Journal of Psychosocial Nursing and Mental Health Services

CNE Article 

Influence of Health Status, Cognitive Function, and Social Capital on Depressive Symptoms in Korean Older Adults

Hyeon-Seung Yun; Sung-Man Bae, PhD

Abstract

The current study explored the influence of health status, cognitive function, and social capital on depressive symptoms in Korean older adults. Data from the National Survey of Older Koreans, conducted by the Korean Institute for Health and Social Affairs in 2017, were used. Social capital was measured by dividing it into structural and cognitive social capital. Final analysis included 6,100 individuals aged ≥65 years (mean age = 72.6, SD = 5.8 years; 3,466 males [56.8%], 2,634 females [43.2%]). Multiple hierarchical regression analysis indicated that age, income, economic activity status, religion, household type, self-rated health, apoplexy (e.g., stroke, cerebral infarction), cognitive function, structural social capital, and cognitive social capital were significant predictors of depressive symptoms in older adults. This study suggests that researchers and clinicians should consider the factors associated with self-rated health, cognitive social capital, and economic status (income and economic activity) to prevent or alleviate depressive symptoms in older adults. [Journal of Psychosocial Nursing and Mental Health Services, 58(10), 24–31.]

Abstract

The current study explored the influence of health status, cognitive function, and social capital on depressive symptoms in Korean older adults. Data from the National Survey of Older Koreans, conducted by the Korean Institute for Health and Social Affairs in 2017, were used. Social capital was measured by dividing it into structural and cognitive social capital. Final analysis included 6,100 individuals aged ≥65 years (mean age = 72.6, SD = 5.8 years; 3,466 males [56.8%], 2,634 females [43.2%]). Multiple hierarchical regression analysis indicated that age, income, economic activity status, religion, household type, self-rated health, apoplexy (e.g., stroke, cerebral infarction), cognitive function, structural social capital, and cognitive social capital were significant predictors of depressive symptoms in older adults. This study suggests that researchers and clinicians should consider the factors associated with self-rated health, cognitive social capital, and economic status (income and economic activity) to prevent or alleviate depressive symptoms in older adults. [Journal of Psychosocial Nursing and Mental Health Services, 58(10), 24–31.]

The world is aging rapidly. Reports suggest that by 2050 the population aged ≥65 years (hereinafter referred to as older adults) will increase to 1.5 billion, and the proportion of older adults will change to approximately 1 in 6 (United Nations [UN], 2019). Particularly, the UN (2019) stated that South Korea is expected to have the highest predicted growth rate among older adults—23%. Among East Asia, which ranked first with a 37% older adult population, Korea had the highest forecasted growth. Korea has already entered an aged society and is expected to account for 42.5% of older adults by 2065 (Korean Statistical Information Services, 2020).

Old age is a time when individuals are vulnerable to depression due to loneliness, decreased physical and cognitive functioning, and decreased social capital, including social networks (Lee & Oh, 2016). According to the 2016 and 2018 National Health Insurance Statistical Yearbook (National Health Insurance Service & Health Insurance Review and Assessment Service, 2017, 2019), the number of older adults receiving treatment for depressive episodes increased from 188,642 in 2016 to 208,445 in 2018. Medical expenses for older adults due to depressive episodes also increased sharply from approximately 89 billion won in 2016 to approximately 1.1 trillion won in 2018. This increase in the depressed population of older adults caused a huge burden of economic and social costs (Chang-Quan et al., 2010; National Health Insurance Service & Health Insurance Review and Assessment Service, 2017, 2019).

Age-related physical diseases are consistently reported as risk factors associated with depression (Vink et al., 2008). Specifically, diabetes (Badescu et al., 2016), angina and myocardial infarction (Almeida et al., 2007), apoplexy (e.g., stroke, cerebral infarction) (Llorca et al., 2015), and osteoarthritis or rheumatoid arthritis (Drosselmeyer et al., 2017; Sharma et al., 2016) have been related to depression. However, the results of previous studies are not consistent. On the other hand, the relationship between self-rated health and depression in older adults showed consistent results (Chang-Quan et al., 2010).

Aging and deterioration in cognitive functions (e.g., memory, executive function, reasoning ability) are closely related. Cognitive function decline may cause decreased self-esteem and psychological atrophy, which in turn increases loneliness and depression (Lee, 2013). Previous studies have identified a relationship between cognitive function and depression (Gale et al., 2012; John et al., 2019). Several studies, including longitudinal studies and meta-analyses, argue that cognitive decline has a direct effect on depression in older adults (Brailean et al., 2017; Yu et al., 2018).

As such, physical illness and cognitive function decline are representative variables related to aging that can act as important risk factors for depression in older adults. Therefore, more effort is needed to identify variables that can alleviate the effects of these risk factors on depression. Social capital began to be used in earnest by French sociologist Bourdieu (1986). Bourdieu (1986) defined social capital as “the aggregate of the actual or potential resources which are linked to possession of a durable network of more or less institutionalized relationships of mutual acquaintance and recognition” (p. 21), whereas Putnam (1995) defined it as “features of social organization such as networks, norms, and social trust that facilitates coordination and cooperation for mutual benefit” (p. 66) (Carpiano, 2006; Jeong, 2019). Social capital has different components and definitions for different sectors and scholars (Bassett & Moore, 2013a,b; Bhandari & Yasunobu, 2009), but the network encompassing social relationships is an essential concept of social capital (Kobayashi et al., 2015; Lin, 1999). Particularly, for older adults, continuous interpersonal relationships, social support, and various social activities through the network not only provide opportunities to obtain other resources, but also contribute to the prevention of depression and promotion of successful aging (Fiori et al., 2006; Kobayashi et al., 2015).

Previous studies have shown that lack of social capital is related to depressive symptoms (Baiyewu et al., 2015; Fujiwara & Kawachi, 2008) and loneliness (Domènech-Abella et al., 2017). Conversely, the more social capital, the more positively individuals perceive their health status (Verhaeghe et al., 2012), and the lower the level of depression. However, most previous studies have a limitation in verifying the relationship between social capital and depression because social capital is limitedly measured by constructing different components of social capital.

In the current study, social capital was divided into quantitative and qualitative aspects. According to Harpham et al. (2002), social capital can be classified as “structural social capital” and “cognitive social capital.” Structural social capital includes “the extent and intensity of associational links or activity” (Harpham et al., 2002, p. 106) and is a quantitative aspect of social capital because it is characterized by what people do in social relationships. Cognitive social capital “covers perceptions, support, reciprocity, sharing, and trust” (Harpham et al., 2002, p. 106), and is a qualitative aspect of social capital because it is characterized by what people feel in social relationships. Therefore, the current study verified the association between depression and social capital in older adults by classifying participation in networking and volunteer activity as structural social capital, and satisfaction with interpersonal relationships and participation in activities as cognitive social capital.

Lastly, among sociodemographic variables related to depression in older adults, gender, age, education, income, economic activity, household type, and religion are consistently associated with depression (Jin, 2018; Vyas & Okereke, 2020). Generally, depressive levels tended to increase among women and with higher age (Glaesmer et al., 2011). The lower the education and economic levels, the higher the level of depression. Economic activity and living alone are associated with depression (Hung et al., 2017; Jin, 2018; Ladin et al., 2010). In addition, some studies have shown that religion contributes to reducing depression (Koenig et al., 2014).

Therefore, the current study aimed to verify the relationships between sociodemographic factors (e.g., gender, age, education, income level, economic activity status, religion, type of household), health status, cognitive function, social capital, and depression in older adults. This study primarily aimed to identify the association between structural and cognitive social capital and depression.

Method

Participants

Data from the National Survey of Older Koreans, conducted by the Korean Institute for Health and Social Affairs (KIHSA) in 2017, were used in this study. The survey's target population included those aged ≥65 years who lived in general residential facilities in 17 cities and provinces nationwide as of 2017. The 2010 Population and Housing Census were used as the sampling frame, and a stratified two-stage cluster sampling method was used for sampling. The survey was conducted by 60 pre-trained professional investigators between June 12, 2017, and August 28, 2018. They visited each house and conducted the survey through a one-on-one interview with older adults. A total of 10,299 older adults (mean age = 74.1, SD = 6.7 years; 4,375 males and 5,924 females) responded, and 6,100 (mean age = 72.6, SD = 5.8 years) were used for final analysis, excluding missing data such as non-response and proxy responses.

Measures

Sociodemographic Variables. Sociodemographic variables included gender, age, education, income level, economic activity status (“Have never worked in life,” “Have worked before, but not now,” or “Currently working”), religion (yes or no), and type of household. Participants were divided into five age groups (65 to 69, 70 to 74, 75 to 79, 80 to 84, ≥85), and education was analyzed by reclassifying participants into five groups (no formal education or less, elementary school, middle school, high school, college and university or higher). Income level was calculated by the method for calculating household equalized income (equalized household income = total household income ÷ number of household members) and divided into four groups (≤8.84 million won, ≤13.6896 million won, ≤21.8539 million won, ≥21.8540 million won). The types of household were classified into four groups (one person, older adult couple, older adult couple living with their adult children, other older adult type) and only two groups (older adult couple and older adult couple living with children) were included for final analysis.

Health Status. Self-rated health was measured using a 5-point Likert scale (1 = very ill health to 5 = very healthy); the higher the score, the better one's perceived health condition. Chronic diseases, such as apoplexy, angina, and myocardial infarction, diabetes, osteoarthritis (degenerative arthritis), and rheumatoid arthritis, were coded as 1 if diagnosed by a physician and endured for >3 months, and 0 if undiagnosed.

Cognitive Function. The Korean version of the Mini Mental State Examination for Dementia Screening (K-MMSE-DS), developed and standardized by Han et al. (2010), was used to understand cognitive function in older adults. The K-MMSE-DS is a test developed based on the detailed questions of the K-MMSE (Kang et al., 1997) and MMSE-KC (i.e., MMSE in the Korean version of the CERAD assessment battery) (Lee et al., 2002) to compensate for the shortcomings of the K-MMSE and MMSE-KC, which standardized the MMSE developed by Folstein et al. (1975) to screen for dementia. This tool comprises 30 questions: 10 questions for orientation, six questions for memory, five questions for attention and calculation, seven questions for language function, and two questions for understanding and judgment. The total score is 30; the higher the score, the higher the cognitive function.

Social Capital. Some questions corresponding to the “leisure activities and social activities” and “old-age and quality of life” sections of the 2017 National Survey of Older Korean Policy Report (KIHSA, 2017), determined through preliminary research (KIHSA, 2016) and expert conference, were used in the current study. Social capital was measured by dividing it into quantitative and qualitative aspects based on the concepts of structural social capital and cognitive social capital (Chang, 2014; Forsman et al., 2012, Harpham et al., 2002). Structural social capital was measured by the sum of participation in social clubs, networking (e.g., alumni, mutual aid group), political and social groups, volunteering activity, and visiting senior citizen and comprehensive community centers. The experience of volunteer activity was recorded as 0 for “Have never done in my life” and 1 for “Have done in the past, but not doing currently” and “Currently doing.” Regarding other participation and facility use, yes was scored 1 and no was scored 0. Cognitive social capital was measured as the sum of the satisfaction of relationships with spouse, children, friends, and regional society, and social/leisure/cultural activities. Each question was scored on a 5-point Likert scale (1= not satisfied at all to 5 = very satisfied); a higher score implied more cognitive social capital.

Depressive Symptoms. The Korean version of the Short-Form Geriatric Depression Scale (SGDS), developed by Yesavage et al. (1982) and validated by Cho et al. (1999), was used to measure the level of depressive symptoms. This inventory comprises 15 questions, and responses include yes or no. Scores range from 0 to 15. Cronbach's alpha for the SGDS was 0.89. Questions included “Have you dropped many of your activities and interests?”; “Do you feel that your life is empty?”; and “Do you often feel helpless?” among others.

Data Analysis

The t test and analysis of variance (ANOVA) were conducted to determine the differences between self-rated health, cognitive function, social capital, and depressive symptoms according to gender and education. In addition, we conducted a correlation and hierarchical multiple regression analysis using SPSS 23.0 to test the influences of sociodemographic variables, factors associated with health status, cognitive function, and social capital on depressive symptoms in Korean older adults. In the first stage, gender, age, education, income level, economic activity status, religion, and type of household were inserted. In the second stage, self-rated health and physician diagnosis of specific chronic diseases were inserted. In the third stage, cognitive function scores were inserted. Finally, in the fourth stage, structural and cognitive social capital scores were inserted.

Results

Sociodemographic Characteristics

Regarding education level, elementary school accounted for the largest percentage (35.6%), and middle and high schools accounted for similar percentages. The income level was the highest at 28.1% in the fourth rank and the lowest with 19.5% in the first rank. Concerning current employment status, the percentage of individuals who responded “Have worked before, but not now” was 55.2%, followed by “Currently working” and “Have never worked in life.” Compared to older adults without religion, 58.7% of older adults were religious. Older adult couple households accounted for the largest proportion of households (79.3%).Table 1 presents the sociodemographic variables and information on predictors.

Sociodemographic Characteristics (N = 6,100)

Table 1:

Sociodemographic Characteristics (N = 6,100)

t test and ANOVA

Table 2 presents the results of the t test performed to identify the gender differences in self-rated health, cognitive function, structural social capital, cognitive social capital, and depressive symptoms. Significant gender differences were found in self-rated health (t = 12.066, p < 0.001), cognitive function (t = 16.889, p < 0.001), structural social capital (t = 1.981, p = 0.048), cognitive social capital (t = 3.703, p < 0.001), and depressive symptoms (t = −8.087, p < 0.001).

t Test Results Between Gender

Table 2:

t Test Results Between Gender

Table 3 presents the results of ANOVA performed to examine the statistical differences between the education levels in the study variables. Significant differences were found between the education levels in self-rated health (F [4, 7273] = 101.633, p < 0.001), cognitive function (F [4, 7262] = 547.405, p < 0.001), structural social capital (F [4, 7273] = 72.728, p < 0.001), cognitive social capital (F [4, 6104] = 57.343, p < 0.001), and depressive symptoms (F [4, 7273] = 95.697, p < 0.001).

Analysis of Variance Results Between Education Level

Table 3:

Analysis of Variance Results Between Education Level

Correlation

Correlation analysis was performed to determine the relationship between measured variables, and the results are shown in Table A (available in the online version of this article). Self-rated health (r = −0.482, p < 0.001) was negatively correlated with depressive symptoms. Apoplexy (r = 0.161, p < 0.001), angina and myocardial infarction (r = 0.075, p < 0.001), diabetes (r = 0.108, p < 0.001), and osteoarthritis (degenerative arthritis) and rheumatoid arthritis (r = 0.166, p < 0.001) were positively correlated with depression. Cognitive function (r = −0.252, p < 0.001) was negatively correlated with depressive symptoms. In addition, structural social capital (r = −0.227, p < 0.001) and cognitive social capital (r = −0.448, p < 0.001) were correlated negatively with depression.

The results of correlation analysis between variables

Table A.

The results of correlation analysis between variables

Hierarchical Multiple Regression

The results indicated that age, income level, economic activity status, religion, type of household, self-rated health, apoplexy, cognitive function, structural social capital, and cognitive social capital had significant influences on depressive symptoms in Korean older adults (Table B, available in the online version of this article). On the other hand, gender, education, angina and myocardial infarction, diabetes, and osteoarthritis (degenerative arthritis) and rheumatoid arthritis did not impact depressive symptoms.

Hierarchical regression analysis

Table B.

Hierarchical regression analysis

First, among the sociodemographic variables, older age was positively associated with more depressive symptoms (β = 0.042, p < 0.001). Higher income levels were negatively associated with depressive symptoms (β = −0.073, p < 0.001). Older adults who were currently employed reported fewer depressive symptoms than those who were currently unemployed (β = −0.070, p < 0.001). Religious older adults reported fewer depressive symptoms than those who did not follow religious practices (β = −0.023, p < 0.05). Furthermore, older adult couples living with their adult children reported more depressive symptoms than those living without children (β = 0.040, p < 0.001).

Second, self-rated health was negatively associated with depressive symptoms (β = −0.334, p < 0.001), and only apoplexy was positively associated with depressive symptoms (β = 0.052, p < 0.001).

Third, cognitive function was negatively associated with depressive symptoms (β = −0.060, p < 0.001).

Fourth, social capital was negatively associated with depressive symptoms. Particularly, higher structural social capital was associated with fewer depressive symptoms (β = −0.032, p < 0.01), and higher cognitive social capital was associated with fewer depressive symptoms (β = −0.300, p < 0.001).

The most powerful predictor was self-rated health, followed by cognitive social capital and income level. The total explanatory rate of the regression model was 37%. The amount of R2 change was 11.4% (p < 0.001) in the first step (sociodemographic variables), 16.6% (p < 0.001) in the second step (factors associated with health status), 0.7% (p < 0.001) in the third step (factors related to cognitive function), and 8.3% (p < 0.001) in the final step (factors associated with social capital).

Discussion

The current study explored the influence of health status, cognitive function, and social capital on depressive symptoms in Korean older adults. It primarily aimed to identify how structural social capital and cognitive social capital played a role in depression in older adults. The following are the main results of this study.

First, depressive symptoms increased significantly with age but decreased with higher income levels. Current employment and religion were also negatively associated with depressive symptoms. However, gender and education were not related to depressive symptoms. These results suggest that older adults who have higher income are likely to have a greater chance of interacting with others through social activities, so that depressive symptoms may decrease. In addition, older adult couples living without children had significantly lower levels of depressive symptoms than those living with children. These results suggest that depression may be increased if children feel burdened as caregivers (Polenick & Martire, 2013).

Second, angina and myocardial infarction, diabetes, and osteoarthritis (degenerative arthritis) and rheumatoid arthritis were not associated with depression in older adults. However, perceived health status and apoplexy were significantly associated with depressive symptoms. These results suggest that subjective perception of one's health status may be more important in preventing depression than the presence of a disease. Apoplexy was the only chronic disease associated with depression. Stroke is caused by acute damage to blood vessels in the brain, specifically, damage to the frontal lobe and the basal ganglia. The areas where the stroke occurs frequently are associated with depression. In other words, mood disorders, such as depression, may occur due to damage to key areas related to emotional processing (Jastorff et al., 2015; Shi et al., 2017).

Third, a decline in cognitive function was associated with depressive symptoms (Lee, 2013). Cognitive function decline can negatively affect performance in various areas of life (e.g., meeting with friends, managing assets), causing stress, which in turn may lead to depressive symptoms (Brailean et al., 2017). Therefore, maintenance of cognitive function may be a major factor in preventing depression in older adults.

Fourth, the most important result in the current study was the significant relationship between social capital and depressive symptoms in older adults. In this study, social capital was divided into structural social capital and cognitive social capital, and both had a negative impact on depressive symptoms. Social capital contributes to healthy aging by enabling the formation and maintenance of interpersonal relationships, providing social support, and participation in various activities. Particularly, cognitive social capital is related to depressive symptoms rather than structural social capital. These results suggest that satisfaction with relationships and social participation are related to depressive symptoms more than the extent and intensity of the connection with others.

One of the contributions of this study was to verify the association of social capital with depression in older adults, while controlling for sociodemographic variables, health status, and cognitive function. Particularly, we identified that cognitive social capital is a stronger predictor of depressive symptoms than structural social capital. The results of this study can be used as basic data to prevent or alleviate depressive symptoms among older adults.

Limitations

The current study had several limitations. First, due to the cross-sectional design, the causality among variables cannot be guaranteed. Second, structural social capital was measured only by participation in social activities. Although this study included structural and cognitive social capital based on previous studies (Chang, 2014; Forsman et al., 2012; Harpham et al., 2002), every component of social capital could not be included due to the limited database. Therefore, future studies should include other components, such as the frequency of social activities, in the analysis.

Implications for Mental Health Nursing

Although constructing social networks through social activities and participation is important, merely increasing the structural social capital may be less helpful in decreasing depressive symptoms because of the psychological aspects of loneliness and lack of support. For example, Korean older adults sometimes visit places, such as the senior citizen center or comprehensive community center, where they socialize with other older adults. Such centers provide opportunities to expand their structural social capital by creating a place for people to meet new community members. However, if older adults do not experience satisfaction or social support through social interactions in these places, the effect of social capital in reducing depressive symptoms may be diminished.

Therefore, the current study suggests that clinicians consider the level of structural and cognitive social capital separately and try to elevate social capital to assess and prevent depression among older adults. The psychological concept of social capital must be understood and accepted by not only psychiatrists and clinical psychologists but also by professional nurses in the mental health field. These individuals could provide improved mental health services through enhanced psychological support for older adults if they attempt to surpass the traditional boundaries.

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Sociodemographic Characteristics (N = 6,100)

Variablen (%)
Gender
  Male3,466 (56.9)
  Female2,634 (43.1)
Age (years)
  65 to 692,332 (38.2)
  70 to 741,647 (27)
  75 to 791,284 (21.1)
  80 to 84616 (10.1)
  ≥85221 (3.6)
Education
  No formal education829 (13.6)
  Elementary school2,168 (35.6)
  Middle school1,233 (20.2)
  High school1,296 (21.2)
  College or university or higher574 (9.4)
Income level (million won)
  1st rank (≤8.84)1,190 (19.5)
  2nd rank (≤13.6896)1,536 (25.2)
  3rd rank (≤21.8539)1,662 (27.2)
  4th rank (≥21.8540)1,712 (28.1)
Economic activity status
  Have never worked in life569 (9.3)
  Have worked before, but not now3,364 (55.2)
  Currently working2,167 (35.5)
Religion
  Yes3,582 (58.7)
  No2,518 (41.3)
Type of the household
  Older adult couple4,840 (79.3)
  Living with adult children1,260 (20.7)

t Test Results Between Gender

VariableMean (SD)t Test
MaleFemale
Self-rated health3.14 (0.994) (n = 3,648)2.86 (0.957) (n = 3,631)12.066***
Cognitive function26.22 (3.203) (n = 3,644)24.77 (4.037) (n = 3,623)16.889***
Structural social capital1.135 (1.051) (n = 3,648)1.086 (1.07) (n = 3,631)1.981*
Cognitive social capital14.554 (2.133) (n = 3,470)14.351 (2.116) (n = 2,639)3.703***
Depressive symptoms3.374 (3.778) (n = 3,648)4.112 (4.005) (n = 3,631)−8.087***

Analysis of Variance Results Between Education Level

VariableSSdfMSFScheffe
Self-rated health
  Between group374.137493.534101.633***a<b<c<d<e
  Within group6,693.4447,2730.92
  Total7,067.5817,277
Cognitive function
  Between group23,216.70345,804.176547.405***a<b<c<d,e
  Within group76,999.5907,26210.603
  Total100,216.2937,266
Structural social capital
  Between group315.402478.85172.728***a<b<c<d<e
  Within group7,885.3247,2731.084
  Total8,200.7267,277
Cognitive social capital
  Between group1,001.4934250.37357.343***a<b<c,d<e
  Within group26,651.5856,1044.366
  Total27,653.0786,108
Depressive symptoms
  Between group5,562.40941,390.60295.697***e<d,c<b<a
  Within group105,686.1537,27314.531
  Total111,248.5627,277

The results of correlation analysis between variables

12345678910111213141516
11
2−.184***1
3−.246***−.077***1
4−.034**−.273***.295***1
5−.261***−.128***−.060***.074***1
6.180***−.045***.099***.053***−.064***1
7−.010−.097***.030.327***−.023*.0021

8−.110***−.153***.214***.185***.161***.011.0131
9−.065***.075***−.043***−044***−.038**−.028*−.013−.186***1
10−.052***.037**.000−.023*−.022*.017−.009−.160***.0161
11.002.023*−.028*−.013−.052***−.031**−.010−.240***.053***.040**1
12.266***.046***−.207***−.099***−.065***.053***−.011−.267***.008−.013.021*1

13−.111***−.249***.388***.196***.056***.068***.023*.235***−.117***.001−.042***−.146***1

14.020−.103***.170***.137***.091***.104***−.069***.152***−.060***−.013−.008−.050***.183***1
15−.048***−.119***.188***.195***.057***.094***−.072***.262***−.088***−.017−.019−.121***.217***.335***1

16.056***.182***−.194***−.222***−.156***−.067***.030**−.482***.161***.075***.108***.166***−.252***−.227***−.448***1

Hierarchical regression analysis

Independent variablesFinal Model (Model 4)

Bβt
(constant)18.11439.298

Gender−.107−.014−1.165
Age.140.0423.690***
Education−.041−.013−1.062
Income level−.255−.073−6.138***
Economic activity status−.431−.070−6.298***
Religion−.175−.023−2.144*
Type of senior citizen household.376.0403.648***

Self-rated health−1.290−.334−28.055***
Apoplexy(Stroke, cerebral infarction).760.0524.937***
Angina, myocardial infarction.157.0111.023
Diabetes.092.010.958
Osteoarthritis(degenerative arthritis), rheumatoid arthritis.161.0191.712

Cognitive function−.069−.060−5.167***

Structural social capital−.115−.032−2.908**
Cognitive social capital−.537−.300−26.405***

R237.0
Adjusted R236.9
Authors

Mr. Yun is Master's Student, and Dr. Bae is Professor, Department of Psychology, Graduate School, Dankook University, Cheonan-si, Chungcheongnam-do, South Korea.

The authors have disclosed no potential conflicts of interest, financial or otherwise.

The authors thank Editage ( www.editage.co.kr) for English language editing.

Address correspondence to Sung-Man Bae, PhD, Professor, Department of Psychology, Graduate School, Dankook University, 119, Dandae-ro, Dongnam-gu, Cheonan-si, Chungcheongnam-do, South Korea 31116; email: spirit73@dankook.ac.kr.

Received: April 29, 2020
Accepted: June 08, 2020
Posted Online: August 26, 2020

10.3928/02793695-20200817-01

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