The study timeframe is 6 months from December 2018 to May 2019 Study Setting

Amarpur a medium sized village located in Gautam Buddha Nagar Tehsil of Gautam Buddha Nagar district, Uttar Pradesh with total 266 families residing. The Amarpur village has population of 1587 of which 827 are males while 760 are females as per Population Census 2011. [20]

Sample Size

Cross-sectional Sample size calculation formula [40]

n= z

²

xp(1-p)/e

²

1+[z

²

xp(1-p)]/e

²

N

Where:

• Z is the confidence level,

• e is the desired level of precision (i.e. the margin of error),

• p is the (estimated) proportion of the population which has the attribute in question,

• q is 1 – p.

• N is the population size

Population family size = 266, Confidence level = 95% (1.96), Margin of error or precision = 5% (0.05) P value= 0.5

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Sample Size = 158 Rounding off to 159 Sampling Technique

Multi-Stage Random Sampling using systematic Random and Sampling formula (N/n) Where;

N= Total number of population, n= Total number of families. Population size= 1587, No of families= 266, 1587/266= 5.9 Rounding off to 5, Thus every 5th family was selected for identifying adult population between the age of 18-65 years. Simple Random sampling for selection of start point from 1st to 5th family by ballot. Simple Random Sampling for selection of 3 participants in each family above the age of 18 years old by ballot, hence a total number of 53 families was included in the study.

Inclusion Criteria

The study included individuals in the age group of 18-69 years, individuals willing to participate in the study. Residents of the study area who have lived in the area for more than 6 months.

Exclusion criteria

Age group below 18 years, pregnant women and psychiatric individuals, individuals not willing to participate in the study. Non-residents of the study area and families that have lived in the area for less than 6 months.

Study Variables

Dependent Variables-Tobacco users, alcohol consumers, unhealthy diet, physical inactivity Independent Variables-Gender, age group, education, income

Data collection tool

Data collection from the study area using World Health Organisation (WHO) standardized closed/open structured questionnaire, a STEP 1 surveillance approach containing socio- demographic factors and behavioral risk factors.

Data collection procedure

Participant were identified between the age of 18-65 years old, they were informed about the purpose and benefit of the research before Initiation of the study. Participants gave informed consent before data collection.

Data Analysis Procedure

After the completion of data collection, data was analyzed using Statistical Package for Social Sciences (SPSS) version 21 software. Descriptive data analysis was used to calculate percentages, frequency and chi-square test to assess the significant association between behavioral risk factors and socio-demographic variables.

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Ethical Consideration

The study was conducted after getting approval from the Institution of Ethics Committee, Noida International University and from the village head where the study took place. The chosen participants of the study were told about the importance and purpose of the research study and informed consent was given before initiation of data collection

RESULTS

Social-DemographicCharacteristicsofTheStudyPopulation

Table1; Social-demographiccharacteristicsofthestudypopulation

Social-

demographics

Variables Frequency

(n=159)

Percentage (%)

Sex Male

Female

85 74

53.5 46.5

Age 18-25

26-33 34-44 45-65

37 58 39 25

23.3 36.5 24.5 15.7 Education Noformalschooling

Primary

schoolSecondary schoolUG/PG

49 44 53 13

30.8 27.7 33.3 8.2

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Income Rs500-10000 Rs10001-20000 Rs20001-30000 Rs30001-40000

88 58 12 1

55.3 36.5 7.5 6 Social-demographiccharacteristicsofthestudypopulation

Thesocial-

demographiccharacteristicsofthestudypopulationillustratedintable1showsthat53.5%weremaleand4 6.5%werefemale.Amongthestudypopulation,theagegroupbetween26-33 years had the highest percentage with 36.5% followed by the age group between 34- 44yearswith24.5%.About55.3%hadanincomeof500-10000rupeespermonthinahousehold,followed by 36.5% with 10001-20000 rupees, 7.5% with 20001-30000 rupees and 6% with30001- 40000rupees’monthlyincome.

FINDINGS RELATED TO THE PREVALENCE OF RISKFACTORS FOR NON-COMMUNICABLEDISEASEIN AMARPUR VILLAGEAPRIL2019

Table2;Prevalenceofrisk factors for non-communicablediseases amongthestudypopulation.

Behavioralriskfactors Frequency(n=159) Percentage(%)

TobaccoUse 59 37.1

AlcoholConsumption 53 33.3

UnhealthyDiet 66 41

PhysicalInactivity 59 37.1

Prevalenceof riskfactorsfornon-communicablediseasesamongthestudypopulation.

As shown in table 2, the prevalence of behaviour risk factors for the study population aretobacco use with 37.1%, alcohol consumption with 33.3%, unhealthy diet 41% and physicalinactivitywith37.1%

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FINDINGS RELATED TO THE ASSOCIATION BETWEEN GENDER ANDBEHAVIORALRISKFACTORS

Table3;Associationbetweengenderandbehavioralriskfactors

Demographics Variables Riskfactors Chi-

square

P- value

TobaccoUse Yes No

GENDER Male

Female

53 30

6 67

54.052 0.000

Alcoholconsumption Male

Female

47 36

6 68

43.008 0.000

FruitsIntake

0 1-3 4-7 Don’t know

MaleF emale

56 20 5 4 43 26 0 5

6.873 0.076

VegetableIntake 1-2 2-3 3-4 5-7 Male

Female

21 47 12 5 14 47 10 3

1.327 0.723

PhysicalInactivity Male

Female

27 55

14 47

4.083 0.253

Associationbetweengenderandbehavioralriskfactors

The association between behaviour risk factors and gender among the study population asshown in table 3. It depicts that, there is a strong significant relation in smoking tobacco

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withp=0.000,alcoholconsumptionwithp=0.000,fruitsintakewithp=0.076andphysicalinactivitywithp

=0.253.Butthereisnosignificantrelation betweengender andvegetablesintake.

FINDINGSRELATEDTOTHEASSOCIATIONBETWEENAGEANDBEHAVIO RALRISKFACTORS

Table4;Associationbetweenageandbehavioralriskfactors

Demographic Variables Behavioral

risk factors

Chi- square

P- value

AGE Smoking

Tobacco Use

Yes No

18-25 14 22 6.360 0.703

26-33 22 35

34-44 11 27

45-65 12 13

AlcoholCons umption

18-25 12 25 5.527 0.786

26-33 22 34

34-44 10 29

45-65 9 16

FruitsIntake

19-25 18 14 9.558 0.387

26-33 37 17

34-44 45-65

29 8

15 7

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VegetablesIntake

18-25 7 22 11.087 0.270

26-33 16 35

34-44 8 24

45-65 4 13

PhysicalInactivity

18-25 9 28 3.708 0.930

26-33 14 42

34-44 12 25

45-65 6 17

Associationbetweenageandbehavioralriskfactors

Table 4 illustrates that there is no association between tobacco use, alcohol consumption,physical inactivity and age but there is a significant association between age and fruits intakewith p=0.387. A much higher association is seen between vegetables intake and age withp=0.270.

FINDINGSRELATEDTOTHEASSOCIATIONBETWEENEDUCATIONANDB EHAVIORALRISKFACTORS

Table5; Associationbetweeneducationandbehavioralriskfactors

Demographic Variables RiskFactors Chi-square P-value

EDUCATION Smoking

Tobacco Use

Yes No

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IlliterateP rimarySec ondary UG/PG

19 30

13 29

23 29

4 9

6.002 0.740

AlcoholCons umption IlliterateP

rimarySec ondary UG/PG

14 34

14 30

19 34

6 6

15.501 0.078

Fruitsintake 0 1-34-7Don’t know

IlliterateP rimarySec ondary UG/PG

37 8 3 2 27 18 0 1 29 17 1 6

8 3 2 0

20.109 0.107

VegetablesIntake 1-2 3-33-4 5-7 IlliterateP

12 28 6 3

6 32 6 0

13 28 7 5

4 6 3 0

10.213 0.334

PhysicalInactivity IlliterateP

9 37

15 27

11 41

6 7

11.623 0.235

Associationbetweeneducationandbehavioralriskfactors

Table5showsthatthereisasignificantassociationbetweeneducationandalcoholconsumptionwithahig herp=0.078,fruitsintakewithp=0.107,vegetablesintakewithp=0.334and physical inactivity with p=0.235. There was no association between smoking tobacco useandeducation.

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FINDINGS RELATED TO THE ASSOCIATION BETWEEN INCOME ANDBEHAVIORALRISKFACTORS

Table6;Associationbetweenincomeandbehavioralriskfactors

Demographic Variables RiskFactors Chi-

square

P- value

INCOME SmokingTobacco

Yes No 500-10000

10001-20000 20001-30000 30001-40000

31 56

24 33

04 07

00 01

13.834 0.128

AlcoholCons umption 500-10000

10001-20000 20001-30000 30001-40000

28 58

23 35

02 10

00 01

4.706 0.859

FruitsIntake

0 1-3 4-7 Don’t know

500-10000 10001-20000 20001-30000 30001-40000

63 21 1 3 31 22 2 3 04 03 2 3 01 00 0 0

23.506 0.005

VegetableIntake 1-22-33-45-7 500-10000

10001-20000 20001-30000 30001-40000

22 51 12 3 11 35 09 3 02 07 01 2 00 01 00 0

5.560 0.783

PhysicalInactivity Yes No

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500-10000 10001-20000 20001-30000 30001-40000

21 63

16 41

03 41

01 00

7.232 0.613

Associationbetweenincomeandbehavioralriskfactors

The association between income and behaviour risk factor as shown in table 6, smokingtobaccousehadap=0.128andsmokelesstobaccousewithp=0.530.Fruitsintakehasap=0.005an dthereisnorelationbetweenalcoholconsumption,vegetableintakeandphysicalinactivity.

DISCUSSION

Non-

communicablediseases(NCDs)arecurrentlyreplacingcommunicablediseasesthatwerethemajorleadi ng cause of death in most countries. Now non communicable disease are the leadingcauseofdeathworldwide[1].Non-

communicablediseasesareaffectingtheentireglobeespeciallyin developing countries, where the social-demographics and economic transition imposes doubleburdenofnon- communicablediseases [22]

The prevalence risk factors of tobacco use (smoking) in this study was found to be (37.1%) withfrequency n=159 (59) among the study population which was high due to psychological factors,low levelsofeducation,peerpressurefromcommunitiesandchanging lifestyleswhichissimilarto the global status report on non-communicable diseases given by Word Health Organisation2014. The impact of these factors are resulting into various diseases leading to high increase inmorbidity rates and mortality rates as well as high level of environmental pollution leading toenvironmental degradation which is highly affecting the population at large.

This is based on theglobalreportonhealthrisks givenbyWorldHealthOrganisation2009[23,24].

The prevalence findings of tobacco use in this study were in contrast with the study done byThankappan et al with results (28%) which had a high burden of tobacco use compared to thestudiesthatwas doneintheUnitedStates [25].

Alcohol consumption was prevalent in this study having (33.1%) with a frequency (n=159) (53)whichissimilartostudiesdonebyKumaretalthatreveledahighburdenofalcoholwith(47.22%)[26].

A comparable alcohol level was seen to be more prevalent especially in poor populationswhichisevidentandmuchhigherinthestudiesdonebyS.Bhattacherjeeetalwith(50.87%).[2 7,28]. A recent study done by Parry et al shows evidence that there is a strong link between alcoholand non‐communicable diseases, in particular cancer, stroke and heart disease, liver disease,pancreatitisanddiabetes [29].

The prevalence of unhealthy diet from this study population was recorded to be (41%) for bothmale and femalethatisinclusiveofbothfruitsandvegetablesintake whichishighercomparabletothestudydonebySrivastavwhichreveledunhealthydiet

tohave9.6%formalesand19.0%forfemales.[2]

Physical Inactivity prevalence results from the study was (37.1%) with frequency (n=159)

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(41)whichis veryhighcomparedtothestudydone byVijayakarthikeyan[10].ThestudydonebyLeeet al show strong evidence that physical inactivity increases the risk of many adverse healthconditions, including major non-communicable diseases such as coronary heart disease, type 2diabetes,breastandcoloncancers,andshortenslife expectancy[30]

The study population highlighted the association between behavioral risk factors and genderamong the study population to have an association in smoking tobacco with p=0.000, alcoholconsumption with p= 0.000, fruits intake was recorded with p=0.076 and physical inactivity withp= 0.253. A strong association was seen between age and alcohol consumption with a higherp=0.078, fruits intake with p=0.107, vegetables intake with p=0.334 and physical inactivity withp=0.235. Education had an association with alcohol consumption of p=0.078, fruits intake withp=0.107,vegetablesintakewithp=0.334andphysicalinactivitywithp=0.235.

The association between income and behaviour risk factor was seen in smoking tobacco use witha p=0.128 and Fruits intake has a p=0.005. The results in this study were similar to the studiesdonebyPatricketalwhichhadanassociationbetweenphysicalinactivityandobesitywithP=

0.093 at 95% and Satia et al which had nutrition levels association with overweight with varianceat 2% and 17% [31,32]. Another study done by Simantov et al, gender was strongly associatedwith increased risk for alcohol use in adolescent boys who were drinking regularly with (95% CI,1.1-5.4)[33]

CONCLUSION

Theprevalenceofmostoftheriskfactorsthatwasusedinthestudyweregenerallyhighacrossthe

sociodemographic groups. The reason for the increase was due to low level of educationandpoorsocial-

economicalbackgroundwhichresultedinahighriskforthehabitofsmoking,alcoholconsumption,physi calinactivity&unhealthydiet.[28]Becauseofthehighprevalencelevels of the various behavioral risk factors, thereis need to address these issues by adoptingvarious health modifications and lifestyle

choices that will help reduce or combat the

diseaseburdenamongthestudypopulationandacrosstheworldatlarge.Theresultshighlightedthe need for carefully and close monitoring, evaluation and control of these non- communicabledisease riskfactors inruralareas ofIndia.

PUBLICHEALTHIMPLICATION

The study was aimed to assess the prevalence of risk factors for NCDs and find out theassociation between demographic variables and behaviour risk factors. The public healthsignificance of this study is to promote health by creating awareness about the dangers ofsmoking and alcohol consumption as well educating the people about the benefits of physicalactivity and eating a diet with more of vegetables and fruits. It can also help reduce the rate ofmorbidity and mortality by assessing the disease burden in the communities. Analyzing andidentifyingthefactorscausingorresponsibleforvariousdiseasesoccurringinthecommunitiescan help make the environment free of pollution. Another health implication of the study is toassessand evaluatetheeconomicdevelopmentandliteracy levelofthestudypopulation.

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LIMITATIONOFTHESTUDY

 Language barrier was the major limitation of the study as the research was conductedin a rural area setting where Hindi was the local language and there was need to hiretranslatorssince the questionnairewasinEnglishlanguage.

 Lack of financial funding and limited time hence only step 1 of the WHO surveillanceapproachwasemployed.

 Due to strong cultural beliefs among the study population, many people refused toparticipate inthestudy.

 Transportationthestudyareawaslimited.

RECOMMENDATIONS

 Ultimately, low level of education was the reason for the high increase in tobacco use,alcohol consumption and physical inactivity. Hence the government, NGOs, charityinstitutions and Humanitarian agencies should collaborate and initiate interventions toaddress these issues like Information Education and Communication(IEC) campaignsto educate people about the harmful effects of tobacco use and alcohol consumption incommunitiesas wellas teachthentheimportanceofphysicalactivity.

 Policy for an intervention study should be encouraged for future researches, accordingtothisstudyfinding.Theprevalenceforthebehaviourrisk

factorswerehighaswellasthe association between the demographic variable and smoking tobacco use, alcoholconsumption, unhealthy diet and physical inactivity. Hence after the IEC is given andmonitored, an experimental study may be explored for example a pre and posttest toexamthe effect,cause andextentofchangeforprobable solutions.

 Poverty was the main reason to which people had unhealthy diet, hence donations canbe put up to help the people in such situation. Health campaigns can also be exercisedto educate people about the benefits of a balanced diet. Majority of the people wereunemployed; therefore, effort can be made in the creation of jobs so as to give them asourceofincome.

 A similar study can be replicated in the same area using the other WHO surveillanceapproachstep 2,3 assessingthephysicalmeasurementsandmetabolicfactors.

ACKNOWLEDGEMENT

Undertaking this research has been a truly life-changing experience for me and it would not havebeen possible to do without the support and guidance that I received from many people.

Firstly, Iwould liketothankthealmightyGodforstrengthening meandseeing methroughoutthisjourney.Thesuccessand finaloutcomeofthisproject requiredalotofguidanceandassistancefrommanypeople. I am extremely privileged to have had

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such great advisors and all that I have done is onlydue to their supervision and assistance. I would like to thank my research guides Prof. Dr. AjokeAkinola Head of Department of Public Health and Dr. Shaheen Khanum Assistant ProfessorSchool of Physiotherapy Noida International University.They have always been so kind andsupportive and they have always been there whenever I ran into a trouble spot or had a questionabout my research or writing. They consistently allowed this research to be my own work, butsteeredmeinthe rightdirectionwhenevertheythoughtIneededit.

Funding: Nofunding sourcesConflictofinterest:Nonedeclared

EthicalApproval:ThestudywasapprovedbytheInstitutionalEthicsCommittee,NoidaInternationalUni versity

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40. https://www.surveymonkey.com/mp/sample-size-calculator/

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Novum exerci soluta no eum. Veri impetus numquam has ex. Vel cu omnis denique eleifend, qui et zril delicatissimi. Consul melius audire id pri, his esse quas voluptatibus an. Tale signiferumque vix ut, probo reque omnium ea nam. Has illud maiestatis at. Eam ne veritus platonem pericula, ad vix sale liber. Laoreet scriptorem at nec. Ex mei idque affert pertinax. Illud nobis tincidunt vim at, ullum utinam nonumes vix no, cum eu nisl vituperatoribus. In duo quas cetero, ei agam aperiam consectetuer nec. Quaestio principes qui at, cu sed noster voluptua efficiendi. No hinc harum mandamus vix, ad doctus dissentias accommodare has. Errem aliquando id per. Sed decore meliore scripserit an, primis apeirian invenire in vim. Brute causae recteque nec an, congue iuvaret ut vis, essent corrumpit adolescens ne mea. Ut eum elit eius brute. Te nemore volumus quaestio mei. Mea ne aperiam fabellas facilisi, veritus invidunt ei mea.

Subtopic

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Figure 1. Sed decore meliore scripserit

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 vel cu omnis denique eleifend

 qui et zril delicatissimi

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 Tale signiferumque vix ut, probo reque omnium ea nam

Has illud maiestatis at. Eam ne veritus platonem pericula, ad vix sale liber. Laoreet scriptorem at nec. Ex mei idque affert pertinax. Illud nobis tincidunt vim at, ullum utinam nonumes vix no, cum eu nisl vituperatoribus. In duo quas cetero, ei agam aperiam consectetuer nec. Quaestio principes qui at, cu sed noster voluptua efficiendi. No hinc harum mandamus vix, ad doctus dissentias accommodare has. Errem aliquando id per. Sed decore meliore scripserit an, primis apeirian invenire in vim. Brute causae recteque nec an, congue iuvaret ut vis, essent corrumpit adolescens ne mea. Ut eum elit eius brute. Te nemore volumus quaestio mei. Mea ne aperiam fabellas facilisi, veritus invidunt ei mea.

Methods (Times New Roman, bold, 12)

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Methodology (Times New Roman, bold, 12)

Data Analysis(Times New Roman, bold, 12)

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Results (Times New Roman, bold, 12)

Discussions (Times New Roman, bold, 12)

Conclusion(Times New Roman, bold, 12)

Novum exerci soluta no eum. Veri impetus numquam has ex. Vel cu omnis denique eleifend, qui et zril delicatissimi. Consul melius audire id pri, his esse quas voluptatibus an. Tale signiferumque vix ut, probo reque omnium ea nam. Has illud maiestatis at. Eam ne veritus platonem pericula, ad vix sale liber. Laoreet scriptorem at nec. Ex mei idque affert pertinax. Illud nobis tincidunt vim at, ullum utinam nonumes vix no, cum eu nisl vituperatoribus. In duo quas cetero, ei agam aperiam consectetuer nec. Quaestio principes qui at, cu sed noster voluptua efficiendi. No hinc harum mandamus vix, ad doctus dissentias accommodare has. Errem aliquando id per. Sed decore meliore scripserit an, primis apeirian invenire in vim. Brute causae

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recteque nec an, congue iuvaret ut vis, essent corrumpit adolescens ne mea. Ut eum elit eius brute. Te nemore volumus quaestio mei. Mea ne aperiam fabellas facilisi, veritus invidunt ei mea.

Limitations and Future Studies (Times New Roman, bold, 12)

Acknowledgement (Times New Roman, bold, 12)

References (APA 6^th edition)

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