Ⅰ. Introduction

1. Background of Research

The average life expectancy of Koreans has been increasing significantly as a consequence of improvements in the living standard and advancements in the medical field. Currently, the proportion of the population aged 65 or above exceeds 14.0% in Korea[3]. However, although the socio-economic environment has improved significantly, the incidence of chronic diseases is steadily rising with increasing age. In the current era, the concept of health focuses on the quality of life as part of life conservation and life extension[14][15]. Although many and the average life expectancy as a consequence of rising income levels and advancements in medical technology, the debate on health status, mental health, and quality of life is ongoing[4][5][6].

A review of previous studies revealed that the quality of life was lower in women than men, in older people, individuals with low educational levels, people belonging to lower income groups, and those with negative health behaviors such as smoking and drinking[20][24][11][15][23][5][18]. In addition, it was determined that life expectancy and healthy lifespan varied based on demographic and sociological characteristics, according to a report on equity and policy tasks by gender and educational level in[2]. The aforementioned studies provide major implications for factors affecting health-related quality of life of adults in Korea, based on demographic and sociological variables. However, there are certain limitation such as not considering various variables related to health behavior and health status. Unlike the current research, existing studies have targeted the general population of the local community, not specific population groups such as adolescents, women, and the elderly. In addition, by using various measurement tools related to the quality of life, it attempted to overcome the bias of the results, which is one of the limitations of existing research. Accordingly , various support and policy improvement implications were presented, while providing information on variables and vulnerable areas that may hinder the quality of life.

Most researchers have focused on the importance of healthy life rather than simply extending life. Accordingly, Korea introduced a general Euro Quality of Life Questionnaire 5-Dimensional Classification (EQ-5D) measurement tool developed by EuroQoL Group, which evaluates health-related quality of life from the 3rd National Health and Nutrition Survey (2005). The greatest significance of EQ-5D is that it enables the calculation of QALY, which allows for a “cost-utility analysis,” which encompasses not only the quantitative aspect of the final outcome of the project, but also the qualitative perspective, given the growing incidence of chronic diseases and an increase in the elderly population. This is the preferred method of evaluating health policies and projects in a demanding situation[1][2].

This study uses standardized data involving a nationwide sample group. Utilizing the raw data of the 7th National Health and Nutrition Survey (2016), we analyzed the factors affecting health-related quality of life of adults using EQ-5D to investigate the factors influencing the health-related quality of life of adults who have secured validity and reliability while overcoming the limitations of the research subject, the limitations of the measurement tools, and the limitations of variables in existing studies related to the quality of life. The main purpose of this study is to prepare basic data for improving the health-related quality of life of Koreans in the future. Furthermore, the findings can be used as comparative data in research on quality of life, as well as to plan and evaluate health promotion projects in local communities.

2. Research Purpose

The purpose of this study is to investigate the factors influencing the health-related quality of life of adults in Korea using EQ-5D. The specific goals of this study are as follows.

Ⅱ. Research Method

1. Research Design & Subjects

This research is a descriptive cross-sectional study conducted as secondary data analysis using the original data of the 7th National Health and Nutrition Survey. The number of participants in the first year of the 7th National Health and Nutrition Survey was 8,150, of which 5,668 were aged 19 and over. Among them, 2,427 (42.8%) were male and 3,241 (57.2%) were female.

2. Research Variables

1) Health-related Quality of Life (EQ-5D)

The EQ-5D index is a comprehensive index of a five-dimensional technical system that responds to the current state of the five categories of motor skills, self-management, daily activities, pain/discomfort, and anxiety/depression at one of three levels. EQ-5D is “Mobility.” “No problem” for the five dimensions of “Self-care,” “Usual activity,” “Pain/Discomfort,” and “Anxiety/Discomfort.” It is composed of three levels of “some problems” and “serious problems.” If the answer to all five items of EQ-5D is 1, EQ-5D=1, and the EQ-5D Index ranges from 0 to 1, and the closer it is to 1, the higher the quality of life. The significance level for statistical significance was considered as 5%.

2) Demographic Characteristics

The demographic and sociological characteristics of the subjects were analyzed by gender, age, household income quartile, educational level, occupation, and marital status. Based on age, the subjects were divided into “young” (aged 20-39), “middle-aged” (40-64), “old” (65-74), and “late elderly” (aged over 75). Household income levels were classified into “lower,” “middle-lower,” “middle-high,” and “higher,” while economic activity status was categorized into “economic activity” and “non-economic activity,” and marital status into “married” and “unmarried.”

3) Health Behavior Factors

Current smoking status, monthly drinking rate, high intensity physical activity, moderate intensity physical activity, walking, average sleep time, and lack of basic medical services were analyzed. The current smoking status is regarded as five packs (100 cigarettes) or more for a lifetime, and it is divided into “non-smokers” and “smokers” based on the current smoking status. Drinking more than once per month in the past year is classified as “drinking.” High-intensity physical activity, moderate-intensity physical activity, and walking were classified into “unimplemented” and “practiced.” Average sleep time was classified into “less than 6 hours,” “6-8 hours,” and “more than 9 hours” by averaging the average daily sleep time during the week and the average daily sleep time during the weekend. Whether or not basic medical services were provided was reclassified into “satisfied” and “not satisfied.”

4) Physical & Mental Health Status Factors

Subjective health status, stress perception rate, and current prevalence of depression, obesity, hypertension, diabetes, dyslipidemia, angina pectoris, atopic dermatitis, myocardial infarction, osteoporosis, arthritis, stroke, and thyroid disease were analyzed. Subjective health status was reclassified into “bad,” “moderate,” and “good.” The stress perception rate was categorized into “received less” and “received a lot.” The current prevalence of depression was classified into “no” and “with.” Obesity was reclassified as “normal” and “obese” based on the underweight and the normal group.

3. Research Analysis

SAS 9.4 Quality of Life Index (EQ-5D Index) and Population Society are indicators that combine the technical systems of five dimensions of health-related quality of life from the 7th National Health and Nutrition Survey using statistical programs. The analysis included academic, health-related behavioral factors, and health-related status factors. In this study, an independent sample t-test and ANOVA were conducted to analyze the frequency analysis to understand the general characteristics of the subject and to determine the difference between the index of life (EQ-5D) and the quality of life index based on health-related factors. In addition, multiple regression analysis was performed stepwise to determine whether the independent variable had an effect on health-related quality of life, and it was also analyzed with a logistic regression model. Accordingly, we tried to compare and analyze the influencing factors affecting the quality of life through multiple regression analysis. The significance level was 5% (α=0.05).

Ⅲ. Research Results

1. Quality of Life According to General Characteristics

Regarding the demographic and sociological factors of the study subjects, the quality of life of males (2,427, 42.8%) was 0.957, and that of females (3,241, 57.2%) was 0.932. According to this, the quality of life of female is lower than that of male(p<.001). Age-wise, the quality of life of youth (1,638, 28.9%) was 0.974; for the middle-aged (2,598, 45.8%), it was 0.941; and for the Early elderly (884, 15.6%), it was 0.894. The quality of life of the Late elderly(548 ,9.7%) was 0.841, and as the age increased, the quality of life was evaluated as lower (p<.001). In terms of household income, the quality of life of the affluent (1,632, 28.8%) was 0.971; that of the middle-high (1,564, 27.6%) 0.964; the middle-lower (1,393, 24.6%) 0.942; and the disadvantaged (1,079, 19.0%) showed a quality of life of 0.870. As a result of post-hoc text, the middle-lower population demonstrated a higher quality of life than their middle-lower counterparts. With regard to educational level, the quality of life of college graduates (2,093, 36.9%) was 0.973, high school graduates (1,774, 31.0%) was 0.962, According to the study, the lower the educational level, the lower the quality of life(p<.001). As regards economic activity, the quality of life of economically active (2,031, 35.8%) was 0.955 and non-economic activity (3,637, 64.2%) was 0.936 (p<.001). In terms of marital status, the quality of life of married people (4,835, 85.3%) was 0.938 and that of the unmarried (836, 14.7%) was 0.968, showing higher quality of life (p<.001).

<Table 1>

Quality of life according to general characteristics

2. Quality of Life According to Health Behavior Factors

The quality of life of smokers (1,037, 18.3%) was 0.950 and that of non-smokers (4,631, 81.7%) was 0.941(p<.001), according to the health behavior of the study subjects. In the monthly drinking rate, the quality of life of the drinking group (3,044, 53.7%) was 0.959 and that of the non-drinking group (2,624, 46.3%) was 0.924, indicating that the quality of life of the drinking group was higher (p<. 001). The quality of life of the group practicing high intensity physical activity (564, 10.0%) was 0.976, and the quality of life of the unimplemented group (5,104, 90.0%) was 0.939 (p<.001). The quality of life of those who practiced moderate-intensity physical activity (1,264, 22.3%) was 0.968, and for those who did not practice (4,404, 77.7%), it was 0.935 (p<.001). The quality of life of the group practicing walking (2,137, 37.7%) was 0.960, and for the group that did not practice (3,531, 62.3%), it was 0.932 (p<.001). The average sleep time was divided into less than 6 hours, 7-8 hours, and more than 9 hours. The quality of life for those who were satisfied with the basic medical services (5,142, 90.7%) was 0.949, and for those who were not satisfied (526, 9.3%), it was 0.885, suggesting that the quality of life was significantly lower (p<.001).

<Table 2>

Quality of life according to health behavior

3. Quality of Life According to Health Factors

According to the health status of the study subjects, the subjective health status was 0.983 for good (1,641, 29.0%), 0.959 for normal (2,887, 50.9%), and 0.843 for bad (1,140, 20.1%) quality of life. The higher the quality of life, the better their subjective health status (p<.001). Those who perceived less stress (4,148, 73.2%) had a quality of life of 0.953, and those with more stress (1,520, 26.8%) had a quality of life of 0.915 (p<.001). As a consequence of selecting and analyzing variables indicating the current prevalence in the health questionnaire, depression, obesity, hypertension, diabetes, dyslipidemia, angina pectoris, myocardial infarction, osteoporosis, arthritis, stroke, and thyroid problems were all significant, except for atopic dermatitis. The better the quality of life, the higher the disease-free condition (p<.001). In particular, in the case of arthritis, the quality of life of individuals suffering from arthritis (684, 12.1%) was 0.840, and the quality of life of those without arthritis (4,984, 87.9%) was 0.957. The quality of life of individuals with depression (177, 3.1%) was 0.777, and those without depression (5,491, 96.9%) had a quality of life of 0.948 (p<.001).

4. Factors Influencing the Quality of Life

1) Factors influencing the quality of life examined through multiple regression analysis

In general characteristics, the quality of life was lower for females, for those who were older, less economically active, and those who were unmarried. Among the variables belonging to health behavior. The quality of life was higher when high-intensity physical activity and walking were practiced, and when necessary medical services were satisfied. The quality of life declined among the variables associated with health status, the higher the subjective health status, the better the quality of life. The higher the perceived stress the greater the incidence of depression, obesity, hypertension, diabetes, angina pectoris, myocardial infarction, osteoporosis, arthritis, and stroke. The better the subjective health status, the higher the quality of life. The lower the perceived stress, the lesser the incidence of depression, obesity, high blood pressure, diabetes, angina pectoris, myocardial infarction, osteoporosis, arthritis, and stroke(p<.05).

<Table 3>

Quality of life according to health status

<Table 4>

Factors influencing quality of life through multiple regression analysis

2) Factors influencing the quality of life examined through logistic regression analysis

Most of the earlier studies have considered the dependent variable as a multiple regression model that can be analyzed constantly in order to identify the factors that affect the health-related quality of life of adults using EQ-5D. However, the five dimensions of EQ-5D can be composed of three levels of “no problem,” ”some problem” and “serious problem,” into the dichotomy of 'no problem' and 'problem', and logistic regression It can also be analyzed as a model. Accordingly, we attempted to generalize the research results by comparing and analyzing the factors influencing the quality of life through multiple regression analysis.

When examining the factors affecting the quality of life through logistic regression analysis, there may be problems with health-related quality of life when the gender is female, as age increases, in the case of non-economic activity, and being unmarried, in terms of marital status. The odds ratio appeared to increase. Additionally, it was found that the higher the income quartile of the household and the higher the educational level, the lower the odds ratio for health-related quality of life.

Among the variables associated with health behavior, the odds of having problems with health-related quality of life were found to decrease when the individual indulged in high-intensity physical activity, when walking was practiced, and when necessary medical services were satisfied. Among the variables pertaining to health status, the higher the subjective health status, the less the odds ratio for health-related quality of life decreased. In addition, as with multiple regression analysis, the higher the stress perception rate, when there is depression, when there is obesity, when there is high blood pressure, when there is diabetes, when there is angina pectoris, when there is myocardial infarction, when there is osteoporosis, when there is arthritis.

Ⅳ. Discussion

This study uses standardized data for a nationwide sample group. Utilizing the raw data of the 7th National Health and Nutrition Survey, we analyzed the factors affecting the health-related quality of life of adults using EQ-5D. The main purpose of this study is to prepare basic data for improving the health-related quality of life of Koreans in the future. Furthermore, it is expected that the results of this study can be used as comparative data in research on quality of life, and can also be used to plan and evaluate health promotion projects in local communities.

In this study, health-related quality of life was largely divided into subjects' general characteristics, health behavior, and health status. According to this study, the lower the age group for both men and women, the higher the level of health-related quality of life. These results are in line with the findings in [8] that reported that the quality of life decreased as the age group increased. This is considered to be the cause of physical and mental health-related problems as a consequence of aging. Additionally, in the relationship with the quality of life according to gender, as in the results of [13][16], the quality of life of women was lower than that of men. In the case of women, it is judged to be related to social problems such as physical, mental, and career breaks following marriage, childbirth, and child rearing in adulthood. Furthermore, it can be related to the current issue of women's rights, and it is believed that related research should be conducted systematically.

Next, the results of this study are in line with the results of [10], the higher the household income quartile, the higher the level of health-related quality of life in the economically active than in the inactive population. These findings suggest that low household income does not facilitate health-related promotion activities for economic reasons, and therefore, has a major impact on the quality of life. With regard to educational level, as in the results of the study in [17], the higher the educational level, the better the health-related quality of life. It has been assessed that the educational level responds more sensitively to the quality of life related to the health of the study subjects and focuses on management. As for marital status [25], the health-related quality of life was higher for unmarried persons than for married persons, as in the research results. These findings can be attributed to the relatively higher age of married people compared to unmarried people. In this regard, the research results of [7] are contrary to the fact that married people have a slightly higher quality of life related to health than unmarried people. Therefore, the necessity of examining the net effect on marital status is raised in the context of correcting the variables that can affect age, gender, and quality of life.

<Table 5>

Factors influencing the quality of life through logistic regression analysis

In terms of quality of life according to the health behavioral factors of the study subjects, the quality of life of the drinking group was higher than that of the non-drinking group in the monthly drinking rate of smokers compared to non-smokers. This can be interpreted as a survivor effect. In essence, it is considered that sick smokers or drinkers may not be able to participate in the study, and thus may appear to be irrelevant because there is a high probability of dying [19][21][24]. Next, in the group that practiced medium intensity, high intensity, physical activity such as walking, the quality of life was higher. Considering previous studies on physical activities and health-related quality of life, it has been reported that moderate physical activity is effective in improving the health-related quality of life[22]. Through this, it is considered necessary to plan continuous exercise and physical activity programs of varying intensity. In terms of average sleep time, the quality of life was the highest when appropriate sleep for 7-8 hours was divided into less than 6 hours, 7-8 hours, and more than 9 hours, followed by less than 6 hours and more than 9 hours. With regard to whether or not basic medical services are satisfied, it can be confirmed that the quality of life of the group that is not satisfied is significantly lower than that of the satisfied group, in line with the previous thesis.

In terms of quality of life according to the health condition of the study subjects, the higher the subjective health condition, the greater the health-related quality of life. Subjective health status is a convincing variable as one of the factors evaluating health because it is the sum of subjective experiences of acute and chronic diseases, and the possibility of being affected by objective health indicators such as medical checkups is also inherent[7][9][22]. The stress perception rate was consistent with the results of previous studies that the lower the level of stress, the higher the quality of life, and stress had a major influence on health-related quality of life[17].

In the health questionnaire, as in previous studies, obesity, hypertension, diabetes, dyslipidemia, angina pectoris, myocardial infarction, osteoporosis, arthritis, cerebral stroke, and thyroid problems were all significant among the cases of chronic diseases indicating the current prevalence. The higher the status, the better the quality of life[9][12]. Similar to the study that highlighted the association between depression and quality of life, this paper also showed that depression lowered the quality of life [9][6]. In this regard, in order to reduce depression and improve the quality of life, social welfare policies should be established, and policy support should be actively provided to prevent and overcome depression.

Ⅴ. Conclusion

This study attempted to elucidate the factors influencing the health-related quality of life of Korean adults based on the EQ-5D index, utilizing data from the National Health and Nutrition Survey. Accordingly, the quality of life index (EQ-5D index), which is an index that combines the technical system of five dimensions of health-related quality of life, and demographic and health-related behaviors was designed using data from the 7th National Health and Nutrition Survey. Health-related factors were included in the model and analyzed. In this study, an independent sample t-test and ANOVA were performed to analyze the frequency analysis to understand the general characteristics of the subject, and to determine the difference between the index of life (EQ-5D) and the quality of life index according to health-related factors. In order to determine the influence of independent variables on health-related quality of life, multiple regression analysis was performed stepwise. Furthermore, we examined logistic regression analysis by categorizing the EQ-5D into “no problem” and “with problem.” The conclusions based on the results of this study are as follows.

First, as factors influencing the quality of life according to the general characteristics of adults in Korea, the health-related quality of life is lower when the gender is female, the higher the age, the less economically active, and being married. Conversely, it was found that the higher the income quartile of the household and the higher the educational level, the higher the health-related quality of life. These findings imply that in order to improve the health-related quality of life, management measures to improve the health-related quality of life of specific genders, especially middle-aged married women are required. And judging that there is a difference in the quality of health-related life according to the level of education and income, and judging the impact, the group with a relatively high level of education and a relatively high income is more active in the quality of life related to health. It has been assessed that they act to enhance the quality of life. Taking these factors into account, it is required to prepare policy alternatives to improve the quality of life related to health in more detail for groups with relatively low socioeconomic status.

Second, the factors that affect the quality of life according to the health behavior of Korean adults primarily affect the quality of life related to health when practicing high-intensity physical activity, walking, and when necessary medical services are met. These findings are of great significance in that they confirmed the same population of adults in Korea. In addition, the findings are significant in that physical activity among the factors of health behavior is something that can be easily obtained without requiring special testing equipment. Specifically, physical activity such as high intensity exercise or walking is an objective indicator that can be easily measured in the health care environment, and physical activity is an essential element for improving lifestyle and can be easily applied in communication with patients. There is a need to more easily access and understand physical activities that can affect the health-related quality of life.

Third, with regard to factors affecting the quality of life based on the health status of Korean adults, the higher the subjective health status, the lower the stress perception rate, depression, obesity, high blood pressure, and diabetes. In addition, the absence of angina pectoris, myocardial infarction, osteoporosis, arthritis, and stroke, etc. were found to improve the quality of life related to health. These findings suggest that the perception questions related to health-related quality of life are closely related to the current health status factors of the study subjects. Therefore, there is a need to prevent health issues such as stress, depression, obesity, high blood pressure, diabetes, angina pectoris, myocardial infarction, osteoporosis, arthritis, and stroke. In terms of policy, it seems imperative to prepare a management plan for this.

The limitations of this study are as follows. First, the data from the National Health and Nutrition Survey are cross-sectional in design. Therefore, the data are limited to explain the causal relationship between the factors and health-related quality of life. Second, the EQ-5D questionnaire used in the study has been answered by subjective judgment rather than medical assessment. Compared to other tools, the sensitivity is somewhat lower and has a ceiling effect[12] . Third, the current state of the disease, treatment effect, side effects of treatment, depression, psychiatric disorders, and other factors related to the disease were not considered, and were not related to diseases such as gender, coping style, social support, cognitive function, etc. There was a limitation in not considering one factor[21]. Despite these limitations, it is meaningful to determine the factors that affect the quality of life with representative data. In this study, health behavior and state of health were influencing the quality of life. Therefore, in order to improve the quality of life, an educational program to promote appropriate health behavior, prevent disease, and improve subjective health status should be devised.