Obesity prevalence and obesity prediction model among Korean men in their 30s and 40s: a 2022 Community Health Survey

Obesity prevalence and obesity prediction model among Korean men in their 30s and 40s: a 2022 Community Health Survey | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Obesity prevalence and obesity prediction model among Korean men in their 30s and 40s: a 2022 Community Health Survey Myeunghee Han This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4040508/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Nov, 2024 Read the published version in BMC Public Health → Version 1 posted 20 You are reading this latest preprint version Abstract Purpose This study aimed to explore general and health-related characteristics according to body mass index (BMI). This study aimed to build a predictive model for BMI in Korean men in their 30s and 40s. Methods Data from 28,388 men aged 30-40 years from the 2022 Community Health Survey wereused. The participants were divided into underweight / normal weight groups (n=14,296, 50.4%) and overweight / obese groups (n=14,092, 49.6%) based on their BMI. Chi-square tests and independent t-tests were used to compare general and health-related characteristics. A prediction model was constructed using decision tree analysis. A split-sample test was conducted to verify the validity of the prediction model. Results The proportions of participants in the underweight / normal and overweight / obese groups were 50.4% and 49.6%, respectively. From the decision tree, the variable of amount of alcohol consumed was the primary factor predicting BMI (chi-square=228.477, p<.001). The characteristics of the group with the highest overweight or obesity rate of 60.6% were that they drank more than seven cups of alcohol at a time, were married, and were sleep-deprived. In contrast, the characteristics of the group with the lowest overweight or obesity rate (38.7%) were that they drank less than six cups of alcohol at a time, got enough or extra sleep time, and smoked less than 15 cigarettes. Conclusions The proportions of participants in the underweight / normal and overweight / obese groups were 50.4% and 49.6%, respectively. From the decision tree, the variable of amount of alcohol consumption was the primary factor predicting BMI. The characteristics of the group with the highest overweight or obesity rate of 60.6% were that they drank more than seven cups of alcohol at a time, were married, and were sleep-deprived. In contrast, the characteristics of the group with the lowest overweight or obesity rate (38.7%) were that they drank less than six cups of alcohol at a time, got enough or over sleep time, and smoked less than 15 cigarettes. Obesity Community Health Survey (CHS) Decision Trees Men Body Mass Index Figures Figure 1 Figure 2 INTRODUCTION 1. Background According to the World Health Organization (WHO), overweight or obesity is a chronic disease caused by abnormal or excessive fat accumulation in the body (1). In 2022, more than 1 billion people worldwide were obese. This number is increasing rapidly (2,3). The World Health Organization (WHO) estimates that by 2025, approximately 167 million adults will be overweight or obese (4). Obesity not only increases the comorbidity of various chronic diseases, such as type 2 diabetes, hypertension, dyslipidemia, metabolic syndrome, cardiovascular diseases, cancer, and musculoskeletal diseases, such as arthritis, back pain, and sleep apnea, but also causes disability. It significantly increases the risk of development, death, and reduces the quality of life (5). Obesity occurs when energy intake is excessive due to a combination of various factors, such as lifestyle habits including diet, physical activity, age, race, genetic factors, and socioeconomic factors (2,3). Specifically, it has been reported that the prevalence of obesity is higher in those who drink higher amounts of alcohol, do not engage in physical activity, or consume excessive energy due to obesity-related lifestyle factors compared to those who do not (2). In Korea, the prevalence of obesity has gradually increased among men of all age groups over the past 20 years (6). However, the highest prevalence of obesity was observed in men in their 30s and 40s (7). Nearly one-third of Korean adults are considered “obese,” with more than 50% of Korean men in their 30s falling in this category (8). Obesity in men was approximately 40.2% compared to 22.1% in women (8). Obesity rates among Korean men, particularly those in their 30s and 40s, are increasing significantly and have become a serious social issue. The risk factors for obesity differ between men and women. Despite the seriousness of these problems and the need for research, it is worth noting that there is a lack of research on obesity in men compared to obesity in women (9). In previous research, obesity has often been studied without distinguishing between sexes or focusing solely on women. Male obesity is a less common topic of study (10). To fill this gap, this study explored factors related to body mass index (BMI) in Korean men in their 30s and 40s and constructed a prediction model for their BMI. 2. Purpose of the Study This study aimed to identify the characteristics according to BMI in Korean male adults in their 30s and 40s. Additionally, we constructed a BMI prediction model. The specific aims were: 1) To compare general and health-related characteristics between two groups (underweight/normal and overweight /obese). 2) To build a prediction model for both groups 3) To build a prediction model for both groups according to age METHODS 1. Study design and data source A cross-sectional study was conducted using data from the 2022 Community Health Survey (CHS), conducted by the Korea Disease Control and Prevention Agency (KDCA). The survey period was from August 16, 2022, to August 31, 2022. Trained interviewers visited the households selected as samples and conducted face-to-face interviews using an electronic survey. The CHS provides data on sociodemographic characteristics, health-related factors, anthropometric measures, and non-communicable diseases. 2. Study participants Initially, the number of subjects in the CHS was 231,785. Only men in their 30s and 40s were selected from the raw data (n=28,394). Subjects who did not answer questions on height or weight (n=6) were excluded because their BMI could not be calculated. Finally, the data from 28,388 participants were used in this study. The BMI is an index that is usually used to categorize adults as overweight and obese (11). A BMI ≥25 kg/m 2 is considered overweight and BMI ≥30 kg/m 2 is considered obese (12). Finally, the number of subjects in the underweight / normal and overweight / obese groups was 14,296 (50.36%) and 14,092 (49.64%), respectively. 3. Ethics Statement This study was approved by the Institutional Review Board of the authors (IRB No.1041495-202403-HR-01-01 ). 4. Measures 4.1General characteristics The general characteristics included factors of age (30s, 40s), residence area (city, province), household type (alone, married, other 1 st generation, couple and unmarried children, single parents and unmarried children, other 2 nd generation, or all 3 rd generation), basic livelihood recipients (no, yes), household income (10,000 won/month), happiness, education level (≤ high school graduate, ≥college graduate), economic activity (no, yes), and marital status (single, married). 4.2Health-related characteristics Factors of health-related characteristics included subject health status (bad, average, good), smoking (no, yes), number of cigarettes (< 15, ≥15), drinking (no, yes), amount of drinking (once) ( ≤6 cups, ≥7 cups), binge drinking (none, <1/month, 1/month, 1/week, daily), food security (sufficient quantity and variety, sufficient quantity but no variety, sometimes insufficient, often insufficient), days of breakfast (none, 1-2, 3-4, 5-7), awareness of nutrition (no, yes), comprehension of nutrition label (no, yes), use of nutrition label (no, yes), subjective oral health status (bad, average, good), difficulty in chewing (bad, average, good), sleeping time (lack, enough, over), depression (no, yes), stress level (rare, little, much, very much), days of strenuous physical activity, days of moderate physical activity, and time of walking. High-risk drinking was defined as drinking more than seven cups for men. The amount of alcohol consumed was divided into two groups; 1) ≤ 6 and 7 cups (13). The Korean version of the Patient Health Questionnaire-9 was used to assess depression. Those with scores above 10 were considered depressed and those with scores below 10 were considered not depressed (14). 4.3 Statistical Methods Data were analyzed using SPSS version 29.0 (IBM Corp., Armonk, NY, USA) for Windows. Due to the sampling design of the CHS, the data analysis in this study considered sampling weights. The significance level was set at p <.05. To compare general and health-related characteristics between the two groups (underweight / normal vs. overweight / obese), chi-square tests for categorical variables and independent t-tests for continuous variables were used. Decision tree analysis was used to build a BMI prediction model. The chi-square automatic interaction detection (CHAID) method was selected for decision tree analysis. This method can produce a decision tree model by considering both the categorical and continuous variables used in this study (15,16). The maximum tree depth was 3. The minimum number of cases for parent and child nodes was 100 and 50, respectively (15,16). Split-sample and cross-validation analyses were used to validate the decision tree model (17). RESULTS 1. General characteristics The general characteristics of the study participants are presented in Table 1. A total of 28,388 men in their 30s and 40s were analyzed and divided into two groups based on BMI. The proportions of participants in the underweight / normal and overweight / obese groups were 50.4% and 49.6%, respectively. The proportion of men in their 40s (56.9%) was higher than that of men in their 30s (43.1%). Approximately 70% of participants lived in cities. The household type of over 50% of the participants was couples and unmarried children. Most participants were not included in basic livelihood recipients. The average monthly household income was 486.63 (10,000 won). Over 80% of the participants had happiness scores greater than 6. Those with educational degrees above college level accounted for 72.3% of the sample. Most participants were economically active and more than half were married. As shown in Table 1, there were significant differences in age (p<.001), household type (p<.001), household income (p=.047), happiness (p=.024), and marital status (p<.001) between the groups. Table 1. General characteristics (N=28,388) Characteristics Category Total (N=28,388) Underweight & Normal (n=14,296, 50.4%) Overweight & Obesity (n=14092, 49.6%) p-value N(%) or M ± SD Age 30’s 12234(43.1) 5883(43.8) 6351(47.1) <.001 40’s 16154(56.9) 8413(56.2) 7741(52.9) Residence area City 19444(68.5) 9845(84.3) 9599(83.7) .110 Province 8944(31.5) 4451(15.7) 4493(16.3) Household type Alone 4384(15.4) 2298(15.2) 2086(14.2) <.001 Married 3694(13.0) 1838(13.6) 1856(14.0) Other 1st generation 478(1.7) 241(1.8) 237(1.7) Couples and unmarried children 14923(52.6) 7349(52.8) 7574(55.2) Single parents and unmarried children 2131(7.5) 1139(7.7) 992(6.4) Other 2nd generation 1063(3.7) 588(3.4) 475(2.8) All 3rd generation 1715(6.0) 843(5.6) 872(5.6) Basic livelihood recipients* No 27872(98.2) 14010(98.2) 13862(98.5) .077 Yes 515(1.8) 285(1.8) 230(1.5) Household income (10,000won/month) 486.63±282.92 482.86±286.07 490.52±279.60 .047 Happiness* (1-10) ≤5 4800(16.9) 2479(17.2) 2321(16.1) .024 ≥6 23584(83.1) 11813(82.8) 11771(83.9) Education level* ≤High school graduate 7858(27.7) 4030(25.1) 3828(24.3) .158 ≥College graduate 20523(72.3) 10263(74.9) 10260(75.7) Economic activity No 1941(6.8) 1033(7.0) 908(6.5) .093 Yes 26446(93.2) 13263(93.0) 13183(93.5) Marital status Single 10687(37.6) 5659(38.5) 5028(34.2) <.001 Married 17699(62.4) 8636(61.5) 9063(65.8) *missing value 2. Differences in health-related characteristics The differences in health-related characteristics between the underweight / normal and overweight / obese groups are presented in Table 2. Over 50% of participants felt that their health was good. The percentage of participants who smoked was 57.7%. The percentage of participants who smoked more than 15 cigarettes per day was 83.2%. Almost all participants drank alcohol (94.6%), and over 50% drank more than seven cups at once. The highest rate of binge drinking was once a week (29.3%). Regarding food security, the proportion of participants who responded that they ate sufficient amounts and a variety of foods was the highest at 75.4%. The rate of not eating breakfast was highest (40.5%). The rates of awareness, comprehension, and use of nutrition labels were 66.9%, 39.9%, and 81.8%, respectively. The highest proportion of subjective oral health and difficulty in chewing were ‘average (47.5%)’ and ‘good (78.6%)’, respectively. The highest proportion of participants had sufficient sleep time (55.3%), and depression was confirmed to be absent in most participants (97.5%). For the stress level, over 50% of subjects showed ‘much’ (58.0%). The average number of days of strenuous physical activity, moderate physical activity, and walking were 1.27, 1.58, and 4.42, respectively. The frequencies of strenuous physical activity, moderate physical activity, and walking were 1.17, 1.04, and 0.79, respectively. There were differences in factors of subject health status (p<.001), smoking (p=.001), number of cigarettes (p<.001), amount of drinking (p<.001), food security (p=.003), days of breakfast (p<.001), awareness of nutrition label (p=.001), use of nutrition label (p=.018), difficulty in chewing (p=.011), sleeping time (p<.001), stress level (p<.001), times of strenuous physical activity (p<.001), times of moderate physical activity (p<.001), and times of walking between both groups (p=.020). Table 2. Health-related characteristics (N=28,388) Characteristics Category Total (N=28,388) Underweight & Normal (n=14,296, 50.4%) Overweight & Obesity (n=14092, 49.6%) p-value N(%), M ± SD Subject health status Bad 1662(5.9) 719(4.6) 943(6.4) <.001 Average 11409(40.2) 5312(36.9) 6097(43.1) Good 15317(54.0) 8265(58.5) 7052(50.4) Smoking* No 8327(42.3) 3990(42.9) 4337(45.5) .001 Yes 11337(57.7) 5775(57.1) 5562(54.5) Amount of cigarettes <15 4772(16.8) 2555(17.5) 2217(15.8) <.001 ≥15 23616(83.2) 11741(82.5) 11875(84.2) Drinking No 1539(5.4) 808(5.0) 731(4.7) .323 Yes 26849(94.6) 13488(95.0) 13361(95.3) Amount of drinking(once)* ≤6cups 11608(47.8) 6409(52.6) 5199(43.0) <.001 ≥7cups 12671(52.2) 5772(47.4) 6899(57.0) Binge drinking* None 4695(19.3) 2561(21.7) 2134(17.8) <.001 <1/month 4721(19.4) 2425(20.3) 2296(19.7) 1/month 5935(24.4) 2927(24.1) 3008(25.2) 1/week 7136(29.4) 3389(27.3) 3747(30.4) Daily 1792(7.4) 879(6.5) 913(6.9) Food security* Sufficient quantity and variety 21417(75.4) 10634(74.5) 10783(76.5) .003 Sufficient quantity but no variety 6341(22.3) 3328(23.3) 3013(21.4) Sometimes insufficient 534(1.9) 288(1.8) 246(1.8) Often insufficient food 94(0.3) 45(0.3) 49(0.3) Days of breakfast (week) None 11507(40.5) 5609(40.6) 5898(43.5) <.001 1-2 3058(10.8) 1462(10.5) 1596(11.8) 3-4 3000(10.6) 1502(10.6) 1498(10.9) 5-7 10823(38.1) 5723(38.3) 5100(33.8) Awareness of nutrition label* No 9388(33.1) 4843(33.7) 4545(31.7) .001 Yes 18976(66.9) 9443(66.3) 9533(68.3) Comprehension of nutrition label* No 11397(60.1) 5692(58.0) 5705(58.2) .730 Yes 7578(39.9) 3751(42.0) 3827(41.8) Use of nutrition label* No 1376(18.2) 657(16.3) 719(18.5) .018 Yes 6201(81.8) 3094(83.7) 3107(81.5) Subjective oral health status Bad 6040(21.3) 3170(21.4) 2870(19.5) <.001 Average 13473(47.5) 6736(47.2) 6737(48.4) Good 8875(31.3) 4390(31.3) 4485(32.1) Difficulty in chewing Bad 2195(7.7) 1152(7.3) 1043(7.0) .011 Average 3875(13.7) 2015(14.3) 1860(13.0) Good 22318(78.6) 11129(78.4) 11189(80.0) Sleeping time Lack(<7h) 9874(34.8) 4682(32.6) 5192(37.1)) 8h) 2826(10.0) 1517(10.2) 1309(9.3) Depression(10) No 27640(97.5) 13951(97.5) 13689(97.2) .082 Yes 716(2.5) 329(2.5) 387(2.8) Stress level* Rare 1130(4.0) 483(3.3) 647(4.6) <.001 Little 6724(23.7) 3210(23.1) 3514(25.7) Much 16462(58.0) 8557(59.9) 7905(56.1) Very much 4071(14.3) 2046(13.6) 2025(13.6) Days of strenuous physical activity 1.27±1.94 1.29±1.95 1.26±1.92 .254 Days of moderate physical activity 1.58±2.17 1.58±2.16 1.58±2.17 .785 Days of walking 4.42±2.62 4.24±2.62 4.20±2.63 .130 Time of strenuous physical activity 1.17±0.54 1.15±0.54 1.19±0.54 <.001 Time of moderate physical activity 1.04±0.47 1.02±0.47 1.06±0.47 <.001 Time of walking 0.79±0.36 0.78±0.36 0.79±0.36 .020 *missing value 3. Prediction model for BMI (total model) The prediction model for BMI is shown in Figure 1. Almost half the participants were overweight or obese. The variable of amount of drinking was the primary factor affecting BMI (chi-square=228.477, p<.001). The rate of overweight or obesity was 44.8% (Node 1). The rate of overweight or obesity in those consuming 1–6 cups of alcohol differed according to sleep duration (chi-square=18.553, p<.001). The BMI of subjects who consumed 6 cups of alcohol with enough or extra sleep (Node 5) differed according to the number of cigarettes smoked (chi-square=12.767, p<.010). Those who consumed 1–6 cups of alcohol at once, slept enough or over time, and smoked less than 15 cigarettes per day showed the lowest rate of being overweight or obese (38.7%). The rate of overweight or obesity in participants who consumed 7–10 cups of alcohol (Node 2) differed according to marital status (chi-square=33.329, p<.001). The rate of overweight or obesity in participants who drank 7–10 cups of alcohol and were married differed according to sleeping time (chi-square=39.248, p<.001). Those who drank 7–10 cups of alcohol, were married, and had a lack of sleep (Node 14) showed the highest rate of overweight or obesity (60.6%). 4. Prediction model for BMI by age The predictive model for BMI according to age is shown in Figure 2. Age was designated as the primary factor that significantly affected BMI. Those in their 40s (Node 1) had a 47.9% rate of being overweight or obese. The rate of overweight or obesity in subjects in their 40s differed according to the amount of alcohol consumed (chi-square=111.572, p<.001). The rates of overweight or obesity in subjects in their 40s who consumed 1–6 cups of alcohol differed according to the number of cigarettes smoked (chi-square=11.018, p=.001). Those who were in their 40s, consumed 1–6 cups of alcohol, and smoked under 15 cigarettes showed the lowest rate of being overweight or obese (38.7%). The rate of overweight or obesity in subjects in their 30s (Node 2) differed according to the amount of alcohol consumed (chi-square=120.249, p<.001). The rate of overweight or obesity among subjects in their 30s who consumed 7–10 cups of alcohol differed according to marital status (chi-square=30.094, p<.001). The subjects who were in their 30s, consumed 7–10 cups of alcohol, and were married (Node 18) showed the highest rate of being overweight or obese (60.6%). 5. Validation tests of prediction model for BMI The results of the validation assessment using split-sample tests are presented in Table 3. For the entire model, the value of risk estimates (RE) was .448 of the training data. This indicates a classification accuracy of 54.2%. In addition, the model set the primary factor as age, and the value of RE was .445. This can be interpreted as a classification accuracy of 55.5%. Table 3. Risk Chart of Decision Trees Model Variables Risk Estimates Standard Error Total Training data .448 .004 Test data .459 .004 Primary factor of age Training data .445 .004 Test data .456 .004 DISCUSSION This study was conducted to predict the BMI of Korean men in their 30s and 40s using data from the 2022 CHS. This study found that more than half of overweight or obese participants were in their 40s. According to the obesity rate statistics for 2022 (18), the rate of obesity was 47.7% in men and 25.7% in women (18). The male age group with the highest obesity rate was the 40s age group, with a rate of 40.7%, which is consistent with the findings of this study. However, another report stated that men in their 30s had the highest prevalence of obesity (8). Notably, the highest rate of obesity was found in men aged 30 to 40 years. According to recent statistics, there has been a steady increase in the rate of obesity among men in their 30s and 40s. Almost half of men between the ages of 30 and 40 were found to be obese, and as of last year (2022), 55.7% of men in their 30s and 53.6% of men in their 40s were classified as obese. (19). Therefore, it is essential to implement proactive interventions to increase awareness and effectively manage obesity and related chronic diseases among these age groups. The results of this study showed that individuals who consumed 7–10 cups of alcohol per week, were married, and experienced sleep deprivation had the highest rates of overweight or obesity. According to age, those who were in their 30s, consumed 7–10 cups of alcohol, and were married showed the highest rate of being overweight or obese (60.6%). The KDCA reported that Korean men in their 30s were more likely to gain excess weight due to prolonged sedentary work and reduced physical activity (8). A study also found that high fat and sodium intakes are linked to abdominal obesity in men in their 30s (20). However, being overweight and obese is not just caused by one factor but a combination of factors such as dietary patterns and lifestyle factors such as sleep deprivation, sedentary lifestyle, chronic disease, medication, and even genetics (21). Although the obesity rate among Korean men in their 30s is increasing rapidly, it is difficult to compare our results with those of other studies. Therefore, there is an urgent need for studies that reflect this situation and explore their distinct characteristics. A previous study showed that alcohol contains 7 kcal/g and can contribute significantly to excess caloric intake (22). However, light-to-moderate alcohol consumption was not found to be related to weight, whereas heavy alcohol consumption was. Additionally, different weight gain outcomes were observed depending on the type of alcohol consumed. Wine consumption appears to prevent weight gain, while there was an association between beer intake > 500 ml/day and abdominal fat accumulation (23,24). According to a systematic review, several previous cross-sectional studies found no relationship between alcohol consumption and BMI in men (25). This is likely because of the complexity of obesity as a condition with many contributing factors, which makes it difficult to determine the independent impact of alcohol consumption on obesity risk (24). As research results on the relationship between drinking and obesity are conflicting, it is necessary to continue research that considers the amount of alcohol consumed, type of alcohol, frequency of drinking, type of food consumed with alcohol, and demographic and physical characteristics of the subjects. Studies have shown that marital status is a socioeconomic factor linked to obesity (26,27). This study found that married men had a higher proportion of overweight or obesity than single men. Similarly, one study found that a higher proportion of single men were underweight than married men (28). On the other hand, some studies found that marriage can positively impact lifestyle habits, such as quitting smoking and a healthy diet, which can help maintain a healthy weight (29). However, it is worth noting that the association between marriage and obesity differs according to race, ethnicity, and sex (30,31). From the results of these studies, it is necessary to develop a family-level obesity management program for married men so that the program can include their families, as they share their lifestyle with them. In addition, more research needs to be conducted on obesity rates among married and unmarried men, so that customized obesity management interventions can be developed based on marital status. This study discovered that individuals who sleep less than seven hours a night are more likely to be overweight or obese (32). Although age and sex differed, previous studies have reported similar findings. A review has mentioned short sleep duration and poor sleep quality are risk factors for developing obesity (33). Additionally, another study found significantly higher rates of overweight or obesity in individuals who slept for shorter periods than in those who slept for longer periods (34). Previous research has suggested that shorter sleep periods are associated with decreased fiber intake and increased consumption of carbohydrates, total cholesterol, and total saturated fat (35). However, the standards for adequate sleep for people in their 30s and 40s vary among studies, making it difficult to determine the relationship between sleep duration and obesity. It was also found that sleep affects diet or physical activity rather than being directly related to obesity. Therefore, it is necessary to determine the appropriate amount of sleep for men in their 30s and 40s. Further research is required to explore the impact of sleep on changes in health behaviors and how sleep is related to obesity. As the obesity rate among Korean men in their 30s and 40s continues to rise, there is an increasing need to develop effective obesity prevention and management programs and encourage active participation from those affected. However, despite the well-established link between obesity and health-related diseases, male obesity rates are still increasing, and men appear hesitant to engage in weight-loss intervention programs (37). Men are generally less concerned about their weight status than women, and tend to lack basic knowledge of nutrition (38,39). Therefore, it is essential to raise awareness in the community about the seriousness of obesity, emphasize the need for obesity management, and emphasize the need for obesity management, provide effective methods for managing obesity. In addition, it is necessary to identify and eliminate obstacles that prevent men from managing obesity. This study is important because it predicted obesity in Korean men in their 30s and 40s. This is a serious condition that has not been extensively investigated. However, this study has some limitations that should be addressed in future research. First, because this was a secondary data analysis, not all significant factors related to BMI in Korean men may have been included. Second, the dataset in this study was collected during the recovery period after a specific situation that may have affected obesity, namely COVID-19, which may have affected obesity. Therefore, the results cannot be generalized with certainty. Third, the data used in this study were self-reported, which poses a disadvantage in that they may not be entirely reliable. Finally, variables proven to be related to obesity in men in previous studies, such as educational level, physical activity, and household income, did not appear significantly in this study. Thus, further studies are necessary to identify the factors that contribute to obesity and use them to develop customized obesity management programs. CONCLUSION In Korea, in male adults in their 30s or 40s, alcohol consumption, frequency of breakfast consumption, amount of smoking, marital status, smoking, and sleeping times were found to be the variables affecting BMI. Individuals who consumed 7–10 cups of alcohol, were married, and had insufficient sleep showed the highest rate of overweight/obesity. To address obesity among men in their 30s and 40s, it is necessary to understand their drinking habits and develop strategies to reduce alcohol consumption. Furthermore, a weight management program that involves the entire family would be more effective than one that focuses solely on men. It is crucial to identify the sleep patterns of overweight or obese individuals and develop strategies to improve sleep quality and duration. Declarations Ethics approval and consent to participate Based on Article 2, Paragraph 2 of the Enforcement Rule of the Bioethics and Safety Act of Republic of Korea, it is excluded from the subject of review as it does not fall under human subject research. However, this study was conducted with approval of review exemption from the researcher’s university. Consent for publication Not applicable. Availability of Data and Materials The datasets generated during the current study are available in the Community Health Survey repository, https://chs.kdca.go.kr/chs/mnl/mnlBoardMain.do. Competing interests The author declare that they have no competing interests. Funding None Author’s contributions MH analyzed and interpreted the data regarding prediction model of cognitive impairment in elderly in Korea. MH performed wrote and revised the draft and final version of this paper. MH read and approved the final manuscript. Acknowledgement Not applicable. References Obesity [Internet]. World Health Organization. 2016. Available from: https://www.who.int/topics/obesity/en/ Nam G. Current status and epidemiology of adult obesity in Korea. J Korean Med Assoc. 2022;65(7):394–9. Korean Society for the Study of Obesity. 2020 Korean Society for the Study of Obesity guidelines for the management of obesity in Korea. Seoul; 2020. World Health Organization. World Obesity Day 2022- Accelerating action to stop obesity [Internet]. 2023 [cited 2023 Jan 13]. 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Korean men becoming more obese due to drinking, lack of exercise [Internet]. The Korea Times. 2023 [cited 2023 Feb 10]. Available from: https://www.koreatimes.co.kr/www/nation/2023/12/113_342667.html NIH. Calculate Your Body Mass Index [Internet]. [cited 2021 May 22]. Available from: https://www.nhlbi.nih.gov/health/educational/lose_wt/BMI/bmicalc.htm Hwang S. Estimation of high-risk drinkers and drinking behavior in Korea. J Environ Heal Sci. 2020;46(1):65–77. Choi H, Choi J, Park K, Joo K, GA H, Ko H, et al. Standarization of the Korean version of patient health questionanire-9 as a screening instrument for major depressive disorder. Korean J Fam Med. 2007;28:114–9. Choi J, Seo D. Decision Trees and Its Applications. J Korean Off Stat. 1999;4(1):61–83. Seo J., Kim M. A Prediction Model for Quality of Life by Resilience in Disaster Female Victims. Korean J Adult Nurs. 2021;33(6):639–48. Choi J, Han S, Kang H, Kim Y. Data mining decision tree by using Answer Tree. SPSS Korea. Seoul; 1998. Kindicator. Obesity rates [Internet]. 204AD [cited 2023 Feb 11]. Available from: https://www.index.go.kr/unify/idx-info.do?idxCd=4239 Korea Disease Control and Prevention Agency. National Health and Nutrition Survey 2022 results [Internet]. 2023. Available from: https://www.kdca.go.kr/filepath/boardDownload.es?bid=0015&list_no=724014&seq=2 Park M, Jung N. Comparison of Physical Activity and Nutrient Intake according to Abdominal Obesity in their 20s and 30s in Korean Men: Data from the Seventh Korea National Health and Nutrition Examination Survey Ⅶ (2016-2018). Korean Soc Sport Sci. 2020;29(4):1113–25. What are overweight and obesity? [Internet]. National Heart, Lung, and Blood Institute. 2023 [cited 2023 Feb 10]. Available from: https://www.nhlbi.nih.gov/health/overweight-and-obesity Aberg F, Farkkila M. Dirnking and obesity: alcoholic liver disease/nonalcoholic fatty liver disease interactions. Semin Liver Dis. 2020;40(2):154–62. Lau K, Baumeister S, Lieb W. The combined effects of alcohol consumption and body mass index on hepatic steatosis in a general population sample of European men and women. Aliment Ther J. 2015;41(5):467–76. Traversy G, Cahput J. Alcohol consumption and obeisty: an update. Curr Obes Rep. 2015;4(1):122–30. Sayon-Orea C, Martinez-Gonzalez M, Bes-Rastrollo M. Alcohol consumption and body weight:a systematic review. Nutr Rev. 2011;69:419–31. Sobal J, Janson K, Frongillo E. Gender, ethnicity, marital status, and body weight in the United States, Obesity. 2009. 2223–2231 p. Tzaotzas T, Vlahavas S, Ppadopoulou E, Kapantais D, Kaklamanou M. Marital status and educational level associated to obesity in Greek adults: data from the National Epidemiological Survey. BMC Public Health. 2010;10:732. Lee J, Shin A, Cho S, Choi J, Kang D, Lee J. Marital status and the prevalence of obesity in a Korean population. Obes Res Clin Pract. 2020;14(3):217–24. Roos E, Lahelma M, Virtanen R, Prattala P. Gender, socioeconomc statis and family status as determinants of food beavior. Soc Sci &Medicine. 1998;46(1519–1529). Klos L, Sobal J. Weight and weddings. Engaged men’s body wieght ideals and wedding weight management behaviors. Appetite2. 13AD;60(1):133–9. Karla H, Jeffery S, Edward F. Gender and marital status calrify association between food insecurity and body weight. J Nutr. 2007;137(6):1460–5. Centers for Disease Control and Prevention. How much sleep do I need? [Internet]. 2022 [cited 2024 Feb 12]. Available from: https://www.cdc.gov/sleep/about_sleep/how_much_sleep.html Beccuti G, Pannain S. Sleep and obesity. Curr Opin Clin Nutr Metab Care. 2013;14(4):402–12. Li Q. The association between sleep duration and excess body weight of the American adult population: a cross-sectional study of the national health and nutrition examination survey 2015–2016. BMC Pubilc Heal. 2021;21:1–9. Grandner M, Jackson N, Gerstner J, Knutson K. Dietary nutrients associated with short and long sleep duration. Data from a nationally representative sample. Appetite2. 2013;64:71–80. Kim D, Park S, Kim Y, Oh K. National health statistics plus: current status of obesity prevalence in Korean adults and related factors [Internet]. Korea Disease Control and Prevention Agency. 2021 [cited 2023 Feb 10]. Available from: https://knhanes.kdca.go.kr/knhanes/sub04/sub04_04_02.do Gray C, Anderson A, Clarke A, Dalziel A, Hunt K, Leishman J. Addressing male obesity: an evaluation of a group based weight management intervention for Scottish men. Jounral Men’s Heal. 2009;6:70–81. Stibbe A. Health and the social construction of masculinity in Men’s health magazine. Men Masc. 2004;7:31–51. Gough B, Conner M. Barriers to healthy eating amongst men: a qualitative analysis. Soc Sci Med. 2006;62:987–95. Additional Declarations No competing interests reported. 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Background\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the World Health Organization (WHO), overweight or obesity is a chronic disease caused by abnormal or excessive fat accumulation in the body (1). In 2022, more than 1 billion people worldwide were obese. This number is increasing rapidly (2,3). The World Health Organization (WHO) estimates that by 2025, approximately 167 million adults will be overweight or obese (4). \u003c/p\u003e\n\u003cp\u003eObesity not only increases the comorbidity of various chronic diseases, such as type 2 diabetes, hypertension, dyslipidemia, metabolic syndrome, cardiovascular diseases, cancer, and musculoskeletal diseases, such as arthritis, back pain, and sleep apnea, but also causes disability. It significantly increases the risk of development, death, and reduces the quality of life (5). \u003c/p\u003e\n\u003cp\u003eObesity occurs when energy intake is excessive due to a combination of various factors, such as lifestyle habits including diet, physical activity, age, race, genetic factors, and socioeconomic factors (2,3). Specifically, it has been reported that the prevalence of obesity is higher in those who drink higher amounts of alcohol, do not engage in physical activity, or consume excessive energy due to obesity-related lifestyle factors compared to those who do not (2). \u003c/p\u003e\n\u003cp\u003eIn Korea, the prevalence of obesity has gradually increased among men of all age groups over the past 20 years (6). However, the highest prevalence of obesity was observed in men in their 30s and 40s (7). Nearly one-third of Korean adults are considered “obese,” with more than 50% of Korean men in their 30s falling in this category (8). Obesity in men was approximately 40.2% compared to 22.1% in women (8).\u003c/p\u003e\n\u003cp\u003eObesity rates among Korean men, particularly those in their 30s and 40s, are increasing significantly and have become a serious social issue. The risk factors for obesity differ between men and women. Despite the seriousness of these problems and the need for research, it is worth noting that there is a lack of research on obesity in men compared to obesity in women (9). In previous research, obesity has often been studied without distinguishing between sexes or focusing solely on women. Male obesity is a less common topic of study (10). To fill this gap, this study explored factors related to body mass index (BMI) in Korean men in their 30s and 40s and constructed a prediction model for their BMI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Purpose of the Study \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aimed to identify the characteristics according to BMI in Korean male adults in their 30s and 40s. Additionally, we constructed a BMI prediction model. The specific aims were:\u003c/p\u003e\n\u003cp\u003e1) To compare general and health-related characteristics between two groups (underweight/normal and overweight /obese).\u003c/p\u003e\n\u003cp\u003e2) To build a prediction model for both groups\u003c/p\u003e\n\u003cp\u003e3) To build a prediction model for both groups according to age \u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003e1. Study design and data source \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA cross-sectional study was conducted using data from the 2022 Community Health Survey (CHS), conducted by the Korea Disease Control and Prevention Agency (KDCA). The survey period was from August 16, 2022, to August 31, 2022. Trained interviewers visited the households selected as samples and conducted face-to-face interviews using an electronic survey. The CHS provides data on sociodemographic characteristics, health-related factors, anthropometric measures, and non-communicable diseases. \u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eStudy participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInitially, the number of subjects in the CHS was 231,785. Only men in their 30s and 40s were selected from the raw data (n=28,394). Subjects who did not answer questions on height or weight (n=6) were excluded because their BMI could not be calculated. Finally, the data from 28,388 participants were used in this study. The BMI is an index that is usually used to categorize adults as overweight and obese (11). A BMI ≥25 kg/m\u003csup\u003e2\u003c/sup\u003e is considered overweight and BMI ≥30 kg/m\u003csup\u003e2 \u003c/sup\u003eis considered obese (12). Finally, the number of subjects in the underweight / normal and overweight / obese groups was 14,296 (50.36%) and 14,092 (49.64%), respectively. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Ethics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of the authors (IRB No.1041495-202403-HR-01-01 ).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1General characteristics \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe general characteristics included factors of age (30s, 40s), residence area (city, province), household type (alone, married, other 1\u003csup\u003est\u003c/sup\u003e generation, couple and unmarried children, single parents and unmarried children, other 2\u003csup\u003end\u003c/sup\u003e generation, or all 3\u003csup\u003erd\u003c/sup\u003e generation), basic livelihood recipients (no, yes), household income (10,000 won/month), happiness, education level (≤ high school graduate, ≥college graduate), economic activity (no, yes), and marital status (single, married). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2Health-related characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFactors of health-related characteristics included subject health status (bad, average, good), smoking (no, yes), number of cigarettes (\u0026lt; 15, ≥15), drinking (no, yes), amount of drinking (once) ( ≤6 cups, ≥7 cups), binge drinking (none, \u0026lt;1/month, 1/month, 1/week, daily), food security (sufficient quantity and variety, sufficient quantity but no variety, sometimes insufficient, often insufficient), days of breakfast (none, 1-2, 3-4, 5-7), awareness of nutrition (no, yes), comprehension of nutrition label (no, yes), use of nutrition label (no, yes), subjective oral health status (bad, average, good), difficulty in chewing (bad, average, good), sleeping time (lack, enough, over), depression (no, yes), stress level (rare, little, much, very much), days of strenuous physical activity, days of moderate physical activity, and time of walking. \u003c/p\u003e\n\u003cp\u003eHigh-risk drinking was defined as drinking more than seven cups for men. The amount of alcohol consumed was divided into two groups; 1) ≤ 6 and 7 cups (13). The Korean version of the Patient Health Questionnaire-9 was used to assess depression. Those with scores above 10 were considered depressed and those with scores below 10 were considered not depressed (14). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Statistical Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were analyzed using SPSS version 29.0 (IBM Corp., Armonk, NY, USA) for Windows. Due to the sampling design of the CHS, the data analysis in this study considered sampling weights. The significance level was set at p \u0026lt;.05. To compare general and health-related characteristics between the two groups (underweight / normal vs. overweight / obese), chi-square tests for categorical variables and independent t-tests for continuous variables were used. \u003c/p\u003e\n\u003cp\u003eDecision tree analysis was used to build a BMI prediction model. The chi-square automatic interaction detection (CHAID) method was selected for decision tree analysis. This method can produce a decision tree model by considering both the categorical and continuous variables used in this study (15,16). The maximum tree depth was 3. The minimum number of cases for parent and child nodes was 100 and 50, respectively (15,16). Split-sample and cross-validation analyses were used to validate the decision tree model (17). \u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003e1. \u0026nbsp; General characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe general characteristics of the study participants are presented in Table 1. A total of 28,388 men in their 30s and 40s were analyzed and divided into two groups based on BMI. The proportions of participants in the underweight / normal and overweight / obese groups were 50.4% and 49.6%, respectively. The proportion of men in their 40s (56.9%) was higher than that of men in their 30s (43.1%). Approximately 70% of participants lived in cities. The household type of over 50% of the participants was couples and unmarried children. Most participants were not included in basic livelihood recipients. The average monthly household income was 486.63 (10,000 won). Over 80% of the participants had happiness scores greater than 6. Those with educational degrees above college level accounted for 72.3% of the sample. Most participants were economically active and more than half were married. As shown in Table 1, there were significant differences in age (p\u0026lt;.001), household type (p\u0026lt;.001), household income (p=.047), happiness (p=.024), and marital status (p\u0026lt;.001) between the groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e General characteristics (N=28,388)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"617\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.24025974025974%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96103896103896%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=28,388)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnderweight \u0026amp; Normal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=14,296, 50.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverweight \u0026amp; Obesity\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=14092, 49.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.253246753246753%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eN(%) or M\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.24025974025974%\" rowspan=\"2\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96103896103896%\"\u003e\n \u003cp\u003e30\u0026rsquo;s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e12234(43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e5883(43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e6351(47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.253246753246753%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.379146919431278%\"\u003e\n \u003cp\u003e40\u0026rsquo;s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e16154(56.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e8413(56.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e7741(52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.24025974025974%\" rowspan=\"2\"\u003e\n \u003cp\u003eResidence area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96103896103896%\"\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e19444(68.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e9845(84.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e9599(83.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.253246753246753%\" rowspan=\"2\"\u003e\n \u003cp\u003e.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.379146919431278%\"\u003e\n \u003cp\u003eProvince\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e8944(31.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e4451(15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e4493(16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.24025974025974%\" rowspan=\"7\"\u003e\n \u003cp\u003eHousehold type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96103896103896%\"\u003e\n \u003cp\u003eAlone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e4384(15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e2298(15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e2086(14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.253246753246753%\" rowspan=\"6\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.379146919431278%\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e3694(13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e1838(13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e1856(14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.379146919431278%\"\u003e\n \u003cp\u003eOther 1st generation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e478(1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e241(1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e237(1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.379146919431278%\"\u003e\n \u003cp\u003eCouples and unmarried children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e14923(52.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e7349(52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e7574(55.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.379146919431278%\"\u003e\n \u003cp\u003eSingle parents and unmarried children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e2131(7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e1139(7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e992(6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.379146919431278%\"\u003e\n \u003cp\u003eOther 2nd generation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e1063(3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e588(3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e475(2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.954070981210855%\"\u003e\n \u003cp\u003eAll 3rd generation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.38204592901879%\"\u003e\n \u003cp\u003e1715(6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.38204592901879%\"\u003e\n \u003cp\u003e843(5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.38204592901879%\"\u003e\n \u003cp\u003e872(5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.899791231732777%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.24025974025974%\" rowspan=\"2\"\u003e\n \u003cp\u003eBasic livelihood recipients*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96103896103896%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e27872(98.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e14010(98.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e13862(98.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.253246753246753%\" rowspan=\"2\"\u003e\n \u003cp\u003e.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.379146919431278%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e515(1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e285(1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e230(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.201298701298704%\" colspan=\"2\"\u003e\n \u003cp\u003eHousehold income\u003c/p\u003e\n \u003cp\u003e(10,000won/month)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e486.63\u0026plusmn;282.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e482.86\u0026plusmn;286.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e490.52\u0026plusmn;279.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.253246753246753%\"\u003e\n \u003cp\u003e.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.24025974025974%\" rowspan=\"2\"\u003e\n \u003cp\u003eHappiness*\u003c/p\u003e\n \u003cp\u003e(1-10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96103896103896%\"\u003e\n \u003cp\u003e\u0026le;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e4800(16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e2479(17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e2321(16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.253246753246753%\" rowspan=\"2\"\u003e\n \u003cp\u003e.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.379146919431278%\"\u003e\n \u003cp\u003e\u0026ge;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e23584(83.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e11813(82.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e11771(83.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.24025974025974%\" rowspan=\"2\"\u003e\n \u003cp\u003eEducation level*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96103896103896%\"\u003e\n \u003cp\u003e\u0026le;High school graduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e7858(27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e4030(25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e3828(24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.253246753246753%\" rowspan=\"2\"\u003e\n \u003cp\u003e.158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.379146919431278%\"\u003e\n \u003cp\u003e\u0026ge;College graduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e20523(72.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e10263(74.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e10260(75.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.24025974025974%\" rowspan=\"2\"\u003e\n \u003cp\u003eEconomic activity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96103896103896%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e1941(6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e1033(7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e908(6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.253246753246753%\" rowspan=\"2\"\u003e\n \u003cp\u003e.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.379146919431278%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e26446(93.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e13263(93.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e13183(93.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.24025974025974%\" rowspan=\"2\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96103896103896%\"\u003e\n \u003cp\u003eSingle\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e10687(37.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e5659(38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e5028(34.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.253246753246753%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.379146919431278%\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e17699(62.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e8636(61.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.540284360189574%\"\u003e\n \u003cp\u003e9063(65.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*missing value\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. \u0026nbsp; Differences in health-related characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe differences in health-related characteristics between the underweight / normal and overweight / obese groups are presented in Table 2. Over 50% of participants felt that their health was good. The percentage of participants who smoked was 57.7%. The percentage of participants who smoked more than 15 cigarettes per day was 83.2%. Almost all participants drank alcohol (94.6%), and over 50% drank more than seven cups at once. The highest rate of binge drinking was once a week (29.3%). Regarding food security, the proportion of participants who responded that they ate sufficient amounts and a variety of foods was the highest at 75.4%. The rate of not eating breakfast was highest (40.5%). The rates of awareness, comprehension, and use of nutrition labels were 66.9%, 39.9%, and 81.8%, respectively. The highest proportion of subjective oral health and difficulty in chewing were \u0026lsquo;average (47.5%)\u0026rsquo; and \u0026lsquo;good (78.6%)\u0026rsquo;, respectively. The highest proportion of participants had sufficient sleep time (55.3%), and depression was confirmed to be absent in most participants (97.5%). For the stress level, over 50% of subjects showed \u0026lsquo;much\u0026rsquo; (58.0%). The average number of days of strenuous physical activity, moderate physical activity, and walking were 1.27, 1.58, and 4.42, respectively. The frequencies of strenuous physical activity, moderate physical activity, and walking were 1.17, 1.04, and 0.79, respectively.\u003c/p\u003e\n\u003cp\u003eThere were differences in factors of subject health status (p\u0026lt;.001), smoking (p=.001), number of cigarettes (p\u0026lt;.001), amount of drinking (p\u0026lt;.001), food security (p=.003), days of breakfast (p\u0026lt;.001), awareness of nutrition label (p=.001), use of nutrition label (p=.018), difficulty in chewing (p=.011), sleeping time (p\u0026lt;.001), stress level (p\u0026lt;.001), times of strenuous physical activity (p\u0026lt;.001), times of moderate physical activity (p\u0026lt;.001), and times of walking between both groups (p=.020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Health-related characteristics (N=28,388)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=28,388)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnderweight \u0026amp; Normal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=14,296, 50.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverweight \u0026amp; Obesity\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=14092, 49.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eN(%), M\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\" rowspan=\"3\"\u003e\n \u003cp\u003eSubject health status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eBad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e1662(5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e719(4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e943(6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e11409(40.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e5312(36.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e6097(43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e15317(54.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e8265(58.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e7052(50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\" rowspan=\"2\"\u003e\n \u003cp\u003eSmoking*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e8327(42.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e3990(42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e4337(45.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"2\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e11337(57.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e5775(57.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e5562(54.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\" rowspan=\"2\"\u003e\n \u003cp\u003eAmount of cigarettes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e\u0026lt;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e4772(16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e2555(17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e2217(15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003e\u0026ge;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e23616(83.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e11741(82.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e11875(84.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\" rowspan=\"2\"\u003e\n \u003cp\u003eDrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e1539(5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e808(5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e731(4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"2\"\u003e\n \u003cp\u003e.323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e26849(94.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e13488(95.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e13361(95.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\" rowspan=\"2\"\u003e\n \u003cp\u003eAmount of\u003c/p\u003e\n \u003cp\u003edrinking(once)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e\u0026le;6cups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e11608(47.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e6409(52.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e5199(43.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003e\u0026ge;7cups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e12671(52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e5772(47.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e6899(57.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\" rowspan=\"5\"\u003e\n \u003cp\u003eBinge drinking*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e4695(19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e2561(21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e2134(17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003e\u0026lt;1/month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e4721(19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e2425(20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e2296(19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003e1/month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e5935(24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e2927(24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e3008(25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003e1/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e7136(29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e3389(27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e3747(30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003eDaily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e1792(7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e879(6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e913(6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\" rowspan=\"4\"\u003e\n \u003cp\u003eFood security*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eSufficient quantity and variety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e21417(75.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e10634(74.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e10783(76.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"4\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003eSufficient quantity but no\u003c/p\u003e\n \u003cp\u003evariety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e6341(22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e3328(23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e3013(21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003eSometimes insufficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e534(1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e288(1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e246(1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003eOften insufficient food\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e94(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e45(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e49(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\" rowspan=\"4\"\u003e\n \u003cp\u003eDays of breakfast\u003c/p\u003e\n \u003cp\u003e(week)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e11507(40.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e5609(40.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e5898(43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003e1-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e3058(10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e1462(10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e1596(11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003e3-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e3000(10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e1502(10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e1498(10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003e5-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e10823(38.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e5723(38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e5100(33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\" rowspan=\"2\"\u003e\n \u003cp\u003eAwareness of\u003c/p\u003e\n \u003cp\u003enutrition label*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e9388(33.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e4843(33.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e4545(31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"2\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e18976(66.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e9443(66.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e9533(68.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\" rowspan=\"2\"\u003e\n \u003cp\u003eComprehension of\u003c/p\u003e\n \u003cp\u003enutrition label*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e11397(60.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e5692(58.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e5705(58.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"2\"\u003e\n \u003cp\u003e.730\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e7578(39.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e3751(42.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e3827(41.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\" rowspan=\"2\"\u003e\n \u003cp\u003eUse of nutrition\u003c/p\u003e\n \u003cp\u003elabel*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e1376(18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e657(16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e719(18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"2\"\u003e\n \u003cp\u003e.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e6201(81.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e3094(83.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e3107(81.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\" rowspan=\"3\"\u003e\n \u003cp\u003eSubjective oral\u003c/p\u003e\n \u003cp\u003ehealth status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eBad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e6040(21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e3170(21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e2870(19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e13473(47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e6736(47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e6737(48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e8875(31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e4390(31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e4485(32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\" rowspan=\"3\"\u003e\n \u003cp\u003eDifficulty in chewing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eBad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e2195(7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e1152(7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e1043(7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"3\"\u003e\n \u003cp\u003e.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e3875(13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e2015(14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e1860(13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e22318(78.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e11129(78.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e11189(80.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\" rowspan=\"3\"\u003e\n \u003cp\u003eSleeping time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eLack(\u0026lt;7h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e9874(34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e4682(32.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e5192(37.1))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003eEnough(7-8h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e15680(55.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e8093(57.2))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e7587(53.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003eOver(\u0026gt;8h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e2826(10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e1517(10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e1309(9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\" rowspan=\"2\"\u003e\n \u003cp\u003eDepression(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e27640(97.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e13951(97.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e13689(97.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"2\"\u003e\n \u003cp\u003e.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e716(2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e329(2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e387(2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\" rowspan=\"4\"\u003e\n \u003cp\u003eStress level*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eRare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e1130(4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e483(3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e647(4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003eLittle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e6724(23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e3210(23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e3514(25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003eMuch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e16462(58.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e8557(59.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e7905(56.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.28985507246377%\"\u003e\n \u003cp\u003eVery much\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.18840579710145%\"\u003e\n \u003cp\u003e4071(14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.985507246376812%\"\u003e\n \u003cp\u003e2046(13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.536231884057973%\"\u003e\n \u003cp\u003e2025(13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDays of strenuous physical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e1.27\u0026plusmn;1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e1.29\u0026plusmn;1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e1.26\u0026plusmn;1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e.254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDays of moderate physical activity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e1.58\u0026plusmn;2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e1.58\u0026plusmn;2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e1.58\u0026plusmn;2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e.785\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDays of walking\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e4.42\u0026plusmn;2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e4.24\u0026plusmn;2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e4.20\u0026plusmn;2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTime of strenuous physical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e1.17\u0026plusmn;0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e1.15\u0026plusmn;0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e1.19\u0026plusmn;0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTime of moderate physical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e1.04\u0026plusmn;0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e1.02\u0026plusmn;0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e1.06\u0026plusmn;0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTime of walking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e0.79\u0026plusmn;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e0.78\u0026plusmn;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e0.79\u0026plusmn;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*missing value\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. \u0026nbsp; Prediction model for BMI (total model)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe prediction model for BMI is shown in Figure 1. Almost half the participants were overweight or obese. The variable of amount of drinking was the primary factor affecting BMI (chi-square=228.477, p\u0026lt;.001). The rate of overweight or obesity was 44.8% (Node 1). The rate of overweight or obesity in those consuming 1\u0026ndash;6 cups of alcohol differed according to sleep duration (chi-square=18.553, p\u0026lt;.001). The BMI of subjects who consumed 6 cups of alcohol with enough or extra sleep (Node 5) differed according to the number of cigarettes smoked (chi-square=12.767, p\u0026lt;.010). Those who consumed 1\u0026ndash;6 cups of alcohol at once, slept enough or over time, and smoked less than 15 cigarettes per day showed the lowest rate of being overweight or obese (38.7%).\u003c/p\u003e\n\u003cp\u003eThe rate of overweight or obesity in participants who consumed 7\u0026ndash;10 cups of alcohol (Node 2) differed according to marital status (chi-square=33.329, p\u0026lt;.001). The rate of overweight or obesity in participants who drank 7\u0026ndash;10 cups of alcohol and were married differed according to sleeping time (chi-square=39.248, p\u0026lt;.001). Those who drank 7\u0026ndash;10 cups of alcohol, were married, and had a lack of sleep (Node 14) showed the highest rate of overweight or obesity (60.6%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. \u0026nbsp; Prediction model for BMI by age\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe predictive model for BMI according to age is shown in Figure 2. Age was designated as the primary factor that significantly affected BMI. Those in their 40s (Node 1) had a 47.9% rate of being overweight or obese. The rate of overweight or obesity in subjects in their 40s differed according to the amount of alcohol consumed (chi-square=111.572, p\u0026lt;.001). The rates of overweight or obesity in subjects in their 40s who consumed 1\u0026ndash;6 cups of alcohol differed according to the number of cigarettes smoked (chi-square=11.018, p=.001). Those who were in their 40s, consumed 1\u0026ndash;6 cups of alcohol, and smoked under 15 cigarettes showed the lowest rate of being overweight or obese (38.7%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe rate of overweight or obesity in subjects in their 30s (Node 2) differed according to the amount of alcohol consumed (chi-square=120.249, p\u0026lt;.001). The rate of overweight or obesity among subjects in their 30s who consumed 7\u0026ndash;10 cups of alcohol differed according to marital status (chi-square=30.094, p\u0026lt;.001). The subjects who were in their 30s, consumed 7\u0026ndash;10 cups of alcohol, and were married (Node 18) showed the highest rate of being overweight or obese (60.6%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.\u0026nbsp; \u0026nbsp;Validation tests of prediction model for BMI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;The results of\u0026nbsp;the validation assessment using split-sample tests\u0026nbsp;are presented in Table 3. For the entire model, the value of risk estimates (RE) was .448 of the training data. This\u0026nbsp;indicates a classification accuracy of 54.2%. In addition, the model set the primary factor as age, and the value of RE was .445. This can be interpreted\u0026nbsp;as a classification accuracy of 55.5%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Risk Chart of Decision Trees\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk Estimates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eTraining data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eTest data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" rowspan=\"2\"\u003e\n \u003cp\u003ePrimary factor of age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eTraining data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eTest data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study was conducted to predict the BMI of Korean men in their 30s and 40s using data from the 2022 CHS. This study found that more than half of overweight or obese participants were in their 40s. According to\u0026nbsp;the obesity rate statistics for 2022\u0026nbsp;(18), the rate of obesity was 47.7% in men and 25.7% in women\u0026nbsp;(18). The male age group with\u0026nbsp;the highest obesity rate was\u0026nbsp;the 40s\u0026nbsp;age group, with a rate of 40.7%,\u0026nbsp;which is\u0026nbsp;consistent with the findings\u0026nbsp;of this study.\u0026nbsp;However, another report stated that men in their 30s had the highest prevalence of obesity\u0026nbsp;(8). Notably, the highest rate of obesity was found in men aged 30 to 40 years. According to recent statistics, there has been a steady increase in the\u0026nbsp;rate of obesity\u0026nbsp;among men in their 30s and 40s. Almost half of men between the ages of 30 and 40 were found to be obese, and as of\u0026nbsp;last year (2022), 55.7% of men in their 30s and 53.6% of men\u0026nbsp;in their 40s were classified as obese.\u0026nbsp;(19). Therefore, it is essential to implement proactive interventions to increase awareness and effectively manage obesity and related chronic diseases among these age groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The results of this study showed that individuals who consumed 7–10 cups of alcohol per week, were married, and experienced sleep deprivation had the highest rates of overweight or obesity. According to age, those who were in their 30s, consumed 7–10\u0026nbsp;cups of alcohol, and were married showed the highest rate of\u0026nbsp;being overweight or obese (60.6%).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The KDCA reported that Korean men in their 30s\u0026nbsp;were more likely to gain excess weight due to\u0026nbsp;prolonged sedentary work and reduced physical activity\u0026nbsp;(8). A study also found that high fat and sodium intakes are linked\u0026nbsp;to abdominal obesity in men in their 30s\u0026nbsp;(20). However, being overweight and obese is not just caused by one factor but a combination of factors such as dietary patterns and lifestyle factors such as sleep deprivation, sedentary lifestyle, chronic disease, medication,\u0026nbsp;and even\u0026nbsp;genetics\u0026nbsp;(21). Although the obesity rate among Korean men in their 30s is increasing rapidly, it is difficult to compare our results with those of other studies. Therefore, there is an urgent need for studies that reflect this situation and explore their distinct characteristics.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;A previous study showed that alcohol contains 7\u0026nbsp;kcal/g and can contribute significantly to excess caloric intake\u0026nbsp;(22). However, light-to-moderate alcohol consumption was not found to be related to weight, whereas heavy alcohol consumption was. Additionally, different weight gain outcomes\u0026nbsp;were observed depending on the type of alcohol consumed. Wine consumption appears to prevent weight gain, while there was an association between beer intake \u0026gt; 500\u0026nbsp;ml/day and abdominal fat accumulation\u0026nbsp;(23,24). According to a systematic review, several previous cross-sectional studies found no relationship between alcohol consumption and BMI in men\u0026nbsp;(25). This is likely because of the complexity of obesity as a condition with many contributing factors, which makes it difficult to determine the independent impact of alcohol consumption on obesity risk\u0026nbsp;(24). As research results on the relationship between drinking and obesity are conflicting, it is necessary to continue research that considers the amount of alcohol consumed, type of alcohol, frequency of drinking, type of food consumed with alcohol, and demographic and physical characteristics of\u0026nbsp;the subjects.\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Studies have shown that marital status is a socioeconomic factor linked to obesity\u0026nbsp;(26,27). This study found that married men had a higher proportion of overweight or obesity than single men. Similarly, one study found that a higher proportion\u0026nbsp;of single men were underweight than married men\u0026nbsp;(28). On the other hand, some studies found that marriage can positively impact lifestyle habits, such as quitting smoking and a healthy diet, which can help maintain a healthy weight\u0026nbsp;(29). However, it is worth noting that the association between marriage and obesity differs according to race, ethnicity, and sex\u0026nbsp;(30,31). From the results of\u0026nbsp;these studies, it is necessary to develop a family-level obesity management program for married men so that the program can include their families, as they share their lifestyle\u0026nbsp;with them. In addition, more research\u0026nbsp;needs to be\u0026nbsp;conducted on obesity rates among married and unmarried men,\u0026nbsp;so that customized obesity management interventions can be developed based on marital status.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;This study discovered that individuals who sleep less than seven hours a night are more likely to be overweight or obese\u0026nbsp;(32). Although age and sex differed, previous studies have reported similar findings. A review has mentioned short sleep duration and poor\u0026nbsp;sleep quality\u0026nbsp;are risk factors for developing obesity\u0026nbsp;(33). Additionally, another study found significantly higher rates of overweight or obesity in individuals who slept for shorter periods than in those who slept for longer periods\u0026nbsp;(34). Previous research has suggested that shorter sleep periods are associated with decreased fiber intake and increased consumption of carbohydrates, total cholesterol, and total saturated fat\u0026nbsp;(35). However, the standards for adequate sleep for people in their 30s and 40s vary among studies, making it difficult to determine the relationship between sleep duration and obesity. It was also found that sleep affects diet or physical activity rather than being directly related to obesity. Therefore, it is necessary to determine the appropriate amount of sleep for men in their 30s and 40s. Further research is required to explore the impact of sleep on changes in health behaviors and how\u0026nbsp;sleep is related to obesity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;As the obesity rate among Korean men in their 30s and 40s continues to rise,\u0026nbsp;there is an increasing need to develop effective obesity prevention and management programs and encourage active participation from those affected. However, despite the well-established link between obesity and health-related diseases, male obesity rates are still increasing, and men appear hesitant to engage in weight-loss intervention programs\u0026nbsp;(37). Men are generally less concerned about their weight status than women, and tend to lack basic knowledge of nutrition\u0026nbsp;(38,39). Therefore, it is essential to raise awareness in the community about the seriousness of obesity, emphasize the need for obesity management, and emphasize the need for obesity management, provide effective methods for managing obesity. In addition, it is necessary to identify and eliminate obstacles that prevent men from managing obesity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study is important because it predicted obesity in Korean men in their 30s and 40s. This is a serious condition that has not been extensively investigated. However, this study has some limitations that should be addressed in future research. First, because this was a secondary data analysis, not all significant factors related to BMI in Korean men may have been included. Second, the dataset in this study was collected during the recovery period after a specific situation that may have affected obesity, namely COVID-19, which may have affected obesity. Therefore, the results cannot be generalized with certainty. Third, the data used in this study were self-reported, which poses a disadvantage in that they may not be entirely reliable. Finally, variables proven to be related to obesity in men in previous studies, such as educational level, physical activity, and household income, did not appear significantly in this study. Thus, further studies are necessary to identify the factors that contribute to obesity and use them to develop customized obesity management programs.\u0026nbsp;\u003c/p\u003e"},{"header":"CONCLUSION ","content":"\u003cp\u003e\u0026nbsp;In Korea, in male adults in their 30s or 40s, alcohol consumption, frequency of breakfast consumption, amount of smoking, marital status, smoking, and sleeping times were found to be the variables affecting BMI. Individuals who consumed 7–10 cups of alcohol, were married, and had insufficient sleep showed the highest rate of overweight/obesity. To address obesity among men in their 30s and 40s, it is necessary to understand their drinking habits and develop strategies to reduce alcohol consumption. Furthermore, a weight management program that involves the entire family would be more effective than one that focuses solely on men. It is crucial to identify the sleep patterns of overweight or obese individuals and develop strategies to improve sleep quality and duration.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on Article 2, Paragraph 2 of the Enforcement Rule of the Bioethics and Safety Act of Republic of Korea, it is excluded from the subject of review as it does not fall under human subject research. However, this study was conducted with approval of review exemption from the researcher\u0026rsquo;s university.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe datasets generated during the current study are available in the Community Health Survey repository, https://chs.kdca.go.kr/chs/mnl/mnlBoardMain.do.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The author declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMH analyzed and interpreted the data regarding prediction model of cognitive impairment in elderly in Korea. MH performed wrote and revised the draft and final version of this paper. MH read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eObesity [Internet]. World Health Organization. 2016. Available from: https://www.who.int/topics/obesity/en/\u003c/li\u003e\n\u003cli\u003eNam G. Current status and epidemiology of adult obesity in Korea. J Korean Med Assoc. 2022;65(7):394\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eKorean Society for the Study of Obesity. 2020 Korean Society for the Study of Obesity guidelines for the management of obesity in Korea. Seoul; 2020. \u003c/li\u003e\n\u003cli\u003eWorld Health Organization. World Obesity Day 2022- Accelerating action to stop obesity [Internet]. 2023 [cited 2023 Jan 13]. Available from: https://www.who.int/news/item/04-03-2022-world-obesity-day-2022-accelerating-action-to-stop-obesity\u003c/li\u003e\n\u003cli\u003eKim B, Kang S, Kang J, Kang S, Kim K, Kim K, et al. 2020 Korean society for the study of obesity guidelines for the management of obesity in Korea. J Obesty Metab Syndr. 2021;30:81\u0026ndash;92. \u003c/li\u003e\n\u003cli\u003eYang Y, Han B, Han K, Jung J, Son J. Obesity Fact Sheet in Korea, 2021: Trends in Obesity Prevalence and Obesity-Related Comorbidity Incidence Stratified by Age from 2009 to 2019. J Obes Metab Syndr. 2022;31(2):169\u0026ndash;77. \u003c/li\u003e\n\u003cli\u003eKorea Centers for Disease Control and Prevention. Korea National Health and Nutrition Examination Survey Fact Sheet. Cheonju; 2020. \u003c/li\u003e\n\u003cli\u003eNo K. More than half of Korean men in 30s \u0026ldquo;overweight.\u0026rdquo; The Korean Herald [Internet]. 2023 Oct 19; Available from: https://www.koreaherald.com/view.php?ud=20231019000573\u003c/li\u003e\n\u003cli\u003eKim K, Shin Y. Male with obesity and overweight. J Obes Metab Syndr. 2020;29(21):18\u0026ndash;25. \u003c/li\u003e\n\u003cli\u003eKim K, Shin Y. Males with obesity and overweight. J Obes Metab Syndr. 2020;29(1):18\u0026ndash;25. \u003c/li\u003e\n\u003cli\u003eKorean men becoming more obese due to drinking, lack of exercise [Internet]. The Korea Times. 2023 [cited 2023 Feb 10]. Available from: https://www.koreatimes.co.kr/www/nation/2023/12/113_342667.html\u003c/li\u003e\n\u003cli\u003eNIH. Calculate Your Body Mass Index [Internet]. [cited 2021 May 22]. Available from: https://www.nhlbi.nih.gov/health/educational/lose_wt/BMI/bmicalc.htm\u003c/li\u003e\n\u003cli\u003eHwang S. Estimation of high-risk drinkers and drinking behavior in Korea. J Environ Heal Sci. 2020;46(1):65\u0026ndash;77. \u003c/li\u003e\n\u003cli\u003eChoi H, Choi J, Park K, Joo K, GA H, Ko H, et al. Standarization of the Korean version of patient health questionanire-9 as a screening instrument for major depressive disorder. Korean J Fam Med. 2007;28:114\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eChoi J, Seo D. Decision Trees and Its Applications. J Korean Off Stat. 1999;4(1):61\u0026ndash;83. \u003c/li\u003e\n\u003cli\u003eSeo J., Kim M. A Prediction Model for Quality of Life by Resilience in Disaster Female Victims. Korean J Adult Nurs. 2021;33(6):639\u0026ndash;48. \u003c/li\u003e\n\u003cli\u003eChoi J, Han S, Kang H, Kim Y. Data mining decision tree by using Answer Tree. SPSS Korea. Seoul; 1998. \u003c/li\u003e\n\u003cli\u003eKindicator. Obesity rates [Internet]. 204AD [cited 2023 Feb 11]. Available from: https://www.index.go.kr/unify/idx-info.do?idxCd=4239\u003c/li\u003e\n\u003cli\u003eKorea Disease Control and Prevention Agency. National Health and Nutrition Survey 2022 results [Internet]. 2023. Available from: https://www.kdca.go.kr/filepath/boardDownload.es?bid=0015\u0026amp;list_no=724014\u0026amp;seq=2\u003c/li\u003e\n\u003cli\u003ePark M, Jung N. Comparison of Physical Activity and Nutrient Intake according to Abdominal Obesity in their 20s and 30s in Korean Men: Data from the Seventh Korea National Health and Nutrition Examination Survey Ⅶ (2016-2018). Korean Soc Sport Sci. 2020;29(4):1113\u0026ndash;25. \u003c/li\u003e\n\u003cli\u003eWhat are overweight and obesity? [Internet]. National Heart, Lung, and Blood Institute. 2023 [cited 2023 Feb 10]. Available from: https://www.nhlbi.nih.gov/health/overweight-and-obesity\u003c/li\u003e\n\u003cli\u003eAberg F, Farkkila M. Dirnking and obesity: alcoholic liver disease/nonalcoholic fatty liver disease interactions. Semin Liver Dis. 2020;40(2):154\u0026ndash;62. \u003c/li\u003e\n\u003cli\u003eLau K, Baumeister S, Lieb W. The combined effects of alcohol consumption and body mass index on hepatic steatosis in a general population sample of European men and women. Aliment Ther J. 2015;41(5):467\u0026ndash;76. \u003c/li\u003e\n\u003cli\u003eTraversy G, Cahput J. Alcohol consumption and obeisty: an update. Curr Obes Rep. 2015;4(1):122\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003eSayon-Orea C, Martinez-Gonzalez M, Bes-Rastrollo M. Alcohol consumption and body weight:a systematic review. Nutr Rev. 2011;69:419\u0026ndash;31. \u003c/li\u003e\n\u003cli\u003eSobal J, Janson K, Frongillo E. Gender, ethnicity, marital status, and body weight in the United States, Obesity. 2009. 2223\u0026ndash;2231 p. \u003c/li\u003e\n\u003cli\u003eTzaotzas T, Vlahavas S, Ppadopoulou E, Kapantais D, Kaklamanou M. Marital status and educational level associated to obesity in Greek adults: data from the National Epidemiological Survey. BMC Public Health. 2010;10:732. \u003c/li\u003e\n\u003cli\u003eLee J, Shin A, Cho S, Choi J, Kang D, Lee J. Marital status and the prevalence of obesity in a Korean population. Obes Res Clin Pract. 2020;14(3):217\u0026ndash;24. \u003c/li\u003e\n\u003cli\u003eRoos E, Lahelma M, Virtanen R, Prattala P. Gender, socioeconomc statis and family status as determinants of food beavior. Soc Sci \u0026amp;Medicine. 1998;46(1519\u0026ndash;1529). \u003c/li\u003e\n\u003cli\u003eKlos L, Sobal J. Weight and weddings. Engaged men\u0026rsquo;s body wieght ideals and wedding weight management behaviors. Appetite2. 13AD;60(1):133\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eKarla H, Jeffery S, Edward F. Gender and marital status calrify association between food insecurity and body weight. J Nutr. 2007;137(6):1460\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eCenters for Disease Control and Prevention. How much sleep do I need? [Internet]. 2022 [cited 2024 Feb 12]. Available from: https://www.cdc.gov/sleep/about_sleep/how_much_sleep.html\u003c/li\u003e\n\u003cli\u003eBeccuti G, Pannain S. Sleep and obesity. Curr Opin Clin Nutr Metab Care. 2013;14(4):402\u0026ndash;12. \u003c/li\u003e\n\u003cli\u003eLi Q. The association between sleep duration and excess body weight of the American adult population: a cross-sectional study of the national health and nutrition examination survey 2015\u0026ndash;2016. BMC Pubilc Heal. 2021;21:1\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eGrandner M, Jackson N, Gerstner J, Knutson K. Dietary nutrients associated with short and long sleep duration. Data from a nationally representative sample. Appetite2. 2013;64:71\u0026ndash;80. \u003c/li\u003e\n\u003cli\u003eKim D, Park S, Kim Y, Oh K. National health statistics plus: current status of obesity prevalence in Korean adults and related factors [Internet]. Korea Disease Control and Prevention Agency. 2021 [cited 2023 Feb 10]. Available from: https://knhanes.kdca.go.kr/knhanes/sub04/sub04_04_02.do\u003c/li\u003e\n\u003cli\u003eGray C, Anderson A, Clarke A, Dalziel A, Hunt K, Leishman J. Addressing male obesity: an evaluation of a group based weight management intervention for Scottish men. Jounral Men\u0026rsquo;s Heal. 2009;6:70\u0026ndash;81. \u003c/li\u003e\n\u003cli\u003eStibbe A. Health and the social construction of masculinity in Men\u0026rsquo;s health magazine. Men Masc. 2004;7:31\u0026ndash;51. \u003c/li\u003e\n\u003cli\u003eGough B, Conner M. Barriers to healthy eating amongst men: a qualitative analysis. Soc Sci Med. 2006;62:987\u0026ndash;95. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Obesity, Community Health Survey (CHS), Decision Trees, Men, Body Mass Index","lastPublishedDoi":"10.21203/rs.3.rs-4040508/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4040508/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aimed to explore general and health-related characteristics according to body mass index (BMI). This study aimed to build a predictive model for BMI in Korean men in their 30s and 40s.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData from 28,388 men aged 30-40 years from the 2022 Community Health Survey wereused. The participants were divided into underweight / normal weight groups (n=14,296, 50.4%) and overweight / obese groups (n=14,092, 49.6%) based on their BMI. Chi-square tests and independent t-tests were used to compare general and health-related characteristics. A prediction model was constructed using decision tree analysis. A split-sample test was conducted to verify the validity of the prediction model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe proportions of participants in the underweight / normal and overweight / obese groups were 50.4% and 49.6%, respectively. From the decision tree, the variable of amount of alcohol consumed was the primary factor predicting BMI (chi-square=228.477, p\u0026lt;.001). The characteristics of the group with the highest overweight or obesity rate of 60.6% were that they drank more than seven cups of alcohol at a time, were married, and were sleep-deprived. In contrast, the characteristics of the group with the lowest overweight or obesity rate (38.7%) were that they drank less than six cups of alcohol at a time, got enough or extra sleep time, and smoked less than 15 cigarettes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe proportions of participants in the underweight / normal and overweight / obese groups were 50.4% and 49.6%, respectively. From the decision tree, the variable of amount of alcohol consumption was the primary factor predicting BMI. The characteristics of the group with the highest overweight or obesity rate of 60.6% were that they drank more than seven cups of alcohol at a time, were married, and were sleep-deprived. In contrast, the characteristics of the group with the lowest overweight or obesity rate (38.7%) were that they drank less than six cups of alcohol at a time, got enough or over sleep time, and smoked less than 15 cigarettes.\u003c/p\u003e","manuscriptTitle":"Obesity prevalence and obesity prediction model among Korean men in their 30s and 40s: a 2022 Community Health Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-15 20:28:00","doi":"10.21203/rs.3.rs-4040508/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-16T12:11:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-14T14:24:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45299239647975511892774931973324311715","date":"2024-10-09T21:41:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86529055292225394461666318044626185036","date":"2024-10-09T15:52:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68458767253370066828588274190085221631","date":"2024-10-07T16:10:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102545267005670953936109728884224002735","date":"2024-10-06T15:48:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131514617539695886927519258744398454956","date":"2024-10-05T11:09:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-04T11:40:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6134661709847263178807382495865374394","date":"2024-10-04T05:51:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-02T14:49:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31762248073470711611861435293365627249","date":"2024-10-02T13:04:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"200990458392438278361097132809002121533","date":"2024-10-02T10:23:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312926207160846033137113628250848414852","date":"2024-10-02T09:00:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108741733414867946295917989071049410724","date":"2024-10-02T08:43:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34964271740675199312434747426038243113","date":"2024-10-02T08:33:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-11T12:08:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-14T05:56:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-13T11:48:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-13T11:48:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-03-08T09:06:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d4a5012b-95b8-4f56-8f1b-599f1ba8f8a4","owner":[],"postedDate":"March 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-25T16:04:48+00:00","versionOfRecord":{"articleIdentity":"rs-4040508","link":"https://doi.org/10.1186/s12889-024-20573-y","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2024-11-18 15:58:08","publishedOnDateReadable":"November 18th, 2024"},"versionCreatedAt":"2024-03-15 20:28:00","video":"","vorDoi":"10.1186/s12889-024-20573-y","vorDoiUrl":"https://doi.org/10.1186/s12889-024-20573-y","workflowStages":[]},"version":"v1","identity":"rs-4040508","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4040508","identity":"rs-4040508","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}