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J Korean Gerontol Nurs > Volume 25(2):2023 > Article
Hwang and Lee: Influence of cognitive function and social support on health-related quality of life of elderly men in partial medically underserved rural areas: A cross-sectional study

Abstract

Purpose

This study aimed to identify factors influencing the health-related quality of life of elderly men in partial medically underserved rural areas.

Methods

The subjects included 182 elderly men aged 65 or older living in the jurisdictions of the Health Care Centers inGyeryong-, Iin-, and Jeongan-myeon, which were medically underserved rural areas in Gongju city, South Korea. Data were collected on October 13 to November 1, 2020, and were analyzed with descriptive statistics, t-test, ANOVA, Scheffé test, Pearson’s correlation coefficient, and hierarchical linear regression.

Results

The subjects’ scored mean 24.20±3.29 points out of 30 in cognitive functions and mean 17.08±9.40 out of 60 in social support. The influential factors included three chronic diseases (β=-.50, p<.001), two chronic diseases (β=-.30, p<.001), one chronic disease (β=-.21, p=.012); fair subjective health status (β=.24, p<.001); good subjective health status (β=.25, p<.001); cognitive functions (β=.17, p=.004); aged 80 or older (β=-.16, p=.006); and brushing teeth three times per day or more (β=.14, p=.009) with an explanatory power of 53.8%.

Conclusion

It is necessary to develop chronic disease management and cognitive function enhancement programs to identify factors influencing the health-related quality of life of elderly men in partial medically underserved rural areas and thus improve their health-related quality of life.

INTRODUCTION

Korea’s demographic structure is expected to become an aged society, with the elderly population aged 65 or older accounting for 17.5% of the total population in 2022, and rapidly reach a super-aged society with the elderly population exceeding 20.6% in 2025 [1]. Because the gender ratio (number of men per 100 women) for those aged 65 or older in 2010 was 68.6, and was projected to be 77.5 in 2021 and is projected to be 86.2 in 2050 [1], it is thought that the number of elderly men will continue to increase, and the life expectancy will be longer [2].
According to a survey of the elderly aged 65 or older in 2020 [3], the prevalence rate of chronic diseases among elderly men was 81.3%, the negative perceptions of subjective health status and need for improved nutrition were higher among the rural elderly than the urban elderly, and the exercise practice rate and exercise recommendations were lower among the rural elderly than the urban elderly. In addition, smoking and drinking rates were higher in men than in women and were higher in the rural elderly than in the urban elderly. Since the health status and behavior of the elderly vary by gender and region, it is important to identify the health-related characteristics of the elderly population by gender and region and to identify the quality of life accordingly.
The geographical location of rural regions can affect or be a determinant of the type of health-seeking behavior [4]. In other words, they experience difficulties in accessing the health care system, and in this case, they become dependent on home treatment and home care. In addition, the health-related quality of life of elderly men in medically underserved rural areas needs attention, as labor is closely linked to health behavior, leading to the postponement of health care until the condition is severe.
Elderly men aged 65 or older in medically underserved rural areas are primarily engaged in resource-based industries, such as agriculture and fisheries, mining, and stock-farming, and have higher health-related labor intensity and higher risk of exposure to harmful environments than elderly women [4]. In addition, life expectancy is 6.0 years shorter for men at 80.6 years and 86.6 years for women, and a healthy life expectancy is 3.4 years shorter for men at 71.3 years and 74.7 years for women [5]. However, the gender ratio of elderly men has been increasing in recent years, which makes it necessary to determine the extent and influencing factors of health-related quality of life in elderly men in medically underserved rural areas. Health-related quality of life is a multidimensional aspect directly related to individual health, refers to the subjective well-being felt by the individual in terms of physical, mental, social, and spiritual, and is effective as an indicator of the daily life function of the elderly [6]. Age, educational background, economic background, educational background, cognitive function, subjective physical symptoms, chronic disease, and subjective health status have been reported as variables affecting the health-related quality of life of the elderly [2,6].
Declining cognitive function showed a significantly lower self-esteem, increased stress, depression, and progression of dementia such as Alzheimer’s disease in the later stages of life [7] and affects the physical health-related quality of life [8]. Aging causes physical decline and negatively affects brain function. This can lead to a decrease in cognitive function, making it difficult to perform activities of daily living and isolating social activities [7], which makes it important to understand the degree of cognitive function in the elderly.
Social support is an important variable that helps people feel a sense of satisfaction, stability, and belonging. In old age, social support decreases due to aging, loss of social roles due to retirement, bereavement, children’s independence [9], and deteriorating quality of life [10]. Therefore, it is necessary to pay more attention to the health of the elderly in medically underserved rural areas than in urban areas and to make efforts to improve their quality of life [11].
Preceding studies on the health-related quality of life of the elderly were conducted on elderly women in rural areas [12], the elderly in urban areas [13,14], and the elderly using elderly centers [15] in Korea, and these studies mainly deal with the relationship between health behavior, social network, self-efficacy, and the health-related quality of life for elderly women [15], so there is a lack of studies on the health-related quality of life for elderly men. In the preceding studies [2], age-related and health-related quality of life influencing factors were studied, targeting the medically underserved population of elderly men with cognitive function and social support as variables, but it was difficult to find studies targeting elderly men in medically underserved rural areas.
Accordingly, this study aims to check cognitive function, social support, and the health-related quality of life for elderly men aged 65 or older in some medically underserved rural areas under the jurisdiction of the Health Care Center and provide basic data on the development of nursing interventions to improve the health-related quality of life by identifying these variables and factors affecting the health-related quality of life.
The purpose of this study is to determine the impact of cognitive function, social support, on the health-related quality of life among the selected elderly men in medically underserved rural areas in South Korea.

METHODS

Ethical statement: This study was approved by the Institutional Review Board (IRB) of Kongju National University IRB No: KNU_IRB_2020-76). Informed consent was obtained from the participants.

1. Study Design

This is a cross-sectional descriptive study to identify health-related quality of life determinants in selected elderly men in medically underserved rural areas.

2. Study Participants

Convenience sampling methods were utilized with elderly men aged 65 or older who lived in the medically underserved rural area of South Korea. Study participants were eligible when they were able to response to questions. However, the elderly who have been diagnosed with psychiatric diseases and dementia and are being administered were excluded.
Referring to the preceding studies [16], which measured the health-related quality of life for the elderly living in the community. The minimum sample size required was 175 in this study, which used the G*Power 3.1.9.2 program (University of Dusseldorf, Dusseldorf, Germany) by setting the Median effect size at 0.15 for multiple regression, 80% test power, a significance level of 0.05, and 16 input variables (14 general characteristics, 2 study variables). Considering the dropout rate, 210 copies of the questionnaire were distributed, and 182 people were targeted, excluding 28 copies with insufficient survey content.

3. Measurements

1) Cognitive Function

For cognitive function, this study used the Korean version of the Mini-Mental State Examination for Dementia Screening (MMSE-DS) tool [17] developed by the Ministry of Health and Welfare to standardize dementia diagnosis and used for dementia screening by public health centers nationwide since 2011. The MMSE-DS ranges from a minimum of 0 to a maximum of 30, with higher scores indicating better cognitive function, where a score of 24 or higher is categorized as “no” cognitive dysfunction, and a score of 24 or less is categorized as cognitive dysfunction being “present” [17]. With a total of 19 questions, the respondent’s cognitive function status is measured with 10 points for orientation awareness, 6 points for memory, 5 points for concentration, 3 points for language ability, 3 points for command execution, 1 point for figure imitation, and 2 points for judgment and common sense. At the time of tool development, the reliability was Cronbach’s α=0.83, and it was 0.81 in this study.

2) Social Support

For social support, the social support measuring tool developed by Jang [18] for institutionalized elderly and the modified tool by Yim and Lee [10] for home-staying elderly were used with permission from the developers of the modified tool. Spouses, children, siblings, friends, and neighbors are the sources of social support, consisting of six questions (2 questions for emotional support and 4 questions for instrumental support) for each support provider. Each question is answered on a 3-point Likert scale ranging from 0 “not at all” to 2 “very much so,” with higher scores indicating higher social support. The social support reliability was 0.81 at the time of tool development, 0.82 in Yim and Lee’s study [10], and it was 0.79 in this study.

3) Health-related Quality of Life

Health-related quality of life refers to the quality of life felt by an individual. For health-related quality of life, this study used EuroQol 5-dimensions (EQ-5D) developed by the EuroQol group [19] with permission from the EuroQol group. The EQ-5D consists of five domains: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression, where participants can respond on a three-point scale: “no problem”, “somewhat of a problem”, and “a serious problem”. The closer the score is to 1, the better the health-related quality of life. In Yang’s study [15], the reliability coefficient of EQ-5D was Cronbach’s α=0.80, and in this study, it was 0.86.

4. Data Collection

In this study, data were collected from October 13 to November 1, 2020, from elderly men aged 65 or older in the jurisdiction of Gyeryong-, Iin-, and Jeongan-myeon Health Care Centers, which are medically underserved rural areas in Gongju city, South Korea. The study manager visited the village hall and the subjects’ residences individually with the permission of the head of the Health Care Center, obtained consent to participate in the study on a 1:1 basis, and received consent to participate in the study in writing. Because the study subjects were elderly aged 65 or older, and it could be difficult for them to fill out the questionnaire due to problems with reading comprehension and vision, the study manager directly read the questionnaire and test paper and recorded the subjects’ answers, so it took about 20 to 25 minutes to complete the questionnaire. Due to COVID-19 that began in 2019, following the COVID-19 infection prevention and control recommendations, participants wore a mask to completely cover their mouth and nose, thoroughly sanitized their hands using hand sanitizer, and maintained a distance of 1 meter.

5. Data Analysis

The collected data were analyzed using the SPSS/WIN 25.0 program (IBM Corp.). Differences in health-related quality of life according to general characteristics of subjects were analyzed by independent sample t-test and one-way ANOVA, and Scheffé test was performed as a post hoc test. Hierarchical regression analysis was used to confirm the relative influence of independent variables among the factors that affect the subject’s health-related quality of life.

6. Ethical Considerations

This study was conducted after obtaining final approval (approval number: KNU_IRB_2020-76) from the Kongju National University Institutional Review Board Committee one day before data collection. Considering the ethical aspects of the study subjects when filling out the questionnaire, study subjects were informed of the purpose and procedures of the study, that anonymity was guaranteed, that participation was voluntary, that they could withdraw from the study, and that there were no penalties for withdrawal.

RESULTS

1. Subjects’ General Characteristics

The subjects of this study were 182 elderly men, with an average age of 74.45±7.34 years, 74 (40.7%) aged 70~79 years, and 108 (59.3%) with an elementary school education or less. Sixty-five subjects (35.7%) had less than 500,000 won in monthly income, 120 subjects (65.9%) had pain, 129 subjects (70.9%) had smoked in the past, 86 subjects (47.3%) were currently drinking, and 73 subjects (40.1%) replied they were subjective healthy. A total of 151 subjects (83.0%) had three or more meals daily, and 81 subjects (44.5%) had poor subjective oral health. A total of 114 subjects (62.6%) did not wear dentures, and 128 subjects (70.3%) brushed their teeth twice a day. Sixty-nine subjects (37.9%) suffered from one chronic disease, 77 subjects (42.3%) took one medication, and 104 subjects (57.1%) mainly used medical institutions (Table 1).

2. The Degree of Cognitive Function, Social Support, and Health-related Quality of Life of Subjects

The mean cognitive function was 24.20±3.29, of which 77 (42.3%) were below 24 with impairment of cognitive function and 105 (57.7%) were above 24 without impairment of cognitive function. The mean social support was 17.08±9.40, of which 10.34±2.36 was from spouses and 8.85±2.24 was from children. Support from siblings had a mean of 6.04±2.36, support from friends had a mean of 6.66±2.97, and support from neighbors had a mean of 5.60±2.14. Health-related quality of life was 0.79±0.22 (Table 2).

3. Differences in Health-related Quality of Life According to the General Characteristics of Subjects

Regarding the differences in health-related quality of life according to general characteristics, there was a significant difference in health-related quality of life according to age (F=21.51, p<.001), education level (F=12.65, p<.001), monthly income (F=11.08, p<.001), presence or absence of pain (t=-4.65, p<.001), smoking or non-smoking (F=3.93, p=.021), drinking or non-drinking (F=5.09, p=.007), subjective health status (F=32.74, p<.001), number of meals per day (t=-2.88, p=.007), subjective oral health (F=17.53, p<.001), presence or absence of dentures (t=-4.86, p<.001), number of times brushing teeth per day (F=9.46, p<.001), number of chronic diseases (F=32.60, p<.001), number of medications taken (F=23.29, p<.001), and primarily using medical institution (F=10.78, p<.001) (Table 3).

4. Correlation Between Subjects’ Cognitive Function, Social Support, and Health-related Quality of Life

Health-related quality of life showed a significant positive correlation with cognitive function (r=.46, p<0.001), but no significant correlation with social support. In other words, the higher the cognitive function, the higher the health-related quality of life (Table 4).

5. Factors Influencing the Subjects’ Health-related Quality of Life

Cognitive function and social support were put as independent variables, and health-related quality of life was put as a dependent variable. To verify the pure influence of the independent variable were converted into a dummy variable and put as control variables. As a result of the regression equation test, the Durbin-Watson value was 1.61, showing no autocorrelation, the tolerance limits of the variables were 0.38 to 0.97, which was more than 0.1, and the variance expansion index, which was 1.00 to 2.66, all less than 10, confirmed that there was no multicollinearity problem.
In Model I, age indicated a significantly lower quality of life for those aged 80 or older than those aged 65~69 (β=-.20, p=.001) and for those wearing dentures than those not wearing dentures (β=-.13, p=.026). Health-related quality of life was significantly higher for those with fair (β=.28, p<.001) and good (β=.31, p<.001) subjective health compared to poor (β=.14, p=.012) and for those brushing their teeth three or more times per day compared to one time per day (β=.14, p=.012). Having one (β=-.18, p=.032), two (β=-.29, p=.001), and three or more (β=-.50, p<.001) chronic diseases had a significantly lower health-related quality of life than having no chronic diseases; the higher the number of chronic diseases, the lower the quality of life, and their explanatory power was 51.8% (F=25.29, p<.001).
In Model II, among the existing control variables, except for denture-wearing status, the variables showed significant results, and cognitive function (β=.17, p=0.004) was found to have a significant effect on health-related quality of life, and the explanatory power increased to 53.8% (F=24.38, p<0.001) (Table 5).

DISCUSSION

This study attempted to identify factors affecting health-related quality of life in elderly men in medically underserved rural areas under the jurisdiction of Health Care Centers.
The mean health-related quality of life in this study was 0.79±0.22, which was favorable and similar to the results of the study by Lee [13], who measured health-related quality of life in elderly men patients with chronic diseases in urban areas. Most of these results show that the degree of health-related quality of life of patients with chronic diseases is thought to be similar because most of the elderly men patients in medically underserved rural areas also have chronic diseases. In the future, a study to confirm the health-related quality of life according to the residential area of urban and rural elderly men is needed. In Yi’s study [20] that measured the health-related quality of life of elderly women, it was slightly higher for urban and rural areas than this study. These results are considered gender-specific, and there is a lack of comparative studies of elderly men in rural areas, so future studies are needed to confirm health-related quality of life by region of residence and gender.
In this study, the number of chronic diseases had the greatest effect on health-related quality of life. In other words, the higher the number of chronic diseases a subject had, the lower the health-related quality of life. Yang et al.’s study [15], which targeted the elderly using urban and rural elderly centers, and Moon and Cha’s study [21], which analyzed the factors influencing the elderly’s health-related quality of life at multiple levels, also showed the number of chronic diseases. The higher the number, the lower the health-related quality of life, which was similar to the results of this study. In Im’s study [8], which targeted community-dwelling elderly women, chronic diseases were a factor affecting health-related quality of life, but in Lee’s study [22] on health-related quality of life by gender, the number of chronic diseases only affected the health-related quality of life of elderly men, but it can be seen that there are differences in study results by gender and region. Because the number of chronic diseases held by the elderly aged 65 or older affects the health-related quality of life, specific strategies are needed to reduce and prevent the number of chronic diseases in the elderly to increase the health-related quality of life of the elderly in medically underserved rural areas.
The second influencing factor was subjective health status: the better the health status, the higher the health-related quality of life. In Kim’s preceding study [11], which targeted the elderly in medically underserved rural areas, and Moon and Cha’s study [21], which targeted the elderly aged 65 or older, the better the subjective health perception, the higher the health-related quality of life, which was similar to this study. These findings suggest that elderly men in medically underserved rural areas are mostly engaged in farming, and rural residents view health as a labor-related state of functioning [4], defined as the ability to work, be productive, and perform daily tasks [4]. In Sohn’s study [23], which targeted rural elderly, the quality of life was higher for those who were able to do farm work than those who were not, supporting this. Therefore, it is necessary to apply and activate various health education and health promotion programs to improve the subjective health status of the rural elderly.
The next influencing factor was cognitive function: the higher the cognitive function, the higher the health-related quality of life. In Yang et al.’s preceding study [15] with the same tool, targeting the elderly using elderly centers, the cognitive function scores were similar to those in this study. In Lee’s study [13] which targeted elderly men patients aged 65 or older who regularly visit outpatient services at a general hospital in Seoul, the cognitive function averages were slightly higher than in this study. These results are thought to be different from the results of this study due to differences in demographic and regional variables in studies that examined cognitive function in elderly women [24], the urban elderly [13,24], and hospitalized elderly patients [13]. Therefore, since the degree of cognitive function differs by gender and region, when developing an intervention program to improve cognitive function, gender, a human variable, and region of residence, a regional variable, should be considered. In addition, in Im’s study [8], which targeted community-dwelling elderly women, cognitive function was found to have a significant effect on physical health-related quality of life, similar to this study. Therefore, since cognitive function is a factor that affects health-related quality of life, it is necessary to measure cognitive function regularly to detect and manage cognitive decline early and to develop and apply programs to improve cognitive function.
The next influencing factor was age: health-related quality of life was lower for those aged 80 and older than for those aged 65 to 69. In Yang et al.’s study [15], which targeted the elderly using urban elderly centers, the primary variable was age, and the higher the age, the lower the health-related quality of life, supporting the results of this study. In Kim and Sohn’s study [12], which targeted rural elderly women, it was found that the older the age, the lower the health-related quality of life. Since medically underserved rural areas are already heading toward being super-aged, it is necessary to develop intervention programs considering regions and age groups to improve health-related quality of life for those aged 65 or older and health-related quality of life for those aged 80 or older.
In this study, brushing teeth three or more times per day had a significantly higher health-related quality of life. In a study of factors related to the quality of life of the elderly in long-term care facilities [25], the more often teeth were brushed daily, the higher the health-related quality of life, similar to this study. These results suggest that a high frequency of daily teeth brushing is thought to prevent oral diseases and improve oral health and health-related quality of life. In a study on the effect of oral health behaviors on health-related quality of life by Shin et al.’s study [26], when teeth were brushed three or more times a day, subjects who engaged in good health behaviors also had higher health-related quality of life [27], which makes it necessary for the subjects to get self-care strategies for proper and regular brushing and continuous oral care.
In this study, whether or not wearing dentures affected the health-related quality of life in Model I, but not in Model II. Lee et al.’s EQ-5D preceding study [28], which targeted a rural population, it was found that denture wearers had lower health-related quality of life than non-wearers, but it did not affect health-related quality of life, supporting the present study. In Jung’s study [29] on the subjective oral health level, the use of dentures was evaluated as low as it caused discomfort, decreased masticatory ability, and increased the possibility of exposure to oral diseases. Therefore, oral health education, such as oral exercise education and proper denture management methods for the elderly using dentures, should be conducted, and health-related quality of life should be increased by improving denture use satisfaction.
In this study, social support did not affect the health-related quality of life. These results are thought to be due to the characteristics of family relationships and friend relationships in the community. In Yim and Lee’s study [10], elderly women accounted for 70.4% of the total respondents, and 29.6% of them had spouses, and among social support, only spouse support affected the quality of life, and there was no effect of other social support variables. Although social support did not have a significant effect on health-related quality of life in Kim et al.’s study [14] of community-dwelling elderly, it has been shown to be an important factor affecting quality of life in other studies [2,11,30], so it is necessary to confirm the effect of social support variables on health-related quality of life in elderly men in the future.
This study is a convenience sample of elderly men aged 65 or older in medically underserved rural areas in three counties, and there are limitations in generalizing these findings to elderly men in medically underserved rural areas in Korea. In addition, the study design was a cross-sectional study to identify factors that had a significantly high health-related quality of life among elderly men in medically underserved rural areas, and there were limitations in explaining the temporal sequence of influencing factors and variables. Nevertheless, this study is meaningful in that it identified factors affecting the health-related quality of life for elderly men in consideration of the aging society in which the gender ratio of elderly men in Korea is gradually increasing. It was confirmed that the number of chronic diseases, subjective health status, cognitive function, age, and number of teeth brushing times per day were influencing factors. This study can provide basic data for the development of multidimensional health management programs such as nutrition management, depression management, and physical activity self-help group formation to improve health-related quality of life for elderly men in medically underserved rural areas. In addition, there is a nursing significance that can provide implications for the establishment of an integrated care foundation for the community.

CONCLUSION

Through this study, the health-related quality of life of some elderly men in medically underserved rural areas was found to be low for subjects with a higher number of chronic diseases and was high for subjects with a lower age, better subjective health status, higher cognitive function, and higher number of brushing times per day. Therefore, it is thought that it can provide evidence for the development of nursing interventions to improve the health-related quality of life of elderly men in medically underserved rural areas.
Based on the results of this study, the followings are suggested. First, since elderly men in medically underserved rural areas have a higher number of chronic diseases and lower health-related quality of life, developing and implementing sustainable chronic disease management programs to manage and prevent chronic diseases and to verify their effectiveness are suggested. Second, detecting and managing cognitive function decline in the early stages by regularly measuring cognitive function and developing and applying a program to improve cognitive function are suggested. Third, elderly men in medically underserved rural areas are advised to participate in health education such as oral health education, education for establishing a healthy drinking culture, and dementia prevention education during farmer’s slack season and to seek ways to operate a local health community. Fourth, establishing a community support system based on the Health Care Center to strengthen the capacity of the Health Care Center and developing a plan to utilize it by reflecting the regional characteristics of medically underserved rural areas are suggested.

NOTES

Authors' contribution
Conceptualization and design - MHH and HKL; Literature search - MHH; Data collection - MHH; Analysis and interpretation - MHH and HKL; Writing-original draft and revision - MHH, HKL; Review and editing - MHH and HKL; Final approval - MHH and HKL
Conflict of interest
No existing or potential conflict of interest relevant to this article was reported.
Funding
None.
Data availability
Please contact the corresponding author for data availability.

ACKNOWLEDGEMENTS

This article is a revision of the first author’s master’s thesis from Kongju National University.

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Table 1.
General Characteristics of the Subject (N=182)
Variable Category n (%)
Age (year) 65~69 64 (35.2)
70~79 74 (40.7)
≥80 44 (24.2)
Education level ≤Elementary school 108 (59.3)
Middle school 45 (24.7)
≥High school 29 (15.9)
Income level (10,000 won/month) <50 65 (35.7)
50~99 44 (24.2)
100~199 45 (24.7)
≥200 28 (15.4)
Pain Yes 120 (65.9)
No 62 (34.1)
Smoking Never 17 (9.3)
Past 129 (70.9)
Current 36 (19.8)
Drinking Never 13 (7.1)
Past 83 (45.6)
Current 86 (47.3)
Subjective health status Good 73 (40.1)
Fair 51 (28.0)
Poor 58 (31.9)
Number of meals ≤2 31 (17.0)
≥3 151 (83.0)
Oral health status Good 47 (25.8)
Fair 54 (29.7)
Poor 81 (44.5)
Denture use Yes 68 (37.4)
No 114 (62.6)
Number of toothbrushing per day 1 26 (14.3)
2 128 (70.3)
≥3 28 (15.4)
Number of chronic disease None 25 (13.7)
1 69 (37.9)
2 56 (30.8)
≥3 32 (17.6)
Number of medication None 31 (17.0)
1 77 (42.3)
2 56 (30.8)
≥3 18 (9.9)
Mainly used medical institutions General hospital 31 (17.0)
Private hospital 104 (57.1)
Public health center 47 (25.8)
Table 2.
The Level of Cognitive Function, Social Support, Health-related Quality of Life (N=182)
Variable n (%) Mean±SD Range
Cognitive function 182 (100.0) 24.20±3.29 0~30
 Cognitive impairment (<24) 77 (42.3) 20.97±1.66 0~30
 Normal (≥24) 105 (57.7) 26.56±1.88 0~30
Social support 17.08±9.40 0~60
 Spouses 10.34±2.36 0~12
 Children 8.85±2.24 0~12
 Siblings 6.04±2.36 0~12
 Friends 6.66±2.97 0~12
 Neighbors 5.60±2.14 0~12
Health-related quality of life 0.79±0.22 0~1

SD=Standard deviation.

Table 3.
The Health-related Quality of Life According to General Characteristics (N=182)
Variable Category M±SD t/F p-value Scheffé
Age (year) 65~69a 0.89±0.15 21.51 <.001 a>b>c
70~79b 0.79±0.20
≥80c 0.64±0.26
Education level ≤Elementary schoola 0.73±0.23 12.65 <.001 a<c
Middle schoolb 0.84±0.20
≥High schoolc 0.94±0.10
Income level (10,000 won/month) 50a 0.70±0.23 11.08 <.001 a<c,d
50~99b 0.76±0.23 b<d
100~199c 0.86±0.19
200d 0.94±0.09
Pain Yes 0.74±0.22 -4.65 <.001
No 0.89±0.19
Smoking Nevera 0.93±0.11 3.93 .021 a>b
Pastb 0.77±0.23
Currentc 0.80±0.19
Drinking Nevera 0.80±0.20 5.09 .007 b<c
Pastb 0.74±0.27
Currentc 0.84±0.15
Subjective health status Gooda 0.88±0.14 32.74 <.001 a,b>c
Fairb 0.85±0.15
Poorc 0.63±0.26
Number of meals ≤2 0.66±0.28 -2.88 .007
≥3 0.82±0.20
Oral health status Gooda 0.89±0.20 17.53 <.001 a,b>c
Fairb 0.86±0.15
Poorc 0.69±0.23
Denture use Yes 0.69±0.24 -4.86 <.001
No 0.85±0.18
Number of toothbrushing per day 1a 0.65±0.28 9.46 <.001 a<b,c
2b 0.79±0.21
≥3c 0.90±0.11
Number of chronic disease Nonea 0.98±0.05 32.60 <.001 a>b,c>d
1b 0.86±0.14
2c 0.76±0.17
≥3d 0.55±0.30
Number of medication Nonea 0.96±0.10 23.29 <.001 a,b>c>d
1b 0.84±0.14
2c 0.70±0.25
≥3d 0.55±0.26
Mainly used medical institutions General hospitala 0.67±0.30 10.78 <.001 a<b,c
Private hospitalb 0.78±0.21
Public health centerc 0.89±0.13

SD=Standard deviation.

Table 4.
Correlations Among Cognitive Function, Social Support, Health-related Quality of Life (N=182)
Variable Cognitive function Social support Health-related quality of life
Cognitive function 1
Social support .04 (.595) 1
Health-related quality of life .46 (<.001) .01 (.895) 1

Values are presented as r (p-value).

Table 5.
Factors Influencing Health-related Quality of Life (N=182)
Variable Category Model Ⅰ*
Model Ⅱ
B β t p-value B β t p-value
Age (year) 65~69 (ref.)
70~79 -0.07 .24 -0.09 .680 -0.06 .37 -0.07 .680
≥80 -0.10 -.20 -3.44 .001 -0.08 -.16 -2.77 .006
Subjective health status Poor (ref.)
Fair 0.14 .28 4.40 <.001 0.12 .24 3.69 <.001
Good 0.14 .31 4.64 <.001 0.11 .25 3.64 <.001
Denture use Yes -0.06 -.13 -2.25 .026 -0.04 -.09 -1.6 0.108
Number of toothbrushing per day 1 (ref.)
2 0.09 .24 0.09 .475 0.08 .28 0.08 .474
≥3 0.08 .14 2.55 .012 0.08 .14 2.65 .009
Number of chronic disease None (ref.)
1 -0.08 -.18 -2.16 .032 -0.09 -.21 -2.53 .012
2 -0.14 -.29 -3.45 .001 -0.15 -.30 -3.69 <.001
≥3 -0.29 -.50 -6.32 <.001 -0.29 -.50 -6.51 <.001
Cognitive function 0.01 .17 2.90 .004

*R2=.54, adj R2=.52, F=25.29, p<.001;

R2=.56, adj R2=.54, F=24.38, p<.001.

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