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Social Support and Cognitive Function in Older Adults.

Introduction

Income level, health status, work capacity, environment, and educational achievement profoundly contour the interpersonal social sphere across ethnicities and genders (Hawkley et al., 2008). Social inequality may be most apparent in conditions of economic deprivation (O'Rand, 2001), with the most socially excluded individuals represented by minorities, those with mental or physical disabilities, and elderly populations (Scharf & Keating, 2012; Scharf, Phillipson, & Smith, 2005).

Although prolonged life expectancy has been estimated to continue along a trajectory of expansion among U.S. citizens (National Academies of Sciences, Engineering, and Medicine, 2015), the consequent socioeconomic impact may result in diminished quality of life (Love-Koh, Asaria, Cookson, & Griffin, 2015). Paradoxically, older adults experiencing reduced mortality rates may not be well enough to enjoy a life extension. Further, the average life expectancy increase is less for those who earn less (Lubetkin & Jia, 2017). Lower income correlates with increased risk of morbidity corroborated by self-reported decrease in number of days spent in good health, adverse behavioral states, and increased diagnoses of chronic lifestyle diseases in late life (Love-Koh et al., 2015). Living below the federal poverty level may exert a seismic impact on residual quality-adjusted life years and exacerbate the national burden of chronic disease (Lubetkin & Jia, 2017; Muennig, Franks, Jia, Lubetkin, & Gold, 2005). Positive human relationships may provide an alternate form of social capital that manifests as individuals habitually engage in interactions of reciprocal benefit (Cattell, 2001). Social support has been shown to facilitate positive mental and physical health outcomes (Berkman, 1995; Cohen & Herbert, 1996; Loprinzi & El-Sayed, 2015; Loprinzi & Joyner, 2016; Seeman, 1996).

Specifically, supportive networks may protect against cardiovascular disease, depression, and hypertension associated with cognitive decline (Seeman, Lusignolo, Albert, & Berkman, 2001). The number of social ties available to an individual may also limit psychological changes, including poor self-efficacy and depression symptoms linked with impaired cognitive function (Bassuk, Glass, & Berkman, 1999; Seeman, McAvay, Merrill, Albert, & Rodin, 1996). Social support is defined in distinct terms of relational fulfillment. Emotional support constitutes social ties marked by some level of intimacy between close friends, family, or romantic partners whereas social support is demonstrated through accumulating points of contact between individuals (Weiss, 1975). When emotional or social support networks either dissolve or are prevented from developing, the pernicious repercussions of loneliness may severely impede mental and physical health (Weiss, 1975).

A large body of prior work indicates that social support has an important influence on cognitive parameters (Arbuckle, Gold, Andres, Schwartzman, & Chaikelson, 1992; Bassuk et al., 1999; Hawkley et al., 2008; Lang, 2001; Scharf & Keating, 2012; Seeman, 1996; Seeman et al., 1996; Shouse, Rowe, & Mast, 2013; Stoykova, Matharan, Dartigues, & Amieva, 2011). However, to our knowledge, no study has examined the association between social support and cognition with a threefold concentration on emotional intimacy in spousal or familial relationships, financial support systems, and range of personal social networks in a large representative cohort. In this study we aim to evaluate this gap in the existing literature, focusing on the potential of both quality and quantity of social support to influence cognitive performance in older adults.

Methods

Study Design

Data from the 1999-2002 National Health and Nutrition Examination Survey (NHANES) were used. The NHANES is an ongoing survey conducted by the National Center for Health Statistics, a major section of the Centers for Disease Control and Prevention. It evaluates a representative sample of noninstitutionalized U.S. civilians selected by a complex, multistage probability design. All procedures for data collection were approved by the National Center for Health Statistics ethics review board, and all participants provided written informed consent prior to data collection.

Participants

Participants were excluded if they had missing data on the study variables or if they self-reported having coronary artery disease or congestive heart failure or having had a stroke or a heart attack. The analyzed sample included 1,874 older adults between sixty and eighty-five years old.

Cognitive Function

The Digit Symbol Substitution Test (DSST; Wechsler, 1958) was used to assess cognitive function among older adults (sixty years of age or older). The DSST, a component of the Wechsler Adult Intelligence Test, is a test of visuospatial and motor speed of processing. It has a considerable executive function component and is frequently used as a sensitive measure of frontal lobe executive functions (Parkin & Java, 1999; Vilkki & Holst, 1991). The DSST was used to assess participant cognitive function tasks of pairing (each digit 1 through 9 has a symbol associated with it) and free recall, in which participants were asked to draw, within two minutes, as many symbols as possible that were paired with numbers. Following the standard scoring method, one point was given for each correctly drawn and matched symbol, and one point was subtracted for each incorrectly drawn and matched symbol; the maximum score was 133.

Social Support

Participants were asked, "Can you count on anyone to provide you with emotional support such as talking over problems or helping you make a difficult decision?" "In the last 12 months, who was the most helpful in providing you with emotional support?" Sources of support that were evaluated included spouse, son, daughter, and sibling. Additionally, participants were asked, "If you need some extra help financially, could you count on anyone to help you?" Regarding the size of their social network, participants were asked, "In general, how many close friends do you have?"

Measurement of Covariates

Covariates included age (continuous; yr), gender, race-ethnicity (Mexican American, non-Hispanic white, non-Hispanic black, or other), measured body mass index (continuous; kg/[m.sup.2]), C-reactive protein (continuous; mg/dL; marker of inflammation), self-reported smoking status (current, former, or never), self-reported diabetes status (yes/no), measured mean arterial pressure (continuous; mmHg; average of four blood pressure measurements), and self-reported physical activity (meeting vs. not meeting guidelines based on [greater than or equal to]2,000 metabolic equivalent of task min-month) (Loprinzi, 2015).

Analysis

All statistical analyses were computed in Stata (version 12). Multivariable linear regression analyses were computed that examined the association between individual sources of social support and cognitive function (outcome variable). Models were computed separately for each source of support. Statistical significance was set at an alpha of 0.05.

Results

Table 1 displays the weighted characteristics of the study variables. Participants, on average, were seventy years old; the majority were female (59%) and non-Hispanic white (83%). The majority of participants received some type of support, but the source of support varied considerably.

Table 2 shows the weighted multivariable regression results examining the association between social support and cognitive function. After adjustments, those who received some type of support (i.e., any support) had a DSST score 6.4 units higher than those reporting no social support ([beta] = 6.4; 95% CI: 2.9-10.0; p = 0.001). The only individual source of support that was significantly associated with cognition was spouse-related support ([beta] = 3.7; CI: 1.9-5.5; p < 0.001). With regard to the relationship between the size of the social support network and cognition, those with a larger supportive network had greater cognitive function. For example, when compared to those with no close friends, those with five and six or more close friends, respectively, had a DSST score 5.7 ([beta] = 5.7; CI: -0.01-11.4; p = 0.05) and 7.8 ([beta] = 7.8; CI: 2.7-12.7; p = 0.003) units higher.

Discussion

Human aging is a process marked by the impending risk of early mortality. Even as the average life expectancy in industrialized countries continues to increase, chronic diseases and prevalence of comorbidities are widespread among elderly populations. Social connectivity may weaken with the death of loved ones and the pernicious decline of personal well-being in late life (Krueger et al., 2009). Nonetheless, attempts to fortify existing social networks and provide avenues for social growth should be encouraged as the paradigm of health promotion is modernized to align with the demands of an increasingly older demographic group. Therefore, the purpose of our study was to broaden the scope of existing knowledge of the relationship between social support and cognition by specifically examining the influence of family relationships, financial support, and social networks on mental performance within a large sample of older U.S. citizens. The main finding of our study was that social support of any degree was associated with a 6.4-unit improvement on the DSST. Additionally, sufficient spousal support and social network size resulted in higher performance on a test of executive cognitive functioning among elderly participants. Our results suggest that expansive social networks may be beneficial to executive cognitive function.

Recent literature supports our analysis. Park and colleagues (2014) provided plausibility for integrating productive engagement opportunities rather than receptive engagement. Productive engagement may include painting, puzzling, volunteering, or crafts in social contexts, among a variety of other activities requiring the consolidation and application of new skill sets. These social activities may attenuate cognitive decline in the domain of executive function, working memory, long-term memory, and reasoning. Receptive engagement is time spent using previous knowledge or skills in conversation to maintain or foster new relationships (Park et al., 2014). For some seniors, regular transportation to and from hubs of social engagement may be difficult due to health or financial constraints. Thus, for some older individuals networking online may a viable alternative to networking in a senior center.

Myhre, Mehl, and Glisky (2016) observed increases in complex working memory, processing speed, and visual scanning after an eight-week interactive Facebook intervention designed to facilitate communication among seniors. There were no positive increases in self-reported social support; however, the small sample consisted of participants with no previous ties to one another. Thus, family, friends, and spousal relationships were not examined.

Future research should examine the effect of online social networking and productive social activities on cognitive function and social support among existing friend groups and loving relationships as well as new contacts. Although we employed an objective measure of cognitive function and our provocative findings provide an important conduit for continued exploration of this topic, subsequent research should overcome the limitations of our study, which include the cross-sectional study design and subjective measure of social support.

In conclusion, this study highlights the association between social support and cognition within a large sample of the U.S. older adult population. Specifically, our findings demonstrated that spouse-related support and larger social networks were favorably associated with higher cognition. We also found that any amount of support was significant in exerting a positive impact on cognitive performance. Thus, clinicians should aim to teach clients in this vulnerable population strategies to effectively enlist social support.

References

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Emily Frith, MS, is a PhD student in the Physical Activity Epidemiology and Exercise Psychology Laboratories, Department of Health, Exercise Science and Recreation Management, at the University of Mississippi, University. Also at the University of Mississippi, University, Paul D. Loprinzi, PhD, is director of Research Engagement, Jackson Heart Study Vanguard Center of Oxford, and director of the Physical Activity Epidemiology Laboratory and the Exercise Psychology Laboratory, School of Applied Sciences.
Table 1 Weighted characteristics of the analyzed sample (N = 1,874)

Variable                              Point estimate   SE

DSST, mean                                 48.7       0.6
Age, mean (yr)                             69.9       0.3
% female                                   59.1
% white                                    83.4
Body mass index, mean (kg/[m.sup.2])       28.0       0.1
C-reactive protein, mean (mg/dL)            0.49      0.02
Arterial pressure, mean (mmHg)             93.3       0.5
% diabetes                                 11.7
% smoker                                   11.6

Table 2 Weighted multivariable regression analysis examining the
association between social support and cognitive function (N = 1,874)

Variable                      [beta]    95% CI     p value

Any support (vs. none)         6.4     2.9-10.0    0.001
Spouse support (vs. none)      3.7     1.9-5.5    <0.001
Son support (vs. none)        -1.6    -3.9-0.62    0.14
Daughter support (vs. none)   -0.64   -2.87-1.59   0.56
Sibling support (vs. none)    -0.34   -2.81-2.11   0.77
Financial support (vs. none)   1.1    -1.9-4.1     0.47
Size of support network
  1 vs. 0                      3.6    -2.6-9.8     0.24
  2 vs. 0                      3.1    -3.2-9.5     0.32
  3 vs. 0                      3.1    -3.0-9.3     0.31
  4 vs. 0                      5.1    -0.54-10.8   0.07
  5 vs. 0                      5.7    -0.01-11.4   0.05
  6 vs. 0                      7.8     2.7-12.7    0.003

Note: Multivariable linear regression models (seven separate models)
were computed for each social support parameter (independent variable).
In all models, covariates included age, gender, race-ethnicity,
measured body mass index, C-reactive protein, self-reported smoking
status, self-reported diabetes status, measured mean arterial pressure,
and self-reported physical activity.
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Author:Frith, Emily; Loprinzi, Paul D.
Publication:Best Practices in Mental Health
Article Type:Report
Date:Sep 22, 2017
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