Ⅰ. Introduction

The World Health Organization (WHO) uses the body mass index (BMI), which is one’s weight in kilograms divided by the square of one’s height in meters, as a measure of obesity. In Korea, BMI is also used as a measure of obesity, and the standard for obesity and normal weight is 25 kg/m2, which is published by the WHO's Asia-Pacific Region and the Korean Society for the Society of Obesity [1].

The direct cause of obesity is a condition in which excessive fat is accumulated in the body due to an increase in the size or number of fat cells owing to consumption being less than intake [2]. It is not simply determined by one factor but is influenced by various factors [3][4]. Additionally, obesity is an imbalance as well as metabolism within the human body. It reduces functionalities and leads to the development of various diseases [5]. As food production rapidly increases and the number of instant foods rises, the number of obese people worldwide is increasing. Women are known to be more interested in weight control for cosmetic purposes, are more sensitive to changes in weight than men, overestimate their bodies, and experience more stress related to their weight [6]. In other words, women are known to be more sensitive to and manage obesity than men, and as of 2021, the obesity rate for men is 46.3% and 26.9% for women [7]. Additionally, the obesity rate for women in their 20s is 15.9%, while for men it is 40%, and for men in their 40s, the obesity rate is the highest at 57.7%, while it is 27.2% for women, representing a difference of more than 30%.

Obesity not only affects appearance but also causes chronic diseases such as high blood pressure, heart disease, cerebral infarction, diabetes, and chronic joint disease, and it has been shown to affect endocrine diseases and certain cancers [4]. Health behaviors, such as exercise, are an important tool in the fight against obesity. Notably, 80.4% of adults feel the need to exercise, but only 3.6% participate in an exercise program. The reasons for being unable to exercise consistently are due to the lack of psychological and time constraints and the inability to experience clear effects from exercise. Although the necessity is recognized, it is evident that there are many limitations to realistic exercise.

Men with office jobs are especially prone to obesity due to the lack of physical activity from sitting at a desk for long hours [8]. Additionally, along with obesity, people are easily exposed to chronic diseases such as high blood pressure, cardiovascular disease, and diabetes. In Korea, deaths from non-communicable diseases account for 79.6% of the total, and among non-communicable diseases, four major chronic diseases, namely, cancer (32.7%), circulatory system diseases (24.7%), diabetes (3.5%), and chronic respiratory diseases (5.5%), account for 66.4% [9].

Obesity has been known to be influenced by sociodemographic factors such as occupation, educational level, economic level (income), and social status, as well as behavioral and dietary patterns [10]. Dietary patterns have been studied in various aspects such as nutrients, food, and eating behavior, and the main focus is on the intake of not only appropriate nutrients but also recommended foods, as well as the improvement of eating behaviors that cause obesity [11]. It is difficult to confirm a clear effect on total energy intake and obesity, which are presumed to have a direct effect on obesity, and it can be assumed that it will be controlled by other factors as research progresses due to differences in energy intake of food according to age and gender [12].

In terms of behavioral patterns, studies have shown that former smokers who quit smoking have higher levels of obesity, alcohol consumption, and obesity in men, and in terms of exercise and physical activity, the lower the amount of exercise and activity, the higher the BMI [13]. Sociodemographic factors such as income level and age have been mainly studied. Among them, there are many studies on the elderly, women's beauty and hormones, and obesity in children and adolescents. Adult studies are conducted on single persons, unmarried households, and the overweight issue [8][9][14]. There is research showing that men have a higher proportion of obesity than women, and that for women, income and obesity are inversely proportional, but for men, it is unrelated to income level and will be affected by factors other than diet and energy intake according to income [15][16].

As the obesity epidemic has grown and the relationship between obesity and chronic disease has become clearer, much research has been conducted on obesity [17][18]. However, there are many obesity studies on women's beauty and hormones, children, and adolescents [19-21]. There are also gender differences in the prevalence of obesity by age group, with men peaking at ages 40 to 45 and women peaking at ages 50 to 55, which is related to hormonal changes in postmenopausal women [22].

In this way, drinking and exercise are explained as the most significant variables in obesity, but there have been relatively few studies on adult men who are frequently exposed to alcohol and do not have many opportunities to exercise. In addition, physical workers appear to have a higher risk of visceral fat accumulation than mental workers [23], so it can be assumed that the relationship between physical activity and obesity is also influenced by a third factor.

Apart from obesity based on BMI, there are differences in anxiety and depression depending on subjective body shape perception. In the group that perceives their subjective body shape as overweight, anxiety and depression are high, and quality of life further declines [24]. In other words, it can be assumed that subjective body shape perception will have a greater psychological impact and influence behavior than obesity based on BMI.

In our society, the obesity rate among men is higher than that among women. Additionally, it is relatively difficult to engage in exercise due to active participation in economic activities. In addition, even manual workers who are physically active are not free from obesity, so it is necessary to practice healthy behaviors to manage obesity, which engenders adult diseases. Accordingly, this study first analyzed the differences between obesity and obesity according to general characteristics, health behavior practice, and subjective body shape perception. In addition, in existing studies on subjective body shape perception and depression and anxiety using data from the National Health and Nutrition Examination Survey [25][26] showed statistically significant differences in depression and anxiety in groups perceived as thin or obese compared to groups with normal subjective body shape perception. There is a statistically significant difference in depression and anxiety across groups. With reference to these existing studies, we further analyzed differences in health behavior practice according to subjective body shape perception.

Ⅱ. Methods

1. Research subject

This study used raw data from the National Health and Nutrition Examination Survey (KNHANES Ⅵ -7, 2018) conducted in 2018. The National Health and Nutrition Survey collects survey data through household member confirmation surveys, health surveys, check-up surveys, and nutritional surveys. There are a total of 7,992 participants in the 7th 3rd year of the National Health and Nutrition Examination Survey (2018), and among them, 1,294 adult males aged 20 to 59 years old who participated in both the health survey and examination survey were selected as the final subjects for the study.

2. Variable definition

This study was designed to investigate the relationship between subjective body shape perception and obesity rates in Korean adult men, and obesity was set as the dependent variable. In the National Health and Nutrition Examination Survey, underweight was defined as a person with a BMI of less than 18.5 kg/m2, normal was defined as a person with a BMI of 18.5 kg/m2 or more but less than 25 kg/m2, and obesity was defined as a person with a body mass index of 25 kg/m2 or more. To compare obese and non-obese groups according to the criteria, underweight and normal are referred to as “non-obese,” and obese is referred to as “obese.”

To a study analyzing factors related to obesity in Korean adult men and women [3] and a previous paper analyzing factors related to obesity in adults in Korea [27][28], we found that among the data from the Korea National Health and Nutrition Examination Survey (KNHANES), the obesity rate was related to variables estimated to exist and were selected as independent variables. A total of 18 independent variables were selected in this study, categorized into 3: general characteristics and health behavior practice characteristics, as well as subjective body shape recognition <Figure 1>.

<Figure 1>

Research frame

3. Analysis method

SPSS 22.0 was used for data analysis, and the data analysis process unfolded as follows. First, descriptive analysis was conducted to determine the distribution of research subjects according to general characteristics and health behavior practice characteristics. Second, a chi-square test was conducted to determine the relationship between general characteristics and health behavior practice characteristics and obesity rates. In addition, we analyzed whether there were differences in health behavior practice by type of subjective body shape perception.

Ⅲ. Results

1. General characteristics of research subjects

Looking at the general characteristics of the subjects of this study, by age, there were 254 people in their 20s (19.6%), 333 people in their 30s (25.7 %), 349 people in their 40s (27.0%), and 358 people in their 50s (27.7%). The area of residence was Seoul with 234 people (18.1%), metropolitan cities and Sejong Special Self-Governing City with 374 people (28.9%), and provinces with 686 people (53.0%). Based on household composition, 316 people (24.4%) were from 1 household and 978 people (75.6%) were from multiple households. Regarding spousal status, 828 people (64.0%) had a spouse and 466 people (36.0%) did not have a spouse.

Considering individual income quartiles, 317 people (24.5%) were low, 317 people (24.5%) were low-middle, 330 people were high-middle (25.5%), and 330 people were high (25.5%). Looking at educational level, 20 people had an elementary school diploma or less (1.6%), 48 (3.7%) were middle school graduates, 328 (25.3%) were high school graduates, and 898 (69.4%) were college graduates or higher. By occupation, 519 people (40.1%) were non-physically active, 573 people (44.3%) were physically active, and 202 people (15.3%) were not economically active. By occupational status, 792 people (61.2%) were wage earners, 202 people (15.3%) were self-employed, 284 people (22.0%) were employers, 16 people (1.2%) were unpaid family workers, and 202 people (15.6%) were “others” <Table 1>.

2. Health behavior practice characteristics of research subjects

In terms of health behavioral practices, 212 (16.4%) were thin, 466 (36.0%) were normal, and 616 (47.6%) were obese, while 714 (55.2%) were trying to control their weight and 580 (44.8%) were not trying to control their weight. In terms of aerobic physical activity, 647 (50.0%) tried, 647 (50.0%) did not try, 661 (51.1%) walked 4 or more days per week, 386 (29.8%) walked 3 or less days per week, and 247 (19.1%) did not try, while 183 (14.1%) lifted weights 4 or more days per week, 273 (21.1%) lifted weights 3 or less days per week, and 838 (64.8%) did not try.

The frequency of alcohol consumption was 307 (23.7%) rarely, 500 (38.6%) one to four times a month, 318 (24.6%) two to three times a week, 129 (10.0%) four or more times a week, and 40 (3.1%) other, while current smoking status was 515 (39.8%) yes and 779 (60.2%) no. The frequency of eating out was 942 (72.8%) often, 267 (20.6%) usually, and 85 (6.6%) rarely; diet status was 270 (20.9%) yes and 1024 (79.1%) no; water intake was 433 (33.5%) inadequate, 369 (28.5%) adequate, and 492 (38.0%) excessive <Table 2>.

3. Obesity rate of study subjects

Looking at the obesity rate of the 1,294 subjects in this study, 605 (46.8%) were “obese,” 26 (2.0%) were “underweight,” and 663 (51.2%) were “normal,” categorized as “non-obese.” When examining the relationship between general characteristics and obesity rates, the difference in obesity rates by age was found to be the highest at 52.9% for those in their 30s, followed by 50.1% for those in their 40s, 44.4% for those in their 50s, and 37.4% for those in their 20s, which was statistically significant (p=0.001). When examining the difference in obesity rates by region of residence, the obesity rate for residents of the provinces was the highest at 47. 9%, followed by 47.9% in Seoul and 42.5% in Gwangju and Sejong Special Self-Governing City, but it was not statistically significant (p=0.146), and the difference in obesity rate by household size was higher in multi-generational households at 49.1% than in single-person households at 39.6%, which was statistically significant (p=0.003). The difference in obesity rates by spousal status was statistically significant (p=0.002), with 59.0% of married people being obese compared to 41.0% of unmarried people.

The difference in obesity rates among personal income quintiles was highest in the upper middle class at 50.0%, followed by 47.6% in the lower middle class, 44.5% in the upper middle class, and 47.6% in the lower middle class, but it was not statistically significant (p=0.452). The difference in obesity rates by educational level was highest among middle school graduates at 47.9%, followed by university graduates at 47.3%, high school graduates at 46.3%, and elementary school or less at 25%, which was not statistically significant (p=0.265). By occupation, non-physically active people had the highest obesity rate at 50.9%, followed by physically active people at 47.3% and non-economically active people at 34.7%, which was statistically significant (p=0.000). By occupational status, self-employed people and employers had the highest obesity rate at 52.1%, followed by unpaid family workers at 50%, wage earners at 47.9%, and other people at 34.7%, which was statistically significant (p=0.001).

4. General characteristics and obesity rates

Results of examining the relationship between general characteristics and obesity rates looking at the difference in obesity rates by age, those in their 30s showed the highest obesity rate at 52.9%, followed by those in their 40s at 50.1%, those in their 50s at 44.4 %, and those in their 20s at 37.4%, which was statistically significant (p = 0.001). By region of residence, looking at the differences in obesity rates, provincial residents had the highest obesity rate at 47.9%; Seoul was 47.9%, followed by metropolitan cities and Sejong Special Self-Governing City at 42.5%, but this was not statistically significant (p = 0.146). The difference in obesity rates by household composition was that multi-generational households had an obesity rate of 49.1%. It was higher than the 39.6% for single-person households, and this was statistically significant (p=0.003). The obesity rate according to the presence or absence of a spouse was 59.0% for those with a spouse, which was higher than the 41.0% for those without a spouse, and this was statistically significant (p = 0.002).

The difference in obesity rates among individual income quartiles is highest in those in the upper middle class at 50.0%. The order was low (47.6%), high (44.5%), and mid-low (47.6%), but this was not statistically significant (p=0.452). Looking at the difference in obesity rates by educational level, middle school graduation was the highest at 47.9%, while 47.3% had a college degree or higher, 46.3% had a high school diploma, and 25% had an elementary school diploma or less, which was not statistically significant (p=0.265). By occupation, non-physically active individuals were found to be the highest at 50.9%, while physically active people were 47.3%, and economically inactive people were 34.7%, and this was statistically significant (p = 0.000). Looking at employment status, the obesity rate was highest among self-employed people and employers at 52.1%, followed by unpaid family workers at 50%. Wage workers (47.9%) and others (34.7%) appeared in that order, and this was statistically significant (p = 0.001)<Table 3>.

5. Relationship between health behavior practice characteristics and obesity rate

Looking at the relationship between obesity rate and subjective body type perception, obesity was the highest at 83.9%, followed by normal (18.2%) and thin (1.4%), which was statistically significant (p = 0.000)<Table 4>.

The obesity rate according to weight control status was 59.2% for those who tried and 31.4% for those who did not try, which was statistically significant (p=0.000). In terms of aerobic physical activity, the obesity rate among those who tried was 47.9% compared to 45.6% among those who did not try, but this was not statistically significant (p=0.403). When looking at days per week of walking, the obesity rate was highest among those who walked three or fewer days per week at 53.6%, followed by those who walked four or more days per week at 45.5% and those who did not walk at 39.3%, which was statistically significant (p=0.001). When examining the number of days per week of strength training, the obesity rate was highest among those who never practiced at 48.6%, followed by 45.1% for 3 or less days per week and 41.0% for 4 or more days per week, which was not statistically significant (p=0.144).

When looking at the difference in obesity rates by drinking frequency, the obesity rate was 53.1% for those who drank 2-3 times a week, 52.7% for those who drank 4 or more times a week, 46.0% for those who drank 1-4 times a month, 42.5% for others, and 39.4% for those who rarely drank, which was statistically significant (p=0.007). When looking at current smoking status, the obesity rate among smokers was 50.1%, which was higher than the obesity rate among non-smokers at 44.5%, but this was not statistically significant (p=0.050).

When looking at the difference in obesity rates by the number of times they ate out, the highest rate was 47.5%, followed by 46.8% for frequent and 38.8% for rare, but this was not statistically significant (p=0.312). The difference in obesity rates by dietary status was statistically significant (p=0.000), with 60.4% practicing a diet compared to 43.2% not practicing a diet. The difference in obesity rates by conscious water intake was statistically significant (p=0.015), with excessive water intake being the highest at 50.8%, followed by meeting 46.9% and not meeting 42.0%<Table 5>.

The results of further analyzing whether there was a difference in health behavior practices based on subjective body shape perception were different from those without distinguishing subjective body shape perception.In weight control, there was a difference in weight control activities based on BMI-based obesity between thin and normal, but there was no difference in weight control activities for obese people. In walking, we found a statistically significant difference at the 0.05 level of significance for obese people. In general, the difference was found at a significance level of 0.1. Drinking alcohol was statistically significant in the group that subjectively perceived themselves as obese. Smoking was the same as drinking, with a difference in the group perceiving themselves as obese. Dietary behaviors differed at the 0.1 level of significance only in the group that perceived themselves as normal. Water intake, which was statistically significant without distinguishing between subjective body perceptions, was not statistically significant after disaggregating by subjective body type.

Ⅳ. Discussion

Obesity among adult men, who are the mainstay of economic activity, is rapidly increasing. Therefore, to determine factors related to obesity in adult men, independent variables were analyzed using general characteristics, health behavior practice characteristics, and subjective body shape perception. Descriptive analysis was performed to determine the distribution of the sample, and a chi-square test was performed to determine statistical significance.

The research results are summarized as follows. First, in terms of general characteristics, those in their 30s had the highest obesity rate, followed by those in their 40s, 50s, and 20s. This is different from the previous study's finding that obesity peaks at the age of 40 to 45 [29]. This is because in Korea, the consumption of meat and instant foods increases, and in men, the proportion of energy intake from grains is lower in the obese group than in the normal group, but the consumption of meat and fish is lower than in the normal group. We support existing research showing that meat consumption is high[12].

In terms of the number of household members, multi-person households were higher at 49.1% than single-person households (39.6%), and in terms of spousal status, the obesity rate was high among spouses. This result supports existing research showing that the BMI of men in single-person households is higher than that of men in multi-person households [30]. However, it may also be caused by differences in dietary patterns and opportunities to practice healthy behaviors deriving from differences in income levels between one- and multi-person households.

In the case of occupation, the order was non-physically active, physically active, and non-economically active, differing from previous studies wherein physically active people had a higher BMI than office workers [21]. In addition, there is a need to further analyze differences in dietary patterns and opportunities to practice healthy behavior by income level. The employment status was in the order of self-employed, employer, wage worker, and unpaid family worker, which appeared to support existing research [31].

Second, in terms of health behavior practice characteristics, there was a significant correlation between subjective body shape awareness, weight control, number of days walking per week, drinking frequency, dietary regimen, and conscious water intake. The group that thought they were obese had the highest obesity rate, and the group that tried to control their weight had the highest obesity rate. In terms of the number of days of walking per week, those who walked less than 3 days a week showed the highest level of obesity, followed by those who walked more than 4 days a week, followed by not walking. Regarding the frequency of drinking, the obesity rate was lowest in the group that rarely drank. This result supports research showing that existing health behavior practices are related to obesity[32]. In particular, drinking can be identified as a major factor to control in managing obesity. Additionally, the obesity rate is significantly higher in the diet group, which appears to indicate that obese men tend to use diet therapy to lose weight. In terms of the amount of water consumed consciously, the higher the water intake, the higher the obesity rate.

Third, there were differences in health behavioral practices by type of subjective body image perception. There was a difference in weight management activities in the thin and normal body perception groups but not in the obese group; diet was statistically significant only in the normal body perception group; walking was different in the normal and obese groups; drinking and smoking were different only in the obese group. Summarizing the results by subjective body shape perception type, only weight control was statistically significant in the thin group, although the significance level was low at 0.1. In the moderate group, there were statistically significant differences in health behavior practices such as weight control, walking, and diet. In the group that perceived themselves as obese, we found differences in health behavior practices in walking, alcohol consumption, and smoking.

Fourth, there were differences in the items of health behavior practice depending on the type of subjective body shape perception. Differences in health behavior practices were identified in weight control in the group that responded that they were obese, in weight control, walking, and diet in the group that responded as average, and in walking, drinking, and smoking in the group that responded that they were obese. The results of the study showed that even those whose body type was thin or average based on BMI made efforts to control their weight, but in the group that perceived themselves as obese, statistical significance could not be confirmed in their efforts to control their weight. In this way, it was discovered that there were differences in health behavior practice depending on BMI, an objective obesity index, and subjective body type perception.

Lastly, there was a difference in the chi-square test results between subjective body shape perception and BMI. First, there was a statistically significant difference in water intake in a chi-square test based on BMI, but there was no statistical difference in subjective body shape perception. The results showing that the higher the BMI, the greater the amount of water intake, and the differences that are not related to the amount of water intake after controlling for subjective body shape perception, are areas where additional research is needed to remove confounding factors between water intake and obesity in the future. In addition, there was a statistically significant difference in drinking and smoking in the chi-square test based on BMI, but in subjective body shape perception, a statistically significant difference was found only in the obese group. This result partially supports existing research showing that drinking and smoking are related to obesity. However, in the group that perceived themselves as average or thin, there was no difference in smoking and drinking, confirming different results according to subjective body shape perception.

Ⅴ. Conclusion

This study was conducted using data from the 2018 National Health and Nutrition Examination Survey conducted by the Ministry of Health and Welfare and the Korea Institute of Health and Social Research to examine the relationship between subjective body shape perception, health behavior practices, and obesity among adult men in Korea. Out of a total of 7,992 participants, 1,294 adult males aged 29 to 59 were selected. The independent variables were divided into general characteristics and health behavior practice characteristics to examine factors related to obesity. Descriptive analysis was conducted to determine the distribution, and chi-square test was conducted to determine the statistical significance.

The results showed that in addition to general characteristics such as age, subjective body image perception, weight control status, number of walking days per week, drinking frequency, dietary status, and conscious water intake were significantly related to health behavioral practices. In particular, we found that there were differences in health behavioral practices depending on the type of subjective body image perception.

There are limitations to this study. First, we used the National Health and Nutrition Examination Survey to utilize data that are representative of Korea, but because the process of calculating the data is complicated and time-consuming, even though we use the recent data, the data used in this study are from 2018, which differs from the current year; thus, it is difficult to reflect the current situation. In addition, in the factors related to exercise practice, only the number of walking days per week was statistically significant, and the items of aerobic exercise and strength training were not significant. In addition, there were differences in results depending on BMI and subjective body shape perception. This means that it is necessary to control the sample into groups with or without matching BMI and subjective body shape perception and then conduct additional research to determine the relationship between BMI and subjective body shape perception.

There are limitations to this study. First, we use the National Health and Nutrition Examination Survey to utilize data that are representative of Korea because the process of calculating the data is complicated and time-consuming; furthermore, this study used 2018 data instead of multi-year data, so it is difficult to generalize the results. In addition, in the factors related to exercise practice, only the number of walking days per week was statistically significant, and the items of aerobic exercise and strength training were not significant. In addition, we expected that irregular nutrient intake due to eating out would be related to obesity, but the results were not significant. This may be due to the fact that there were many missing values and the sample size was small, thereby limiting the accuracy of the results.

However, this study suggests that subjective body shape perception and BMI do not match, and it entails analyzing health behavior practices according to subjective body shape perception separately. Through this, it is discovered that there is a difference in health behavior management depending on the subjectively perceived body type, and in the process of implementing obesity management policies in the future, it would be desirable to recommend the practice of health behavior by reflecting the subjective body type perception recognized by the individual.