Social Network,Activity Participation,and Cognition

Abstract

This study examined how two domains of engagement—social network and activity participation—associate with objective and subjective cognitive function in later life. Specific consideration was given as to how these two spheres intersect in regard to recall and memory. The analytic sample included Europeans aged 60 and older drawn from the fourth wave of the Survey of Health Ageing and Retirement in Europe in which a new name-generated social network inventory was implemented. Multivariate analyses revealed that activity participation yielded stronger positive associations with word recall and self-rated memory than social network alone. However, the interactions indicate that this association lessened in strength for both the objective and subjective cognitive outcome measures as social network resources increased. The findings suggest that the social component of activity participation may be partially contributing to the positive role that such engagement has on cognitive well-being in later life.

Keywords

engagement connectedness leisure recall subjective memory

Introduction

There is ample evidence that psychosocial factors affect cognition in late adulthood (Agrigoroaei & Lachman, 2011; Ybarra et al., 2008; Zunzunegui, Alvarado, Del Ser, & Otero, 2003), although cognitive ability or limitation may also affect the maintenance of psychosocial resources (Green, Rebok, & Lyketsos, 2008; Hovbrandt, Stahl, Iwarsson, Horstmann, & Carlsson, 2007; Johnson, Whitlatch, & Menne, 2014). The key psychosocial component of successful aging, as theorized by Rowe and Kahn (1997) is the realm of “engagement with life.” The engagement domain in their widely cited model is composed of two specific components: the maintenance of interpersonal relations and productive activities. The former reflects the field of social networks while the latter refers to the area of activity participation. The aim of the current analysis is to shed light on the relative role of these two components of engagement vis-à-vis cognitive function in older age. Our purpose is to clarify if and how the respective aspects of engagement are related to cognitive function and whether they interact in their association with this same outcome.

Cognition

Cognition is a key functional area that has major implications for the quality of later life (Ament, de Vugt, Verhey, & Kempen, 2014; Johansson, Marcusson, & Wressle, 2012; Mol, van Boxtel, Willems, Verhey, & Jolles, 2009). The cognitive sphere spans several skills and tasks that range from learning and memory to executive function and attention (Ayalon, Heinik, & Litwin, 2010). The domain of recall and memory is a key measure of cognitive function that is addressed in the research literature (Dong, Simon, Beck, & Evans, 2014; Federman, Sano, Wolf, Siu, & Halm, 2009; Wolinsky et al., 2011).

Several studies of memory consider both objective and subjective measures, insofar as these two different facets of recall are only weakly related to one another (Crumley, Stetler, & Horhota, 2014). Moreover, the strength of the association between these two indicators does not increase in advanced age when cognitive decline becomes more prevalent (Schmidt, Berg, & Deelman, 2001). Objective and subjective measures are also found to correlate with different explanatory factors; objective memory is more relatively related to medical comorbidity (Fischer, Jiang, & Schweizer, 2010) while subjective memory is related more to affect (Trepanier & Nolin, 1997) and to personality factors (Park et al., 2012). Therefore, analyses that consider both objective and subjective measures of memory offer a greater breadth in examining its varied facets.

Social Network and Cognition

Social network is the collection of meaningful ties that people variously maintain over the life course (Litwin, 2010). Personal or confidant networks (McPherson, Smith-Lovin, & Brashears, 2009; Sluzki, 2010) represent the most meaningful of the ties that one maintains, those which generally involve the exchange of support as well as frequent close emotional contact. Studies frequently find social networks to be related to good cognitive function (Fratiglioni, Wang, Ericsson, Maytan, & Winblad, 2000; Holtzman et al., 2004), and their absence—as measured by social isolation—to be related to cognitive decline (Cacioppo & Hawkley, 2009).

Some studies raise questions, however, as to the nature and the timing of the effect of social network on cognition. Green, Rebok, and Lyketsos (2008) found a cross-sectional effect of social network on cognition in Baltimore, for example, but not a longitudinal one. A study in London found only weak associations between a widely used social network scale and impaired memory (Iliffe et al., 2007). Some inquiries find social networks to act primarily as modifiers in the association between other factors such as neuropathology and the cognitive outcome in question, rather than as the source of main effects (Bennett, Arnold, Valenzuela, Brayne, & Schneider, 2014). Yet other studies find that it is the support that is provided by social networks rather than the structure of the network, per se, that seems to make the difference in relation to cognition (Krueger et al., 2009). Further complicating an already complex field, a study of a sample of persons aged 85 and older living in an American retirement complex found that among the network characteristics, it was the diversity of the ties in the network that mattered most among the network measures for the performance of cognitive tasks (Keller-Cohen, Fiori, Toler, & Bybee, 2006).

Social Network, Activity, and Cognition

One potential reason for the lack of clarity in the network/cognition nexus is that the network variable in many studies includes the additional domain of activity as well. Activity participation most usually refers to one’s engagement in social, physical, intellectual, and/or recreational pursuits or pastimes (Leung et al., 2010). Involvement in activity is considered to provide protective benefits from age-related cognitive decline (Bielak, 2010). Although the activity variable is generally meant to capture the active engagement of people in the execution of a range of tasks, activities also have a role in facilitating and nurturing social ties. This is because they are most frequently carried out in the company of others. As such, activity may be also said to encompass components of social network.

Studies that consider both social network characteristics and activity participation tend to suggest that the latter maybe more important than the former for cognitive functioning. For example, data from a study in Taiwan showed that participation in social activities outside the family seemed to have a bigger impact on cognitive function than did the number of social contacts with family or nonrelatives (Glei et al., 2005). A longitudinal study of non-Hispanic African Americans and whites aged 65 and older in Chicago examined a composite measure of cognition in relation to summary measures of social network (number of children, relatives, and friends seen at least monthly) and what the investigators defined as social and productive activities (attending religious services, museum visits, activities outside the home, and full- or part-time work). The results revealed that social network and social and productive activity were both related to initial cognitive function as well as to reduced cognitive decline, but the effect of social and productive activity was greater (Barnes, de Leon, Wilson, Bienias, & Evans, 2004). Another American study employed mixed models in which cognitive function was regressed on social activity, controlling for network size and a host of other confounders (James, Wilson, Barnes, & Bennett, 2011). The results revealed that the more socially active among the respondents experienced less cognitive decline, even after controlling for network size.

What remains unclear in prior research that examines these associations is whether it is the “doing” aspect of activity that contributes to cognitive function, or the social network aspect of “doing with.” Thus, additional examination is required to disentangle how different aspects of engagement, in terms of activity participation or social ties, relate with cognitive functioning.

Research Aim

Given the varied findings reported thus far, it is evident that greater clarity is required in order to understand the relative roles of social network and activity participation in relation to late life cognitive function. The aim of this analysis, therefore, is to clarify how social network and activity participation intersect in relation to objective and subjective memory. The two measures of cognitive function employed in the current study, objective word recall and subjective self-rated cognitive performance, sensitize the analysis to different facets of memory and recall in later life. Social network is measured by means of a recently constructed composite social network index that is based upon data from a state-of-the-art, name-generated network inventory. Activity participation is measured as a count of participation in a broad range of activities. A key innovation of the current inquiry is that we also consider the interaction between social network and activity participation vis-à-vis the selected cognition outcomes as a means to better distinguish the interrelated components of these two domains of engagement.

Research Design

Data and Sampling

The current study is based on data from the fourth wave of the Survey of Health, Ageing, and Retirement in Europe (SHARE). Collected in 2010–2011, through probability sampling in sixteen participating countries, the fourth wave included persons aged 50 and older and their partners of any age. The present analysis included respondents and spouses aged 60 and above in order to focus on the age when most of older Europeans have retired from paid employment. This sampling age criterion was selected to sensitize the findings to the relationship between voluntary (i.e., nonpaid) activity, social network, and cognition. In addition, those respondents aged 60+ for whom a proxy answered the survey (n = 1499) were not included in the final analytical sample because the social network module and the self-rated memory question were not answered by proxies. The initial analytic sample thus numbered 38,777 respondents. Data were gathered face-to-face by means of computer-assisted personal interviews that were uniformly conducted in each of the participating countries.

Study Variables

One of the two key independent variables in the current study was social network. In the fourth wave of the SHARE survey, a name generating inventory was introduced in which respondents were asked to personally identify up to six persons with whom they discussed important matters and one additional person of choice (Litwin, Stoeckel, Roll, Shiovitz-Ezra, & Kotte, 2013). The SHARE wave four sample yielded a mean of 2.5 cited persons (SD = 1.6). Thus, the instrument employed in SHARE allowed a cogent representation of the respondents’ most meaningful interpersonal environment, that is, a list of their confidants. The inventory also gathered additional information (name interpreters) about each cited person, including questions about the nature of the relationship (e.g., child or friend), geographic proximity, frequency of contact, and degree of emotional closeness.

For the purposes of the present analysis, the social network variable was operationalized using a newly constructed summary scale of the data that were collected by means of the SHARE network inventory (Litwin & Stoeckel, 2014). The scale incorporates the five main characteristics of social network into one composite measure in order to capture the key facets of social network resources within a single indicator. These characteristics include (1) the number of persons cited (network size), (2) the number of cited social network members living within 25 km (proximity), (3) the number of cited persons with weekly or more contact (frequency), (4) the number of cited persons with very or extremely close emotional ties (support), and (5) the number of different types of relationships present within the network (diversity). The first four of these measures were scored as follows: (0 = 0, 1 = 1, 2 = 2–3, 3 = 4–5, and 4 = 6–7 persons cited). The fifth measure counted the number of different relationship categories [(a) spouse, (b) other family, including children, (c) friend, and (d) other] that were present in the network (0–4). For each of these individual components of the scale, the underlying assumption is that having more social network members in each category is representative of stronger network resources.

Principal component factor analysis confirmed that the 5 items in the scale loaded on a single factor. Reliability analysis yielded a Cronbach’s α of .93. The total raw score on the scale ranged from 0 to 20. We calculated and employed a calibrated version of scale according to the following conversion: 0 = 0, 1 = 1–5, 2 = 6–10, 3 = 11–15, and 4 = 16–20. By default, survey respondents who did not identify any social network members received a score of zero. The resultant scale achieved a normal distribution in the analytical sample, with a mean of 1.88 and a standard deviation of 0.88.

The other key independent variable in the analysis was activity participation. It was measured as a count of the number of different activity types in which the respondent reported having taken part within the previous 12 months. The activity types included categories reflecting the key areas identified in the late life activity literature: social, physical, intellectual, and recreational pursuits. The list of activities was drawn from eight undertakings or pursuits that are queried by SHARE question AC035, along with two additional measures of physical activity that are asked in the survey (BR015 and BR016).

The specific activities probed were (1) volunteer work, (2) educational courses, (3) sport or social clubs, (4) religious organization events, (5) political or community events, (6) games involving others, (7) reading, (8) word or number games, (9) physically vigorous sports or activities, and (10) moderate energy activities like gardening or taking walks. An activity was included in the generated count variable if respondents reported participating in the particular activity at a minimum of one time per month. Activities were not differentiated by type (i.e., physical activity, social activity, and intellectual activity) insofar as several categories overlap. For example, sports clubs include both social and physical activity, while educational courses include both intellectual and social activity. The activities score thus ranged from 0 to 10, with higher scores representative of a greater breadth of activities in which respondents are engaged.

The dependent variable of interest in the current inquiry was cognitive function, which was addressed in terms of recall and memory. Based upon the literature review, the analysis employed two indicators of this function, one objective and the other subjective. The objective indicator was a combined score of two memory measures utilized in the SHARE survey pertaining to word recall. Respondents were presented with a list of 10 words and were asked to immediately recall the list of words as an indicator of verbal learning. Delayed verbal recall was then assessed 5 min later when respondents were asked again to list the 10 words that were previously told to them. The objective recall measure that was utilized in the present analysis combined the total number of words learned and recalled (0–20). Higher scores represent better recall and, correspondingly, better cognitive function.

The subjective measurement was gathered by means of a query in which respondents were asked to rate their memory at the present time. The answer categories reflect an ordinal scale that ranges from excellent, through very good, good, fair and poor. For the purposes of this analysis, the answers were reverse coded from the raw data so that the orientation of the variable aligns with the objective cognitive measure. Thus, higher scores on the subjective measure of self-perceived memory performance represent, in the current analysis, better cognitive functioning.

In the final part of the analysis, we employed an interaction term to capture the interrelationship of social network and activity participation vis-à-vis cognition. The interaction term was created by multiplying the number of activities (0–10) by the social network scale value (0–4), both of which are continuous scores. A positive value for the effect of the interaction term would imply that the greater the social network resources (the social network scale score), the greater (more positive) the effect of activity on recall and self-rated memory is. Correspondingly, the higher the activity level (number of activities), the greater (more positive) the effect of social network resources on recall and self-rated memory is. Conversely, a negative value for the effect of the interaction term would imply that the greater the social network score, the lesser the effect of activity on recall and self-rated memory is. In the same way, the higher the number of activities, the lesser the effect of social network on recall and self-rated memory would be.

Several control variables were considered in the present study in order to minimize confounding. Age was considered, given the demonstrated decline of cognitive function in late life (Ayalon et al., 2010). It was entered into the analyses as a categorical variable differentiating between respondents aged 60–69, 70–79, and 80 years and older.

Additional socioeconomic controls included, gender, marital status, work status, education, household income, and country of residence. Gender was dichotomous (female = 1, male = 0). Marital status was a dichotomous variable distinguishing between those who were married or partnered (1) and the never married, divorced, or widowed (0). Working status was entered to control for paid employment (1). Highest level of education attained was grouped into three categories based on the standardized coding of the International Standard Classification of Education (ISCED-97) that is utilized by SHARE. Education was distinguished between primary (ISCED-97 = 0–2), secondary (ISCED-97 = 3), and postsecondary (ISCED-97 = 4–6).

Household income, in Euros, was based upon values adjusted for purchasing power parity by country and household size square root. Missing values with regard to the financial indicators that comprise the income measure were imputed by SHARE headquarters. The income values were divided into quintiles at the following cutoff points, calculated on the complete Wave 4 SHARE sample: lowest: < €824, mid-low: €825–€6,215, mid: €6,216–€13,383, mid-high: €13,384–€24,577, and highest: €24,578+. Country of residence was entered as a series of categorical dummy variables. Spain served as the country reference category in the multivariate phase of the analysis because its averages on the outcome measures were similar to the average of the sample as a whole.

Functionality and physical health measures were also included in the analyses as controls. A count of limitations with activities of daily living (ADL), range 0–6, controlled for the level of dependency that each respondent had with tasks such as bathing, dressing, or toileting. Difficulty with mobility and functions, such as walking 100 m or climbing stairs, was measured as a 0–10 count variable. The number of chronic health conditions, such as diabetes or heart conditions, was also entered as a health control variable (0–11). A final function measurement included as a control was the count of depressive symptoms (0–12), as measured on the Euro-D Scale, in which high scores were an indicator of worse mental health.

Statistical Analysis

The analyses of this study were separated into three stages. First, a univariate description of the sample was executed. Second, the bivariate associations between each of the independent variables and the dependent cognition variables, recall and self-rated memory, were examined. Finally, a hierarchical ordinary least squares (OLS) regression was executed for each of the two cognitive functioning outcome measures. Respondents missing values on one or more of the model variables were removed from the multivariate analyses. In the first stage of the respective regressions, the cognitive functioning variables were regressed on the social network scale and the activity measure. In the second model, the control variables were added, including, age, socioeconomic characteristics, country, and health. In the third model, the interaction term of the range of activities and social network scale was entered to consider if and how activity participation and social network ties interact with each other to alter the respective associations with cognition in later life.

Results

The univariate description of the analytic sample is displayed in Table 1. The average objective cognitive word recall score was 8.28 out of a possible highest score of 20. The mean for the subjective self-rated memory measure was 2.88, equivalent to a rating of almost “very good.” As noted earlier, the mean social network scale score for the sample as a whole was 1.88, indicating a moderate level of social network resources. On average, the sample participated in approximately 3.4 different activities on a monthly basis or more frequently.

Table 1.

Cognitive Outcomes, Social Network, Activity, and Control Variables: Univariate and Descriptive Analysis of Older Europeans Aged 60+.

Analysis Variables	Range	Mean (SD)	%	Bivariate Results^a
Analysis Variables	Range	Mean (SD)	%	Cognition	Self-Rated Memory
Word recall cognition	0–20	8.368 (3.67)
Self-rated memory	1–5	2.88 (0.95)
SN scale	0–4	1.88 (0.88)		0.164***	0.060***
Number of activities	0–10	3.43 (1.83)		0.407***	0.266***
Age category				3,078.66***	828.54***
60–69			49.4
70–79			34.1
80+			16.5
Gender (female)			55.8	−8.929***	7.634***
Marital status (married/partner)			68.5	−19.824***	−10.162***
Work status (working)			9.5	−35.231***	−20.891***
Country^b				174.87***	195.26***
Education				2,718.57***	942.91***
Primary			45.8
Secondary			31.0
Postsecondary			23.2
Household income				412.31***	276.61***
Quintile 1 (lowest)			20.0
Quintile 2			20.5
Quintile 3			21.7
Quintile 4			20.1
Quintile 5			17.7
ADL limitation count	0–6	0.28 (0.88)		−0.220***	−0.191***
Mobility difficulty count	0–10	1.95 (2.45)		−0.294***	−0.300***
Chronic conditions	0–11	1.97 (1.59)		−0.163***	−0.208***
EURO-D	0–12	2.62 (2.31)		−0.249***	−0.320***

Note. Percentage and Mean (SD) calculated on weighted observations. ADL = activities of daily living; SN = social network.

^aBivariate Test: t-test (italic); Pearson R ² (boldface); F-Statistic (bold italic).

^bCountry-specific descriptives available upon request. ***p < .001.

The sample was majority female (55.6%). About half of the sample was aged 60–69 while nearly one fifth were aged 80 or older. Approximately two thirds were married or partnered and less than a 10th were still employed and working for a salary. Education levels varied. Fewer than one quarter attained postsecondary degrees, approximately one third obtained a bachelor-level degree and almost a half had completed only a primary school level of education. The household income quintiles were mostly evenly distributed across the study sample. In general, the sample survey was healthy and had high levels of functionality. The average number of ADL limitations was less than one. Respondents had, on average, two functionality and mobility limitations and two chronic conditions. Average number of depressive symptoms was between two and three on the EURO-D scale. Due to length restrictions, the country distribution of the sample is not presented here but is available upon request.

The bivariate associations of the control variables with the two cognitive outcome variables are also presented in Table 1. All variables were found to have significant associations with both objective word recall cognition and subjective self-rated memory. Consequently, they were all taken into account in the multivariate phase of the analysis. In addition, we note that the Pearson correlation between social network and activity participation was of moderate strength (r = .23, not shown).

Two hierarchical OLS regressions comprised the multivariate analysis phase, in which the associations between cognitive functioning, social network, and activity were examined while controlling for socioeconomic and health characteristics. The net associations between objective cognitive recall and the study variables are presented in Table 2. In the following sentences, we compare the relative strength of the respective associations by contrasting their standardized beta coefficients. Model 1 shows that without controlling for background and other confounding factors, activity participation (β = .389) had a greater positive association with cognitive recall abilities than did social network resources (β = .074). This pattern remained consistent in Model 2 after the entering of control variables in the model, although the association with activity decreased in magnitude. The findings in Model 3, after the insertion of the interaction term between social network and cognitive recall, show that the main effects of the interaction variables retained the same pattern. That is, in the absence of social network resources, activity participation yielded a stronger positive association with better cognitive recall (β = .191) than social network resources did in the absence of activity participation (β = .073). However, the interaction term revealed that the positive association between activity participation and objective cognitive recall lessened as social network resources increased (β = −.035). Figure 1 displays the result of the interaction graphically.

Table 2.

Word Recall Cognition, Social Network, and Activity: OLS Regression.

	Model 1			Model 2^c			Model 3^c
	Coefficient	SE	β	Coefficient	SE	β	Coefficient	SE	β
Social network scale^a	0.309***	.020	.074	0.203***	.1589	.049	0.280***	.039	.067
Number of activities^a	0.781***	.010	.389	0.340***	.011	.170	0.383***	.022	.191
Age^b
Age 70–79				−1.118***	.037	−.146	−1.119***	.037	−.146
Age 80+				−2.459***	.050	−.246	−2.458***	.050	−.246
Gender (female)				0.811***	.034	.011	0.813***	.034	.110
Marital status (married/partner)				0.118**	.037	.015	0.117**	.037	.015
Work (working)				0.438***	.058	.035	0.438***	.010	.033
Education^b
Secondary				1.084***	.040	.138	1.080***	.040	.138
Postsecondary				1.782***	.045	.207	1.785***	.045	.207
Household income^b
Quintile 2				0.156*	.074	.017	0.154*	.074	.017
Quintile 3				0.120*	.079	.023	0.195*	.079	.022
Quintile 4				0.529***	.084	.058	0.524***	.085	.058
Quintile 5				0.689***	.089	.072	0.685***	.089	.072
ADL limitations^a				−0.195***	.023	−.044	−0.194***	.023	−.044
Mobility limitations^a				−0.031**	.010	−.021	−0.031**	.010	−.021
Chronic condition count^a				−0.008	.012	−.003	−0.009	.012	−.004
EURO-D^a				−0.149***	.008	−.094	−0.148***	.008	−.093
Interaction
Social Network × Activity							−0.022*	.010	−.035
Constant	5.110***	.048		5.574***	.112		5.430***	.128
R ²	.170			.325			.325

Note. n = 36,571. ADL = activities of daily living; OLS = ordinary least squares.

^aVariable ranges: Social network scale = 0-4; Number of activities = 0-10; ADL limitations = 0-6; Mobility limitations = 0-10; Chronic condition count = 0-11; EURO-D = 0-12.

^bReference category: Age (60-69); Education (Primary); Household income (Quintile 1 – lowest).

^cModel controlled for country; findings available upon request.

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

Figure 1.

Association between activity participation and objective cognitive recall—by Social Network Scale.

Similar findings were revealed in the second OLS regression model in which associations between the study variables and subjective cognitive functioning (self-rated memory) were explored (see Table 3). Activity participation was consistently found to yield a stronger positive association with subjective cognitive self-rating of memory in comparison to social network resources, as seen in the standardized beta coefficients across the models. We note that the social network scale did not at first obtain significant associations with the self-rated memory outcome (Models 1 and 2). However, when the interaction term of social network and activity participation was entered (Model 3), the main effect of the social network scale and the subjective cognition outcome became significant. The interaction findings revealed that the positive association between activity participation and subjective self-rating of memory lessened as social network resources strengthened (β = −.049; see also Figure 2).

Table 3.

Self-Rated Memory, Social Network, and Activity: OLS Regression

	Model 1			Model 2^c			Model 3^c
	Coefficient	SE	β	Coefficient	SE	β	Coefficient	SE	β
Social network scale^a	−0.002	.006	−.002	−0.001	.005	−.001	0.029**	.011	.027
Number of activities^a	0.139***	.003	.267	0.037***	.003	.070	0.053***	.006	.102
Age^b
Age 70–79				−0.125***	.010	−.063	−0.013***	.010	−.063
Age 80+				−0.221***	.014	−.085	−0.022***	.014	−.085
Gender (female)				0.067***	.010	.035	0.068***	.010	.035
Marital status (married/partner)				−0.042***	.010	−.021	−0.043***	.010	−.021
Work (working)				0.103***	.031	.032	0.103***	.016	.032
Education^b
Secondary				0.142***	.011	.069	0.141***	.011	.069
Postsecondary				0.254***	.013	.113	0.255***	.013	.114
Household income^b
Quintile 2				−0.030	.021	−.013	−0.030	.021	−.013
Quintile 3				−0.041	.022	−.018	−0.043*	.022	−.019
Quintile 4				−0.011	.024	−.004	−0.013	.024	−.005
Quintile 5				−0.041	.025	.016	0.039	.025	.016
ADL limitations^a				0.004	.006	.004	0.005	.006	.004
Mobility limitations^a				−0.040***	.003	−.102	−0.040***	.003	−.102
Chronic condition count^a				−0.023***	.003	−.039	−0.024***	.003	−.040
Euro-D^a				−0.075***	.002	−.189	−0.075***	.002	−.182
Interaction
Social Network × Activity							−0.009***	.003	−.049
Constant	2.414***	.014		2.913***	.031		2.860***	.046
R ²	.071			.207			.208

Note. n = 36,812. ADL = activities of daily living; OLS = ordinary least squares.

^aVariable ranges: Social network scale = 0-4; Number of activities = 0-10; ADL limitations = 0-6; Mobility limitations = 0-10; Chronic condition count = 0-11; EURO-D = 0-12.

^bReference category: Age (60-69); Education (Primary); Household income (Quintile 1 – lowest).

^cModel controlled for country; findings available upon request.

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

Figure 2.

Association between activity participation and subjective self-rated memory—by Social Network Scale.

Further analyses performed separately on each of the age-groupings (not shown) revealed that the associations presented above were not uniform within each age-group. Specifically, the observed interaction between social network and activity in relation to the objective recall measure was significant mainly among the young-olds, those aged 60–69 (β = −.054). In addition, the interaction between social network and activity vis-à-vis subjective memory was significant primarily among the old-olds, that is, respondents aged 80 and older (β = −.098).

Discussion

The purpose of this study was to consider the interrelationship between social network and activity participation vis-à-vis cognitive health in a large sample of European adults aged 50 and older. We addressed the research question by means of a composite social network scale that was based upon data obtained from a name-generated network inventory and by using an activity count that takes a wide range of pursuits into account. The cognition outcome in the study was recall and memory, measured objectively and subjectively. The analysis controlled for several possible background and health confounders.

The adjusted model in the multivariate analysis showed that social network connectedness and the extent of activity participation were independently related to better recall—the objective cognitive measure that was used in this study. However, activity participation showed a relatively stronger association with this cognitive outcome. This same trend emerged in relation to the subjective self-rated memory outcome for activity participation. Again, activity participation exhibited a stronger association with self-rated memory in comparison to social network. These findings align with prior research (Barnes et al., 2004; Glei et al., 2005; James et al., 2011) and provide additional empirical support for the assertion that engagement with activities is particularly beneficial in later life. Stated differently, these results show that both social network and activity participation seem to matter in relation to cognitive health in later life, and activity ostensibly matters more so.

What this research demonstrates, however, is that the distinction between social network and activity participation associations with cognition is more nuanced than the findings from the first two models of each OLS regression otherwise suggest. In Model 3, the interaction between network and activity was added to the objective and subjective cognition regression models. The results showed that in the absence of any activity participation, having greater social network resources was associated with higher cognitive recall scores and better self-rated memory. Likewise, in the absence of any social network, participating in more activities was related to similarly positive cognitive outcomes.

However, the negative association of the interaction term shows that although the association between activity participation and cognition remains positive in terms of the interaction with social network resources, the extent of the positive benefit of the activity count on cognition decreases when greater social network resources are available. That is, the positive correlation of activity seems to diminish as one’s social network connectedness increases. Furthermore, among respondents with fewer network resources (or none at all), the positive association between increasing engagement in types of activities and subjective memory increased substantially, while among those with the greatest amount of network resources, the same association increased to only a minor degree. This finding suggests that at least part of the role of activity vis-à-vis cognition may be in its social function, that is, its capacity to serve as a source for social contact. Consequently, the net effect of activity as a possible cognitive stimulant may be more modest than is generally assumed.

Also noteworthy are the slightly different trends observed in this analysis when considering the objective and the subjective cognition measures. The general effect of activity on cognition in relation to social network was similar for both the objective and subjective measures of cognition, in that as social network resources increased, the positive association of increased activity participation lessened. However, the measure of activity participation showed a stronger beta association with subjective self-rated memory measure when interacted with social network, yielding a crossover interaction effect at the higher levels of activity scores.

Thus, for older adults who otherwise did not indicate having close personal ties, the participation in a variety of activities yielded a stronger positive association with self-rated memory than for those with greater social network connections. This finding implies that being engaged in a large variety of activities was primarily beneficial for persons with limited or no social ties. This again supports the assertion that it is the social component of activity participation that has a major benefit in later life, particularly within the realm of self-rating of memory. Therefore, public policy that seeks to promote “active and healthy aging” should not only encourage participation in a range of activities among all older adults but should focus specifically on those who appear to be more socially isolated.

Separate regressions by age-group in a supplementary analysis that was briefly summarized showed that the observed interactions mattered in terms of objective recall mainly among the young-olds, while the same was relevant in relation to subjective memory primarily among the oldest respondents. These findings suggest a differential explanation of the observed associations. The principal life course challenge of persons in their 60s is their exit from the workforce, which potentially diminishes their range of social ties as they reduce their former contacts with coworkers. As was found, activities can compensate for reduced network size in maintaining objective cognitive function. As for the oldest respondents, those 80+ in age, activity participation may serve as a replacement for social isolation. The findings revealed that in this age-group category, those without any social network seemed to have gained the most benefit from activity in regard to self-rated memory.

One limitation of this study is the cross-sectional nature of its design. Some investigators contend that it may be the case that cognition spurs activity rather than the opposite case, in which activity participation improves cognitive function. For example, in a study of a 1936-born Scottish cohort at age 70, activity was found to be related to cognition (Gow, Corley, Starr, & Deary, 2012). But when childhood IQ (as well as adult social class) was taken into account, most of the activity types lost their correlation with cognition. Such reverse causality is an interesting line of interpretation that warrants further scrutiny utilizing longitudinal data. The current analysis was unable to longitudinally test the extent of the association between the social and the cognitive realms toward this end. To date, the name-generated SHARE social network data are only available in Wave 4. However, the cross-sectional nature of the current study is not necessarily a shortcoming, given that the major effects of activity on cognition, as was reported in the longitudinal study by Green and associates (2008) noted earlier, tended to be baseline effects. Thus, what matters most may well be the initial measurement of the relevant associations, as one obtains in cross-sectional studies such as the present analysis.

Additional exploration is also warranted to do more explicit analyses of how different subsets of activity types intersect with social network resources. For example, does this same pattern persist when comparing participation in formal group memberships (i.e., volunteer city council member) versus participating in more informal group activities such as playing cards with friends? Research that further explores the unique contribution of the social components of activity engagement would bring additional clarification as to how the social network component of activities drives the positive outcomes of an active aging lifestyle.

Although not a primary aim of the current study, the data showed several previously observed associations with the control variables. Older age as well as poorer health and mental health were associated with lesser recall. Education and income were associated with greater recall. Women showed better recall, but no association emerged for marital or work status. These findings not only highlight the independence of the associations between social network and activity participation and cognition, they also give further backing to the direction of the key associations that emerged from this study. Interestingly, marital status was associated with poorer subjective memory and income was unrelated. These unexpected associations emphasize the unique nature of the subjective cognitive measure and the need to consider it further in future research.

In conclusion, this study sheds light on the complex interrelationship between social network and activity participation in relation to cognition in later life. It also raises some questions as to the actual role of activity participation vis-à-vis cognition. It is our interpretation, based upon the present research findings, that the “doing with” aspect of activity, in addition to the “doing,” per se, is important in the maintenance of cognitive health.

Footnotes

Authors’ Note

This article uses data from SHARE wave 4 release 1.1.1, as of March 28, 2013 (DOI: 10.6103/SHARE.w4.111).

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: The SHARE data collection has been primarily funded by the European Commission through the 5th Framework Program (project QLK6-CT-2001-00360 in the thematic program quality of life), through the 6th Framework Program (projects SHARE-I3, RII-CT-2006-062193, COMPARE, CIT5-CT-2005-028857, and SHARELIFE, and CIT4-CT-2006-028812), and through the 7th Framework Program (SHARE-PREP, No. 211909, SHARE-LEAP, No. 227822, and SHARE M4, No. 261982). Additional funding from the U.S. National Institute on Aging (U01 AG09740-13S2, P01 AG005842, P01 AG08291, P30 AG12815, R21 AG025169, Y1-AG-4553-01, IAG BSR06-11, and OGHA 04-064) and the German Ministry of Education and Research as well as from various national sources is gratefully acknowledged (see for a full list of funding institutions).

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