How Does the Frequency of Eating-Alone among Older People in Korea Affect Their Health and Dietary Behavior?
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National Institute of Agricultural Sciences, 166 Nongsaengmyeong-ro, Wanju 55365, Republic of Korea
2
Department of Food Science and Nutrition, Dongseo University, Busan 47011, Republic of Korea
3
Department of Medical Nutrition, Graduate School of East-West Medical Science, Kyung Hee University, Yongin 17104, Republic of Korea
4
Nutrition Care Services, Seoul National University of Bundang Hospital, Seongnam 13620, Republic of Korea
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Nutrients 2023, 15(9), 2085; https://doi.org/10.3390/nu15092085
Submission received: 4 March 2023
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Revised: 20 April 2023
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Accepted: 22 April 2023
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Published: 26 April 2023
(This article belongs to the Section Geriatric Nutrition)
Abstract
:This study examined the relationship between general population characteristics and diet-related factors pertaining to eating alone for older adults (65 years and older) in Korea. This study used the Korea National Health and Nutrition Examination Survey (KNHANES), 2016–2020, and the target population was 7037 Koreans aged 65 years or older who participated in the nutritional survey and health interview. Eating alone variables were classified as follows. Eating together all day means “eating together”, eating only one meal a day means “1/day”, eating two meals a day alone means “2/day”, and “3/day” means eating three meals a day alone. The main results are as follows. The rate of moderate or severe food insecurity was 3.41% in the “eating together” group to 7.86% in the “3/day” group, which was 4.45% higher in the “3/day” group. Fruit + vegetable intake among food intake lowered by about 35 g from 301.2 g in the “eating together” group to 266.2 g in the “3 day” group. In addition, as a result of analyzing the prevalence of depression using the PHQ-9 score, the “3/day” group had a 1.775 to 2.464 times higher risk of depression than the “eating together” group. Finally, EQ-5D variables and quality of life scores were significantly lowered from the “eating together” group to the “3/day” group. Overall, higher frequency of eating alone was associated with food safety, essential food intake, and quality of life. Based on these results, it is thought that a dietary life support program such as the eating together program is necessary to improve the quality of life of the older people who eat alone.
1. Introduction
The number of people who eat alone is increasing in Korea. In the past, Korean culture was characterized as collective, with eating together as a social norm. The eating-alone habit has emerged as a typical eating pattern in modern Korean society and even a new acronym, “Hon-Bab”, has been created to describe it [1,2,3,4]. The increase in the age of first marriage and the prevalence of single-person households due to the aging population due to a change in the population’s age structure are the main reasons for the increase in eating alone. This change can be seen as a cultural phenomenon of a modernized society emphasizing efficiency, even in how effectively people use break time, or as a social structure change toward individualization [5,6,7,8]. According to the Statistics Korea report, single-person households comprised 20.0% of the entire population in 2005, 27.2% in 2015, and 32.4% in 2021, rapidly increasing over the last 16 years [9]; it mentioned that eating alone occurs in single-person households or among people in their 20s and 30s. However, eating alone now is becoming more common in people of all ages and generations, not just the younger generation [3,4,10]. The disadvantages of eating alone include a lack of intake of various nutrients and food groups, as fast foods (e.g., ramen, bread, gimbap, and sandwiches) are generally preferred [11,12,13]. According to previous research, eating alone leads to insufficient protein and calcium intake and is associated with an excess intake of carbohydrates and sodium, leading to nutritional imbalance. The risks associated with eating alone for obesity, hypertension, and metabolic syndrome are high, increasing concerns over eating habits, health, and nutrition management [14,15,16].
It has been reported to cause adverse effects on diet quality and eating habits, particularly in older people, due to the high frequency of eating alone, low dietary diversity, high meal-skipping rates, and inadequate consumption of various nutrients and food categories [17,18,19,20,21,22]. It was also found that cognitive function was reduced significantly and nutritional status was poorer for the older adults who ate alone compared to those who ate together; eating alone was a significant risk factor for high levels of social isolation and depression symptoms. [17,20,23,24,25,26,27,28]. Additionally, eating alone was associated with higher mortality risk and feeling unhappy, resulting in lower quality of life and the increased likelihood of multiple comorbidities [17,21,29,30,31]. Based on the results of previous studies reporting the risk factors associated with eating alone, it seems to affect both physical and mental health negatively.
Previous studies about eating alone conducted in Korea did not focus on older adults. For example, the topics of these studies were “the comparison of perceptions about eating alone and eating behaviors between male and female university students”, “the relationship between nutrient intake and metabolic syndrome for middle-aged adults who eat alone”, “comparison between eating with family and eating alone in adolescents”, and “the effects of eating alone or eating together with the family on pediatric mental and physical health.” Additionally, a study on older individuals that investigated the association between eating alone and depression in older Korean females did not examine the frequency of eating alone as an eating behavior in relation to health-related indices [12,32,33,34,35,36]. Thus, the purpose of this study is to determine the frequency of eating alone among older adults aged 65 years or older [12,32,33,34,35,36]. In addition, we aim to describe the general characteristics of this population and then identify individual eating habits. Further, we intend to learn how food and nutrient intake affect depression and quality of life. We hope that the results of this study, obtained by identifying the relationship between the frequency of eating alone as an eating pattern and mental health risk factors, will help older adults maintain an appropriate diet and manage their health status in preparation for the hyper-aging population.
2. Materials and Methods
2.1. Research Data
This study was a sub-study of the Korea National Health and Nutrition Examination Survey (KNHANES) conducted in 2016 and 2020. The KNHANES administered by the Korean Ministry of Health and Welfare and a stratified multistage probability design were used with subject selection from sampling units and using household registries. KNHANES is a nationwide, population-based, cross-sectional study aimed at the health and nutrition status of the noninstitutionalized civilian Korean population. In addition, it provides basic data for health policies such as improvement of people’s nutrition, disease prevention, and development of health promotion programs [37]. The KNHANES consists of health interviews, health examinations, and nutritional surveys, among which the nutritional survey aims to understand the food and nutritional intake and dietary habits of Koreans.
In this study, a 24-h recall method was used as a nutrition survey. Food and nutrient intake and dietary behavior were investigated and smoking, alcohol consumption, exercise, mental health, and life quality were examined in a health survey [37].
2.2. Subjects
This study selected older people over 65 years old who participated in the nutritional survey and health interview of the KNHANES 2016–2020. Some of the subjects who consumed less than 500 kcal or more than 5000 kcal (112 people) were excluded. Outlier data on subjects who did not participate in the dietary survey (24-h recall survey) were also excluded (1254 people). A total of 7037 people were selected in the study (Figure 1). The KNHANES data used in this study were approved by the KCDA (Korea Disease Control and Prevention Agency) Institutional Review Board (IRB approval numbers: 2018-01-03-P-A, 2018-01-03-C-A, and 2018-01-03-2C-A). Among these, the 2016 and 2017 KNHANES were exempt from review regarding research ethics based on the Bioethics and Safety Act from 2015 to 2017 [38].
2.3. Meal Pattern According to the Frequency of Eating Alone
In this study, we used the variables recorded in the “24-h recall to measure the eating alone variable.” First, respondents who consumed more than two daily meals were targeted. The variables “meals with other people” (variable name: N_meal_w) and “number of meals” (variable name: N_meal) were used. “Eating together group” consisted of people who had all daily meals—namely breakfast, lunch, and dinner—with their family members or others throughout the day; “1/day eating alone group” for those who ate alone only once a day; “2/day eating alone group” for those who ate alone twice a day, and “3/day eating alone group” for those who ate alone thrice a day.
2.4. General Characteristics
General characteristics of the subjects, such as gender, age, residential area, marital status, household number, education level, household income, and job status were analyzed. Among these characteristics, marital status was classified as single or married, residential area was classified as urban or rural area, and household number was classified as “1 person”, “2 people”, “3 people”, “4 people”, “5 people” and “over 6 people”. The education levels were classified as “Middle school or less”, “High school or less” and “College or more”. Household income level was classified using the household income level variables in KNHANES and job status was classified as employed or unemployed.
2.5. Health Related Characteristics
For health behavior, smoking, drinking, stress, exercise, and obesity status were analyzed. Obese status was classified as underweight (BMI < 18.5 kg/m2), normal (18.5 kg/m2 ≤ BMI < 23.0 kg/m2), overweight (23.0 kg/m2 ≤ BMI < 25.0 kg/m2), and obese (BMI ≥ 25 kg/m2).
2.6. Dietary Behavior
Dietary behaviors were analyzed for snack consumption, food security, and eating-out frequency. For snack intake, using the meal-related variables in the 24-h recall data, those who chose snacks were classified as “yes” and those who did not were classified as “no”. Food security was determined by using variables of dietary life and classified by referring to previous studies [39,40].
2.7. Food and Nutrient Intake
Food intake was categorized into 17 food groups using food classification codes and food intake in the food (24-h recall) data; individual intake was obtained for each survey subject. In addition, energy, carbohydrate, protein, fat, Ca, P, Fe, Na, K, vitamin A, carotene, retinol, thiamine, riboflavin, niacin, and vitamin C intake and contribution ratios were calculated using the daily intake for each nutrient.
2.8. PHQ-9
The Patient Health Questionnaire-9 (PHQ-9) was used in the National Health and Nutrition Examination Survey to measure depression and the questionnaire consisted of nine items. For each question, subjects were instructed to choose one of: “none” (0 points), “several days” (1 point)”, “more than a week” (2 points), or “almost every day” (3 points) and score all of the corresponding questions. The total score was calculated by adding them up. A higher PHQ-9 score means more depressive symptoms.
2.9. EQ-5D
Quality of life was measured using the Euro Quality of Life five-dimension (EQ-5D) tool. EQ-5D is a five-item measurement tool that can be easily used to determine the level of physical health, mental health, and social health. The items are composed of motor ability, self-management, daily activities, pain/inconvenience, and anxiety/depression and are evaluated as “no problem”, “some problem”, and “severe problem”, respectively. The score is calculated by applying a weight to each item value and the value range is 162 between −1 point and 1 point, and the lower the score, the lower the quality of life [36].
2.10. Statistical Analysis
The KNHANES data was obtained by stratified multi-stage sampling rather than a simple random sampling method. The collected data were analyzed with a consideration of weight, strata variable (KSTRATA), and cluster variable (Primary Sampling Unit, PSU). Among them, general matters, dietary factors, and categorical variables such as health-related factors, frequency analysis (frequency analysis) was performed to determine the frequency (n) and the weighted percentage (weighted %) and the significance test was a chi-square test. For qualitative variables, descriptive analysis was performed and the mean and standard error were expressed. For the significance test of these continuous variables, the p for trend value was obtained using the SURVEYREG procedure. Among them, in the case of nutrients and food intake, the adjusted p for trend value was obtained by correcting for age and energy intake. In addition, multiple logistic regression analysis was conducted to analyze the relationship between the prevalence of eating alone and the prevalence of depression. Multiple regression analysis was conducted to determine whether the prevalence of eating alone was related to quality of life. All statistical analyzes were performed using the SAS ver. 9.4 package (SAS Institute, Cary, NC, USA), and the significance level was set at α = 0.05.
3. Results
3.1. General Characteristics
Table 1 presents the general characteristics of the target population. According to the results of the 24-h recall survey, the proportion of males was higher than that of females in the “eating together” group, while the proportions of females were higher in the “1/day eating alone”, “2/day eating alone”, and “3/day eating alone” groups (p < 0.0001). The proportion of females and the mean age increased in proportion to the frequency of eating alone. The mean ages for the “eating together” group and the “3/day eating alone” group were 72.82 and 74.19 years old, respectively (p < 0.0001). Regarding the area of residence, more subjects in the “3/day eating alone” group lived in “urban” rather than “rural” areas, compared to those in the “eating together” group (p < 0.0001). The “household number” of subjects, which is the proportion of families consisting of two members, was the highest in the “eating together” and “1/day eating alone” groups. In contrast, the proportions of the families consisting of one and two members were similar to each other in the “2/day eating alone” group and that of one member was the highest (55.06%) in the “2/day eating alone” group (p < 0.0001). Regarding education level, “middle school or less” was the highest and the higher the frequency of eating alone, the lower the level of education. As for household income, the proportion of “low” was high in the “3/day eating alone” group and for “job status”, the proportion of “3/day eating alone” was higher in the “unemployed” group than in the “employed” group (p < 0.05).
3.2. Meal Pattern According to the Frequency of Eating Alone
Table 2 shows meal patterns according to the frequency of eating alone. Significant differences were found in all results (p < 0.001). Of all subjects aged 65 or older, 42.37% ate all three meals daily with other people. The higher the frequency of eating alone, the lower the proportion of subjects: 23.58% for “1/day eating alone”, 20.10% for “2/day eating alone”, and 13.95% for “3/day eating alone.” Most in the “eating together group” were found to eat with “family” and “family and other people.” In the “1/day eating alone” group, the “family + alone” rate was the highest; in the “2/day eating alone” group, “only alone” was 24.92%, “family + alone” was 39.31%, and “other people + alone” was 35.77%. The results from analyzing eating alone during the day in the “1/day eating alone” group “Breakfast (B)” was 40.28%, “Lunch (L)” was 38.99%, and “Dinner (D)” was 20.73%, demonstrating that eating alone was higher for breakfast and lunch than dinner. In the “2/day eating alone” group, the “B + D” rate was the highest (47.23%), followed by “B + L” (33.30%), and “L + D” was the least (19.47%), showing that eating alone was the most frequent for breakfast. For “Cooking place for a solo meal”, for all of the “1/day eating alone”, “2/day eating alone”, and “3/day eating alone” groups, “only home (H)” was the most frequent, followed by “home + commercial (H + C)”, showing that older adults tend to prepare food at home to eat alone.
3.3. Health-Related Characteristics According to the Frequency of Eating Alone
Table 3 presents the subjects’ health-related factors. The proportion of “non-smokers” was the highest in all groups (p < 0.0001), whereas drinking “<1/year” had the highest proportion (p < 0.0001). Additionally, the higher the frequency of eating alone, the lower the frequency of drinking. For stress, the subjects who reported a relatively higher number of meals eaten alone were likely to answer “feel it very much” (p < 0.0001). However, there was no significant difference between the exercise group and the obese group.
3.4. Dietary Behavior According to the Frequency of Eating Alone
Table 4 presents the subjects’ dietary behavior based on the frequency of eating alone. Snack intake rate was lower when eating alone more frequently (p < 0.01). In the “food security” variable, the ratio of “Enough food—secure” decreased as the frequency of eating alone increased. In the “eating together” and “1/day eating alone” groups, the proportion of “enough food—secure” was high, measured by “all of our family could eat enough food and a variety of foods as much as they wanted.” In contrast, in the “2/day eating alone” and “3/day eating alone” groups, the mild food insecurity rate was high, measured by “all of our family could eat enough food but could not eat various kinds of food.” Regarding eating out, a higher frequency of eating alone was associated with eating out more frequently. The “3/day eating alone” group showed the highest proportion of rarely eating out (p < 0.0001).
3.5. Food Intake in Their Meal According to Consumption Frequency of Eating Alone
Table 5 presents the results from analyzing the subject’s food intake depending on the frequency of eating alone. The results show that the higher the frequency of eating alone, the lower the intake of “total food”, “Fruit”, “Meat, poultry, and their products”, and “Fish and shellfish”, and the greater the intake of “cereal and grain products” and “legumes and their products.” When adjusted for age, gender, and energy intake, the intake of “total food”, “vegetables”, “fruit”, “meat, poultry, and their products”, “seaweed”, and “seasonings” was significantly reduced (adjusted p for trend < 0.05).
3.6. Nutrient Intake Depending on the Consumption Frequency of Eating Alone
Table 6 presents the results of analyzing the subjects’ nutrient intake. After adjusting for gender, age, and energy intake (adjusted p for trend < 0.0001), the higher the frequency of eating alone, the lower the intake of protein, sodium, potassium, vitamin A, carotene, and niacin. Additionally, the rate of carbohydrate intake increased as the number of people eating alone increased, while the intake of protein and fat was significantly reduced.
3.7. Depression-Related Survey Results
Table 7 presents the results of evaluating the subject’s depression using PHQ-9. The total scores for the “3/day eating alone”, “2/day eating alone”, “1/day eating alone”, and “eating together” groups were 2.40, 2.06, 1.80, and 1.56, respectively, indicating that the more frequently people ate alone, the more depressed they felt. Looking at the prevalence of depression against the frequency of eating alone, the numbers for the “3/day eating alone”, “2/day eating alone”, “1/day eating alone”, and “eating together” groups were 6.16%, 3.89%, 3.35%, and 2.70%, respectively, indicating that a higher frequency of eating alone was linked to a higher prevalence of depression.
3.8. Association between the Prevalence of Depression and the Frequency of Eating Alone
The results of the logistic regression analysis to identify the relationship between the frequency of eating alone and depression are provided in Table 8. Using the “eating together” group as a reference, the unadjusted Model 1 revealed a trend of increasing prevalence of depression as the frequency of eating alone increased (p for trend d < 0.0001). Model 2, adjusted for gender, age, and energy intake, showed a significant increase in the prevalence of depression regardless of gender, age, or energy intake (p for trend < 0.01). However, statistical significance did not exist in Model 3, which was adjusted additionally for exercise, alcohol consumption, stress status, smoking, and obesity status. Model 4 was adjusted for food security, snacking, the frequency of eating out in a week, the frequency of having breakfast in a week, marital status, occupation, education level, and household income.
3.9. Quality of Life Survey Results
Table 9 shows the results of using EQ-5D to examine the subjects’ quality of life. The quality of life in the “3/day eating alone” group was significantly lower than the “eating together”, “1/day eating alone”, and “2/day eating alone” groups in all of the “mobility” (p < 0.001), “self-management” (p < 0.05), “daily activities” (p < 0.0001), “pain/inconvenience” (p < 0.05), and “anxiety/depression” (p < 0.01) items.
3.10. Relationship between the Frequency of Eating Alone and Quality of Life
Table 10 presents the results of the relationship between the frequency of eating alone and quality of life. Using the “eating together” group as a reference, the unadjusted Model 1 showed significantly decreasing quality of life as the frequency of eating alone increased. In Models 2 and 3, with stepwise adjustment for age, gender, energy intake, family size, exercise, alcohol consumption, stress status, smoking, obesity status, food security, snacking, and the frequency of eating out per week, quality of life tended to decrease as the frequency of eating alone increased (p for trend < 0.05). In contrast, no significant difference was found in Model 4, which was adjusted additionally for food security, snacking, eating out frequency per week, marital status, occupation, education level, and household income.
4. Discussion
The “eating alone” population is increasing due to the increase in single-person households and other relevant societal changes, including individualization. Additionally, such a phenomenon is not merely due to personal preferences but also reflects social, structural, and cultural changes [24]. Several previous studies have reported that eating alone negatively influences health for various reasons without clarifying the reasons behind its increase [18,24,41,42,43]. Reports suggest that a lack of diverse nutrient and food intake should be associated with increased levels of social isolation, depression, and decreased quality of life [24,44]. This study identified 7037 older adults from the 2016–2020 KNHANES database and examined their health, diet, food and nutrient intakes, depression, and quality of life that depended on the frequency of eating alone.
Previous studies about eating alone using the KNHANES dataset defined eating alone based on respondents answering “no” to the dietary life survey question about eating meals (i.e., breakfast, lunch, and dinner) together with family members and other people. In contrast, this study analyzed more objectively the frequency of eating alone and the presence or absence of eating together using the 24-h dietary recall method. We found a gender difference in the “eating alone” condition. The proportion of men was higher than that of women only in the “eating together” group, whereas the proportion of women was higher in all other “eating alone” groups. In a previous study on Korean adults aged 19 years or older, the male ratio was higher than that of females in the “eating together” and “1/day eating alone” groups. Compared to the higher proportion of women in the other “eating alone” groups, i.e., eating alone for two or three meals per day, we found that the frequency of eating alone was higher among women than among men in older adults. The average ages of subjects in the “eating together” and “eating alone” groups were 72.82 and 74.19 years, respectively. Thus, the higher the frequency of eating alone, the higher the age. In research involving adults and eating alone, the average age of those who ate alone for one meal per day and three meals per day was 45.3 and 54.7 years, respectively [44]. Previous studies reported that 7% or 12.2% of Korean adults eat three meals alone a day, whereas our study targeted older adults and found that those eating all three meals alone per day (3/day eating alone) were as high as 13.95%, higher than the adult group [45,46]. We also analyzed the data after classifying the subjects into “65–74 years old” and “≥75 years old” groups. The rate at which the frequency of eating alone increased gradually decreased in the “65–74 years of age” group, while for the “≥75 years old or older” group, “eating alone once a day”, “twice a day”, and ”thrice a day” were 35.07%, 43.69%, and 50.3%, respectively. Thus, the older respondents were, the more often they ate a daily meal alone.
The results of analyzing the frequency of eating alone (a solo daily meal) among subjects in this study show that for those who have one meal alone per day, the solo meal was breakfast, lunch, or dinner 40.28%, 38.99%, and 20.73% of the time, respectively. Thus, the rate of eating alone is higher for breakfast or lunch than for dinner, which is consistent with the results of a study with Korean adults [46]. Additionally, most subjects answered “two” regarding family size. Older adults were more likely to eat together with family members than other people who were not family members. The highest rate of eating all three meals alone daily was 55.06% for those with a single-member family. This indicates that older adults eating alone are more significantly affected by the number of family members. For older adults who live alone, the frequency of eating alone will likely increase, leaving them deficient in healthy eating and nutrient consumption. Thus, interventions must be taken to prevent imbalanced meals and maintain correct dietary habits.
In the “cooking place of solo meal” survey item for the subjects of this study, the ratio of “only at home” was 65.82% in all eating alone groups regardless of the frequency of eating alone, meaning that most of the solo meals were prepared at home, which is higher than that for adults. Thus, providing services such as nutrition management that focus on homemade, balanced and healthy meals may be beneficial for older adults who eat alone.
Looking at the subject’s “food security”, the rates of “enough food security” (i.e., all members within the family could eat a sufficient quantity and variety of foods) were higher in the “eating together” and “1/day eating alone” groups. In contrast, the proportion of “mild food insecurity” (i.e., everyone in the family ate enough food, but not a good variety) was high in the “eating alone two meals a day” and “eating alone three meals” groups. This indicates that the higher the frequency of eating alone, the lower the food security. This can be interpreted to mean that the frequency of eating alone is higher in lower-income households.
Furthermore, the more frequently people eat alone, the less frequently they eat out. Snack intake also decreased as the frequency of eating alone increased. Thus, the number of meals eaten alone was related to total food intake, food safety, snack intake, and the frequency of eating out.
Looking at the consumption of food groups according to the frequency of eating alone, food groups such as vegetables, fruit, meat, poultry, seaweed, and seasoning showed a significantly lower intake as the frequency of eating alone increased (p for trend < 0.05). The total intake of “fruit” and “vegetables” in the “eating together” group was 301.2 g, while it was 304.2 g, 250.1 g, and 266.2 g in the “eating alone” groups, i.e., one meal a day, two meals a day, and three meals a day, respectively. Thus, in all groups, the consumption was less than 400 g, which the World Health Organization (WHO) and the World Cancer Research Fund (WCRF) recommend. This is consistent with the results from previous studies on middle-aged adults eating alone that found the frequency of eating alone increased as the frequency of vegetable and fruit intake decreased. It is also consistent with the results from studies on older Japanese adults eating alone that found the consumption of green and yellow vegetables and fruits was lower in the older adults eating alone than that in older adults eating together with others [18,19,20,32,45]. This means that people who eat alone consume relatively fewer vegetables and fruit and overall fruit and vegetable intake was low for older adults in all groups. In particular, the consumption of fresh fruit and vegetables was insufficient to a greater degree in older adults who ate alone more frequently, indicating that this population needs a varied diet that includes fresh fruit and vegetables, new recipes, and the development of food products, and education emphasizing the intake of fruit and vegetables to maintain a healthy and balanced life [46].
Food groups were affected by the frequency of eating alone. The intake of “meat, poultry, and their products” was significantly lower as the frequency of eating alone increased. A previous study of Korean adults eating alone also reported a lower intake of the same food group. In a study of middle-aged men, the more men ate alone, the less calcium and protein-based energy they consumed, while their fat intake increased. Therefore, “meat, poultry, and their products” have become a common food group, of which both adults and older people ate less when they ate alone more frequently [32,47]. This explains the result of significantly reduced protein intake concurrent with gradually increasing carbohydrate intake for an increasing number of people eating alone. Our study targeted older adults and showed that “meat, poultry, and their products” intake was significantly reduced, pointing towards lower protein intake. Thus, we learned that protein intake could be lowered as the number of people eating alone increases, regardless of age.
We examined the relationship between the number of people eating alone and the stress subjects felt. The higher the frequency of eating alone, the more significant the proportion of “feel it very much” responses. This indicates that a high frequency of eating alone might be related to older adult stress. Examining the connection between depression and the frequency of eating alone in models adjusted for gender, age, and energy intake, we found that depression was significantly higher in the “eating three meals a day alone” group. No significant difference was found when the model was adjusted for other variables. However, previous studies investigating depression revealed a relationship between dinner accompanied by other people and depression in Korean adults. Other studies reported that the greater the number of solo meals, the higher the rate of depression. Notably in a previous study on older adults, eating alone was reported as a significant risk factor for high PHQ-9 scores, indicative of depression [30,44,48]. This result is consistent with a previous study using the geriatric depression scale (GDS) in the elderly over 65 years of age, which indicated that older adults who ate alone had higher rates of depression than those who ate with others [46].
The frequency of eating alone differed in terms of the areas of residence. In the case of urban residents, it was shown to be gradually increasing along with the frequency of eating alone, consisting of 73.51%, 81.19%, 80.9%, and 78.75% in the “eating together”, “eating alone one meal a day”, “eating alone two meals a day”, and “eating alone three meals a day” groups. Conversely, rural residents reported a slower decline in the frequency of eating alone than urban residents.
Rural residents are less likely to eat alone than urban dwellers. Additionally, they frequently engage in social support or activities more closely tied to women’s organizations and village halls, enhancing their sense of community. These might help them mitigate the feeling of loneliness and lower their level of depression. Thus, encouraging community activities for healthier and more regular meals as well as providing community kitchens for older adults residing in urban areas who frequently eat alone—the population of lone eaters would be large in urban areas—may assist in securing mutual exchange, interaction, and food security among older adults, ultimately improving their mental health and quality of life [49,50]. Evaluating older adults’ quality of life in relation to eating alone extends the concept of health to cover the disease-free state, including overall well-being, social well-being, and mental health. This study also investigated quality of life using the EQ-5D score to find that the higher the frequency of eating alone, the lower the quality of life. We also found a significant relationship between older adults’ eating alone habits and quality of life scores, confirming its close relationship with various health variables [30].
This study has several limitations, which are described as follows. First, it is a cross-sectional study using the KNHANES dataset, so a causal relationship between eating behaviors depending on the frequency of eating alone, nutrient intake, and mental health could not be identified. Additionally, there was no information on the reasons for eating alone, i.e., whether it was voluntary or involuntary; information on eating snacks, whether alone or with others, was absent. The characteristics of eating habits, including the number of meals, meal time, and whether the meal was cooked or was an instant food, were not reflected in this study. Furthermore, an individual’s routine diet might not have been considered because intake status was analyzed using 24-h recall data. Additionally, there are multifactorial causes of loneliness and depression among older people, including family environment, presence or absence of a spouse, subjective health status, contact with neighbors and friends, living conditions, living environments, sociocultural perspectives, and other factors. A limitation of this study is that it did not include all of these factors.
Despite these limitations, this study targeted older adults aged 65 or more from the KNHANES dataset and conducted weighted data analysis to secure the sample’s representativeness for older Korean adults.
This study identified the relationship between dietary behavior, food and nutrient intake, and mental health in older adults eating alone, reflecting current social and cultural conditions. Previous studies defined eating alone as answering “no” to a dietary life survey question about eating together with family members or others based on meals (i.e., breakfast, lunch, and dinner). In contrast, this study is advantageous because we used objective data based on the 24-h dietary recall method to categorize the frequency of eating alone and analyze the prevalence of eating together. Therefore, eating alone is not a mere act of eating; it is closely related to one’s health status and influences mental health and quality of life. Thus, older adults who often eat alone deserve attention not only in terms of meal improvement, but also in terms of social, cultural, and social welfare perspectives—developing appropriate health policies and nutrition education programs to promote dietary life management and mental health. Our findings in this study could be used as substantial evidence for establishing guidelines for dietary management among older adults who frequently eat alone.
5. Conclusions
Among older adults who frequently eat alone throughout the day, the intake of “vegetables”, “fruit”, “meat, poultry, and their products”, “seaweed”, “milk and dairy products”, and “seasonings” was reduced, exhibiting low nutritional intakes of protein, sodium, potassium, vitamin A, carotene, and niacin. Although the food groups and single nutrient intakes used in this study could not determine the overall composition of the diet, they showed how meal patterns might differ depending on how often one eats alone. Thus, the results from our study could be useful for appropriate nutrition management and meal planning for older adults who frequently eat alone. Additionally, our results show that a higher frequency of eating alone influences depression and quality of life, indicating a need for future welfare services customized for older adults that could improve their health status.
Older adults who eat alone have different characteristics from age groups, so studies about eating alone improve our understanding of its frequency and the patterns of the meals eaten alone based on different genders and regions. The results from such studies could help establish suitable strategies for adequate nutrition and health management among older adults who frequently eat alone.
Author Contributions
Conceptualization, S.K., Y.-K.P., K.H.H. and Y.K.; study design S.K., Y.-K.P., K.H.H. and Y.K. methodology, Y.K.; formal analysis, S.K. and Y.-K.P.; investigation, S.K. and Y.K.; writing—original draft preparation, S.K., K.H.H. and Y.K.; writing—review and editing, S.K., K.H.H. and Y.K.; validation, S.K., K.H.H. and Y.-K.P.; visualization, S.K. and Y.-K.P.; supervision, S.K., Y.-K.P. and Y.K. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the agro-food based basic research of the Korea Rural Development Administration grant (Project No. PJ017251).
Institutional Review Board Statement
The KNHANES data used in this study were approved by the KDCA Institutional Review Board (IRB approval numbers: 2018-01-03-P-A, 2018-01-03-C-A, and 2018-01-03-2C-A). Among these, KNHANES was exempt from review regarding research ethics based on the Bioethics and Safety Act from 2015 to 2017.
Informed Consent Statement
Not applicable.
Data Availability Statement
All data were obtained from the Korea Disease Control and Prevention Agency and are available with the permission of the Korea Disease Control and Prevention Agency. The data in this study were from the Korea National Health and Nutrition Examination Survey.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Lee, H.J.; Yoon, J.Y. The Effect of Eating with Others on Depression among Community-dwelling Older Adults by Family Arrangement. Korea Gerontol. Soc. 2019, 39, 415–427. [Google Scholar] [CrossRef]
- Jung, T.Y. Historical and Cultural Analyses of Collective Features of Korean Society. Korean J. Soc. Personal. Psychol. 2010, 24, 53–76. [Google Scholar]
- Nam, S.Y. Proposal of application service for ‘Honbab-jok’according to increasing single households focused on service design methodology. J. Commun. Des. 2016, 55, 114–125. [Google Scholar]
- Jo, M.; Choen, H. People who eat alone: Characteristics by types and their satisfaction. Consum. Policy Educ. Rev. 2018, 141, 127–148. [Google Scholar] [CrossRef]
- Lee, E.J.; Lee, K.R. Comparison of solo eating perception and dietary behaviors according to gender in the Seoul and Gyonggi-do regions. J. Korean Soc. Food Cult. 2018, 33, 531–542. [Google Scholar]
- Herman, C.P.; Polivy, J. Normative influences on food intake. Physiol Behav. 2005, 86, 762–772. [Google Scholar] [CrossRef] [PubMed]
- Park, J.N.; Cheon, H.J. Leisure-alone Folks: Their Experiences and Meaning. J. Leis. Stud. 2012, 10, 87–105. [Google Scholar]
- Park, M.; Ahn, B. Impacts of food consumption lifestyle on the expenditure for the processed food: Using cluster analysis and matching method. J Rural Dev. 2014, 37, 25–58. [Google Scholar]
- Statistics Korea. Available online: https://www.kostat.go.kr/portal/korea/kor_nw/1/1/index.board?bmode=read&aSeq=415446 (accessed on 29 January 2023).
- Jung, H. Y The Effects of Solo Diners’ Self-Determined Motivation for Solitude and Solo Dining Propensity on Solo Dining Satisfaction. Master’s Thesis, Sejong University, Seoul, Republic of Korea, 2017. [Google Scholar]
- Chae, W.; Ju, Y.J.; Shin, J.; Jang, S.I.; Park, E.C. Association between eating behaviour and diet quality: Eating alone vs. eating with others. Nutr J. 2018, 17, 117. [Google Scholar] [CrossRef]
- Lee, Y.; Oh, Y.J.; Cho, W.; Jo, P.K. Differences in solo eating perceptions and dietary behaviors of university students by gender. J. Korean Diet. Assoc. 2015, 21, 57–71. [Google Scholar] [CrossRef]
- Lee, S.H. The main choice menu for the eating-alone group. In ‘Food Safety Day’ Weekly Honbap Symposium; Ministry of Food and Drug Safety: Osong, Republic of Korea, 2017; pp. 13–33. [Google Scholar]
- Shin, G.; Kim, Y.; Kim, M. Nutritional evaluation of convenience meals in convenience stores near the universities. Korean J. Community Nutr. 2017, 22, 375–386. [Google Scholar] [CrossRef]
- Ford, E.S. Prevalence of the metabolic syndrome defined by the international diabetes federation among adults in the U.S. Diabetes Care 2005, 28, 2745–2749. [Google Scholar] [CrossRef] [PubMed]
- Kim, M.R.; Kim, H.J.; Kim, J.H.; Park, B. Sex difference in the relationship between evening meal-sharing and prevalence of metabolic syndrome: The 2013–2014 Korean National Health and Nutrition Examination Survey. Korean J. Fam. Pract. 2018, 8, 125–130. [Google Scholar] [CrossRef]
- Kimura, Y.; Wada, T.; Okumiya, K.; Ishimoto, Y.; Fukutomi, E.; Kasahara, Y.; Matsubayashi, K. Eating alone among community-dwelling Japanese elderly: Association with depression and food diversity. J. Nutr. Health Aging 2012, 16, 728–731. [Google Scholar] [CrossRef]
- Reicks, M.; Davey, C.; Anderson, A.K.; Banna, J.; Cluskey, M.; Gunther, C.; Jones, B.; Richards, R.; Topham, G.; Wong, S.S. Frequency of eating alone is associated with adolescent dietary intake, perceived food-related parenting practices and weight status: Cross-sectional Family Life, Activity, Sun, Health, and Eating (FLASHE) Study results. Public Health Nutr. 2019, 22, 1555–1566. [Google Scholar] [CrossRef]
- Ishikawa, M.; Takemi, Y.; Yokoyama, T.; Kusama, K.; Fukuda, Y.; Nakaya, T.; Nozue, M.; Yoshiike, N.; Yoshiba, K.; Hayashi, F.; et al. “Eating Together” Is Associated with Food Behaviors and Demographic Factors of Older Japanese People Who Live Alone. J. Nutr. Health Aging 2017, 21, 662–672. [Google Scholar] [CrossRef] [PubMed]
- Tanaka, I.; Kitamura, A.; Seino, S.; Nishi, M.; Tomine, Y.; Taniguchi, Y.; Yokoyama, Y.; Narita, M.; Shinkai, S. Relationship between eating alone and dietary variety among urban older Japanese adults. [Nihon Koshu Eisei Zasshi]. Jpn. J. Public Health 2018, 65, 744–754. [Google Scholar]
- Tani, Y.; Kondo, N.; Noma, H.; Miyaguni, Y.; Saito, M.; Kondo, K. Eating alone yet living with others is associated with mortality in older men: The JAGES cohort survey. J. Gerontol. B Psychol. Sci. Soc. Sci. 2018, 73, 1330–1334. [Google Scholar] [CrossRef]
- Huang, Y.C.; Cheng, H.L.; Wahlqvist, M.L.; Lo, Y.T.C.; Lee, M.S. Gender differences in longevity in free-living older adults who eat-with-others: A prospective study in Taiwan. BMJ Open 2017, 7, e016575. [Google Scholar] [CrossRef]
- Li, C.L.; Tung, H.J.; Yeh, M.C. Combined effect of eating alone and a poor nutritional status on cognitive decline among older adults in Taiwan. Asia Pac. J. Clin. Nutr. 2018, 27, 686–694. [Google Scholar]
- RYU, H.S.; Lee, M.A. Eating Alone and Depressive Symptoms of Korean Older Adults: Examining Differential Effects by Gender. Surv. Res. 2019, 20, 1–27. [Google Scholar] [CrossRef]
- Jung, S.H.; Kim, J.H. The Relationship Between Eating Breakfast with Someone and Depression: Based on Korean National Health and Nutrition Examination Survey. Health Soc. Welf. Rev. 2019, 39, 580–601. [Google Scholar] [CrossRef]
- Tani, Y.; Sasaki, Y.; Haseda, M.; Kondo, K.; Kondo, N. Eating alone and depression in older men and women by cohabitation status: The JAGES longitudinal survey. Age Ageing 2015, 44, 1019–1026. [Google Scholar] [CrossRef] [PubMed]
- Kuroda, A.; Tanaka, T.; Hirano, H.; Ohara, Y.; Kikutani, T.; Furuya, H.; Iijima, K. Eating alone as social disengagement is strongly associated with depressive symptoms in Japanese community-dwelling older adults. J. Am. Med. Dir. Assoc. 2015, 16, 578–585. [Google Scholar] [CrossRef]
- Yiengprugsawan, V.; Banwell, C.; Takeda, W.; Dixon, J.; Seubsman, S.A.; Sleigh, A.C. Health, happiness and eating together: What can a large Thai cohort study tell us? Glob. J. Health Sci. 2015, 7, 270–277. [Google Scholar] [CrossRef]
- Ford, E.S.; Li, C. Metabolic syndrome and health-related quality of life among US adults. Ann. Epidemiol. 2008, 18, 165–171. [Google Scholar] [CrossRef]
- Song, E.G.; Yoon, Y.S.; Yang, Y.J.; Lee, E.S.; Lee, J.H.; Lee, J.Y.; Park, S.Y. Factors associated with eating alone in Korean adults: Findings from the sixth Korea National Health and Nutrition Examination Survey, 2014. Korean J. Fam. Pract. 2017, 7, 698–706. [Google Scholar] [CrossRef]
- Eckel, R.H.; Barouch, W.W.; Ershow, A.G. Report of the national heart, lung, and blood institute-national institute of diabetes and digestive and kidney diseases working group on the pathophysiology of obesity-associated cardiovascular disease. Circulation 2002, 105, 2923–2928. [Google Scholar] [CrossRef]
- Kim, H.K.; Chung, J. Associations of the eating alone behavior with nutrient intake, obesity and metabolic syndrome in middle-aged adults based on the 2013~2017 korean national health and nutrition examination survey. J. Nutr. Health 2019, 52, 435–448. [Google Scholar] [CrossRef]
- Cha, S.; Lee, H. Analyzing adolescent family meal vs. alone meal: Focusing on adolescent time use and family characteristics. J. Fam. Relat. 2018, 22, 135–156. [Google Scholar] [CrossRef]
- Sung, S.; Kwon, S. Effect of eating with family or alone on the self-rated mental or physical health-the elementary school children in daejeon area. Korean J. Community Nutr. 2010, 15, 206–226. [Google Scholar]
- Cho, S.E.; Nam, B.; Seo, J.S. Impact of eating-alone on depression in korean female elderly: Findings from the sixth and seventh korea national health and nutrition examination survey, 2014 and 2016. Mood Emot. 2018, 16, 169. [Google Scholar]
- Yang, J.S.; Bae, E.J.; Hong, J.E.; Kang, J.; Jeon, W.J. Eating alone among korean elderly association with depressive symptoms: The korean national health and nutrition examination, 2013. Korean J. Fam. Pract. 2017, 7, 904–908. [Google Scholar] [CrossRef]
- Oh, K.; Lee, J.; Lee, B.; Kweon, S.; Lee, Y.; Kim, Y. Plan and Operation of the 4th Korea National Health and Nutrition Examination Survey (KNHANES IV). Korean J. Epidemiol. 2007, 29, 139–145. [Google Scholar]
- Korea Centers for Disease Control and Prevention. Guideline for Seventh Korea National Health and Nutrition Examination Survey (KNHANES Ⅶ); Korea Centers for Disease Control and Prevention: Osong, Republic of Korea, 2018; pp. 1–303. [Google Scholar]
- Shim, J.S.; Oh, K.W.; Nam, C.M. Association of Household Food Security with Dietary Intake; Based on the Third (2005) Korea National Health and Nutrition Examination Survey (KNHANES III). J. Nutr. Health 2008, 41, 174–183. [Google Scholar]
- Lee, S.J.; Lee, K.W.; Oh, J.E.; Cho, M.S. Nutritional and health consequences are associated with food insecurity among Korean elderly: Based on the fifth (2010) Korea National Health and Nutrition Examination Survey (KNHANES V-1). J. Nutr. Health 2015, 48, 519–529. [Google Scholar] [CrossRef]
- Kong, J.S.; Min, K.B.; Min, J.Y. Temporary Workers’ Skipping of Meals and Eating Alone in South Korea, The Korean National Health and Nutrition Examination Survey for 2013–2016. Int. J. Environ. Res. Public Health 2019, 16, 2319. [Google Scholar] [CrossRef]
- Rah, W.; So, J.; Park, E.C.; Lee, S.A.; Jang, S.I. Association between family dinner and BMI in adults: Data from the 2013 to 2015 Korean National Health and Nutrition Examination Survey. Public Health Nutr. 2019, 22, 681–688. [Google Scholar] [CrossRef]
- Son, H.; Kim, H. Influence of Living Arrangements and Eating Behavior on the Risk of Metabolic Syndrome: A National Cross-Sectional Study in South Korea. Int. J. Environ. Res. Public Health 2019, 16, 919. [Google Scholar] [CrossRef]
- Ahn, J.H.; Park, Y.K. Frequency of eating alone and health related outcomes in Korean adults: Based on the 2016 Korea National Health and Nutrition Examination Survey. J. Korean Diet. Assoc. 2020, 26, 85–100. [Google Scholar]
- Lee, K.W.; Shin, D. Association between Eating Alone Patterns and Mental Health Conditions by Region among Korean Adults. Korean J. Community Nutr. 2021, 26, 441–454. [Google Scholar] [CrossRef]
- Tani, Y.; Kondo, N.; Takagi, D.; Saito, M.; Hikichi, H.; Ojima, T.; Kondo, K. Combined effects of eating alone and living alone on unhealthy dietary behaviors, obesity and underweight in older Japanese adults: Results of the JAGES. Appetite 2015, 95, 1–8. [Google Scholar] [CrossRef] [PubMed]
- Kwon, Y.; Kim, S. How Much Does Plant Food (Fruits and Vegetables) Intake Affect the Prevention of Periodontal Disease in the Korean Elderly? Nutrients 2022, 14, 4589. [Google Scholar] [CrossRef] [PubMed]
- Donini, L.M.; Savina, C.; Cannella, C. Eating habits and appetite control in the elderly: The anorexia of aging. Int. Psychogeriatr. 2003, 15, 73–87. [Google Scholar] [CrossRef] [PubMed]
- Williams, K.L.; Galliher, R.V. Predicting depression and self–esteem from social connectedness, support, and competence. J. Soc. Clin. Psychol. 2006, 25, 855–874. [Google Scholar] [CrossRef]
- Mundel, E.; Chapman, G.E. A decolonizing approach to health promotion in Canada: The case of the Urban Aboriginal Community Kitchen Garden Project. Health Promot. Int. 2010, 25, 166–173. [Google Scholar] [CrossRef]
Figure 1.
The flowchart for subject samples of this study.
Table 1.
General characteristics according to the frequency of eating alone.
| Variables | Eating Together n = 3060 (42.37%) | Eating Alone | p-Value (2) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| 1/Day n = 1574 (23.58%) | 2/Day n = 1394 (20.10%) | 3/Day (3) n = 1009 (13.95%) | |||||||
| n | % (1) | n | % | n | % (1) | n | % | ||
| Gender | <0.0001 | ||||||||
| Male | 1519 | 52.03 | 686 | 45.48 | 473 | 36.50 | 357 | 38.53 | |
| Female | 1541 | 47.97 | 888 | 54.52 | 921 | 63.50 | 652 | 61.47 | |
| Age | <0.0001 | ||||||||
| 65–74 | 1844 | 60.54 | 1006 | 64.92 | 757 | 56.30 | 476 | 49.70 | |
| ≥75 | 1216 | 39.46 | 568 | 35.07 | 637 | 43.69 | 533 | 50.30 | |
| Average (Mean, SE) | 72.82 a | 0.12 | 72.32 b | 0.16 | 73.28 a | 0.16 | 74.19 c | 0.18 | <0.0001 (3) |
| Region | <0.0001 | ||||||||
| Urban | 2075 | 73.51 | 1206 | 81.19 | 1046 | 80.90 | 740 | 78.75 | |
| Rural area | 985 | 26.49 | 368 | 18.81 | 348 | 19.10 | 269 | 21.25 | |
| Marital status | <0.0001 | ||||||||
| Married | 3035 | 99.80 | 1568 | 99.77 | 1373 | 98.46 | 995 | 98.78 | |
| Single | 5 | 0.2 | 6 | 0.23 | 21 | 1.54 | 14 | 1.22 | |
| Household number | <0.0001 | ||||||||
| 1 | 112 | 2.85 | 255 | 11.92 | 656 | 36.44 | 662 | 55.06 | |
| 2 | 2145 | 65.10 | 871 | 51.63 | 477 | 36.18 | 214 | 25.30 | |
| 3 | 476 | 18.26 | 251 | 20.08 | 150 | 16.12 | 73 | 11.22 | |
| 4 | 155 | 6.43 | 98 | 7.79 | 53 | 5.72 | 28 | 3.97 | |
| 5 | 103 | 4.16 | 62 | 5.95 | 40 | 3.93 | 22 | 3.47 | |
| ≥6 | 69 | 3.19 | 37 | 2.64 | 18 | 1.62 | 10 | 0.98 | |
| Education level | <0.0001 | ||||||||
| Middle school or less | 1925 | 64.60 | 953 | 63.23 | 917 | 69.45 | 702 | 75.93 | |
| High school or less | 561 | 20.98 | 289 | 21.71 | 216 | 18.92 | 136 | 16.88 | |
| College or more | 358 | 14.42 | 190 | 15.05 | 134 | 11.63 | 62 | 7.18 | |
| Household income | <0.0001 | ||||||||
| Low | 1306 | 40.99 | 603 | 36.79 | 744 | 50.58 | 665 | 60.10 | |
| Middle-low | 933 | 30.26 | 494 | 29.99 | 339 | 23.39 | 195 | 23.21 | |
| Middle-high | 492 | 17.11 | 280 | 19.26 | 180 | 15.69 | 100 | 11.32 | |
| High | 319 | 11.63 | 187 | 13.95 | 122 | 10.33 | 42 | 5.37 | |
| Job status | 0.0274 | ||||||||
| Employed | 1018 | 33.47 | 521 | 36.15 | 384 | 30.43 | 273 | 30.32 | |
| Unemployed | 1829 | 66.52 | 911 | 63.85 | 883 | 69.57 | 629 | 69.68 | |
(1) Weighted %. (2) p-value by chi-square test. (3) p-value by one-way ANOVA. a–c Different superscript letters mean “significantly different” among groups at α = 0.05 level by Tukey’s multiple range comparison.
Table 2.
Consumption pattern according to the frequency of eating alone.
| Variables | Eating Together n = 3060 | Eating Alone | ||||||
|---|---|---|---|---|---|---|---|---|
| 1/Day n = 1574 | 2/Day n = 1394 | 3/Day n = 1009 | ||||||
| n | % (1) | n | % | n | % | n | % | |
| Eating partner among daily meal | ||||||||
| Everything-alone | - | - | - | - | 349 | 24.92 | 1009 | 100.00 |
| Family | 2251 | 73.77 | - | - | - | - | - | - |
| Family + alone | - | - | 982 | 62.9 | 514 | 39.31 | - | - |
| Other people | 69 | 2.19 | - | - | - | - | - | - |
| Other people + alone | - | - | 289 | 17.7 | 531 | 35.77 | - | - |
| Family + other people | 740 | 24.0 | - | - | - | - | - | - |
| Family + other people + alone | - | - | 303 | 19.37 | - | - | - | - |
| Alone meal in daily meal | ||||||||
| Breakfast (2) | - | - | 628 | 40.28 | - | - | - | - |
| Lunch (3) | - | - | 611 | 38.99 | - | - | - | - |
| Dinner | - | - | 335 | 20.73 | - | - | - | - |
| B (3) + L (4) | - | - | - | - | 431 | 33.30 | - | - |
| B + D (4) | - | - | - | - | 628 | 47.23 | - | - |
| L + D | - | - | - | - | 262 | 19.47 | - | - |
| B + L + D | - | - | - | - | - | - | 1009 | 100.00 |
| Cooking place of solo meal | ||||||||
| Only home | - | - | 1190 | 74.30 | 979 | 69.06 | 690 | 65.82 |
| Only commercial | - | - | 374 | 25.18 | 85 | 5.98 | 23 | 2.01 |
| Only institution | - | - | 10 | 0.53 | 3 | 0.24 | 5 | 0.48 |
| H (5) + C (6) | - | - | - | - | 318 | 24.05 | 282 | 30.86 |
| H + I (7) | - | - | - | - | 6 | 0.34 | 3 | 0.23 |
| C + I | - | - | - | - | 3 | 0.33 | 3 | 0.30 |
| H + C + I | - | - | - | - | - | - | 3 | 0.29 |
(1) Weighted %, (2) B: Breakfast, (3) L: Lunch, (4) D: Dinner, (5) H: Home, (6) C: Commercial place, (7) I: Institution.
Table 3.
Health-related characteristics according to consumption frequency of eating alone.
| Variables | Eating Together n = 3060 | Eating Alone | p-Value (2) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| 1/Day n = 1574 | 2/Day n = 1394 | 3/Day n = 1009 | |||||||
| n | % (1) | n | % | n | % | n | % | ||
| Smoking status | <0.0001 | ||||||||
| Current smoker | 253 | 9.2 | 135 | 9.1 | 150 | 11.8 | 80 | 8.2 | |
| Ex-smoker | 990 | 33.7 | 450 | 30.0 | 304 | 23.6 | 234 | 25.5 | |
| Non-smoker | 1777 | 57.2 | 960 | 61.0 | 910 | 64.6 | 673 | 66.3 | |
| Drinking status | 0.0005 | ||||||||
| <1/year | 1911 | 61.9 | 992 | 62.8 | 956 | 68.6 | 706 | 70.5 | |
| 1–4/month | 595 | 20.6 | 296 | 19.5 | 224 | 17.4 | 146 | 14.6 | |
| 2–3/week | 269 | 9.4 | 150 | 10.1 | 103 | 7.3 | 67 | 7.8 | |
| ≥4/week | 248 | 8.2 | 109 | 7.6 | 84 | 6.7 | 67 | 7.1 | |
| Stress status | <0.0001 | ||||||||
| Feel it very much | 99 | 2.9 | 48 | 2.3 | 62 | 4.2 | 42 | 4.8 | |
| Feel it somewhat | 406 | 13.1 | 219 | 13.3 | 208 | 15.6 | 169 | 17.9 | |
| Feel it a little | 1628 | 55.4 | 860 | 57.6 | 637 | 47.7 | 433 | 45.1 | |
| Feel it rarely | 882 | 28.6 | 420 | 26.8 | 453 | 32.5 | 338 | 32.2 | |
| Exercise | 0.5960 | ||||||||
| <1/week | 2310 | 79.3 | 1135 | 78.3 | 1058 | 81.5 | 751 | 81.3 | |
| 1–2/week | 104 | 3.9 | 52 | 3.7 | 39 | 3.5 | 30 | 3.8 | |
| 3–4/week | 145 | 5.4 | 74 | 5.4 | 58 | 4.6 | 46 | 5.8 | |
| ≥5/week | 291 | 11.4 | 175 | 12.6 | 113 | 10.4 | 76 | 9.1 | |
| Obesity status | 0.2358 | ||||||||
| Underweight (BMI < 18.5 kg/m2) | 75 | 2.5 | 54 | 3.6 | 47 | 3.4 | 30 | 3.7 | |
| Normal (18.5 kg/m2 ≤ BMI < 23.0 kg/m2) | 1058 | 36.1 | 508 | 31.9 | 459 | 33.7 | 350 | 36.3 | |
| Overweight (23.0 kg/m2 ≤ BMI < 25.0 kg/m2) | 794 | 25.7 | 403 | 27.4 | 348 | 25.5 | 247 | 26.3 | |
| Obesity (BMI ≥ 25.0 kg/m2) | 1092 | 35.7 | 590 | 37.1 | 516 | 37.3 | 354 | 33.7 | |
(1) Weighted %, (2) p-value by chi-square test.
Table 4.
Dietary behavior according to consumption frequency of eating alone.
| Variables | Eating Together n = 3060 | Eating Alone | p-Value (2) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| 1/Day n = 1574 | 2/Day n = 1394 | 3/Day n = 1009 | |||||||
| n | % (1) | n | % | n | % | n | % | ||
| Snack consumption | 0.0003 | ||||||||
| Yes | 2794 | 91.63 | 1443 | 91.98 | 1251 | 89.73 | 876 | 86.72 | |
| No | 266 | 8.36 | 131 | 8.02 | 143 | 10.27 | 133 | 13.28 | |
| Food security | <0.0001 | ||||||||
| Enough food—secure | 1628 | 53.34 | 784 | 50.63 | 594 | 44.39 | 412 | 43.74 | |
| Mildly food insecure | 1330 | 43.24 | 711 | 45.04 | 681 | 48.31 | 503 | 48.40 | |
| Moderately food insecure | 86 | 2.66 | 71 | 3.73 | 99 | 6.19 | 69 | 5.69 | |
| Severely food insecure | 15 | 0.75 | 8 | 0.60 | 18 | 1.12 | 21 | 2.17 | |
| Eating-out Frequency | <0.0001 | ||||||||
| ≥2/day | 32 | 1.02 | 24 | 1.46 | 13 | 1.11 | 10 | 1.09 | |
| 1/day | 94 | 3.68 | 74 | 5.43 | 40 | 3.05 | 18 | 1.93 | |
| 5–6/week | 188 | 6.32 | 129 | 8.22 | 106 | 7.79 | 27 | 2.62 | |
| 3–4/week | 232 | 7.79 | 144 | 9.23 | 97 | 7.25 | 44 | 4.33 | |
| 1–2/week | 765 | 25.53 | 416 | 26.08 | 352 | 25.39 | 239 | 23.43 | |
| <1/week | 1064 | 33.64 | 494 | 31.80 | 429 | 30.25 | 323 | 31.69 | |
| seldom | 685 | 22.02 | 293 | 17.79 | 357 | 25.17 | 347 | 34.90 | |
(1) Weighted %, (2) p-value by chi-square.
Table 5.
Food intake in their meal according to consumption frequency of eating alone.
| g/Day | Eating Together n = 3060 | Eating Alone | Unadjusted p for Trend (1) | Adjusted p for Trend (2) | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1/Day n = 1574 | 2/Day n = 1394 | 3/Day n = 1009 | ||||||||
| Mean | SE | Mean | SE | Mean | SE | Mean | SE | |||
| Total food | 1315.7 | 17.8 | 1298.4 | 19.7 | 1160.2 | 20.7 | 1178.0 | 26.5 | <0.0001(−) | <0.0866(−) |
| Cereals and grain products | 158.9 | 5.5 | 161.0 | 6.0 | 142.7 | 4.9 | 156.1 | 6.7 | <0.0010(+) | 0.0987(+) |
| Potatoes and starches | 20.5 | 1.6 | 28.5 | 3.4 | 20.8 | 2.4 | 18.6 | 2.4 | 0.0967(−) | 0.0511(−) |
| Sugars and sweets | 3.4 | 0.2 | 3.4 | 0.4 | 2.7 | 0.2 | 2.9 | 0.3 | 0.0785(−) | 0.0847(+) |
| Legumes and their products | 22.4 | 1.3 | 28.5 | 2.7 | 22.1 | 1.7 | 30.0 | 3.2 | 0.0450(+) | 0.0994(+) |
| Seeds and nuts | 4.1 | 0.4 | 4.4 | 0.5 | 4.3 | 0.8 | 4.4 | 0.9 | 0.0724(+) | 0.0882(+) |
| Vegetables | 188.2 | 6.8 | 180.0 | 7.2 | 152.1 | 6.8 | 166.3 | 9.2 | 0.0620(−) | 0.0477(−) |
| Mushrooms | 2.8 | 0.3 | 2.4 | 0.4 | 1.2 | 0.2 | 1.5 | 0.4 | 0.0672(−) | 0.0994(−) |
| Fruit | 113.0 | 6.3 | 124.2 | 7.2 | 98.0 | 6.5 | 99.9 | 8.8 | 0.0315(−) | 0.0351(−) |
| Meat, poultry, and their products | 35.2 | 2.2 | 34.0 | 2.3 | 30.7 | 2.9 | 25.7 | 2.9 | 0.0129(−) | 0.0022(−) |
| Eggs | 10.8 | 0.7 | 11.0 | 0.9 | 8.8 | 0.8 | 9.2 | 1.0 | 0.01675(−) | 0.0884(−) |
| Fish and shellfish | 60.0 | 3.6 | 58.8 | 3.9 | 45.8 | 3.5 | 42.9 | 5.6 | 0.0453(−) | 0.0729(−) |
| Seaweed | 18.3 | 1.8 | 21.6 | 2.6 | 13.9 | 1.8 | 14.3 | 3.7 | 0.0675(−) | 0.0324(−) |
| Milks and dairy products | 30.7 | 2.0 | 34.0 | 2.9 | 38.3 | 3.4 | 39.2 | 3.9 | 0.0612(+) | 0.0667(+) |
| Oils and fats | 2.3 | 0.1 | 2.2 | 0.1 | 1.8 | 0.2 | 1.7 | 0.2 | 0.0887(−) | 0.2187(+) |
| Beverages | 62.1 | 4.3 | 72.5 | 5.8 | 52.1 | 5.2 | 35.3 | 4.9 | 0.0665(−) | 0.3664(−) |
| Seasonings | 16.6 | 0.7 | 15.7 | 0.7 | 12.9 | 0.6 | 12.9 | 0.9 | 0.0998(−) | 0.0111(−) |
| Other food | 3.1 | 0.5 | 3.8 | 0.8 | 3.8 | 0.8 | 5.0 | 1.3 | 0.0679(+) | 0.0775(−) |
(1) p for trend was calculated by the SURVEYREG procedure in SAS. (2) Adjusted for gender, age, and energy intake.
Table 6.
Nutrient intake according to consumption frequency of eating alone.
| g/Day | Eating Together n = 3060 | Eating alone | Unadjusted p for Trend (1) | Adjusted p for Trend (2) | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1/Day n = 1574 | 2/Day n = 1394 | 3/Day n = 1009 | ||||||||
| Mean | SE | Mean | SE | Mean | SE | Mean | SE | |||
| Energy (kcal) | 1682.7 | 16.7 | 1653.9 | 21.5 | 1517.4 | 20.2 | 1576.5 | 22.5 | <0.0001(−) | 0.5353(+) |
| Carbohydrates (g) | 285.0 | 2.5 | 279.8 | 3.8 | 261.3 | 3.2 | 277.3 | 3.7 | <0.0001(−) | 0.0657(+) |
| Protein (g) | 58.0 | 0.8 | 56.0 | 0.9 | 50.3 | 0.9 | 51.3 | 1.0 | <0.0001(−) | <0.0001(−) |
| Fat (g) | 29.3 | 0.7 | 28.3 | 0.6 | 26.0 | 0.7 | 25.6 | 0.8 | <0.0001(−) | 0.2612(+) |
| Calcium (mg) | 464.8 | 7.8 | 457.8 | 9.5 | 426.8 | 9.4 | 434.5 | 10.7 | 0.0006(−) | 0.8470(+) |
| Phosphorus (mg) | 935.5 | 11.4 | 905.1 | 12.9 | 829.4 | 13.2 | 861.8 | 15.7 | <0.0001(−) | 0.1460(−) |
| Iron (mg) | 11.3 | 0.2 | 11.0 | 0.2 | 10.0 | 0.2 | 10.2 | 0.2 | <0.0001(−) | 0.0550(−) |
| Sodium (mg) | 3012.0 | 43.7 | 2815.0 | 51.0 | 2606.4 | 53.8 | 2678.5 | 74.1 | <0.0001(−) | 0.0316(−) |
| Potassium (mg) | 2729.3 | 35.5 | 2633.1 | 43.4 | 2409.1 | 42.9 | 2441.2 | 51.2 | <0.0001(−) | 0.0012(−) |
| Vitamin A (μg RE) | 350.2 | 15.0 | 293.5 | 7.2 | 286.6 | 9.8 | 265.1 | 10.1 | <0.0001(−) | 0.0003(−) |
| Carotene (μg) | 2914.8 | 81.6 | 2558.2 | 73.4 | 2434.9 | 93.9 | 2255.5 | 95.4 | <0.0001(−) | <0.0001(−) |
| Retinol (μg) | 1.2 | 0.0 | 1.1 | 0.0 | 1.1 | 0.0 | 1.1 | 0.0 | 0.0327(−) | 0.1363(−) |
| Thiamine (mg) | 107.2 | 12.4 | 80.2 | 3.5 | 83.6 | 5.0 | 77.1 | 5.0 | <0.0001(−) | 0.8884(−) |
| Riboflavin (mg) | 1.3 | 0.0 | 1.2 | 0.0 | 1.1 | 0.0 | 1.1 | 0.0 | <0.0001(−) | 0.1725(−) |
| Niacin (mg) | 10.8 | 0.2 | 10.4 | 0.2 | 9.4 | 0.2 | 9.5 | 0.2 | <0.0001(−) | 0.0032(−) |
| Vitamin C (mg) | 59.9 | 1.7 | 59.2 | 2.1 | 56.7 | 2.4 | 52.7 | 2.3 | 0.0158(−) | 0.3947(−) |
| Energy Contribution (%, SE) | ||||||||||
| Carbohydrates | 71.4 | 0.3 | 71.6 | 0.3 | 72.3 | 0.3 | 73.2 | 0.4 | <0.0001(+) | 0.0055(+) |
| Protein | 13.7 | 0.1 | 13.5 | 0.1 | 13.1 | 0.1 | 12.9 | 0.1 | <0.0001(−) | <0.0001(−) |
| Fat | 14.9 | 0.2 | 14.9 | 0.2 | 14.6 | 0.3 | 13.9 | 0.3 | 0.0100(−) | 0.0233(−) |
(1) p for trend was calculated by the SURVEYREG procedure in SAS. (2) Adjusted for gender, age, and energy intake.
Table 7.
Prevalence and mean score regarding item of Patient Health Questionnaire according to consumption frequency of eating alone.
Table 7.
Prevalence and mean score regarding item of Patient Health Questionnaire according to consumption frequency of eating alone.
| Patient Health Questionnaire (1) | Eating Together n = 3060 | Eating Alone | ||||||
|---|---|---|---|---|---|---|---|---|
| 1/Day n = 1574 | 2/Day n = 1394 | 3/Day n = 1009 | ||||||
| Mean | SE | Mean | SE | Mean | SE | Mean | SE | |
| 0.24 | 0.02 | 0.19 | 0.02 | 0.27 | 0.03 | 0.32 | 0.04 |
| 0.18 | 0.02 | 0.24 | 0.03 | 0.29 | 0.03 | 0.35 | 0.05 |
| 0.55 | 0.03 | 0.58 | 0.04 | 0.67 | 0.05 | 0.71 | 0.06 |
| 0.52 | 0.03 | 0.51 | 0.04 | 0.57 | 0.05 | 0.62 | 0.05 |
| 0.20 | 0.02 | 0.19 | 0.03 | 0.29 | 0.03 | 0.33 | 0.04 |
| 0.12 | 0.01 | 0.14 | 0.02 | 0.21 | 0.03 | 0.25 | 0.04 |
| 0.11 | 0.01 | 0.13 | 0.02 | 0.14 | 0.02 | 0.16 | 0.03 |
| 0.09 | 0.01 | 0.10 | 0.02 | 0.10 | 0.02 | 0.11 | 0.02 |
| 0.12 | 0.02 | 0.09 | 0.02 | 0.19 | 0.03 | 0.20 | 0.03 |
| Total score | 1.56 | 0.09 | 1.80 | 0.14 | 2.06 | 0.15 | 2.40 | 0.23 |
| Prevalence of depression (n, weighted %) | ||||||||
| Non-depression (<10) (2) | 1726 | 97.30 | 867 | 96.65 | 755 | 96.11 | 568 | 93.84 |
| Depression (≥10) (3) | 64 | 2.70 | 30 | 3.35 | 38 | 3.89 | 40 | 6.16 |
(1) Q. 1–9. Score (0: Not at all, 1: Several days 2: A week or higher, 3: Everyday). (2) Total score of Patient Health Questionnaire (PHQ)-9 < 10, (3) Total score of PHQ-9 ≥ 10.
Table 8.
Relationship between daily consumption of eating alone and prevalence of depression.
| Eating Together | 1/Day | 2/Day | 3/Day | p for Trend | |
|---|---|---|---|---|---|
| Prevalence of depression | |||||
| Model 1 | 1 (1) | 1.410 (0.613–1.936) (2) | 1.437 (0.898–2.297) | 2.464 (1.527–3.975) | 0.0001 (+) |
| Model 2 | 1 | 1.093 (0.647–1.847) | 1.982 (0.742–1.882) | 2.152 (1.302–3.555) | 0.0010 (+) |
| Model 3 | 1 | 1.253 (0.716–2.193) | 0.942 (0.553–1.604) | 1.775 (1.012–3110) | 0.1286 (+) |
| Model 4 | 1 | 1.103 (0.613–1.985) | 0.938 (0.543–1.622) | 1.573 (0.871–2.838) | 0.4364 (+) |
(1) Reference, (2) Odd ratio (95% Confidence Interval). Model 1: Unadjusted. Model 2: Adjusted for gender, age, and energy intake. Model 3: Adjusted for gender, age, family size, energy intake, exercise, alcohol consumption, stress status, smoking, and obese status. Model 4: Adjusted for gender, age, family size, energy intake, exercise, alcohol consumption, stress status, smoking, obese status, food security, snack, eating-out frequency per week, breakfast frequency per week, marital status, occupation, education level, and household income.
Table 9.
Ratio of item of quality of life according to consumption frequency of eating alone.
| Variables | Eating Together n = 3060 | Eating Alone | p-Value | ||||||
|---|---|---|---|---|---|---|---|---|---|
| 1/Day n = 1574 | 2/Day n = 1394 | 3/Day n = 1009 | |||||||
| n | % | n | % | n | % | n | % | ||
| Mobility | <0.0001 | ||||||||
| M1. No problems | 1838 | 60.75 | 966 | 62.23 | 781 | 58.12 | 475 | 48.30 | |
| M2. Some/moderate problems | 986 | 30.23 | 461 | 27.87 | 467 | 31.22 | 409 | 39.80 | |
| M3. Extreme problems | 36 | 0.96 | 14 | 0.66 | 29 | 1.70 | 24 | 1.99 | |
| Non-respondence | 200 | 8.06 | 133 | 9.24 | 117 | 8.97 | 100 | 9.91 | |
| Self-management | 0.0413 | ||||||||
| SM1. No problems | 2583 | 83.68 | 1312 | 83.33 | 1124 | 81.05 | 791 | 79.21 | |
| SM2. Some/moderate problems | 251 | 7.52 | 117 | 6.52 | 145 | 9.52 | 110 | 10.32 | |
| SM3. Extreme problems | 26 | 0.74 | 11 | 0.84 | 9 | 0.52 | 7 | 0.56 | |
| Non-respondence | 200 | 8.06 | 134 | 9.31 | 116 | 8.91 | 100 | 9.91 | |
| Daily activities | 0.0002 | ||||||||
| DA1. No problems | 2332 | 76.21 | 1217 | 77.74 | 996 | 72.72 | 678 | 68.95 | |
| DA2. Some/moderate problems | 487 | 14.59 | 211 | 12.42 | 258 | 16.85 | 216 | 19.78 | |
| DA3. Extreme problems | 41 | 1.14 | 13 | 0.59 | 24 | 1.51 | 13 | 1.32 | |
| Non-respondence | 200 | 8.06 | 133 | 9.24 | 116 | 8.91 | 101 | 9.95 | |
| Pain/Inconvenience | 0.0392 | ||||||||
| PI1. No problems | 1849 | 61.09 | 956 | 60.84 | 780 | 57.93 | 535 | 53.70 | |
| PI2. Some/moderate problems | 874 | 26.58 | 412 | 25.73 | 407 | 27.75 | 308 | 30.28 | |
| PI3. Extreme problems | 133 | 4.13 | 72 | 4.12 | 91 | 5.41 | 63 | 5.95 | |
| Non-respondence | 204 | 8.20 | 134 | 9.31 | 116 | 8.91 | 102 | 10.06 | |
| Anxiety/Depression | 0.001 | ||||||||
| AD1. No problems | 2477 | 80.01 | 1260 | 80.21 | 1065 | 76.11 | 727 | 73.04 | |
| AD2. Some/moderate problems | 352 | 10.81 | 168 | 9.88 | 180 | 13.06 | 159 | 15.46 | |
| AD3. Extreme problems | 28 | 1.03 | 13 | 0.66 | 29 | 1.58 | 20 | 1.54 | |
| Non-respondence | 203 | 8.14 | 133 | 9.24 | 120 | 9.25 | 102 | 9.96 | |
| EQ-5D (1) index (2) (Mean, SE) | 0.82 a | 0.008 | 0.821 a | 0.011 | 0.800 ab | 0.010 | 0.773 b | 0.012 | <0.0001 |
(1) EQ-5D: Euro Quality of Life-5 Dimension. (2) EQ-5D index = 0.05 + 0.096(M2) + 0.418(M3) + 0.046(SM2) + 0.136(SM3) + 0.051(DA2) + 0.208(DA3) + 0.037(PI2) + 0.151(PI3)+ 0.043(AD2) + 0.158(AD3) + 0.05(N3: The case given at least two points or more in more than one item). a,b different superscript letters mean significantly different among groups at the α = 0.05 level by Tukey’s multiple range comparison.
Table 10.
Relationship between daily consumption of eating alone and quality of life.
| Eating Together | 1/Day | 2/Day | 3/Day | p for Trend | |
|---|---|---|---|---|---|
| Dependent Variable: Quality of Life | |||||
| Model 1 | 0 (1) | 0.0077 (−0.0019~0.0174) (2) | −0.0152 (−0.0269~−0.0037) | −0.0347 (−0.0482~−0.0211) | <0.0001 (−) |
| Model 2 | 0 | 0.0074 (−0.0019~0.0166) | −0.0030 (−010140~0.0079) | −0.0205 (−0.0333~−0.0077) | 0.0057 (−) |
| Model 3 | 0 | 0.0056 (−0.0034~0.0146) | −0.0024 (−0.0129~0.0080) | −0.0153 (−0.0271~−0.0034) | 0.0273 (−) |
| Model 4 | 0 | 0.0066 (−0.0025~0.0156) | 0.0047 (−0.0058~0.0152) | −0.0040 (−0.0173~0.0091) | 0.9571 (−) |
(1) Reference, (2) β-coefficients (95% Confidence Interval), Model 1: Unadjusted; Model 2: Adjusted for gender, age, and energy intake; Model 3: Adjusted for gender, age, energy intake, family size, exercise, alcohol consumption, stress status, smoking, obese status, food security, snack, and eating-out frequency per week; Model 4: Adjusted for gender, age, family size, energy intake, exercise, alcohol consumption, stress status, smoking, obesity status, food security, snacking, eating-out frequency per week, marital status, occupation, education level, and household income.
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Kwon, Y.; Hong, K.H.; Park, Y.-K.; Kim, S. How Does the Frequency of Eating-Alone among Older People in Korea Affect Their Health and Dietary Behavior? Nutrients 2023, 15, 2085. https://doi.org/10.3390/nu15092085
AMA Style
Kwon Y, Hong KH, Park Y-K, Kim S. How Does the Frequency of Eating-Alone among Older People in Korea Affect Their Health and Dietary Behavior? Nutrients. 2023; 15(9):2085. https://doi.org/10.3390/nu15092085
Chicago/Turabian StyleKwon, Yongseok, Kyung Hee Hong, Yoo-Kyung Park, and Sohye Kim. 2023. "How Does the Frequency of Eating-Alone among Older People in Korea Affect Their Health and Dietary Behavior?" Nutrients 15, no. 9: 2085. https://doi.org/10.3390/nu15092085
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