Factors Affecting the Health-related Quality of Life of Older Adults with Unmet Healthcare Needs Based on the 2018 Korean National Health and Nutrition Examination Survey: A Cross-Sectional Study

Article information

J Korean Acad Fundam Nurs. 2022;29(2):258-268
Publication date (electronic) : 2022 May 31
doi : https://doi.org/10.7739/jkafn.2022.29.2.258
1)Visiting Professor, Department of Nursing, Kunjang Univerity College, Gunsan, Korea
2)Associate Professor, College of Nursing ․ Research Institute of Nursing Science, Jeonbuk National University, Jeonju, Korea
Corresponding author: Nho, Ju-Hee College of Nursing, Jeonbuk National University 567 Baekje-daero, Deokjin-gu, Jeonju 54896, Korea Tel: +82-63-270-3108, Fax: +82-63-270-3127, E-mail: jhnho@jbnu.ac.kr
Received 2022 February 24; Revised 2022 April 19; Accepted 2022 May 12.

Trans Abstract


This study aimed to identify the factors influencing the health-related quality of life (HRQoL) of older adults with unmet healthcare needs.


Data from the 2018 Korean National Health and Nutrition Examination Survey were used. From a pool of 7992 participants, a final sample of 153 participants was analyzed using complex descriptive statistics, independent t-test, and multiple regression analysis.


In general, young age, being married, employment, higher income, no hypertension, health insurance coverage as an employee, and living with someone were associated with higher the HRQoL. The following factors were significantly associated with lower HRQoL scores: activity restriction (B=−0.15, p<.001), poor perceived health status (B=−0.14, p<.001), and higher depressive symptom score (B=−0.01, p=.002). The explanatory power was approximately 58%, and the model was deemed suitable (Wald F=16.86, p<.001).


This study provides insights into the influence of various characteristics of older adults with unmet healthcare needs on their HRQoL. Healthcare providers should develop strategies to reduce these unmet healthcare needs.


In Korea, the proportion of older adults aged 65 years or older was 17.5% in 2021, accounting for more than 15.6% of the total population. Importantly, the country is aging faster than expected [1]: the proportion of older adults is expected to reach 20.0% in 2025, 30.0% in 2036, and 46.1% in 2065. Moreover, by 2025, Korea is expected to become a super-aged society with 10 million older adults. Meanwhile, medical needs and usage also increase with age due to aging-induced health declines, the prevalence of chronic diseases, and difficulties in medical service use due to various causes that negatively affect health-related quality of life (HRQoL) and perceived health status [2]. Given Korea's rapidly aging society, we must recognize the need to maintain individual health identified by medical experts and the fact that individuals need to uphold their own health. When participants do not receive the medical services they seek or which medical experts consider nec-essary, these needs are referred to as unmet healthcare needs [3].

Unmet healthcare needs are used as a criterion to eval-uate the national healthcare system. This is done by identifying the accessibility of medical care and is useful for de-termining whether patients’ demand for medical services is met [4]. Unmet healthcare needs may arise because of issues of availability, accessibility, and acceptability [3]. For example, in 2018, the proportion of Korean older adults with unmet healthcare needs is 17.5%, higher than that for all adults at 16.2% [5]. Thus, older adults are experiencing medical inequality regarding unmet healthcare needs. Therefore, the risk of spreading diseases and death is higher among older adults [6]. This is because as older adults have lower access to medical services, existing diseases may develop complications or worsen. Therefore, in Korea, unmet healthcare needs should be reduced and improvement measures should be implemented [7].

Studies have shown that 24.3% of individuals over 80 years old, 21.9% of those in their 70s, and 19.9% of those in their 60s experience unmet healthcare needs; thus, these needs seem to increase with age. Further, the unmet healthcare needs these individuals experienced were solvency (46.2%), convenience (22.7%), and accessibility limits (4.6%); this indicates that economic reasons are a major cause of unmet healthcare needs [8]. Moreover, women experience more unmet healthcare needs than men on each metric. Meanwhile, lower educational attainment is associated with higher limitations on one's daily life, suggesting its relevance [8,9].

Unmet healthcare needs may be associated with an individual's HRQoL. HRQoL refers to the physical and mental state of perceived well-being related to an individual's purpose, expectation, interest within culture, and values [10]. Studies report that depression, health promotion behavior, social support, and emotional stability affect HRQoL [11,12]. Further, being older, less educated, and less economically active are associated with a higher prevalence of chronic diseases that affect physical functioning and HRQoL [8,13]. Meanwhile, some report that daily activity restrictions and perceived health status have the greatest influence on HRQoL. Furthermore, mental health issues, such as depression and stress, can negatively affect HRQoL [14]. Indeed, one study reported that the higher the unmet healthcare needs, the lower the overall QoL [15]. As noted before, when people experience unmet healthcare needs, the disease worsens, and the possibility of complications increases; this can worsen QoL or even lead to death [16]. Considering Korea's aging society and older adults who particularly experience unmet healthcare needs, we need to identify factors that affect the HRQoL of these older adults. This can help develop programs that can effectively manage these factors.

One useful model for understanding factors affecting HRQoL is Ferrans et al.'s model [17]. Ferrans et al. [18] modified Wilson and Cleary's HRQoL model [18] to create an integrated model of the biomedical paradigm, which focuses on the cause of disease, and the social science paradigm, which focuses on patient functioning and overall well-being [17,18]. This model explains QoL in a dynamic and multidimensional manner and has been used to identify influencing factors in studies on unmet healthcare needs [17]. Therefore, this study uses Ferrans’ modified HRQoL model and its classification of factors (individual characteristics, biological function, symptoms, functional status, general health perceptions, and environmental characteristics) to explore the factors influencing QoL in older adults with unmet healthcare needs. Individual characteristics include demographic characteristics such as gender, age, marital intimacy, and employment. Biological function includes comorbidity and disease. Symptoms comprises subjective experiences and perceptions, such as mental health (depression and stress). Functional status consists of physical, social, role, and psychological functions, such as activity restriction. General health perceptions include the individual's mental health and subjective health evaluation, such as perceived health status. Finally, environmental characteristics include interpersonal relationships, health insurance, and regional factors such as living area [16,17].

In summary, older adults with unmet healthcare needs experience several problems, particularly regarding HRQoL. This study aimed to identify the factors influencing HRQoL in older adults with unmet health care needs. The specific aims were to identify: i) the general characteristics of unmet healthcare needs in older adults; ii) differences in HRQoL according to individual characteristics, biological function, symptoms, functional status, general health perceptions, and environmental characteristics of older adults with unmet healthcare needs; and iii) the factors influencing the HRQoL of these older adults.


1. Research Design

This cross-sectional correlational study was conducted to identify the characteristics of unmet healthcare needs and factors influencing HRQoL in older adults with unmet healthcare needs. In particular, this is a secondary data analysis study based on Ferrans’ model [17] and uses data from the 2018 seventh Korean National Health and Nutrition Examination Survey (KNHANES).

2. Data Sources

This study used data from the seventh KNHANES, which was conducted in 2018. KNHANES is conducted annually with the approval of the Research Ethics Review Committee of the Korea Center for Disease Control and Prevention. The KNHANES uses an annual sampling design, including a stratified, two-stage probability-cluster survey of a representative Korean population sample. The survey includes interviews, health behaviors and health examination studies, and nutrition surveys. This study utilized KNHANES medical use data to assess unmet medical needs, which were defined as answering “ yes” to the question “ did you ever need treatment (examination or treatment) at a hospital (excluding dentistry) in the last year?”

Of the 7,992 participants who participated in the 2018 KNHANES, data on 1,653 older adults (aged 65 years or older) were extracted. Of these, data on 153 participants were selected and analyzed, excluding 1,497 participants who did not have unmet healthcare needs and three who had missing values or responded with “unknown.” (Figure 1)

Figure 1.

Flowchart of the study population.

3. Measurement

1) Individual characteristics

Individual characteristics that were considered included gender, age, marital status, employment, education level, household income level, drinking, and smoking. Age was classified into “65∼69,” “70∼79,” and “80 or above.” Marital status was classified into “ married,” “ bereavement,” and “ others.” Education level was classified as “ elementary school or less,” “ middle school,” “ high school,” and “ college or higher.” Household income was classified into four groups, according to the instructions for using KNHANES, as follows: “ upper (>3 million KRW),” “ middle-high (2∼3 million KRW),” “ middle-low (100∼200 million KRW),” and “ lower (≤100 million KRW).” For the question, “ how often do you drink alco-hol?” those who drank more than once a month were classified as “ yes,” and those who did not drink were classified as “ no.” For smoking, currently smoking (both daily and occasional smoking) was classified as “ yes,” whereas smoking in the past but not now was classified as “ no.”

2) Biological function

Biological function includes chronic diseases such as hypertension, diabetes mellitus, and cancer. Responses to the question on whether a doctor had diagnosed the participant with hypertension, diabetes mellitus, and cancer were classified into “ yes” and “ no.”

3) Symptoms

Symptoms were classified as “ depressive symptoms” or “ perceived stress.” Depressive symptoms were measured using the Korean version of the Patient Health Questionnaire-9 (PHQ-9). The PHQ-9 comprises nine items, each scored from 0‒3, with higher scores indicating higher depression rates [19]. The cutoff score for depression in the PHQ-9 is greater than or equal to 10[20]. Following another study [6], responses on perceived stress were dicho-tomized as “ yes” (“ feeling a lot of stress” or “ feeling very much stress in everyday life”) or “ no”(responses of “ feeling less stress,” “ feeling a little stress,” or “ barely feeling stress in everyday life”).

4) Functional status

Functional status was classified as “ yes” or “ no” according to the participant's response to the question: “ Are you currently restricted from daily life and social activities due to health problems or physical or mental disorders?”

5) General health perceptions

General health perceptions were classified as “ good” (“ very good” and “ good”), “ normal”(“ normal”), or “ poor” (“ very poor” and “ poor”) based on the question, “ How do you feel about your health in general?”

6) Environmental characteristics

Participants’ environmental characteristics included health insurance, private insurance, living area, and living type. Health insurance was classified into “ self-employed,” “ employee,” and “ dependent.” Private insurance was classified into “ yes” and “ no” according to membership. The participants’ living area was classified into “ urban” and “ rural,” and the living type was reclassified into “ alone” and “ with someone.”

7) Health-related Quality of life

HRQoL was measured using the EuroQoL-5 Dimension (EQ-5D). The EQ-5D has been used in the KNHANES since 2005 and was developed by the EuroQoL Group [21]. The EQ-5D is divided into five dimensions: mobility, self-care, usual activity, pain/discomfort, and anxiety/ depression. Each dimension is measured using a three- point Likert scale, “ no problem,” “ moderate problem,” and “ severe problem,” ranging from 0 (dead) to 1 (full health). Here, the EQ-5D was calculated according to instructions for use in the KNHANES.

4. Ethical Consideration

This study was approved by the institutional review board of the researcher's medical institution (IRB no. 2022- 02-007) and submitted a pledge to implement statistics data user compliance and to remain true to the original data of the KNHANES. Data were collected using unique num-bers, making it difficult to identify individuals. Confiden-tiality and anonymity were secured because no personal information of participants was collected.

5. Statistical Analysis

Since the KNHANES was conducted using a complex sample design, an analysis plan file with household and individual weights (basic weight and correlation analysis weight) was created and analyzed using the IBM SPSS/ WIN 26.0 program (IBM Corp., Armonk, NY, USA). For weighting, the inclusion error due to the difference in the number of households and population between the sample design and survey times, unequal extraction rate, and the non-response error of non-participants in the survey were corrected; the aim was to improve the representativeness and accuracy of the related estimate.

For older adults’ characteristics, frequency and weight-ed percentage, estimated mean, and standard error were computed using complex sample frequency analysis. Com-plex sample t-tests were used, with significance set at p<.05. The effects of physical and psychological health-related factors on HRQoL, according to sedentary time of older adults living alone, were analyzed using linear regression analysis. The differences in HRQoL according to participant characteristics were tested using independent t-tests. Finally, the factors influencing participants’ HRQoL were assessed using multiple regression analysis.


1. Participants' General Characteristics

The total number of participants was 153, with 70.7% women, and the average age was 73.77±0.48. 41.1% of participants were aged between 70-79, whereas 26.5% were over 80. Overall, 47.5% of participants were married, 58.4% were unemployed, 78% had graduated from elementary school, and 56.7% were in the lower household income group. Regarding biological function, the order of prevalence of chronic diseases was as follows: hypertension (54.4%), diabetes mellitus (20.1%), and cancer (7.5%). Regarding symptoms, the mean score for depressive symptoms was 5.17; 18.0% of participants scored more than the threshold for depression (≥10), that is, suf-fered from depression. Further, 35.5% of participants perceived stress in their daily lives, and 29.5% of participants had activity restrictions, while 51.0% perceived their health status as poor. Regarding health insurance, 34.8% of the self-employed and 58.4% of all participants did not have private insurance. Regarding living areas, 74.9% lived in urban areas, whereas 66.6% lived with someone (Table 1).

Participants General Characteristics (N=153)

2. HRQoL according to the General Characteristics of the Unmet Healthcare Needs of Older Adults

Younger age was associated with a higher HRQoL score (F=4.52, p=.015); participants who were “65-69 years old” had higher HRQoL scores than those “80 years or older.” Married participants had significantly higher HRQoL ratings than those whose status was “ bereavement” and “ others”(F=7.83, p=.001). HRQoL was highest for college graduates (F=4.40, p=.007). HRQoL was higher for participants belonging to the “ upper” and “ middle-high” income group than those in the “ lower” income group (F= 4.80, p=.005).

In terms of biological function, the HRQoL score was higher for participants who did not suffer from hypertension than for those who did (t=2.24, p=.029). Regarding symptoms, those who did not report depressive symptoms and perceived stress had a higher HRQoL score than those who did (t=4.36, p<.001; t=2.60, p =.012, respectively). Participants who reported activity restriction had significantly higher HRQoL scores than those who did not (t=6.76, p<.001). In general, those with good health perceptions showed a higher HRQoL score than those with poor perception (F=74.29, p<.001).

Regarding environmental characteristics, HRQoL was significantly higher for those with private insurance (t= −3.16, p=.002). Finally, the HRQoL score for those living with someone was significantly higher than for those living alone (t=2.35, p=.022) (Table 2).

Health-related Quality of Life according to General Characteristics (N=153)

3. Factors Influencing the HRQoL of Older Adults with Unmet Healthcare Needs

Activity restrictions (B=−0.15, p <.001), and negative perceived health perception were significantly associated with a lower HRQoL score (B=−0.14, p<.001). A higher depressive symptom score was associated with a lower HRQoL score, indicating that depression significantly affected HRQoL (B=−0.01, p=.002). The explanatory power of these variables’ ability to explain HRQoL in older adults with unmet healthcare needs was approximately 58%, with the model being deemed suitable (Wald F=16.86, p<.001)(Table 3).

Factors related to Participants' Health-related Quality of Life (N=153)


This study identified factors influencing the HRQoL of older adults experiencing unmet healthcare needs using data from the seventh KNHANES conducted in 2018. Based on Ferrans’ HRQoL model [17], individual characteristics, biological function, symptoms, functional status, general health perceptions, and environmental characteristics were identified, categorized, and analyzed. The results show that activity restriction, perceived health status, and depressive symptoms affected HRQoL in older adults with unmet healthcare needs. These results can be used to identify ways to improve participants’ HRQoL by addressing their unmet healthcare needs.

Activity restriction had the greatest impact on the HRQoL of older adults with unmet healthcare needs. This result is consistent with another study, which showed that it was a major factor influencing HRQoL in older adults of all ages [22]. Suffering from chronic diseases can restrict the activities of older adults; for example, some report that 19-87% of older adults suffering from chronic diseases experienced activity restrictions [23]. In this study, more than 50% of participants had at least one chronic disease; thus, the sample appropriately reflects the characteristics of the participants. However, since this study did not con-firm the relationship between activity restriction, chronic disease, and HRQoL, further research should examine the relationship between these variables. In addition, detailed intervention directions for each activity restriction area should be developed by comparing and analyzing the differences in HRQoL according to the activity restriction area.

Depression was reported to be higher in older adults than all adults because of physical illness, loss of family, and the loss of their role due to aging [24,25]. Depression can be reduced by expanding the social support system network to receive attention and support [25]. One effective way to reduce depression is to increase social support. Importantly, a method that uses an integrated community approach is needed to assess and prevent depression in older adults at an early stage. In addition, small group gatherings should be organized in the local community for older adults who lack contact with their families and chil-dren; furthermore, older adults should be supported to participate in the program [26].

Perceived health status is the subjective satisfaction of older adults with their health and their lack of confidence in their health [27]. The perceived health status of older adults is also related to physical, psychological, and social factors. Therefore, an integrated institutional care service is required to positively recognize the perceived health status of older adults with unmet healthcare needs. In particular, to reduce their depression and change their perceived health status, the causes of unmet healthcare needs should be identified in detail, and the QoL can then be improved through systematic medical care.

Here, most older adults who experienced unmet medical needs were women. This may be attributed to the soci-ocultural characteristics of older adult women. They often lack adequate preparation for old age in a male-centered, patriarchal society and report higher unmet healthcare needs from physical, social, and economic disadvantages than men [28]. Therefore, policies that consider gender should be prioritized while improving medical access. Further, nursing interventions for older adults should re-flect the differences in methods according to gender [29].

Regarding age, unmet needs were higher for those aged 70∼79 years than 65∼69 years, consistent with previous studies [28]. As unmet healthcare needs are related to diseases, complications, and the mortality rate of older adults [7], continuous monitoring and measures to reduce unmet healthcare needs are required. To reduce unmet needs, interventions should be tailored to the characteristics of each individual target to provide customized health and medical services and expand support for human resources and mobility services. To address older adults’ lack of economic and social resources, measures are needed to lower the burden of health and medical expenses; further, access to medical care must be improved through the social support of the local community.

As shown in the literature, those who lived alone and had lower education and income had a higher prevalence of unmet medical experience [9]. These are characteristic of vulnerable populations who are highly likely to be ex-posed to harmful environments. The HRQoL of these individuals is negatively impacted by economic difficulties and social alienation [30]. Thus, effective interventions are required to resolve this health inequality. In other words, since the HRQoL of older adults is closely related to their economic status, a broader economic support policy and linking social support networks within the community can solve social alienation in old age; further, various social activity programs can be helpful. This also affects their access to medical services. For example, education and income are related to HRQoL [14]. Further, this study showed that the level of activity restriction differed according to income level; that is, the degree of medical service use differed according to socioeconomic solvency. In addition, the HRQoL score was significantly higher in the absence of hypertension, which is a chronic disease. Others also report that HRQoL decreases with chronic diseases, and the HRQoL of hypertensive patients is related to the practice of steady health care behavior and that strategies are needed to improve it [31].

Older adults with depression and stress had lower HRQoL. This suggests the need for mental health care programs and interventions, as well as physical factors, which is in line with the literature [9]. Further, individuals who perceived their health to be bad reported lower HRQoL scores than those who reported good. In addition, individuals with private insurance had higher HRQoL scores than those without private insurance. This can be considered a major influencing factor because the burden of disease treatment is low with insurance.

Finally, HRQoL scores were higher for those who live with someone than those who live alone. People may live alone because of changes in social structure, divorce, and bereavement. This finding is consistent with previous research [32], which indicates that these individuals experience difficulties, depression, anxiety, poor housing conditions, lower HRQoL, income level, health status, and social interest. In addition, older adults who involuntarily live on their own need support. Moreover, social policies that help reduce unmet healthcare needs, as well as address health vulnerabilities and inequalities are warranted.

This study has limitations. First, participants with unmet healthcare needs were selected based on answers to a single question. Future research is needed on selecting such participants by considering social or organizational factors. Second, the revised Wilson and Cleary HRQoL model describes the effect of individual and environmental characteristics on biological function and the interactions between an individual and their environment. However, this study did not list the results of these effects and interactions. Despite these limitations, this study con-tributes to the literature by revealing the HRQoL of older people who experience unmet healthcare needs, identifying the factors affecting HRQoL, and obtaining basic data for improving HRQoL. Researchers and practitioners can use this study and the associated data to promote personal and socioeconomic health policies to improve the HRQoL of older adults who experience unmet healthcare needs.


This study identified factors that affect the HRQoL of older adults who experience unmet healthcare needs using the 2018 data from the seventh KNHANES survey. This study's findings show that these older adults are often from the vulnerable class; thus, interventions should consider the physical, psychological, and environmental factors of older adults. In addition, nursing interventions and policies are needed to improve the HRQoL of these older adults. Finally, the development and application of various individualized programs that can reduce depression and improve subjective health status are required.



The authors declared no conflict of interest.


SStudy conception and design acquisition - SS-J and NJ-H; Data collection - NJ-H; Data analysis & Interpretation - SS-J and NJ-H; Drafting & Revision of the manuscript - SS-J and NJ-H.


1. Statistics Korea. Statistical indicators [Internet] Daejeon: Statistics Korea; 2022. [cited 2021 December 09]. Available from: https://kosis.kr/statHtml/statHtml.do?orgId=101&tblId=DT_1BPA002&vw_cd=&list_id=&scrId=&seqNo=&lang_mode=ko&obj_var_id=&itm_id=&conn_path=E1&docId=03842&markType=S&itmNm.
2. Lord SR, Murray SM, Chapman K, Munro B, Tiedemann A. Sit-to-stand performance depends on sensation, speed, bal-ance, and psychological status in addition to strength in older people. The Journal of Gerontology Series A. 2002;57(8):M539–M543. https://doi.org/10.1093/gerona/57.8.M539.
3. Donabedian A. Aspects of medical care administration: speci-fying requirements for health care. Cambridge: Harvard University Press; 1973. p. 65–68.
4. Cunningham PJ, Hadley J, Kenney G, Davidoff AJ. Identifying affordable sources of medical care among uninsured persons. Health Services Research. 2007;42(1p1):265–285. https://doi.org/10.1111/j.1475-6773.2006.00603.x.
5. Lee HJ, Huh SI. Unmet health care needs and impact of type of household among the elderly in Korea. The Korean Journal of Health Economics and Policy. 2017;23(2):85–108.
6. Nho JH, Park SK. Factors affecting unmet healthcare needs of low-income overweight and obese women in Korea: analysis of the Korean National Health and Nutrition Examination Survey. Korean Journal of Women Health Nursing. 2021;27(2):93–103. https://doi.org/10.4069/kjwhn.2021.05.06.
7. Diamant AL, Hays RD, Morales LS, Ford W, Calmes D, Asch S, et al. Delays and unmet need for health care among adult pri-mary care patients in a restructured urban public health system. American Journal of Public Health. 2004;94(5):783–789. https://doi.org/10.2105/ajph.94.5.783.
8. Jung YH. Limited activities and unmet medical care on the Korean health panel. Korea Institute for Health and Social Affairs. 2012;120(1):1–8.
9. Choi HY, Rhu SY. Factors associated with the types of unmet health care needs among the elderly in Korea. The Korean Journal of Health Service Management. 2017;11(2):65–79. https://doi.org/10.12811/kshsm.2017.11.2.065.
10. Jo MW, Lee SI. General population time trade-off values for 42 EQ-5D health states in South Korea. Journal of Preventive Medicine and Public Health. 2007;40(2):169–176.
11. Kim YS. The study of the impact of the Family type on the health promoting behavior and physical and mental health of elderly people. Health and Social Welfare Review. 2014;34(3):400–429. https://doi.org/10.15709/hswr.2014.34.3.400.
12. Kim SY, Choi KW, Oh HY. Relationships of social networks to health status among the urban low-income elderly. The Korean Journal of Rehabilitation Nursing. 2010;13(1):53–61.
13. Kwon MJ. Factors influencing quality of life elderly who live alone, depending on gender. Journal of Digital Convergence. 2018;17(1):365–373. https://doi.org/10.14400/JDC.2019.17.1.365.
14. Lee SH. Gender difference in influencing factors on health related quality of life among the elderly in community. Journal of Digital Convergence. 2013;11(12):523–535. https://doi.org/10.14400/JDPM.2013.11.12.523.
15. Lee JW. Effect of unmet healthcare needs on quality of life. Journal of the Korea Acacemia-Industrial cooperation Society 2020;21(9):283–290. https://doi.org/10.5762/KAIS.2020.21.9.283.
16. Diamant AL, Hays RD, Morales LS, Ford W, Calmes D, Asch S, et al. Delays and unmet need for health care among adult pri-mary care patients in a restructured urban public health system. American Journal of Public Health. 2004;94(5):783–789. https://doi.org/10.2105/ajph.94.5.783.
17. Ferrans CE, Zerwic JJ, Wilbur JE, Larson JL. Conceptual model of health-related quality of life. Journal of Nursing Scholar-ship. 2005;37(4):336–342. https://doi.org/10.1111/j.1547-5069.2005.00058.x.
18. Wilson IB, Cleary PD. Linking clinical variables with health-related quality of life. A conceptual model of patient outcomes. JAMA. 1995;273(1):59–65.
19. Kroenke K, Spitzer RL, Williams JB. The PHA-9: validation of a brief depression severity measure. Journal of General Internal Medicine. 2001;16(9):606–613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x.
20. Snijkers JTW, van den Oever W, Weerts ZZRM, Vork L, Mujagic Z, Leue C, et al. Examining the optimal cutoff values of HADS, PHQ-9 and GAD-7 as screening instruments for depression and anxiety in irritable bowel syndrome. Neurogastroenterology and Motility. 2021;33(12):e14161. https://doi.org/10.1111/nmo.14161.
21. van Reenen M, Oppe M. EQ-5D-3L user guide basic information on how to use the EQ-5D-3L instrument. Version 5.1. Rotterdam, the Netherlands: EuroQoL Group; 2015. p. 1–22.
22. Kim EK. Age difference in factors associated with health-related quality of life among elderly. Journal of the Korean Data Analysis Society. 2017;19(5):2807–2823.
23. Hwang HS, Choi JH, Kim SK. Factors affecting activity restriction in the elderly with chronic disease: using data from the 8th period of the National Health and Nutrition Examination Survey. Journal of the Korea Convergence Society. 2021;12(11):359–369. https://doi.org/10.15207/JKCS.2021.12.11.359.
24. Jang KO. Effects of elderly people's frail prevention program on subjective health status, depression, physical fitness and quality of life for in senior center participation of the elderly. Journal of the Korea Academia-Industrial cooperation Society. 2017;18(5):47–58. https://doi.org/10.5762/KAIS.2017.18.5.47.
25. Lee HS. The factors influencing health related quality of life in the elderly: focused on the general characteristics, health hab-its, mental health, chronic diseases, and nutrient intake status: data from the fifth Korean National Health and Nutrition Examination Survey (KNHANES V), 2010∼2012. Korean Journal of Community Nutrition. 2014;19(5):479–489. https://doi.org/10.5720/kjcn.2014.19.5.479.
26. Jang HY, Lee HI. Factors influencing unmet healthcare needs among elderly living alone. Journal of the Korean Data Analysis Society. 2017;19(6):3317–3392.
27. Ko YH. The relationships among the physical competence, subjective health status, and health promoting behavior of elderly participating in health activity program. Journal of Digital Convergence. 2016;14(12):571–581. https://doi.org/10.4400/JDC.2016.14.12.00.
28. Chae HJ, Kim MJ. Unmet healthcare needs and related factors according to gender differences in single-person household. Korean Journal of Women Health Nursing 2020;26(1):93–103. https://doi.org/10.4069/kjwhn.2020.03.23.
29. Moon JH, Kang MA. The prevalence and predictors of unmet medical needs among the elderly living alone in Korea: an application of the behavioral model for vulnerable populations. Health and Social Welfare Review 2016;36(2):480–510. https://doi.org/10.15709/hswr.2016.36.2.480.
30. Kim JI. Levels of health related quality of life (EQ-5D) and its related factors among vulnerable elders receiving home visiting health care services in some rural areas. Journal of Korean Academic Community Health Nursing. 2013;24(1):99–109. https://doi.org/10.12799/jkachn.2013.24.1.99.
31. Lee KE, Cho EH. Factors influencing health related quality of life patients with hypertension: Based on the 5 th Korean National Health and Nutrition Examination Survey. The Journal of the Korea Contents Association. 2016;16(5):399–409. https://doi.org/10.5392/JKCA.2016.16.05.339.
32. Kim YJ, Cho SJ, Hwang BD. Factors associated with unmet healthcare needs according to households. The Korean Journal of Health Service Management. 2018;12(2):39–49. https://doi.org/10.12811/kshsm.2018.12.2.039.

Article information Continued

Figure 1.

Flowchart of the study population.

Table 1.

Participants General Characteristics (N=153)

Factors Variables Categories n or Estimated M± SE Weighted %
Individual characteristics Gender Women 111 70.7
Men 42 29.3
Age 65∼69 52 32.4
70∼79 70 41.1
≥80 31 26.5
Marital status Married 81 47.5
Bereaved 52 39.7
Others 20 12.8
Employment Yes 64 41.6
No 89 58.4
Education level ≤ Elementary school 115 78.0
Middle school 16 8.7
High school 13 7.7
≥ College 9 5.5
Household income Upper (>3 million KRW) 8 4.9
Middle-high (2∼3 million KRW) 20 13.8
Middle-low (1∼2 million KRW) 37 24.6
Lower (≤1 million KRW) 87 56.7
Drinking Yes 33 24.0
(≥1 time /month) No 120 76.0
Current smoking Yes 11 6.6
No 142 93.4
Biological function Chronic disease
  HTN Yes 87 54.4
  DM Yes 34 20.1
  Cancer Yes 12 7.5
Symptom status Depressive symptoms Yes (≥10) 24 18.0
No (<10) 128 82.0
Perceived stress Yes 51 35.5
No 102 64.5
Functional status Activity restriction Yes 46 29.5
No 107 70.5
General health perception Perceived health status Poor 86 51.0
Ordinary 53 37.9
Good 14 11.1
Environmental characteristics Health insurance Self-employed 47 34.8
Employee 91 54.1
Dependent 15 11.1
Private insurance Yes 64 41.6
No 88 58.4
Living area Urban 107 74.9
Rural 46 25.1
Living type Alone 49 33.4
With someone 104 66.6

Unweighted count (frequency) DM=diabetes mellitus; HTN=hypertension; SE=standard error.

Table 2.

Health-related Quality of Life according to General Characteristics (N=153)

Factors Variables Categories Mean± SE t or F (p)
Individual characteristics Gender Women 0.77±0.03 1.93
Men 0.84±0.03 (.059)
Age 65∼69 a 0.85±0.02 4.52
70∼79 b 0.80±0.02 (.015)
≥80 c 0.71±0.04 a> c
Marital status Married a 0.85±0.02 7.83
Bereaved b 0.75±0.03 (.001)
Others c 0.69±0.05 a> b, c
Employment Yes 0.80±0.02 -0.47
No 0.79±0.02 (.643)
Education level ≤ Elementary school a 0.78±0.02 4.40
Middle school b 0.79±0.04 (.007)
High school c 0.89±0.04 a< c, d; b< d
≥ College d 0.91±0.04
Household income Upper a 0.89±0.03 4.80
Middle-high b 0.82±0.04 (.005)
Middle-low c 0.86±0.03 a, b> d
Lower d 0.75±0.03
Drinking Yes 0.81±0.04 -0.60
(≥1 time/month) No 0.79±0.02 (.549)
Current smoking Yes 0.78±0.07 0.16
No 0.79±0.02 (.876)
Biological function Chronic disease
  HTN Yes 0.76±0.03 2.24
No 0.83±0.02 (.029)
  DM Yes 0.82±0.02 -0.94
No 0.79±0.02 (.349)
  Cancer Yes 0.77±0.07 0.38
No 0.79±0.02 (.705)
Symptoms Depressive symptoms Yes 0.60±0.52 4.36
No 0.83±0.15 (<.001)
Perceived stress Yes 0.73±0.03 2.60
No 0.83±0.02 (.012)
Functional status Activity restriction Yes 0.61±0.04 6.76
No 0.87±0.01 (<.001)
General health perceptions Perceived health status Poor a 0.68±0.02 74.29
Ordinary b 0.89±0.02 (<.001)
Good c 0.97±0.01 a< b< c
Environmental characteristics Health insurance Self-employed 0.75±0.03 2.53
Employee 0.83±0.02 (.089)
Dependent 0.74±0.06
Private insurance Yes 0.85±0.02 -3.16
No 0.75±0.03 (.002)
Living area Urban 0.81±0.02 1.48
Rural 0.75±0.02 (.145)
Living type Alone 0.73±0.04 2.35
With someone 0.83±0.02 (.022)

DM=diabetes mellitus; HTN=hypertension; SE=standard error;

Holm-Bonferroni method.

Table 3.

Factors related to Participants' Health-related Quality of Life (N=153)

Variables Categories B SE t p 95% CI
Age (year) 65∼69 0.02 0.03 0.52 .608 -0.05∼0.08
70∼79 -0.02 0.03 -0.63 .531 -0.08∼0.04
Marital status Married 0.04 0.04 0.97 .334 -0.04∼0.12
Bereaved -0.08 0.05 -1.68 .099 -0.17∼0.02
Education level ≤ Elementary school 0.01 .043 0.27 .790 -0.07∼0.10
Middle school -0.02 0.05 -0.29 .775 -0.01∼0.09
High school 0.02 0.05 0.51 .612 -0.07∼0.11
Household income Upper 0.04 0.04 1.09 .282 -0.03∼0.11
Middle-high 0.01 0.04 0.37 .716 -0.06∼0.09
Middle-low 0.02 0.04 0.60 .554 -0.05∼0.10
HTN No -0.01 0.03 -0.18 .857 -0.06∼0.05
Depressive symptoms -0.01 0.00 -3.17 .002 -0.02∼-0.00
Perceived stress No -0.00 0.03 -0.11 .916 -0.07∼0.06
Activity restriction Yes -0.15 0.04 4.27 <.001 0.08∼0.22
Perceived health status Poor -0.14 0.03 -5.03 <.001 -0.19∼-0.08
Ordinary -0.03 0.03 -0.95 .344 -0.08∼0.03
Private insurance No -0.04 0.02 -1.67 .101 -0.09∼0.01
Living type Alone -0.05 0.04 -1.10 .276 -0.14∼0.04
R2=.58, F=16.86, p<.001

CI=confidence interval; HTN=hypertension; SE=standard error; Reference values: age (≥80), marital status (other), educational level (≥college), income (lower), HTN (yes), subjective stress (yes), activity restriction (yes), perceived health status (good), private insurance (yes), living type (living with someone).