

























 

 Epidemiology and Society Health Review| ESHR 
Vol. 7 No. 1, 2025, pp. 1-11 ISSN 2656-6052 (online) | 2656-1107 (print) 
         http://journal2.uad.ac.id/index.php/eshr/index                                                   eshr@ikm.uad.ac.id 

 

 

      10.26555/eshr.v7i1.9933  

 
 
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Review Article 

Risk Factors for Obesity: A Systematic Review 
 
Ami Poniasih1* 
1 Faculty of Public Health, Universitas Ahmad Dahlan, Yogyakarta, Indonesia 
 
* Correspondence: 2207053016@webmail.uad.ac.id  
 
Received 22 January 2024; Accepted 25 December 2024; Published 31 January 2025 

ABSTRACT 

Background: Obesity is excessive fat accumulation due to an imbalance between energy 
intake and energy use. Between 2010 and 2018, there was an increase in the prevalence 
of overweight (3.9% annual average) and obesity (8% yearly average) among adults aged 
over 18. This study aims to overview the factors associated with the incidence of obesity. 
Method: This systematic literature review generated the data using electronic sources: 
Google Scholar and PubMed. Articles were selected using keywords: risk factors, obesity, 
physical activity, and food intake. The inclusion criteria used were free full text published in 
Bahasa or English between 2018 and 2023. Exclusion criteria: Literature review articles and 
full text are not freely available. 
Results: The existence of a high-calorie consumption pattern can increase the incidence 
of obesity, and lack of physical activity can cause the accumulation of body fat, which 
contributes to obesity. The findings from this study can be used to determine risk factors for 
obesity. 
Conclusion: This systematic literature review article concludes that various factors are 
associated with obesity, including food intake, physical activity, and smoking. 

Keywords: Obesity; Risk factors; Food intake; Physical activity 

INTRODUCTION 

Non-communicable diseases (NCDs) are one of the leading causes of death and disability in 
the world. Obesity is one of the main risk factors for non-communicable diseases such as type 
2 diabetes, cardiovascular disease, and several types of cancer, as well as lung, digestive, 
kidney, endocrine, musculoskeletal, neurological, and mental health disorders. In 2019, there 
were an estimated 5 million deaths from obesity-related NCDs, which is equivalent to 12% of 
all NCD deaths.1 

Obesity is when a person has excess fat in the body, so the person has health risks. Body 
Mass Index (BMI) is an indicator used to determine whether someone is obese.2 In obesity, 

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central obesity is also used, a condition of excess fat accompanied by a buildup of visceral fat 
in the middle abdominal area.3 

UNICEF, 2022 stated that in 2010-2018, there was an increase in the prevalence of 
overweight (3.9% annual average) and obesity (8% average annually) among adults aged 
over 18 years. Overweight and obesity were more common in women than men (15.1 % in 
women, 12.1 % in men; and 29.3 % in women, 14.5 % in men) and in urban compared to rural 
areas (14.6 % in urban areas, 12.2 in rural areas; and 25.1 % in urban areas, 17.8 % in rural 
areas). The rise of industrialization and globalization in the food sector has led to significant 
shifts in people's eating behaviours. The existing food industry plays a substantial role in 
influencing dietary patterns, particularly in urban areas.4 The prevalence of central obesity 
(defined as a waist circumference > 90 cm for men and > 80 cm for women) based on 
Indonesia Basic Health Research (RISKESDAS) data for Indonesians aged 15 years and over 
is also high, with a national average of 31 %, where this figure is higher in women compared 
to men (46.7 % in women, 15.7 % in men) and higher in urban compared to rural areas (35.1 
% in women, 25.9 % in men.5 

The prevalence of Non-Communicable Diseases (NCDs) tends to increase over time. 
Referring to RISKESDAS data, by 2013, the obesity rate was 14.8%; in 2018, it was 21.8%5. 
In the prevalence of excess weight, there was a slight increase from 2013 (13.5%) to 2018 
(13.6%). Disparities in the prevalence of obesity are seen in several provinces, and these 
differences differ from the national prevalence values. Apart from that, RISKESDAS data also 
shows an increase in the prevalence of central obesity in residents aged > 15 years from 
26.6% (2013) to 31.0% (2018).3 As individuals age, they face an increased risk of weight gain 
due to a decline in metabolic rate and a reduction in muscle mass.6 Our body functions will 
decline as we age, leading to decreased physical activity. This can cause an imbalance 
between the intake of calories and the energy used to perform activities. 7 

Obesity can be found in children, teenagers, and adults. More than 1.4 billion adults are 
overweight, and more than 500 million adults in the world are obese (WHO, 2008). Obesity is 
closely related to the incidence of NCDs and causes death in 2.8 million adults each year 
(WHO, 2013).8 Sixty-five per cent of the world's population lives in countries where obesity 
and overweight kill more people than underweight). At least 2.8 million adults die every year 
due to overweight and obesity. In addition, overweight and obesity have a risk of developing 
diabetes (44%), ischemic heart disease (23%) and cancer (7%-41%). Factors that influence 
obesity include genetic factors, environmental factors, and drug/hormonal factors. According 
to research by Nugraha (2010), 30% of obesity is caused by genetic factors.3 However, 
hereditary factors are unclear as the cause of obesity. Chronic excess energy intake can 
cause overweight and obesity. The existence of a sedentary physical activity pattern (lack of 
movement) causes the energy expended to be less than optimal, thereby increasing the risk 
of obesity.9 

Research in the literature on risk factors for obesity is essential to compare this disease's 
development over time continuously. This is why this systematic review is conducted to 
determine the risk factors that cause obesity. 

 

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METHOD 

Study Design 

This research was a systematic review that followed PRISMA and PICOS principles during 
the data collection and analysis. The author uses PICOS (Population, Intervention, 
Comparison, Outcome, and Study Design) with the following criteria: 
1. Population criteria   : adult patients (>18 years) 
2. Intervention/exposure criteria : BMI 
3. Outcome Criteria    : Odds Ratio (OR) or p-value 
4. Study Design Criteria  : Cross-sectional 
5. Language    : The study published in English or Bahasa 
The article was obtained from Google Scholar and PMC databases from 2018 to 2023. 
 
Search Course 

The article search process is limited to Google Scholar and PubMed databases. The keywords 
used are risk factors, obesity, physical activity, and food intake. The inclusion criteria for the 
articles used were the last 5 years and open access. The exclusion criteria in this study were 
age < 18 years and the literature review method. The initial article search yielded 1,007 
articles. Eight articles were included for analysis after identifying the relevance of the title, 
theme, and target. The analysis in this article followed Prisma Flow Diagrams (Figure 1) 

Data Collection 

The author took relevant data based on the study design. Researchers conducted the entire 
data collection process, and disagreements will be resolved. The data analyzed in this study 
used eight articles. The data collected for each article is as follows: Author, year, title, 
population, sample, type of research, data collection, and significant findings—output in the 
form of Odds Ratio (OR) with Confidence Interval. 

Assessment of Risk of Bias of Individual Studies and Measurements 

The authors used STROBE to evaluate the quality of observational studies. The STROBE tool 
covers 22 quality assessment domains, including abstract, background, objective, study 
design, location, participants, variables, data sources, bias, study size, quantitative variables, 
statistical methods, participants, descriptive data, output data, primary results, other analysis, 
summary of results, limitations, interpretation, generalization, and funding. The quality of each 
item assessed will be assessed as complete, incomplete, and incomplete based on each 
colour. The authors carried out a study assessment. After data extraction, the author obtained 
the outcome in the form of OR, which was then tabulated in table form. 

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Figure 1. The flowchart of the paper selected in this study 

RESULTS 

We yield 1,007 articles from 2 databases, Google Scholar and PubMed. Eight articles were 
obtained and included in the analysis after identifying the relevance of the title, theme, and 
target. The article selection results correspond to the Prisma (Figure 1). This systematic review 
used the STROBE checklist to evaluate the quality of individual studies. Generally, the quality 
of the studies in this systematic review is good because green dominates the STROBE 
assessment. 

STROBE Checklist 

A summary of the study assessment is shown in Figure 2. The assessment of the quality of 
this study begins with an assessment of the title and abstract, where a total of 8 studies were 
obtained that had complete criteria. 

 
 
 
Author 

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Thicke et al. 
(2020) 

                      

Gupta et al. 
(2020) 

                      

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Information: 

  Criteria not met 

Criteria Not Fulfilled 

Criteria Fulfilled 

Figure 2. STROBE checklist 

DISCUSSION  

This systematic review carried out an analysis of 8 articles in each study. Overall, the quality 
of the studies carried out in the analysis is of good quality. The author found five articles related 
to physical activity that supported obesity: articles 8, 9, 10, 11, and 12. Based on these eight 
articles, the analysis revealed that lack of physical activity was a risk factor for obesity. 
Physical activity is any body movement that increases energy expenditure. For physical 
activity to be beneficial, it should be done for 30 minutes a day or 150 minutes every week at 
moderate intensity. Physical activities can be carried out in various situations and places.10 

Adults should engage in regular physical activity; doing physical activity is better than no 
physical activity. There is evidence of a dose-response relationship between the volume of 
physical activity and several health outcomes, such as death from cardiovascular disease 
(CVD), as well as the incidence of cancer and diabetes. Health benefits occur when physical 
activity levels are below recommendations, thus supporting the claim that some physical 
activity is better than none. More physical activity is better, although the relative benefits tend 
to decrease at higher levels of physical activity.11 

Thin ZT et al. (2020) conducted research with a sample size of 100 people aged 18 - 60. It 
showed that being overweight and obese were related to the habit of consuming fast food in 
their free time (AOR = 8.93, 95% CI 2.54–31.37). Other results found that overweight and 
obesity (AOR = 3.55, 95% CI 1.63–7.73) were positively associated with light-intensity 
physical exercise and sedentary leisure activities in free time (AOR = 3.32, 95% CI 1.22–9.03). 
From this research, respondents who engage in physical activity tend not to become 
overweight or obese.12 

Bogale et al., 
(2019) 

                      

Zulkarnain et 
al., (2020) 

                      

Sumael et 
al., (2020) 

                      

Koryaningsih 
et al. (2019) 

                      

Arifani et al. 
(2021) 

                      

Pakaya et 
al., (2020) 

                      

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 Epidemiology and Society Health Review| ESHR 
Vol. 7 No. 1, 2025, pp. 1-11 ISSN 2656-6052 (online) | 2656-1107 (print) 

      http://journal2.uad.ac.id/index.php/eshr/index                                                                                                                                        eshr@ikm.uad.ac.id 

 

 

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Table 2. shows the synthesis of the eight articles included in the analysis. The variables associated with obesity incidence are physical activity.  

Table 2. Study assessment uses the STROBE Checklist 

 
No Author Name, 

Year 
Research Title Sample Data Collection Important Findings 

1 Thicke et al. 
(2020) 

Association between body 
mass index and ready-to-eat 
food consumption among 
sedentary staff in Nay Pyi 
Taw union territory, 
Myanmar 
 

400 respondents Face-to-face interviews 
and measurements. 
Multiple logistic 
regression analysis to 
estimate adjusted odds 
ratios (AOR) and 95% 
confidence intervals (CI). 

Sedentary Staff who consumed RTE meals once or more per 
month were almost five times more likely to be overweight and 
obese (AOR = 4.78, 95% CI 1.44-15.85) than those who 
consumed RTE meals less frequently. In addition, five factors, 
namely being older than 32 years (AOR = 3.97, 95% CI 1.82-
8.69), preference for ready-to-eat foods (AOR = 8.93, 95% CI 
2.54-31.37), light physical exercise intensity (AOR = 3.55, 95% CI 
1.63-7.73), sedentary leisure activities (AOR = 3.32, 95% CI 1.22-
9.03), and smoking (AOR = 5.62, 95% CI 1.06-29.90) were 
positively associated with overweight and obesity. 
 

2 Gupta et al. 
(2020) 

Association between the 
frequency of television 
watching and overweight and 
obesity among women of 
reproductive age in Nepal: 
Analysis of data from the 
Nepal Demographic and 
Health Survey 2016 
 

Six thousand 
thirty-one women 
aged 15-49  

Multilevel logistic 
regression was performed 
to find factors associated 
with overweight and 
obesity. 

Results: Approximately 35% of participants were overweight or 
obese (overweight: 23.7% and obesity: 11.6%). 
Watching television at least once a week is associated with 
overweight and obesity in women of childbearing age living in 
urban Nepal. 

3 Bogale, et al., 
(2019) 

Determinant factors 
of overweight/ obesity 
among federal ministry civil 
servants in Addis Ababa, 
Ethiopia: a call for sector-

Employees who 
work in the 
ministry. 
Sample 532 
respondents 

Data were entered into 
EPI-INFO version 7 
software and analyzed 
with SPSS version 23. 
Associated factors were 
identified using a 

In multivariable logistic regression analysis, variables that 
showed statistically significant associations with obesity and 
overweight using a p-value < 0.05 were age, income, alcohol 
consumption, 10 minutes of walking per day, and physical 
activity (sports). 

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No Author Name, 
Year 

Research Title Sample Data Collection Important Findings 

wise occupational health 
program 

multivariable binary 
logistic regression model 

4 Zulkarnain, et 
al., (2020) 

The relationship between 
exercise and smoking habits 
and abdominal obesity in 
productive age employees 
 

Sample 103 
respondents 

A questionnaire using the 
Chi-Square Test 

There is a significant relationship between exercise habits and 
abdominal obesity, and there is no relationship between smoking 
and abdominal obesity. 

5 Sumael, et al., 
(2020) 
 

The Relationship between 
Physical Activity and the 
Incidence of Obesity at the 
Pangolombian Community 
Health Center 
 

Sample 85 
respondents 

A questionnaire using the 
Chi-Square Test 

There is a significant relationship between physical activity and 
the incidence of obesity at the Pangolombian Community Health 
Center 

6 Koryaningsih, 
et al., (2019) 

The Relationship Between 
Energy Intake and Physical 
Activity and Obesity in 
Female Workers 

Sample = 54 Interviews, questionnaires 
Chi-Square Test 

The result showed that p-values for energy, fat and physical 
activities were 0,366, 0,638, and 0,189, respectively, which 
means there is no relationship between energy intake, fat intake, 
physical activity and body mass index. There was no correlation 
between the intake of energy and fat and physical activity to 
Body Mass Index. 

7 Arifani, et al., 
(2021) 

Risk Behavior Factors 
Associated with the Incident 
of Obesity in Adults in 
Banten Province in 2018 
 
 
 

A sample of 
12,718 adults 
aged 20-60 

Interviews, questionnaires 
Chi-Square Test 

There is a relationship between smoking behaviour, risky food 
consumption behaviour (sweet foods, sugary drinks, soft drinks 
and instant food) and physical activity with the incidence of 
obesity, and there is no relationship between fatty food 
consumption behaviour and the incidence of obesity. 

8 Pakaya, et al., 
(2020) 
 

The relationship between 
physical activity and 
consumption patterns on the 
incidence of central obesity 
in public transportation 
drivers 

Sample = 201 Interviews, questionnaires 
Chi-Square Test 
 

 

 

 

 

There is a relationship between physical activity and the 
incidence of central obesity in public transportation drivers in 
Gorontalo. There is no relationship between carbohydrate 
consumption and the incidence of central obesity in public 
transportation drivers in Gorontalo City. There is a relationship 
between fat consumption and the incidence of central obesity in 
public transportation drivers in Gorontalo City. There is no 
relationship between carbohydrate consumption and the 
incidence of central obesity in public transportation drivers in 
Gorontalo City. 

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Vol. 7, No. 1, 2025, pp. 1-11 ISSN 2656-6052 (online) | 2656-1107 (print) 
      http://journal2.uad.ac.id/index.php/eshr/index                                                   eshr@ikm.uad.ac.id 

 

  

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Research in Ethiopia with a sample size of 525 respondents reported the risk factor for obesity 
is that adults who do not walk 10 minutes per day are more likely to be overweight and obese 
[AOR=11.28, 95% CI 5.96–21.36] compared to respondents who walk for 10 minutes every 
day. Likewise, respondents who did not do physical activity (exercise) [AOR=2.42% 95% CI 
1.36–4.30] were 2.42 times more likely to experience overweight/obesity than those who did 
physical activity. This is because respondents spend more time sitting and less time doing 
physical work, which causes them to lead a sedentary lifestyle.13 

Previous research stated that the results of statistical tests using the Chi-Square test obtained 
a p-value of 0.02 or p-value <0.05. So, it can be concluded that there is a significant 
relationship between physical activity and the incidence of obesity in respondents at the 
Pangolombian Community Health Center. 14  This research aligns with research conducted by 
Gupta et al. (2016) that states that lack of physical activity is related to the prevalence of 
overweight and obesity. Regular physical activity can help control overweight and obesity.15 

The relationship between food intake and the incidence of obesity using the Chi-Square 
statistical test obtained a p-value = 0.038 (p-value < 0.05), meaning that there is a significant 
relationship between food intake and the incidence of obesity in female workers at the crab 
factory in Prapag Kidul Village and Prapag Lor, Losari District, Brebes Regency. The 
relationship between physical activity and the incidence of obesity in this study obtained a p-
value = 0.017, which means there is a significant relationship between physical activity and 
the incidence of obesity among female workers in crab factories in Prapag Kidul and Prapag 
Lor Villages, Losari District, Brebes Regency.16 

Research in Banten with respondents in this study as many as 12,718 adults aged 20-60 years 
who were taken using probability proportional to size using the Chi-Square test, which stated 
that the behaviour of consuming sweet foods, sweet drinks, soft drinks, and instant food have 
a p-value <0.05 while fatty foods have a p-value >0.05. The p-value means that there is a 
relationship between the consumption of sweet foods, sweet drinks, soft drinks, and instant 
food and the incidence of obesity, and there is no relationship between fatty foods and the 
incidence of obesity. The relationship between physical activity and the incidence of obesity 
obtained a p-value of 0.002, which means that there is a relationship between physical activity 
and the incidence of obesity in research subjects. From the results of the analysis, it is also 
known that the OR p-value of physical activity on the incidence of obesity shows that 
respondents with heavy physical activity can reduce the risk of obesity by 1.15 times greater 
than respondents who have moderate activity.17 

The prevalence of overweight and obesity, especially in women, was increasing in Nepal. A 
previous study in South Asia found television viewing to be a risk factor for overweight and 
obesity among women of reproductive age. Thirteen urban women who watched television at 
least once a week were 40% more likely to be overweight or obese than those who did not 
(AOR: 1.4, 95% CI: 1.1–1.7; p-value <0.01). In contrast, no significant association between 

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overweight or obesity and television viewing frequency was observed among rural women. 
Overall, women who watched television at least once a week were 1.3 times more likely to be 
overweight or obese than women who never watched television on a diet (AOR: 1.3, 95% CI: 
1.0–1.7; p-value <0.05).15 

Two other studies support that physical activity affects obesity, but the benchmark is central 
obesity. Zulkarnain et al. (2020) found a significant relationship between exercise habits and 
abdominal obesity with a p-value =0.037.18. Lack of physical activity causes the energy 
expended not to be optimal, which can increase the risk of nutrition, overweight and obesity.3 
Pakaya's research (2020) states a significant relationship exists between physical activity and 
the incidence of central obesity in public transportation drivers in Gorontalo City in 2020 with 
a p-value of 0.048 < α 0.05. Other results stated no relationship between carbohydrate 
consumption and the incidence of central obesity in public transportation drivers in Gorontalo 
City with a Fisher's Exact value = 0.774 > 0.05. There is a relationship between fat 
consumption and the incidence of central obesity in public transportation drivers in Gorontalo 
City, with p-value = 0.00 < 0.05. There is no relationship between carbohydrate consumption 
and the incidence of central obesity in public transportation drivers in Gorontalo City, with a p-
value = 1> 0.05.19 

Articles related to smoking habits are articles numbers 1, 4 and 7. Thicke et al. stated that 
smoking is positively associated with overweight and obesity with a value (AOR = 5.62, 95% 
CI 1.06–29.90).12 Research conducted by Arifani shows a relationship between smoking 
behaviour and obesity incidence with a value (p = 0.000, OR = 2.660).17 Other studies show 
different results, stating that there was no relationship between smoking and abdominal 
obesity (p=0.720).18 If we are exposed to nicotine from cigarette smoke, it can change the way 
the brain regulates food by reducing appetite through homeostatic and reward processes. 
After 7 days of exposure to cigarette smoke, it can cause an energy deficit characterized by 
decreased food intake and increased energy expenditure. This is associated with weight loss. 
This effect only occurs when belly fat.19 

In addition to behavioural factors, obesity is also influenced by genetic factors. Children with 
obese parents are at 3 times greater risk of obesity as adults and experience an increase of 
almost 10 times if their parents are obese.17 Genetic factors consist of hereditary factors that 
come from their parents. The results of the study stated that children who come from parents 
with average weight have a 10% risk of obesity. If one of the parents is obese, the risk 
becomes 40-50%; if both parents are obese, the hereditary factor becomes 70-80%.  

CONCLUSION 

Eight articles supported the research, with five appropriate articles. According to the literature, 
the incidence of obesity can be influenced by several factors, including consumption patterns, 
physical activity, genetic factors and environmental factors. A consumption pattern that is high 
in calories can increase the incidence of obesity, and a lack of physical activity can cause the 
accumulation of body fat, which contributes to obesity. 

 

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Declarations 

Authors' contribution 

AP is mainly in charge of designing, data collection, analysis, and writing articles.  

Funding statement 

 This research has not received external funding 

Conflict of interest 

There is no conflict of interest in this research.  

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