ABSTRACT
This study was conducted to analyze diet and health-related factors based on the income level of single-adult households using data from the Korea National Health and Nutrition Survey (KNHANES). Among those who participated in the 2016–2018 KNHANES, 951 single-adult households aged 19 to 64 were selected, and factors such as general characteristics, physical characteristics, dietary behaviors, health behaviors, and the prevalence of chronic diseases were analyzed. The high-income group had higher frequency of eating out, better dietary status generally, higher recognition rate of nutrition labels than the other groups. The rate of alcohol consumption and smoking was higher in the high-income group of single-adult households, while the rates of anxiety and depression were higher in the low-income group. Additionally, the use of dietary supplements decreased as income level decreased. Among chronic diseases, hypertension, diabetes, and dyslipidemia had the lowest prevalence in the middle-income group and the highest prevalence in the low-income group. These results suggest that diet and health behaviors vary by income level in single-adult households and may be associated with the prevalence of chronic diseases. Future systematic studies should be conducted to determine the causal relationships between these factors.
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Keywords: Single person; Income; Diet; Health; Chronic disease
INTRODUCTION
A single-adult household is defined as a household where an individual lives alone and independently supports themselves through activities such as cooking and sleeping [
1]. It has been reported that individuals in single-adult households, who live alone without anyone to rely on, are more vulnerable to alcohol consumption and smoking, which can lead to health problems [
2]. Additionally, single-adult households are often more susceptible to negative demographic characteristics and face poorer mental health outcomes, such as depression, suicidal tendencies, and unhealthy behaviors [
3].
A study examining the association between eating alone, depression, and quality of life among unmarried adults found that those who ate alone had higher depression scores and lower quality of life scores compared to those who ate with others [
4]. These findings suggest that psychological issues, such as social isolation and stress, may negatively impact eating habits in single-adult households, increasing their risk of chronic diseases due to unstable eating behaviors. A study on the prevalence of metabolic syndrome in single-adult households found that they had the highest prevalence, at 37.2%, compared to 35.1% in two-person households and 25.8% in households with three or more people [
5]. Compared to multi-person households, individuals in single-adult households were more likely to skip breakfast, smoke, drink alcohol, and have an inadequate diet. They were also at a higher risk for hyperglycemia, hypertension, and metabolic syndrome [
6].
Low income is associated with poorer health outcomes compared to higher income due to limited access to healthcare resources, healthcare services, and unhealthy lifestyle habits. Kim [
7] reported that among adult women aged 35 and older, lower income was linked to lower employment status, lower education levels, and a higher prevalence of chronic diseases such as diabetes, hypertension, hyperlipidemia, and angina, compared to other groups. Among women, those in the lower income tended to have a greater dependence on carbohydrates and showed lower adherence to dietary guidelines, particularly in terms of consuming fresh foods and maintaining variety in their diets [
8].
Previous studies on single-adult households have focused on various groups, including the elderly, adolescents, women, and middle-aged individuals across the life cycle. Most of these studies have compared single-adult households to multi-person households [
8,
9,
10]. Furthermore, studies on single-adult households [
11,
12] have primarily focused on quality of life, dietary routines, consumption, and dietary patterns, which limits the ability to assess the impact of socioeconomic status on diet and health-related factors within single-adult households.
This study aims to investigate the association between income level, dietary behaviors, and health status in single-adult households. To achieve this, we conducted a comparative analysis of dietary and health-related behaviors across different income levels in single-adult households, using data from the Korea National Health and Nutrition Examination Survey (KNHANES). The findings of this study may provide foundational evidence for the development of nutrition programs aimed at preventing future health issues in single-adult households.
MATERIALS AND METHODS
Data and study participants
This study utilized the raw data from the 7th period (2016–2018) of the KNHANES. From a total of 24,269 subjects, 2,324 subjects were selected from single-adult households, and 1,184 subjects were selected from individuals aged 19–64 years. Of these, 1,005 individuals whose nutrient intake fell within the range of 500 kcal to 5,000 kcal were selected, and 951 individuals with no missing values on analytic variables were included in the study. Income levels were divided into ‘low’ (n = 266), ‘mid-low’ (n = 270), ‘mid-high’ (n = 219), and ‘high’ (n = 196) quartiles based on the average monthly household equalized income (monthly household income/
household size
) from the 7th KNHANES. This study was conducted in compliance with regulations after approval waiver from the Chonnam National University Institutional Review Board (1040198-231010-HR-148-01).
Age, sex, education, house type, and residential area were selected as variables representing the general characteristics of the participants (
Table 1). Height, weight, body mass index (BMI), and waist circumference were chosen as variables to assess the physical characteristics of the subjects. BMI was calculated by dividing weight (kg) by the square of height (m
2).
Table 1Demographic characteristics of the participants in single-adult households
Table 1
|
Variables |
Total (n = 951) |
|
Age (yr) |
|
|
19–29 |
192 (20.2) |
|
30–39 |
163 (17.1) |
|
40–49 |
169 (17.8) |
|
≥ 50 |
427 (44.9) |
|
Sex |
|
|
Male |
491 (51.6) |
|
Female |
460 (48.4) |
|
Education |
|
|
≤ High school |
582 (61.2) |
|
≥ College |
369 (38.8) |
|
House type |
|
|
Detached house |
518 (54.5) |
|
Apartment |
312 (32.8) |
|
Multi-household |
74 (7.8) |
|
Others |
47 (4.9) |
|
Residential area |
|
|
Urban |
776 (81.6) |
|
Rural |
175 (18.4) |
Dietary behaviors
Frequency of eating out, breakfast frequency, lunch frequency, dinner frequency, and food insecurity were used as variables to examine the eating behaviors of single-adult households by income level. The frequency of eating out was categorized into the following: ‘more than twice a day,’ ‘once a day,’ ‘5–6 times a week,’ ‘3–4 times a week,’ ‘1–2 times a week,’ ‘1–3 times a month,’ and ‘rarely.’ Breakfast and lunch frequencies were categorized as ‘5–7 times per week,’ ‘3–4 times per week,’ ‘1–2 times per week,’ and ‘rarely,’ while dinner frequency was categorized as ‘5–7 times per week,’ ‘3–4 times per week,’ and ‘0–2 times per week.’ Food insecurity was categorized as ‘sufficient and varied,’ ‘sufficient but not varied,’ and ‘not always enough.’
To examine the level of nutrition knowledge, nutrition education experience, recognition of nutrition labels, use of nutrition labels, and interest in nutrients listed on nutrition labels were used. Nutrition education experience, recognition of nutrition labels, and use of nutrition labels were categorized as ‘yes’ and ‘no.’ Nutrient interests were categorized as ‘calories,’ ‘carbohydrates,’ ‘sugars,’ ‘protein,’ ‘fat,’ ‘saturated fat,’ ‘trans fat,’ ‘cholesterol,’ ‘sodium,’ and ‘others.’
Health characteristics
The following variables were selected to examine the health behaviors of the participants: alcohol consumption, smoking, health checkups, stress, anxiety and depression, and dietary supplement use. Alcohol consumption frequency was reclassified into three categories: ‘more than once a week,’ ‘less than once a week,’ and ‘not at all in the past year.’ Smoking status was categorized based on a combination of lifetime smoking and current smoking. Individuals who reported having smoked five or more cigarettes in their lifetime and currently smoke were classified as ‘smokers,’ those who had smoked in the past but do not currently smoke were categorized as ‘ex-smokers,’ and those who had never smoked were labeled as ‘non-smokers.’ Health checkup attendance was based on whether participants had attended a health checkup in the past two years. Stress was categorized as ‘very much,’ ‘a lot,’ ‘a little,’ or ‘not at all.’ Anxiety and depression were classified as ‘yes’ for individuals who reported being very anxious or depressed, and ‘no’ for those who reported being somewhat anxious or depressed or not anxious or depressed. Dietary supplement use was based on a ‘yes’ or ‘no’ response to the question, “Have you taken dietary supplements consistently for more than two weeks in the past year?”
The following variables were selected to analyze the prevalence of chronic diseases by income level in single-adult households: obesity, hypertension, diabetes, dyslipidemia, atopic dermatitis, and allergic rhinitis. The prevalence of obesity was categorized as ‘underweight’ if the BMI was less than 18.5 kg/m2, ‘normal’ if the BMI was between 18.5 kg/m2 and 24.9 kg/m2, and ‘obese’ if the BMI was 25 kg/m2 or higher. The prevalence of hypertension, diabetes, dyslipidemia, atopic dermatitis, and allergic rhinitis was categorized as ‘yes’ or ‘no.’
Statistical analysis
Since the raw data from the KNHANES used in this study were collected through stratified cluster sampling rather than simple random sampling, composite sample analysis was conducted. A composite sample general linear model was employed to test for significant differences in physical characteristics by income level, and a composite sample χ2 test was used to examine significant differences in eating behavior characteristics, nutrition knowledge, health behaviors, and the prevalence of chronic diseases. Statistical analysis was performed using SPSS Statistics 26 (IBM Corporation, Armonk, NY, USA). Statistical significance was determined at a significance level of p < 0.05.
RESULTS
Demographic and physical characteristics
The general characteristics of the participants are shown in
Table 1. As previously reported [
13], age (p < 0.001), sex (p = 0.001), and education (p < 0.001) were significantly different among the general characteristics by income level.
Table 2 shows the physical characteristics by income level in single-adult households. For men, there are no significant differences in physical characteristics based on income level. However, for women, significant differences are observed in height (p = 0.021), waist circumference (p = 0.002), and BMI (p = 0.028) by income level. The ‘lower’ income group had the shortest height, and both waist circumference and BMI increased as income decreased.
Table 2Physical characteristics by income level in single-adult households
Table 2
|
Variables |
Income level |
p value |
|
Low (n = 266) |
Mid-low (n = 270) |
Mid-high (n = 219) |
High (n = 196) |
|
Height (cm) |
|
|
|
|
|
|
Male |
172.1 ± 0.7 |
172.1 ± 0.7 |
172.7 ± 0.7 |
172.8 ± 0.5 |
0.829 |
|
Female |
157.6 ± 0.6a
|
158.8 ± 0.5ab
|
160.5 ± 0.7b
|
159.7 ± 0.7b
|
0.021 |
|
Weight (kg) |
|
|
|
|
|
|
Male |
70.74 ± 1.30 |
72.52 ± 1.68 |
72.81 ± 1.11 |
73.74 ± 0.88 |
0.313 |
|
Female |
58.83 ± 1.01 |
59.85 ± 0.86 |
57.59 ± 1.07 |
57.53 ± 1.17 |
0.306 |
|
Waist circumference (cm) |
|
|
|
|
|
|
Male |
84.56 ± 1.11 |
84.74 ± 1.32 |
85.23 ± 0.83 |
85.76 ± 0.71 |
0.799 |
|
Female |
79.14 ± 1.30b
|
78.54 ± 0.92b
|
74.68 ± 1.04a
|
74.03 ± 1.09a
|
0.002 |
|
BMI (kg/m2) |
|
|
|
|
|
|
Male |
23.86 ± 0.37 |
24.40 ± 0.49 |
24.42 ± 0.34 |
24.67 ± 0.26 |
0.387 |
|
Female |
23.71 ± 0.42b
|
23.74 ± 0.34b
|
22.34 ± 0.42a
|
22.54 ± 0.40a
|
0.028 |
Dietary behaviors
Table 3 shows the eating behaviors by income level in single-adult households. While the frequency of breakfast, lunch, and dinner did not differ significantly by income, significant differences were observed in the frequency of eating out (p < 0.001), food insecurity (p < 0.001), and recognition of nutrition labels (p = 0.033). The frequency of eating out among single-adult households was higher for those with higher incomes, and higher income was associated with better dietary quality. Among single-adult households, those with ‘medium’ income showed the highest awareness of nutrition labels, while individuals with higher incomes generally exhibited greater recognition of nutrition labels.
Table 3Dietary behaviors by income level in single-adult households
Table 3
|
Variables |
Income level |
p value |
|
Low (n = 266) |
Mid-low (n = 270) |
Mid-high (n = 219) |
High (n = 196) |
|
Frequency of eating out |
|
|
|
|
< 0.001 |
|
≥ Twice/day |
20 (13.2) |
35 (15.9) |
52 (27.4) |
44 (27.9) |
|
Once/day |
32 (15.9) |
62 (24.2) |
64 (31.2) |
60 (32.5) |
|
5–6/wk |
23 (10.5) |
44 (17.0) |
42 (17.6) |
35 (15.8) |
|
3–4/wk |
34 (15.5) |
36 (17.0) |
23 (10.6) |
27 (12.9) |
|
1–2/wk |
72 (23.3) |
44 (14.1) |
18 (7.1) |
20 (6.8) |
|
1–3/mon |
58 (15.3) |
33 (7.6) |
17 (5.2) |
9 (3.9) |
|
Rarely |
27 (6.4) |
16 (4.2) |
3 (0.9) |
1 (0.2) |
|
Breakfast frequency |
|
|
|
|
0.430 |
|
5–7/wk |
135 (37.8) |
134 (38.4) |
82 (33.0) |
80 (35.2) |
|
3–4/wk |
31 (12.3) |
44 (19.2) |
31 (14.7) |
35 (18.2) |
|
1–2/wk |
38 (22.2) |
38 (16.3) |
32 (16.5) |
25 (15.4) |
|
Rarely |
62 (27.7) |
54 (26.2) |
74 (35.7) |
56 (31.2) |
|
Lunch frequency |
|
|
|
|
0.060 |
|
5–7/wk |
215 (82.2) |
239 (87.8) |
201 (92.1) |
173 (86.5) |
|
3–4/wk |
24 (9.6) |
15 (6.4) |
15 (6.8) |
17 (10.1) |
|
1–2/wk |
16 (4.5) |
8 (3.0) |
1 (0.2) |
4 (2.2) |
|
Rarely |
11 (3.7) |
8 (2.7) |
2 (0.9) |
2 (1.3) |
|
Dinner frequency |
|
|
|
|
0.506 |
|
5–7/wk |
235 (87.8) |
222 (80.9) |
187 (84.6) |
162 (81.4) |
|
3–4/wk |
26 (10.5) |
38 (15.3) |
27 (12.6) |
30 (16.5) |
|
0–2/wk |
5 (1.7) |
10 (3.7) |
5 (2.8) |
4 (2.1) |
|
Food insecurity |
|
|
|
|
< 0.001 |
|
Sufficient and varied |
79 (33.1) |
118 (46.7) |
117 (56.9) |
135 (69.4) |
|
Sufficient but not varied |
137 (50.2) |
144 (50.3) |
100 (42.3) |
61 (30.6) |
|
Not always enough |
50 (16.7) |
8 (2.9) |
2 (0.8) |
0 (0.0) |
|
Nutrition education experience |
|
|
|
|
0.215 |
|
Yes |
13 (3.5) |
7 (2.2) |
12 (5.8) |
8 (2.8) |
|
No |
253 (96.5) |
263 (97.8) |
207 (94.2) |
188 (97.2) |
|
Recognition of nutrition labels |
|
|
|
|
0.033 |
|
Yes |
200 (82.1) |
214 (82.8) |
198 (90.4) |
174 (88.8) |
|
No |
66 (17.9) |
56 (17.2) |
21 (9.6) |
22 (11.2) |
|
Use of nutrition labels |
|
|
|
|
0.421 |
|
Yes |
76 (33.3) |
80 (33.4) |
79 (38.6) |
60 (30.2) |
|
No |
190 (66.7) |
190 (66.6) |
140 (61.4) |
136 (69.8) |
|
Nutrients of interest |
|
|
|
|
0.411 |
|
Calorie |
36 (19.3) |
33 (14.6) |
39 (19.4) |
22 (11.4) |
|
Carbohydrate |
3 (0.8) |
4 (1.7) |
3 (1.7) |
1 (0.7) |
|
Sugars |
10 (4.0) |
11 (5.2) |
8 (3.3) |
6 (2.7) |
|
Protein |
5 (3.1) |
8 (2.8) |
4 (1.7) |
3 (1.3) |
|
Fat |
2 (0.3) |
1 (0.3) |
3 (0.9) |
3 (1.4) |
|
Saturated fat |
3 (1.6) |
0 (0.0) |
1 (0.7) |
1 (0.5) |
|
Trans fat |
4 (1.5) |
6 (2.1) |
7 (3.1) |
6 (3.0) |
|
Cholesterol |
6 (1.1) |
8 (2.6) |
2 (0.6) |
7 (3.1) |
|
Sodium |
6 (1.4) |
9 (4.2) |
11 (6.5) |
10 (5.6) |
|
Others |
1 (0.1) |
0 (0.0) |
1 (0.7) |
1 (0.5) |
Health behaviors
The health behaviors by income level in single-adult households are shown in
Table 4. Significant differences were found in alcohol consumption (p < 0.001), smoking (p = 0.021), health checkup (p < 0.001), anxiety and depression (p < 0.001), and dietary supplement use (p = 0.040) based on income level. The prevalence of drinking alcohol ‘more than once a week’ was higher among individuals with higher incomes: 21.0% in the low-income group, 25.6% in the mid-low-income group, 27.3% in the mid-high-income group, and 41.4% in the high-income group. The proportion of smokers also increased with income: 34.7% in the low-income group, 33.3% in the mid-low-income group, 35.2% in the mid-high-income group, and 42.1% in the high-income group, while the proportion of non-smokers decreased with income. Health checkup attendance rates were higher among those with greater incomes: 48.3% in the low-income group, 57.2% in the mid-low-income group, 71.0% in the mid-high-income group, and 74.0% in the high-income group. The use of dietary supplements was lowest in the low-income group at 42.5%, compared to 55.1% in the mid-low-income group, 48.8% in the mid-high-income group, and 53.9% in the high-income group. While there were no significant differences in stress levels by income, anxiety and depression were less common in higher income groups: 19.5% in the low-income group, 9.7% in the mid-low-income group, 6.5% in the mid-high-income group, and 4.1% in the high-income group.
Table 4Health behaviors by income level in single-adult households
Table 4
|
Variables |
Income level |
p value |
|
Low (n = 266) |
Mid-low (n = 270) |
Mid-high (n = 219) |
High (n = 196) |
|
Alcohol drinking |
|
|
|
|
< 0.001 |
|
> Once/wk |
55 (21.0) |
64 (25.6) |
51 (27.3) |
67 (41.4) |
|
≤ Once/wk |
131 (55.8) |
156 (60.2) |
135 (61.4) |
103 (50.7) |
|
None |
80 (23.1) |
50 (14.2) |
33 (11.3) |
26 (7.9) |
|
Smoking |
|
|
|
|
0.021 |
|
Smokers |
92 (34.7) |
77 (33.3) |
68 (35.2) |
72 (42.1) |
|
Ex-smokers |
35 (12.0) |
42 (15.2) |
30 (13.5) |
40 (22.0) |
|
Non-smokers |
139 (53.3) |
151 (51.4) |
121 (51.4) |
84 (35.9) |
|
Health checkup |
|
|
|
|
< 0.001 |
|
Yes |
137 (48.3) |
173 (57.2) |
163 (71.0) |
151 (74.0) |
|
No |
129 (51.7) |
97 (42.8) |
56 (29.0) |
45 (26.0) |
|
Stress |
|
|
|
|
0.924 |
|
Very much |
19 (6.4) |
13 (5.6) |
14 (8.1) |
10 (5.5) |
|
A lot |
69 (25.5) |
66 (28.0) |
60 (27.8) |
42 (22.2) |
|
A little |
148 (56.1) |
156 (55.1) |
119 (54.5) |
119 (61.3) |
|
Not at all |
30 (11.9) |
35 (11.4) |
26 (9.6) |
25 (11.1) |
|
Anxiety and depression |
|
|
|
|
< 0.001 |
|
Yes |
61 (19.5) |
28 (9.7) |
17 (6.5) |
11 (4.1) |
|
No |
205 (80.5) |
242 (90.3) |
202 (93.5) |
185 (95.9) |
|
Dietary supplement |
|
|
|
|
0.040 |
|
Yes |
122 (42.5) |
155 (55.1) |
115 (48.8) |
114 (53.9) |
|
No |
144 (57.5) |
115 (44.9) |
104 (51.2) |
82 (46.1) |
Prevalence of chronic diseases
Table 5 shows the prevalence of chronic diseases by income level in single-adult households. Hypertension (p < 0.001), diabetes (p < 0.001), and dyslipidemia (p < 0.001) exhibited significant differences across income levels. The prevalence of hypertension was 19.0% in the low-income group, 14.6% in the mid-low-income group, 4.6% in the mid-high-income group, and 12.8% in the high-income group. The prevalence of diabetes was 10.0% in the low-income group, 6.3% in the mid-low-income group, 0.5% in the mid-high-income group, and 3.1% in the high-income group. The prevalence of dyslipidemia was 16.6% in the low-income group, 10.7% in the mid-low-income group, 3.9% in the mid-high-income group, and 5.8% in the high-income group. The prevalence of hypertension, diabetes, and dyslipidemia was highest in the low-income group and lowest in the mid-high-income group.
Table 5Prevalence of chronic diseases by income level in single-adult households
Table 5
|
Variables |
Income level |
p value |
|
Low (n = 266) |
Mid-low (n = 270) |
Mid-high (n = 219) |
High (n = 196) |
|
Obesity |
|
|
|
|
0.207 |
|
Under weight |
20 (6.7) |
12 (5.6) |
11 (6.9) |
2 (1.0) |
|
Normal |
162 (63.9) |
175 (61.9) |
143 (61.8) |
126 (63.3) |
|
Obesity |
84 (29.4) |
83 (32.4) |
65 (31.3) |
68 (35.7) |
|
Hypertension |
|
|
|
|
< 0.001 |
|
Yes |
59 (19.0) |
51 (14.6) |
16 (4.6) |
29 (12.8) |
|
No |
207 (81.0) |
219 (85.4) |
203 (95.4) |
167 (87.2) |
|
Diabetes |
|
|
|
|
< 0.001 |
|
Yes |
33 (10.0) |
24 (6.3) |
3 (0.5) |
7 (3.1) |
|
No |
233 (90.0) |
246 (93.7) |
216 (99.5) |
189 (96.9) |
|
Dyslipidemia |
|
|
|
|
< 0.001 |
|
Yes |
58 (16.6) |
39 (10.7) |
14 (3.9) |
17 (5.8) |
|
No |
208 (83.4) |
231 (89.3) |
205 (96.1) |
179 (94.2) |
|
Atopy dermatitis |
|
|
|
|
0.294 |
|
Yes |
9 (4.2) |
3 (1.1) |
7 (4.0) |
3 (2.2) |
|
No |
257 (95.8) |
267 (98.9) |
212 (96.0) |
193 (97.8) |
|
Allergic rhinitis |
|
|
|
|
0.159 |
|
Yes |
43 (20.9) |
40 (15.6) |
26 (11.7) |
31 (15.5) |
|
No |
223 (79.1) |
230 (84.4) |
193 (88.3) |
165 (84.5) |
DISCUSSION
This study utilized data from the 7th KNHANES (2016–2018) to identify differences in eating behaviors, health behaviors, and the prevalence of chronic diseases by income group among 951 adult single-adult households aged 19–64 years. When comparing physical characteristics, the study found no significant differences by income level for men. However, for women, higher income was associated with greater height, while lower income was associated with higher BMI and waist circumference. A previous study [
8], which analyzed women aged 20 years and older by income group, found that height was greater in the highest income group, and waist circumference was significantly smaller in the highest income group compared to other income groups. These findings can be interpreted in conjunction with previous research indicating that low-income populations tend to have higher carbohydrate-dependent diets, lower rates of physical activity, and more frequent salty eating behaviors [
8].
Although there were no significant differences in breakfast skipping rates by income level in this study, breakfast skipping rates were high across all groups. This finding is similar to the results of a previous study [
14], which examined the eating behavior of single-adult households in metropolitan areas. That study found that the proportion of people who regularly eat three meals a day was low, breakfast skipping was frequent, and the main reasons for skipping breakfast were lack of time and inconvenience. This study also examined differences in eating behavior characteristics by income level among single-adult households and found that higher incomes were associated with more frequent dining out and better dietary status. The result on higher frequency of dining out in higher income group may be associated with spending more time at work. Kang and Jung [
15] reported that single-adult households in the upper-middle-income group had a higher breakfast skipping rate compared to multi-person households, and that higher income was linked to a greater frequency of eating out. Similarly, Heo and Sim [
14] found that single-adult households in metropolitan areas often ate out two to three times a day. Lee [
16] suggested that in order to mitigate the negative effects of a diet centered around eating out, which is often high in sugar, fat, and sodium, it is necessary to encourage the restaurant industry to offer balanced meals, implement a nutrition labeling system, and enhance nutrition education to improve consumers’ ability to make healthier eating choices. Previous studies have also shown that the rate of nutrition label checking is higher in multi-person households compared to single-adult households with the lowest income level. For single-adult households, the rate of checking nutrition labels is lower, but it increases with income level [
15]. This is consistent with our finding, which indicated that the higher the income of single-adult households, the greater their recognition and interest in nutrition labels. Based on these results, for individuals with higher incomes, education on making healthier eating-out choices is necessary, while for those with lower incomes, education on the importance of checking nutrition labels would be valuable.
In this study, higher incomes of single-adult households were associated with significantly higher rates of drinking and smoking, which is consistent with previous study [
9]. That study found that the prevalence of high-risk drinking among women aged 35 years and older was lowest in the lower-income group and higher in the higher-income group. Additionally, among older men, the prevalence of not drinking was highest in the lower-income group, while the prevalence of drinking tended to be higher in the higher-income group [
17]. Regarding smoking, a study of adults aged 50 years and older [
18] found that the number of cigarettes smoked per day was higher in the high-income group than in the low-income group, and the number of alcoholic drinks consumed per day was also higher in the high-income group. This suggests significant differences in drinking and smoking rates by income level, similar to the results of this study. A related study [
2] reported that policies to prevent and reduce smoking and alcohol consumption are being implemented at the national level, including regulation through significant price increases and restrictions on advertising. This study suggests that high-income groups may not be affected by these economic factors, and that the prevalence of drinking and smoking is higher among high-income groups due to cultural preferences for these behaviors. Therefore, it is necessary to develop prevention policies and education programs to change the perceptions of not only low-income groups, who are economically vulnerable, but also high-income groups.
In our study, higher income levels in single-adult households were associated with higher health checkup rates, which is similar to the results of Lim [
19], who found that higher income was associated with higher cancer screening rates. Another previous study [
9] also found that higher income levels were associated with higher rates of health checkups and cancer screening among women aged 35 and older. This indicates the need for various policies to ensure that low-income groups, which are vulnerable, can actively engage in health-related behaviors and disease prevention without being negatively impacted by socioeconomic factors. A previous study [
20] that examined the mental health of single-adult households found significant differences in depression and suicidal ideation by income level. Depression was more pronounced in the low-income group, and similar results were observed in this study, with higher levels of anxiety and depression among the low-income group.
This study compared the prevalence of chronic diseases by income and found that hypertension, diabetes, and dyslipidemia had a lower prevalence in the high-income group and a higher prevalence in the low-income group. This is consistent with a previous study by Oh et al. [
9], which found that the prevalence of chronic diseases decreased as income increased among women aged 35 and older. These results suggest that the higher prevalence of chronic diseases in low-income groups may be due to limited access to systematic healthcare and persistently unstable lifestyles. They also highlight the need for national-level management and support for economically vulnerable groups to prevent these conditions.
This study has the following limitations. First, since the study is cross-sectional, it limits the ability to establish clear causal relationships between dietary health behaviors and income level. Second, the study was limited to single-adult households aged 19 to 64 and only considered differences in income level, so variations by sex or age were not examined. Therefore, future studies should account for these factors and conduct various analyses, such as regression analysis, to explore the relationship between dietary habits, health status, and income level. Despite these limitations, this study provides significant information that dietary and health behaviors by income level among single-adult households are potentially associated with chronic diseases.
CONCLUSION
The study found that higher-income single-adult households ate out more frequently, had better diets, and were more likely to recognize nutrition labels. Alcohol consumption and smoking were higher in the high-income group, while anxiety and depression were more prevalent in the low-income group. Additionally, the use of dietary supplements was lower in the low-income group. Regarding chronic diseases, the prevalence of hypertension, diabetes, and dyslipidemia was lowest in the mid-high-income group and highest in the low-income group. These results suggest that diet and health behaviors vary by income level among single-adult households and may be associated with chronic diseases. Future systematic studies should be conducted to determine the causal relationships between these factors.
NOTES
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Conflict of Interest: The authors declare that they have no competing interests.
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Author Contributions:
Conceptualization: Jung BM, Choi MK.
Formal analysis: Han MH, Jung BM, Choi MK.
Investigation: Han MH.
Methodology: Han MH, Jung BM.
Writing - original draft: Jung BM, Choi MK.
Writing - review & editing: Han MH, Jung BM, Choi MK.
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