Bridging Potential in Friend Networks and Cognitive Function in Older Korean Americans

Abstract

Objectives

To examine the associates of bridging potential in networks of friends and its impact on cognitive function in older Korean Americans.

Methods

Data came from 308 participants in Central Florida who were part of the Study of Older Korean Americans (SOKA). Using data from a name generator of three close friends in local Korean communities, non-overlapping networks were quantified for bridging potential. Linear regression models for bridging potential and cognitive function were examined.

Results

Bridging potential was greater among those with stronger social ties with friends in general and higher levels of activity engagement. The direct benefit of bridging potential on cognitive function was also confirmed. In addition, bridging potential was found to mediate the effect of social ties with friends on cognitive function.

Discussion

Findings underscore the interconnectedness of older immigrants’ social and cognitive function and shed light on interventions.

Keywords

social network analysis bridging potential cognitive status older immigrants

Introduction

Since the early gerontological theories (e.g., activity theory, disengagement theory, continuity theory, social convoy theory, socioemotional selectivity theory), the intersection of social networks and health has been at the core of attention (Bengtson & Settersten, 2016; Roth, 2020). Social networks, often quantified by the number of family and friends and the frequency of contact with them, offer a structure of social relations and serve as a “support bank” from which older individuals can draw help and assistance in times of need (Antonucci et al., 1990; Berkman et al., 2000; Lubben et al., 2006). There is an accumulated body of evidence that social networks not only provide direct benefits to older individuals’ health and well-being but also buffer the negative consequences of late-life stresses such as health decline, caregiving, and bereavement (Pearlin & Bierman, 2013). The health risks associated with a deprived social network or social isolation have been identified as a public health concern (Agency for Healthcare Research and Quality, 2019).

Although attention has been focused on physical and mental health consequences, emerging studies also report that social networks are closely linked with cognitive health (Evans et al., 2019; Kuiper et al., 2016). Cognitive function may be positively influenced by interpersonal connectedness (Evans et al., 2019; Kuiper et al., 2016) and engagement in diverse activities (Hertzog et al., 2008; Kelly et al., 2017; Lee et al., 2021). Active engagement with life, known as a critical element of successful aging (Rowe & Kahn, 2015), stimulates brain activities, helping older adults maintain and further enhance their cognitive function (Hertzog et al., 2008; Kelly et al., 2017). In particular, the ties and activities with friends, rather than with family members, are known to be beneficial to promoting cognitive function (Ihle et al., 2018; Sharifian et al., 2020), and such findings are in line with the task-specific model of network mobilization (Pierce et al., 1996). For personal care, family members often serve as a primary source of support. However, for social and leisure activities, many older individuals prefer friends due to their age-comparability, shared interests, and feelings of companionship.

Another construct of particular relevance to cognitive health is the bridging phenomena in social networks, where an individual is connected with people who themselves are not directly tied with each other (Burt, 1992). Having non-overlapping social networks, in which not all members know each other, may enable individuals to access a greater range of support sources and to control their own personal autonomy and independence (Burt, 1992; Cornwell, 2009, 2011). Furthermore, those with higher bridging potential may be more likely to serve as go-betweens or gatekeepers in their social relationships, which may be health-enhancing in itself (Cornwell, 2009). Recent studies suggest that older adults’ social position with greater bridging potential promotes cognitive enrichment and delays cognitive deterioration through exposures to diverse social stimuli and the flow of novel information (Ellwardt et al., 2015; Kelly et al., 2017).

Although social network is conventionally measured with survey instruments on the number of network members and frequency of contact (e.g., Lubben Social Network Scale), assessing bridging potential has been more challenging as it requires Social Network Analysis (SNA) modules with name generating prompts (Borgatti et al., 2018; Perry et al., 2018). The National Social Life, Health, and Aging Project (NSHAP) is exceptional in that it collected data on personal network structures at a population-level and contributed to exploring the concept and health implications of network bridging potential (e.g., Cornwell, 2009; 2011; Goldman & Cornwell, 2015). It should be noted that the SNA data gathered in the NSHAP are egocentric: each participant (i.e., ego) generates an independent list of network members (i.e., alters) and only their direct ties are considered. On the other hand, the sociometric approach includes data from both egos and alters and thus allows assessments of both direct and indirect connections, directionalities, and group dynamics (Borgatti et al., 2018; Perry et al., 2018). Because it is labor-intensive and time-consuming to identify full network structures of the entire population, the sociometric approach has often been used in spatially defined settings (e.g., nursing home, prison, classroom). However, the egocentric approach may be used with age peers in ethnic minority communities where friend networks are tightly bounded. In such cases, the dataset is likely to include both egos and the nominated alters, which permits the assessment of community-level network structures and group dynamics (Jang et al., 2016).

For bridging potential, we used one of Burt’s (1992) measures of structural holes, known as effective size. A structural hole represents the absence of a tie between two alters within an ego’s network (Borgatti et al., 2018). Effective size is the most common indicator of structural holes, defined as an ego’s degree (i.e., the number of alters nominated by an ego) minus the average number of ties that each alter has with other alters. Figure 1 illustrates possible network configurations when each participant is asked to report three close friends. The far-left diagram (a) is when all three friends reported are already connected with one another (no bridging potential), and the far-right diagram (e) is when none of the friends are connected (full bridging potential). The situations where some but not all friends are connected (b, c, and d) indicate partial bridging potential. Instead of a dichotomization that lumps together all cases with partial and full bridging potentials (b to e), the effective size approach considers both network size and connectivity within network members in quantifying the extent to which each participant is involved in non-overlapping social groups (Borgatti et al., 2018; Perry et al., 2018). For instance, the bridging potential score depicted in the diagram (a) is one (3 minus 2), where the ego’s network size is three and the average number of ties that the alters have with one another is two. In the case depicted in the diagram (e), the bridging potential score is three (3 minus 0), where the ego’s network size is three and the average number of ties that three alters have is zero. Allowing a range of scores with higher scores indicating a greater potential for network bridging, this indicator has gained popularity (Borgatti et al., 2018; Perry et al., 2018).

The present investigation focused on older Korean Americans living in Central Florida. Numbering approximately 1.8 million, Korean Americans are the fifth largest Asian American subgroup (Pew Research Center, 2017). Because most of these Korean immigrants have come to the U.S. since the Immigration Act of 1965, the current generation of older Korean Americans is predominantly foreign-born and challenged by linguistic and cultural barriers (Pew Research Center, 2017). Including less than 2% of the total Korean population in the U.S. (Social Explorer, 2021), Florida is considered as an area with low Korean population density, and its Korean residents, particularly older members, form a tight-knit and cohesive ethnic community (Jang et al., 2016). Studies on older Korean Americans have also demonstrated the health benefits of social ties in general (Hong et al., 2011; Park et al., 2013) and ties with friends in particular (Roh et al., 2015). It has been reported that older Korean Americans in Florida have ethno-centric peer-networks due to their cultural and linguistic barriers and that friends in ethnic communities play a critical role as emotional confidants (Jang et al., 2016). However, attention has rarely been paid to the bridging potential in friend networks and its impact on cognitive function.

Based on the aforementioned review and targeting older Korean Americans, the aims of the present study were (1) to identify how bridging potential in networks of core friends in ethnic communities is associated with general social ties with friends and activity engagement and (2) to examine how bridging potential predicts cognitive function. We hypothesized that (1) bridging potential would be greater among those with stronger social ties with friends and higher levels of activity engagement and that (2) bridging potential would enhance the cognitive function of older Korean Americans. Understanding the determinants of bridging potential and its impact on cognitive function would offer implications for ways to enhance older immigrants’ social and cognitive function.

Methods

Participants

Data for the present investigation came from the Study of Older Korean Americans (SOKA). The SOKA surveys were conducted with community-dwelling Korean immigrants aged 60 or older in several states representing an array of Korean population density in the United States. The present investigation focused on the Florida site, which is optimal for the SNA assessment with its low Korean population density (1.7%; Social Explorer, 2021) and tight connections among older Korean residents in local ethnic communities (Jang et al., 2016). The surveys took place at various locations and events (e.g., churches, temples, grocery stores, small group meetings, and cultural events) in Tampa from April to July in 2017. Developed through a back-translation and reconciliation method, the survey was designed to be self-administered, but trained interviewers were available for anyone who needed assistance. The SNA name-generating prompt was included as part of the questionnaire; each participant was asked to list the names of three close friends in local Korean communities. The community-based non-probability sampling methods yielded data from 308 participants. None of the participants had more than 5% data missing on study variables, and all were included in the analyses.

Measures

Bridging Potential

Participants were asked to list three close friends in the Korean community. This SNA name-generating prompt yielded egocentric data of egos (the study participants) and three alters (e.g., close friends), a majority of whom were also our survey participants. As described earlier, bridging potential scores were calculated by subtracting the average number of ties that each friend nominated (alter) has to other alters from the number of friends that each participant (ego) has. Although at least two network members are required for the score calculation, those who listed fewer than two friends were retained for the overall assessment of the friend networks in the community. Participants with no nominated friend (n = 30) were assigned for a score of zero and those with one nominated friend (n = 37) for a score of one.

Social Ties with Friends

Three items on friends were selected from the Lubben Social Network Scale (Lubben et al., 2006). The items address the number of friends whom the participant (1) sees or hears from at least once a month, (2) feels at ease talking about private matters, and (3) feels close enough to call for help. The respondent answered each question by selecting a value on a six-point scale (ranging from 0 = none to 5 = nine or more). Ranging from 0 to 15, higher scores indicate stronger ties with friends. The scale has been translated into Korean and shown to have good psychometric properties (e.g., Hong et al., 2011; Jang et al., 2020). Internal consistency of the scale in the present sample was high (α = .89).

Activity Engagement

Using a list of six social activities (e.g., religious meetings, senior and community activities, social group meetings, leisure or sporting activities, volunteering activities, and education/learning activities), participants were asked to report how often they attended each activity on a 4-point scale (ranging from 0 = never to 3 = very often). Total scores could range from 0 to 18, with higher scores indicating a greater level of activity engagement. Reflecting the diversities in the type of activities included, internal consistency was marginally acceptable (α = .61).

Cognitive Function

The Mini-Mental State Examination (MMSE) (Folstein et al., 1975) was used as an index of global cognitive function. The MMSE includes items on time and place orientation, memory recall, attentional and computational capabilities, language ability, three-stage commands, pentagon drawing, judgment, and comprehension. Responses for each item were scored as 0 (incorrect) or 1 (correct), and total scores could range from 0 to 30. A score of 24 or below has been used as an indication of cognitive impairment (Folstein et al., 1975). The psychometric properties of the Korean version of the MMSE have been validated (e.g., Han et al., 2010); internal consistency in the present sample was satisfactory (α = .74).

Covariates

Sociodemographic variables included age (in years), gender (0 = male and 1 = female), marital status (0 = not married and 1 = married), and education (0 = high school graduation or lower and 1 = higher than high school graduation).

The length of stay in the United States (in years) and acculturation were included as measures of immigration-related characteristics. The level of acculturation was assessed with a 12-item inventory of acculturation (Jang et al., 2007), addressing English proficiency, media consumption, food consumption, social relationship, sense of belonging, and familiarity with culture and customs. Each response was coded from 0 to 3, and total scores could range from 0 to 36, with a higher score indicating a greater level of acculturation to mainstream American cultures. Internal consistency in the present sample was high (α = .91).

Chronic diseases and functional disability were used as indicators of physical health. Total count for the checklist of 10 chronic diseases and conditions common in older populations (e.g., diabetes, cancer, arthritis, heart disease, high blood pressure) was used as a continuous format. Functional disability was assessed with a scale (Fillenbaum, 1988) that included activities of daily living (ADL) and instrumental activities of daily living (IADL). It included 16 activities (e.g., walking, bathing, dressing, managing medication), and participants were asked to indicate how they could perform each activity. Responses were coded as 0 (without help), 1 (with some help), or 2 (unable to do). Total scores could range from 0 (no functional disability) to 32 (severe functional disability). Internal consistency of the scale in the present sample was high (α = .87).

As an indicator of mental health status, depressive symptoms were measured by the Patient Health Questionnaire 2 (PHQ 2), a short form of the PHQ 9 (Kroenke et al., 2001). Participants were asked to report how often, over the past 2 weeks, they had been bothered by problems such as “little interest or pleasure in doing things” and “feeling down, depressed or hopeless.” Each item was scored on a 4-point scale ranging from 0 (not at all) to 3 (nearly every day). Total scores could range from 0 to 6, with higher scores indicating greater levels of depressive symptoms. Internal consistency of the scale in the present sample was high (α = .76).

Analytical Strategy

Using UCINET 6 for Window (Borgatti et al., 2002), data from the name-generating prompts were managed and bridging potential scores were calculated. After reviewing sample characteristics and correlations among study variables, multivariate regression analyses were conducted. All models were tested with an initial adjustment of covariates (age, gender, marital status, education, years of residence in the U.S., chronic medical conditions, functional disability, and depressive symptoms). For the aim 1, the regression model for bridging potential was tested with social ties with friends and activity engagement. The aim 2 was pursued with a series of regression models for cognitive function. First, social ties with friends and activity engagement were considered. Subsequently, bridging potential was added to the model to examine its independent contribution to the model. All analyses were conducted using IBM SPSS Statistics 27 (IBM Corp., Armonk, NY).

Results

Descriptive Characteristics of the Sample

Sample characteristics are summarized in Table 1. The mean age was 71.6 (SD = 7.59), with a range from 60 to 94. About 65% were women, more than three quarters (78.5%) were married, and 41% had received an education greater than high school graduation. The years of residence in the United States ranged from 1 to 57 years, with an average of 35.2 (SD = 12.4), and acculturation scores averaged 16.2 (SD = 7.44). The scores for chronic medical conditions, functional disability, and depressive symptom averaged 1.32 (SD = 1.26), .94 (SD = 2.47), and .90 (SD = 1.50), respectively. The scores for social ties with friends and activity engagement averaged 7.54 (SD = 3.63) and 3.79 (SD = 2.53), respectively. Bridging potential scores ranged from 0 to 10.4, with a mean of 2.77 (SD = 1.48). For cognitive function, the MMSE scores ranged from 17 to 30, with a mean of 27.7 (SD = 2.17). Using the suggested cut-off score (≤24; Folstein et al., 1975), 8.6% of the sample fell into the category of cognitive impairment.

Table 1.

Descriptive Characteristics of the Sample (n = 308).

Variable	Value
Age, years, M ± SD (range)	71.6 ± 7.59 (60–94)
Gender (female), %	64.9
Marital status (married), %	78.5
Education (>high school graduation), %	41.4
Years in the U.S., M ± SD (range)	35.2 ± 12.4 (1–57)
Acculturation, M ± SD (range)	16.2 ± 7.44 (0–34)
Chronic medical conditions, M ± SD (range)	1.32 ± 1.26 (0–10)
Functional disability, M ± SD (range)	.94 ± 2.47 (0–32)
Depressive symptoms, M ± SD (range)	.90 ± 1.50 (0–6)
Social ties with friends, M ± SD (range)	7.54 ± 3.63 (0–15)
Activity engagement, M ± SD (range)	3.79 ± 2.53 (0–17)
Bridging potential, M ± SD (range)	2.77 ± 1.48 (0–10.4)
Cognitive function, M ± SD (range)	27.7 ± 2.17 (17–30)

Multivariate Regression Models for Bridging Potential and Cognitive Function

Prior to multivariate analyses, bivariate correlations among study variables were assessed. All variables were associated in the expected direction, and no sign of collinearity was observed. Higher bridging potential scores were negatively associated with functional disability but positively associated with social ties with friends and activity engagement. Greater levels of cognitive function were associated with younger age, male gender, higher educational attainment, fewer chronic medical conditions, lower functional disability, stronger social ties with friends, higher levels of activity engagement, and a greater potential for bridging.

Regression models for bridging potential and cognitive function are summarized in Table 2. In the model for bridging potential, among covariates, only functional disability was found to be significant. Individuals with a higher physical function demonstrated a greater bridging potential. It was notable that both social ties with friends and activity engagement were found to be significant; bridging potential increased with a stronger tie with friends and a greater engagement in activities. A total of 16% of the variance of bridging potential was accounted for by the model.

Table 2.

Regression Models for Bridging Potential and Cognitive Function (n = 308).

	B (SE)
	Bridging Potential	Cognitive Function
Age	−.01 (.01)	−.06** (.02)	−.06** (.02)
Female	.12 (.19)	−.54 (.29)	−.57 (.28)
Married	−.17 (.20)	.22 (.30)	.27 (.29)
> High school graduation	−.15 (.18)	.99*** (.28)	1.04*** (.27)
Years in the U.S.	.00 (.01)	.01 (.01)	.01 (.01)
Acculturation	−.02 (.01)	.02 (.02)	.01 (.02)
Chronic medical conditions	.11 (.07)	−.11 (.10)	−.13 (.09)
Functional disability	−.08* (.03)	−.06 (.05)	−.04 (.05)
Depressive symptoms	−.01 (.05)	−.08 (.08)	−.08 (.08)
Social ties with friends	.10*** (.02)	.10** (.04)	.07 (.04)
Activity engagement	.13** (.04)	.06 (.05)	.02 (.05)
Bridging potential	—	—	.29** (.09)
ΔR²	.16***	.17***	.03**
Overall R²	.16***	.17***	.20***

Note. B = unstandardized regression coefficient, SE = standard error.

*p < .05, **p < .01, ***p < .001.

In the initial model for cognitive function, younger age, higher education, and stronger ties with friends were associated with higher MMSE scores. A total of 17% of the variance was explained in the initial model. The subsequent entry of bridging potential added 3% of the explained variance to the model, demonstrating the positive effect of bridging potential on cognitive function. It was notable that social ties with friends became non-significant when bridging potential was included in the model. This elucidation of the mediating effect led to a further analysis with the PROCESS macro for SPSS (Hayes, 2018). We found that the indirect effect of social ties with friends on cognitive function through bridging potential was significant (B [SE] = .04 [.01]), as evidenced by the 95% bootstrap confidence interval for the indirect effect not containing zero (.01, .06). The finding suggests that the impact of general social ties with friends on cognitive function is mediated by the enhanced potential for bridging. In other words, larger networks of friends offer the context for diversified social relationships, which in turn leads to better cognitive function. In this sense, the cognitive benefit associated with social ties with friends is optimized by bridging potentials.

Discussion

Moving beyond the conventional approach to addressing the health impact of social connectedness via survey instruments, we attended to an understudied construct‐bridging potential with an innovative use of the SNA approach. The study is in line with the emerging literature demonstrating the benefits of non-overlapping social networks or social positioning as a broker between unconnected network members (Cornwell, 2009, 2011; Goldman & Cornwell, 2015). Our approach was unique in that (1) data on bridging potential were yielded from the SNA name generating prompt, (2) older Korean Americans from a tight-knit ethnic community were targeted, (3) networks and bridging potentials within the core circles of friends in ethnic communities were focused, and (4) cognitive function was considered as a health outcome. Our findings not only lend support to the hypothesized role of bridging potential as a promoter of the cognitive health but also elucidate mechanisms underlying social and cognitive function.

The present sample of older Korean Americans demonstrated a wide range of bridging potential. In both bivariate and multivariate analyses, physical functioning, social ties with friends, and activity engagement were identified as significant associates of bridging potential. These findings add credibility to the idea that older individuals with such potentials are likely to have prominence in their social circles (e.g., Cornwell, 2009). The vital role of functional ability was also notable; functional limitation poses restrictions not only to daily activities but also to social engagement (Fingerman et al., 2021; Rosso et al., 2013). Particularly for participation in events and activities which require mobility, functional independence is essential. It is plausible that older individuals’ physical function enables them to build and maintain relationships with friends and to actively engage in social activities (Fingerman et al., 2021; Rosso et al., 2013), fostering the diversity within social arenas.

Better cognitive function was observed among those with younger age, higher education, and stronger ties with friends, and these findings are consistent with previous studies reporting the age-related cognitive decline (Deary et al., 2009), the function of education as cognitive reserve (Stern, 2003), and the cognitive benefits of social networks in general (Evans et al., 2019; Kuiper et al., 2016) and those of friends in particular (Ihle et al., 2018; Sharifian et al., 2020). As the task-specific model of network mobilization (Pierce et al., 1996) posits, friends play an important role in meeting older individuals’ needs for social engagement, further enhancing cognitive function. Under the cultural valuation of familism, networks of family have been conventionally emphasized for older Asian Americans (Guo & Stensland, 2018); however, our findings call attention to the benefits that the voluntary relationships with friends might offer, particularly in the area of cognitive health.

Supporting the proposed hypotheses, bridging potential in networks of core friends within ethnic communities was found to have a direct benefit to cognitive function. Having a greater potential for bridging among network members implies the possession of non-overlapping social networks, which allows exposures to more diverse social stimuli and new sources of information and support (Ellwardt et al., 2015; Kelly et al., 2017). Furthermore, maintaining and coordinating resources and contacts with various network members may help older adults keep their brain active and allow them to exercise relational power and control in their daily life (Cornwell, 2009, 2011).

Albeit not included in the hypotheses, an interesting pattern of mediation emerged. Bridging potential not only had a direct effect on cognitive function but also mediated the impact of friend networks on cognitive function. Bridging potential enhanced the importance of friendship ties for cognition, suggesting that older immigrants with a broader range of friends have better cognitive health. This finding advances our understanding of the direct cognitive benefits from friend networks and bridging potential, elucidating the indirect path between social and cognitive function. In general, having many friends and keeping strong ties with them are beneficial; however, not all individuals have the ability to link and consolidate the networks of friends who are not connected to each other. Indeed, it has been suggested that those with bridging potential are the ones who can transfer a pool of resources among various network members, enjoy the bargaining power in social exchanges, and achieve a great sense of independence and control (Burt, 1992; Cornwell, 2009, 2011).

Regardless of whether the benefit comes from a broader range of resources, made available by a non-overlapping set of friends, or whether having a non-overlapping set of friends places the individual in a socially advantaged position, a network with greater bridging potentials optimized the cognitive health outcomes associated with social ties with friends. It should be noted that activity engagement was a significant predictor of bridging potential but not of cognitive function. The latter finding is inconsistent with the studies reporting a significant impact of engagement, across diverse activities, on cognitive function (e.g., Hertzog et al., 2008; Kelly et al., 2017; Lee et al., 2021). This discrepancy warrants further investigation with refined measures that clearly distinguish social and physical activities.

To ensure meaningful interpretations of the findings, some limitations to the present investigation should be noted. The foremost concern is related to the use of a non-representative sample and cross-sectional design, which limits our ability to generalize the findings to a larger population and to draw causal inferences. Moreover, older individuals with functional and cognitive challenges were underrepresented in this volunteer sample of community-dwelling older adults. Although we conceptualized bridging potential as a predictor to cognitive function, the reverse-causation is also possible. It is plausible that older individuals with higher cognitive performance are more active in social activities and better-positioned for network bridging. Future studies should consider longitudinal approaches to properly address the directionalities between social and cognitive function, as well as their dynamic changes over time. Second, an important methodological concern stems from the inherent limitation of egocentric network data, which cannot fully address a comprehensive network structure unless all alters are included in the study (Borgatti et al., 2018; Perry et al., 2018). Recognizing this limitation, we intentionally selected an ethnic community where its members are tightly bounded. However, the name generator prompt only allowed for a maximum of three friends and not all of them proved to be study participants. Therefore, the scope of the present investigation is limited to a core circle of friends, and the potential for bridging could be biased. It should also be noted that the present investigation was focused on friendship within Korean communities. Rationale for this focus came from the ethno-centric nature of older Korean Americans’ social networks (Jang et al., 2016), but the approach was also validated by the fact that in a question on social relationships 90% of the present sample reported that they “never” or “rarely” attended meetings or social gatherings with people who were not Korean. However, future studies need to consider networks of friends with diverse racial and ethnic backgrounds and bridging potentials across them. Finally, due to a small sample size, the number of predictor variables included in our multivariate models was limited. Although the selected variables explained a statistically significant amount of the variance in the proposed models, a substantial proportion remains unexplained. It is recommended that future studies consider other explanatory variables such as personality traits (e.g., extraversion and openness) and personal control for bridging potential and cognitive function.

Despite these limitations, the study findings uniquely contribute to advancing our knowledge and providing practical implications. Considering that bridging potential is an important structural feature of friend networks that may be associated with cognitive enhancement, intervention efforts should focus on ways to diversify older individuals’ social networks and empower them to play proactive roles in their social relationships. As the opportunities for social relationships and engagement are often disrupted among older immigrants (Hong et al., 2011; Park et al., 2013; Roh et al., 2015), attention should be paid to community-based programs and services geared toward their social integration and connectedness. Given that those with bridging potential are likely to have attitudinal and behavioral dispositions to serve as an opinion leader or social confidant among peers, they could potentially serve as an efficient agent for peer-led interventions on health promotion and diffusion of innovation in their communities. Addressing the core elements of successful aging (Rowe & Kahn, 2015), our findings underscore the interconnectedness of older immigrants’ social and cognitive function and shed light on interventions.

Footnotes

Acknowledgments

The authors would like to thank Dr. Kyungmin Kim for the initial network data management.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by National Institute on Agin (grant no: R01AG047106).

ORCID iD

Yuri Jang

References

Agency for Healthcare Research and Quality (2019). Addressing social isolation to improve the health of older adults: A rapid review. Rockville, MD: U.S. Department of Health and Human Services. https://effectivehealthcare.ahrq.gov/sites/default/files/pdf/rapid-social-isolation-older-adults-final.pdf.

Antonucci

T. C.

Fuhrer

Jackson

J. S.

(1990). Social support and reciprocity: A cross-ethnic and cross-national perspective. Journal of Social and Personal Relationships, 7(4), 519-530. https://doi.org/10.1177/0265407590074008.

Bengtson

V. L.

Settersten

(Eds), (2016). Handbook of theories of aging. New York: Springer Publishing Company.

Berkman

L. F.

Glass

Brissette

Seeman

T. E.

(2000). From social integration to health: Durkheim in the new millennium. Social Science & Medicine, 51(6), 843-857. https://doi.org/10.1016/S0277-9536(00)00065-4.

Borgatti

S. P.

Everett

M. G.

Freeman

L. C.

(2002). UCINET for Windows: Software for social network analysis. Harvard, MA: Analytic Technologies.

Borgatti

S. P.

Everett

M. G.

Johnson

J. C.

(2018). Analyzing social networks. Thousand Oaks, CA: Sage.

Burt

R. S.

(1992). Structural holes. Cambridge, MA: Harvard University Press.

Cornwell

(2009). Network bridging potential in later life: Life-course experiences and social network position. Journal of Aging and Health, 21(1), 129-154. https://doi.org/10.1177/0898264308328649.

Cornwell

(2011). Independence through social networks: Bridging potential among older women and men. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 66(6), 782-794. https://doi.org/10.1093/geronb/gbr111.

10.

Deary

I. J.

Corley

Gow

A. J.

Harris

S. E.

Houlihan

L. M.

Marioni

R. E., ...

Starr

J. M.

(2009). Age-associated cognitive decline. British Medical Bulletin, 92(1), 135-152. https://doi.org/10.1093/bmb/ldp033.

11.

Ellwardt

Van Tilburg

T. G.

Aartsen

M. J.

(2015). The mix matters: Complex personal networks relate to higher cognitive functioning in old age. Social Science & Medicine, 125, 107-115. https://doi.org/10.1016/j.socscimed.2014.05.007.

12.

Evans

I. E.

Martyr

Collins

Brayne

Clare

(2019). Social isolation and cognitive function in later life: A systematic review and meta-analysis. Journal of Alzheimer's Disease, 70(s1), S119-S144. https://doi.org/10.3233/JAD-180501.

13.

Fillenbaum

G. G.

(1988). Multidimensional functional assessment: The Duke Older Americans Resources and Services procedures. Hillsdale, NJ: Lawrence Erlbaum.

14.

Fingerman

K. L.

Y. T.

Huo

Birditt

K. S.

Charles

S. T.

Zarit

(2021). Functional limitations, social integration, and daily activities in late life. The Journals of Gerontology: Series B. 125, 107-115. https://doi.org/10.1093/geronb/gbab014.

15.

Folstein

M. F.

Folstein

S. E.

McHugh

P.R.

(1975). “Mini-Mental State“: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatry Research, 12(3), 189-198. https://doi.org/10.1016/0022-3956(75)90026-6.

16.

Goldman

A. W.

Cornwell

(2015). Social network bridging potential and the use of complementary and alternative medicine in later life. Social Science & Medicine, 140, 69-80. https://doi.org/10.1016/j.socscimed.2015.07.003.

17.

Guo

Stensland

(2018). A systematic review of correlates of depression among older Chinese and Korean immigrants: What we know and do not know. Aging & Mental Health, 22(12), 1535-1547. https://doi.org/10.1080/13607863.2017.1383971.

18.

Han

J. W.

Kim

T. H.

Jhoo

J. H.

Park

J. H.

Kim

J. L.

Ryu

S. H.

Kim

K. W.

(2010). A normative study of the Mini-Mental State Examination for Dementia Screening (MMSE-DS) and its short form (SMMSE-DS) in the Korean elderly. Journal of Korean Geriatric Psychiatry, 14(1), 27-37.

19.

Hayes

A. F.

(2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (2nd ed.). New York, NY: Guilford Press.

20.

Hertzog

Kramer

A. F.

Wilson

R. S.

Lindenberger

(2008). Enrichment effects on adult cognitive development: Can the functional capacity of older adults be preserved and enhanced?. Psychological Science in the Public Interest, 9(1), 1-65. https://doi.org/10.1111/j.1539-6053.2009.01034.x.

21.

Hong

Casado

B. L.

Harrington

(2011). Validation of Korean versions of the Lubben Social Network Scales in Korean Americans. Clinical Gerontologist, 34(4), 319-334. https://doi.org/10.1080/07317115.2011.572534.

22.

Ihle

Oris

Baeriswyl

Kliegel

(2018). The relation of close friends to cognitive performance in old age: The mediating role of leisure activities. International Psychogeriatrics, 30(12), 1753-1758. https://doi.org/10.1017/S1041610218000789.

23.

Jang

Kim

Chiriboga

King-Kallimanis

(2007). A bidimensional model of acculturation for Korean American older adults. Journal of Aging Studies, 21(3), 267-275. https://doi.org/10.1016/j.jaging.2006.10.004.

24.

Jang

Kim

Park

N. S.

Chiriboga

D. A.

(2016). Emotional confidants in ethnic communities: Social network analysis of Korean American older adults. Journal of Aging and Health, 28(2), 324–340. https://doi.org/10.1177/0898264315591002

25.

Jang

Powers

D. A.

Park

N. S.

Chiriboga

D. A.

Chi

Lubben

(2020). Performance of an abbreviated Lubben Social Network Scale (LSNS-6) in three ethnic groups of older Asian Americans. The Gerontologist. 46(10), 2067-2075 https://doi.org/10.1093/geront/gnaa156.

26.

Kelly

M. E.

Duff

Kelly

Power

J. E. M.

Brennan

Lawlor

B. A.

Loughrey

D. G.

(2017). The impact of social activities, social networks, social support and social relationships on the cognitive functioning of healthy older adults: A systematic review. Systematic Reviews, 6(1), 1-18. https://doi.org/10.1186/s13643-017-0632-2.

27.

Kroenke

Spitzer

R. L.

Williams

J. B. W.

(2001). The PHQ‐9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606-613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x.

28.

Kuiper

J. S.

Zuidersma

Zuidema

S. U.

Burgerhof

J. G.

Stolk

R. P.

Oude Voshaar

R. C.

Smidt

(2016). Social relationships and cognitive decline: A systematic review and meta-analysis of longitudinal cohort studies. International Journal of Epidemiology, 45(4), 1169-1206. https://doi.org/10.1093/ije/dyw089.

29.

Lee

Charles

S. T.

Almeida

D. M.

(2021). Change is good for the brain: activity diversity and cognitive functioning across adulthood. The Journals of Gerontology: Series B, 76(6), 1036-1048. https://doi.org/10.1093/geronb/gbaa020.

30.

Lubben

Blozik

Gillmann

Iliffe

von Renteln Kruse

Beck

J. C.

Stuck

A. E.

(2006). Performance of an abbreviated version of the Lubben Social Network Scale among three European community-dwelling older adult populations. The Gerontologist, 46(4), 503-513. https://doi.org/10.1093/geront/46.4.503.

31.

Park

N. S.

Jang

Lee

B. S.

J. E.

Haley

W. E.

Chiriboga

D. A.

(2013). An empirical typology of social networks and its association with physical and mental health: A study with older Korean immigrants. Journals of Gerontology: Series B. Psychological Sciences and Social Sciences, 70(1), 67-76. https://doi.org/10.1093/geronb/gbt065.

32.

Pearlin

L. I.

Bierman

(2013). Current issues and future directions in research into the stress process. Handbook of the sociology of mental health (pp. 325-340). Dordrecht: Springer.

33.

Perry

B. L.

Pescosolido

B. A.

Borgatti

S. P.

(2018) Egocentric network analysis: Foundations, methods, and models (Vol. 44). Cambridge: Cambridge University Press.

34.

Pew Research Center (2017). Koreans in the U.S. fact sheet. Washington, DC: Pew Research Center. Retrieved from https://www.pewsocialtrends.org/fact-sheet/asian-americans-koreans-in-the-u-s/.

35.

Pierce

G. R.

Sarason

B. R.

Sarason

I. G.

(1996). Handbook of social support and the family. Boston, MA: Springer.

36.

Roh

Lee

Y. S.

Lee

K. H.

Shibusawa

Yoo

G. J.

(2015). Friends, depressive symptoms, and life satisfaction among older Korean Americans. Journal of Immigrant and Minority Health, 17(4), 1091-1097. https://doi.org/10.1007/s10903-014-0021-z.

37.

Rosso

A. L.

Taylor

J. A.

Tabb

L. P.

Michael

Y. L.

(2013). Mobility, disability, and social engagement in older adults. Journal of Aging and Health, 25(4), 617-637. https://doi.org/10.1177/0898264313482489.

38.

Roth

A. R.

(2020). Social networks and health in later life: A state of the literature. Sociology of Health & Illness, 42(7), 1642-1656. https://doi.org/10.1111/1467-9566.13155.

39.

Rowe

J. W.

Kahn

R. L.

(2015). Successful aging 2.0: Conceptual expansions for the 21st century. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 70(4), 593-596. https://doi.org/10.1093/geronb/gbv025.

40.

Sharifian

Kraal

A. Z.

Zaheed

A. B.

Sol

Zahodne

L. B.

(2020). Longitudinal associations between contact frequency with friends and with family, activity engagement and cognitive functioning. Journal of the International Neuropsychological Society, 26(8), 815-824. https://doi.org/10.1017/S1355617720000259.

41.

Social Explorer (2021). 2019 American Community Survey 1-year estimate: Asian by specific origin. Retrieved from https://www.socialexplorer.com/tables/ACS2019/R12782942.

42.

Stern

(2003). The concept of cognitive reserve: A catalyst for research. Journal of Clinical and Experimental Neuropsychology, 25(5), 589-593. https://doi.org/10.1076/jcen.25.5.589.14571.