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. Author manuscript; available in PMC: 2009 Oct 7.
Published in final edited form as: Exp Aging Res. 2009 Jan–Mar;35(1):45–60. doi: 10.1080/03610730802545028

SOCIAL ENGAGEMENT AND COGNITIVE FUNCTION IN OLD AGE

Kristin R Krueger 1, Robert S Wilson 2, Julia M Kamenetsky 2, Lisa L Barnes 3, Julia L Bienias 4, David A Bennett 5
PMCID: PMC2758920  NIHMSID: NIHMS133742  PMID: 19173101

Abstract

We examined the association of diverse measures of social engagement with level of function in multiple cognitive domains in 838 persons without dementia who had a mean age of 80.2 (SD = 7.5). Social network size, frequency of social activity, and level of perceived social support were assessed in linear regression models adjusted for age, sex, education, and other covariates. Social activity and social support were related to better cognitive function, whereas social network size was not strongly related to global cognition. The results confirm that higher level of social engagement in old age is associated with better cognitive function but the association varies across domains of social engagement.

Social engagement refers to maintenance of social connections and participation in social activities (Bassuk, Glass, & Berkman, 1999). Recent research suggests that older people who are more socially engaged tend to have a higher level of cognitive function (Barnes, Mendes de Leon, Wilson, Bienias, & Evans, 2004; Bassuk et al., 1999; Holtzman et al., 2004; Yeh & Liu, 2003; Zunzunegui, Alvarado, del Ser, & Otero, 2003) compared to less engaged persons. With few exceptions (Barnes et al., 2004), these studies have not assessed nonsocial experiences such as cognitive activity and physical activity that have also been associated with level of cognitive function (Dustman, Emmerson, & Shearer, 1994; Wilson et al., 1999) and rate of cognitive decline (Weuve et al., 2004; Wilson, Mendes de Leon, et al., 2002). Thus, the extent to which the association of social engagement with cognitive function reflects other lifestyle factors has not been extensively investigated. In addition, knowledge about the bases of these associations remains limited. One reason for this uncertainty is that both social engagement and cognitive function are multidimensional constructs but have often been assessed with brief unidimensional measures.

In the present study, we examined the relation of social engagement to level of cognitive function in older persons from the Rush Memory and Aging Project, a clinical-pathologic study of risk factors for common chronic conditions of old age. We used three measures of social engagement: social network size, frequency of participation in social activities, and perceived level of social support. Cognition was assessed with a battery of 19 performance tests administered in an approximately 1-h session. Based in part on a factor analysis of the tests at baseline, composite measures of episodic memory, semantic memory, working memory, processing speed, and visuospatial ability were derived (Wilson et al., 2002). By constructing these composite measures, we were able to examine the association of social engagement with multiple cognitive domains. In analyses, we tested whether higher levels of each indicator of social engagement was associated with higher level of cognition, whether these associations varied across cognitive domains, and whether other affective, lifestyle, and health-related variables could account for these associations.

METHODS

Participants

Persons participating in this study are from the Rush Memory and Aging Project, a prospective clinical-pathologic study of risk factors for common chronic conditions of old age (Bennett et al., 2005). They were primarily recruited from subsidized housing facilities and continuous care retirement communities in the Chicago metropolitan area. Following a presentation about the study, individuals rated their interest in participation, and those who expressed interest were subsequently contacted by project personnel who provided more detailed information and obtained informed consent. The study was approved by the Institutional Review Board of Rush University Medical Center.

Upon enrollment in the project, all participants underwent a uniform clinical evaluation, which included a self-report medical history, cognitive function testing by trained neuropsychological technicians, and a complete neurological examination by trained nurses. On the basis of this examination and an in-person evaluation of the participant, an experienced clinician (neurologist, geriatrician, or geriatric nurse practitioner) classified each person with respect to dementia, using the criteria of the joint working group of the National Institute of Neurologic and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (McKhann et al., 1984).

Between September 1997 and July 2006, 3397 persons attended presentations about the study and 2231 indicated that they were ‘very’ or ‘somewhat’ interested in participating. By July 2006, 1102 of those expressing interest had enrolled and completed baseline evaluation. Of these participants, 197 had missing social variable data and were excluded. We excluded an additional 67 people who met criteria for dementia, leaving 838 persons eligible for analyses. They had a mean age of 80.2 (SD = 7.5), a mean of 14.4 years of formal schooling (SD = 3.0), a mean score of 27.9 (SD = 2.1) on the Mini-Mental State Examination, and 75% were women and 91% were white and non-Hispanic.

Assessment of Social Engagement

Social engagement was assessed with measures of social activity frequency, size of social networks, and perceived social support. Frequency of social activity was assessed by asking how often during the past year participants engaged in six common types of activities that involve social interaction (1: go to restaurants, sporting events, or teletrack, or play bingo; 2: go on day trips or overnight trips; 3: do unpaid community/volunteer work; 4: visit relatives’ or friends’ houses; 5: participate in groups, such as senior center, VFW, Knights of Columbus, Rosary Society, or something similar; 6: attend church or religious services; Mendes de Leon, Glass, & Berkman, 2003). Persons rated each activity on a 5-point scale, with 5 indicating participation in the activity every day or nearly every day, 4 indicating participation several times a week, 3 for several times a month, 2 for several times a year, and 1 for once a year or less. Item responses were summed and averaged to yield a total score. In prior research in this cohort, higher scores on this measure have been associated with higher levels of socioeconomic status (Wilson, Scherr, Schneider, Tang, & Bennett, 2007) and psychosocial functioning (Barnes et al., 2007).

We quantified social network size with standard questions (Cornoni-Huntley, Brock, Ostfeld, Taylor, & Wallace, 1986) about the number of children, family, and friends each participant had and how often they had seen them. Social network size was the number of these individuals seen at least once per month, as previously described (Barnes et al., 2004).

Social support was assessed with four questions (items 1, 2, 5, 10) from the Multidimensional Scale of Perceived Social Support (Zimet, Dahlem, Zimet, & Farley, 1988). These four items (e.g., “There is a special person who is around when I am in need”) make up the Significant Other subscale of the questionnaire, as established in factor analytic studies (Cheng & Chan, 2004; Zimet, Powell, Farley, Werkman, & Berkoff, 1990). Participants rated agreement with each statement on a 5-point scale, and item scores were averaged so that the total score ranged from 1 to 5, with higher scores denoting more social support.

Assessment of Cognitive Function

Cognitive function was assessed with a battery of 19 performance tests. The Mini-Mental State Examination (Folstein, Folstein & McHugh, 1975) was used for descriptive purposes but not in analyses. The 19 performance tests were used in analyses. There were seven episodic memory measures: Word List Memory, Recall, and Recognition (Morris et al., 1989) and immediate and delayed recall of Story A from Logical Memory of the Wechsler Memory Scale–Revised (Wechsler, 1987) and of the East Boston Story (Albert et al., 1991; Wilson, Beckett, et al., 2002). Semantic memory was assessed with a 15-item version (Morris et al, 1989) of the Boston Naming Test (Kaplan, Goodglass, & Weintraub, 1983), Verbal Fluency (Morris et al., 1989; Wilson, Beckett, et al., 2002), and a 15-item version (Wilson, Beckett, et al., 2002) of the National Adult Reading Test (Nelson, 1982). Working memory tests included Digit Span Forward and Digit Span Backward (Wechsler, 1987) and digit ordering (Cooper & Sagar, 1993; Wilson, Beckett, et al., 2002). Four measures of perceptual speed were administered: the oral version of the Symbol Digit Modalities Test (Smith, 1982), Number Comparison (Ekstrom, French, Harman, & Kermen, 1976; Wilson, Beckett, et al., 2002), and two measures from a modified version (Wilson et al., 2005) of the Stroop Neuropsychological Screening Test (Trenerry, Crosson, DeBoe, & Leber, 1989): number of color names correctly read in 30s minus the number of errors and number of colors correctly named in 30s minus the number of errors. Visuospatial ability was assessed with a 15-item version of Judgment of Line Orientation (Benton, Sivan, Hamsher, Varney, & Spreen, 1994) and a 16-item version of Standard Progressive Matrices (Raven, Court, & Raven, 1992).

To minimize floor and ceiling artifacts and other sources of measurement error, composite measures of two or more individual tests were used in analyses. Based in part on a previous factor analysis (Wilson et al., 2005), summary measures of episodic memory (seven tests), semantic memory (three tests), working memory (three tests), perceptual speed (four tests), and visuospatial ability (two tests) were formed along with a measure of global cognition based on all 19 tests. In each case, individual tests were converted to z scores, using the mean and standard deviation from the entire cohort, and then the z scores were averaged to yield the composite measure. Further information about the individual tests and the derivation of these composite measures is published elsewhere (Wilson, Barnes, et al., 2003; Wilson et al., 2005).

Assessment of Other Covariates

Depressive symptomatology was assessed with a 10-item version (Kohout, Berkman, Evans, & Cornoni-Huntley, 1993) of the Center for Epidemiologic Studies Depression Scale (Radloff, 1977). Persons were asked if they had experienced each of 10 symptoms (e.g., “I felt sad”) much of the time during the past week. The score was the number of symptoms experienced. Scores on this scale have been shown to correspond well with scores on the original version of the scale (Kohout et al., 1993) and to predict dementia (Wilson, Barnes, et al., 2002; Wilson, Mendes de Leon, Bennett, Bienias, & Evans, 2004) and mortality (Wilson, Bienias, Mendes de Leon, Evans, & Bennett, 2003) in old age.

The personality traits of neuroticism, indicative of distress proneness, and extraversion, indicative of sociability, were measured with 6-item versions of the standard 12-item scale of each trait from the NEO Five-Factor Inventory (Costa & McCrae, 1992). Persons rated agreement with each neuroticism item (item numbers 1, 6, 21, 36, 41, 51; e.g., “I often feel inferior to others”) and each extraversion item (item numbers 2, 7, 17, 27, 37, 52; e.g., “I like to have a lot of people around me”) on a 5-point scale. Item scores ranged from 0 to 4, with higher scores denoting a higher level of the trait. Item scores were summed and multiplied by two to make the total scores (range: 0 to 48) more comparable to the standard 12-item scales. In a separate group of 932 older persons without dementia from the Rush Religious Orders Study (Wilson, Bienias, Evans, & Bennett, 2004), the 6-item neuroticism measure had a correlation of 0.90 with the standard 12-item scale, and the 6-item extraversion measure had a correlation of 0.91 with the standard 12-item scale, supporting the validity of the brief measures.

Persons rated their current frequency of participation in nine cognitively stimulating activities (e.g., reading a book, visiting a library) on a 5-point scale, with 5 indicating participation in the activity every day or about every day and 1 indicating participation once a year or less. We focused on common activities in which seeking or processing information was central and which had minimal social or physical demands. Item scores were averaged to yield a summary measure of cognitive activity that has been shown to have adequate short-term temporal stability and positive associations with education and cognitive ability (Barnes, Wilson, Mendes de Leon, & Bennett, 2006; Wilson, Barnes, & Bennett, 2003; Wilson et al., 2005).

Frequency of physical activity was assessed with questions adapted (McPhillips, Pellettera, Barrett-Connor, Wingard, & Criqui, 1989) from the 1985 Health Interview Survey (1985 Health Interview Survey, 1985). Persons were asked if they had participated in each of five activities (e.g., walking for exercise, calisthenics) during the past 2 weeks, and if so, the number of times and mean time per occasion. Minutes in each activity were summed and divided by 120 to yield a summary measure of hours per week of physical activity, as described elsewhere (Wilson, Mendes de Leon, et al., 2002).

The presence of seven chronic medical conditions was determined from medical history (i.e., diabetes, hypertension, heart disease, cancer, thyroid disease, head injury) or from history plus examination (i.e., stroke). The number of conditions present was used as a measure of chronic illness, as described elsewhere (Wilson, Mendes de Leon et al., 2002; Wilson, Beckett, Bienias, Evans, & Bennett, 2003).

Disability was assessed with the Katz scale (Katz, Ford, Moskowitz, Jackson, & Jaffe, 1963). Participants indicated whether they could independently perform each of six daily living activities: walking, bathing, dressing, eating, getting from bed to chair, and toileting. The score was the number of activities that the person was unable to perform independently.

Data Analysis

We examined the association of three measures of social engagement with global cognition in a series of linear regression analyses, controlling for age, sex, and education. We initially entered each measure of social engagement separately and then entered the three measures together. Subsequent analyses added terms for depressive symptoms, neuroticism, and extraversion; for cognitive and physical activities; or for chronic illness and disability. We then repeated this set of analyses separately for measures of specific cognitive domains in lieu of the measure of global cognition. All models were validated graphically and analytically. Programming was done in SAS (SAS Institute Inc., 2000).

RESULTS

Distribution of Social Engagement Measures

Social activity scores ranged from 1 to 4.2 (mean = 2.6, SD = 0.6), with higher scores indicating more activity. Social activity was inversely related to age, neuroticism, and disability and positively related to education, extraversion, and cognitive and physical activities (Table 1). Social network size ranged from 0 to 66 persons seen at least monthly (mean 6.9, SD = 5.8). Social support ranged from 1 to 5 (mean = 4.3, SD 0.7), with higher scores indicating more support. Social network size and social support were positively related to each other and social activity, and they had similar patterns of correlations with other covariates (Table 1), consistent with the idea that they are each facets of a broader construct such as social engagement.

Table 1.

Correlations of social engagement measures with each other and covariates*

Range Mean SD Social
network		 Social
support		 Age Education CES-D Neuroticism Extraversion Cognitive
activity		 Physical
activity		 Chronic
illness		 Disability
Social activity 1−4.2 2.56 .59 .26 .17 −.17 .17 −.06 −.16 .31 .45 .15 −.01 −.18
Social network 0−66 6.62 5.85 − .20 −.08 .01 −.08 −.10 .23 .10 .09 .00 −.02
Social support 1−5 4.31 .71 − − .13 .06 −.17 −.19 .29 .15 .00 .03 −.07
Age 54−100 80.16 7.51 – −.04 .01 .04 −.11 −.10 −.06 −.01 .05
Education 3−28 14.37 3.00 – −.23 −.26 .04 .36 .06 −.02 −.05
CES-D 0−10 1.34 1.80 – .51 −.19 −.17 −.08 .08 .12
Neuroticism 0−44 15.56 7.21 – −.29 −.20 −.05 −.00 .09
Extraversion 14−48 31.39 5.97 – .16 .13 −.01 −.06
Cognitive activity 1−4.67 3.16 .68 – .12 −.04 −.15
Physical activity 0−35 3.04 3.64 – −.02 −.07
Chronic illness 0−6 1.44 1.07 – .05
Disability 0−5 .196 .64 –
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*

p < .01 for correlations with an absolute value of .09 or more.

Social Engagement and Global Cognitive Function

We examined the relation of social engagement to level of global cognition by entering the three social engagement indicators separately and then together in linear regression models. These models and all subsequent analyses controlled for the potentially confounding effects of age, sex, and education. When the indicators were entered separately, both social activity and social support had a positive association with global cognition: estimate (unstandardized beta weight) for social activity = 0.173, SE = 0.029, p < .001, 3% increment in adjusted R2 compared to a model with only demographic variables; and social support = 0.087, SE = 0.023, p < .001, 1% increment in R2. Social network was not associated with global cognition: social network = 0.005, SE = 0.003, p = 0.73. With all three social variables entered simultaneously, results were not substantially changed: estimate for social activity 0.161, SE = 0.029, p < .001; estimate for social support = 0.069, SE = 0.023, p = .003; estimate for social network = .000, SE = 0.003, p = .882; 4% increment in R2. In subsequent analyses, therefore, we entered social activity and social support simultaneously and dropped social network.

Because social engagement is associated with depression and personality, we repeated the analysis controlling for depressive symptomatology and for the personality traits of neuroticism and extraversion (Table 2, model B). In this analysis, the associations of social activity and social support with global cognition were reduced by about 25%, but remained significant.

Table 2.

Relation of social engagement to global cognitive function and specific cognitive domains*

Model A
 Model B
 Model C
 Model D
Outcome Model term Estimate (SE) p value Estimate (SE) p value Estimate (SE) p value Estimate (SE) p value
Global cognition Social activity .160 (.030) <.001 .152 (.030) <.001 .069 (.030) .022 .147 (.029) <.001
Social support .068 (.023) .003 .050 (.024) .034 .052 (.022) .019 .066 (.023) .004
Episodic memory Social activity .177 (.038) <.001 .164 (.040) <.001 .125 (.041) <.003 .171 (.039) <.001
Social support .023 (.030) .444 .005 (.032) .881 .013 (.030) .678 .020 (.030) .510
Semantic memory Social activity .117 (.036) .001 .104 (.037) .005 −.026 (.037) .491 .107 (.037) .004
Social support .055 (.029) .056 .037 (.030) .211 .034 (.027) .212 .055 (.029) .058
Working memory Social activity .099 (.045) .027 .102 (.046) .027 .049 (.048) .310 .087 (.045) .055
Social support .107 (.036) .003 .094 (.037) .011 .094 (.035) .008 .105 (.036) .003
Perception speed Social activity .273 (.044) <.001 .263 (.045) <.001 .084 (.045) .061 .251 (.044) <.001
Social support .117 (.035) <.001 .089 (.036) .014 .089 (.033) .007 .114 (.035) .001
Visuospatial ability Social activity .092 (.045) .040 .102 (.046) .027 .026 (.048) .586 .074 (.045) .101
Social support .089 (.036) .012 .079 (.037) .030 .076 (.035) .031 .089 (.035) .012
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*

Estimated from linear regression models adjusted for age, sex, and education. In addition, model B adjusted for depressive symptoms and personality, model C for cognitive and physical activity, and model D for chronic illness and disability. The estimates are unstandardized beta weights.

Because cognitive and physical activities are related to social engagement and cognition, we repeated the initial model with terms added for cognitive activity score and physical activity score (Table 2, model C). Compared to the initial analysis, the effect for social activity was reduced by about 60% but remained significant, and the effect for social support was reduced by less than 25%, and remained significant.

To see if poor health could account for the relation of social engagement to global cognition, we repeated the initial model with terms added for number of chronic medical conditions and for level of disability on the Katz scale (Table 2, model D). The results of this analysis were comparable to the initial analysis.

Social Engagement and Function in Specific Cognitive Domains

To determine whether social engagement was related to some forms of cognition and not others, we repeated the analyses with each of the five summary measures of specific cognitive domains as outcomes in place of the measure of global cognition. Frequency of social activity was positively related to level of function in each cognitive domain in the initial analyses (Table 2, model A). Adjustment for depressive symptomatology and personality (Table 2, model B) or for chronic illness and disability (Table 2, model D) did not substantially affect results. Conversely, controlling for cognitive and physical activities (Table 2, model C) substantially reduced the association between social activity and cognition in all domains except for episodic memory, which was reduced by about 15% but remained significant.

In the initial analysis (Table 2, model A), social support was positively related to level of function in working memory, perceptual speed, and visuospatial ability, but not in episodic or semantic memory. Results were comparable after adjusting for depressive symptoms and personality, cognitive and physical activities, and chronic illness and disability.

DISCUSSION

We examined the relation of multiple indicators of social engagement to level of cognitive function in more than 800 older persons without clinical signs of dementia. More frequent participation in social activities and a higher level of perceived social support were associated with higher level of cognitive functioning.

Higher levels of social activity and social integration have been associated with better cognitive functioning in prior research (Barnes et al., 2004; Bassuk et al., 1999; Zunzunegui et al., 2003), as in the present study. In two studies that did not observe the association, social activity assessment was based on activity during a single day in one (Hilleras, Jorm, Herlitz, & Winblad, 1999) and on three dichotomous items in the other (Aarsten, Smits, van Tilburg, Knipscheer, & Deeg, 2002), suggesting that some of the inconsistency in previous research may be due to variability in how social activity=social integration was defined and how adequately it was assessed.

Previous studies have found a positive association between perceived social support and global cognitive function in older people (Holtzman et al., 2004; Lee & Shinkai, 2005; Yeh & Liu, 2003), consistent with the present results. Controlling for chronic illness and disability had no effect on the relation of social support to cognition in the present study. By contrast, adjustment for depression and personality and for cognitive and physical activities reduced the association by approximately 25% (from an estimate of 0.068 in the original model to 0.050 and 0.052, respectively). This reduction suggests that affect and activity lifestyle may partially account for the relation of perceived social support to cognition.

Social network size was not related to cognitive function in this cohort, though there was a nearly significant correlation in initial analyses without other measures of social engagement in the model. Larger network size has been associated with higher level of cognitive function in some previous studies (Barnes et al., 2004; Holtzman et al., 2004), but evidence has been weak (Zunzunegui et al., 2003) or mixed in others (Arbuckle, Gold, Andes, Schwartzman, & Chaikelson, 1992). These results suggest that social network size is not strongly related to level of cognitive function in old age. It may be that satisfaction with social relationships within one's network is more important than number of social contacts (Fratiglioni, Wang, Ericsson, Maytan, & Winblad, 2000).

The basis of the association between these diverse indicators of social engagement and cognitive function is uncertain. Frequency of cognitive (Wilson et al., 1999, 2005) and physical (Dustman et al., 1994) activities is also positively related to cognitive function in older persons. In this study, the modest association of social activity with global cognitive function was reduced by over 50% when we controlled for cognitive and physical activities, but remained significant. This suggests that social activities also involve some degree of cognitive and physical activities, consistent with prior research (Christensen & MacKinnon, 1993), and also account for a unique, albeit small, portion of the variance in cognition. We found little evidence that depression, personality, or chronic illness could account for the relation of social engagement indicators with cognitive function.

A novel feature of this study is that we examined the association of social engagement with function in different domains of cognition. More frequent social activity was related to better function in multiple domains of cognition. In contrast, a consistent effect of social support was observed for only three domains: working memory, perceptual speed, and visuospatial ability. This finding was unexpected. It suggests that having supportive relationships has more to do with problem-solving abilities and processing efficiency than with storage of information.

This study has several strengths. A relatively large number of participants was studied so that there was adequate power to observe the associations of interest even after controlling for selected covariates. We used previously established composite measures of different cognitive domains, reducing measurement error and permitting examination of selective associations of social engagement with cognition. Finally, we used multiple indicators of social engagement given the multidimensional nature of this construct.

The study has important limitations as well. Analyses are based on a selected group of participants so that it will be important to determine whether the results generalize to defined populations of older people. In addition, the findings are cross-sectional. Longitudinal studies will be needed to elucidate the direction of the association between social engagement and cognition.

Acknowledgments

This research was supported by National Institute on Aging grants R01 AG17917 and R01 AG022018, and the Illinois Department of Public Health. The authors thank the many Illinois residents for participating in the Rush Memory and Aging Project; Traci Colvin, MPH, and Tracy Hagman for coordinating the study; Todd Beck, MS, for statistical programming; George Dombrowski, MS, and Greg Klein for data management.

Footnotes

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Contributor Information

Lisa L. Barnes, Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA

Julia L. Bienias, Rush Institute for Healthy Aging and Department of Internal Medicine, Rush University Medical Center, Chicago, Illinois, USA

David A. Bennett, Rush Alzheimer's Disease Center and Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois, USA

REFERENCES

  1. 1985 Health Interview Survey . National Center for Health Statistics. U.S. Public Health Service; Hyatsville, MD: 1985. (Series 10). Publication No. 160 PHHS (PHS) 86−1568. [Google Scholar]
  2. Aartsen MJ, Smits CHM, van Tilburg T, Knipscheer KCPM, Deeg DJH. Activity in older adults: Cause or consequence of cognitive functioning? A longitudinal study on everyday activities and cognitive performance in older adults. Journal of Gerontology: Psychological Sciences. 2002;57B:153–162. doi: 10.1093/geronb/57.2.p153. [DOI] [PubMed] [Google Scholar]
  3. Albert M, Smith L, Scherr P, Taylor J, Evans D, Funkenstein H. Use of brief cognitive tests to identify individuals in the community with clinically diagnosed Alzheimer's disease. International Journal of Neuroscience. 1991;57:167–178. doi: 10.3109/00207459109150691. [DOI] [PubMed] [Google Scholar]
  4. Arbuckle TY, Gold DP, Andes D, Schwartzman A, Chaikelson J. The role of psychosocial context, age, and intelligence in memory performance of older men. Psychology and Aging. 1992;7:25–36. doi: 10.1037//0882-7974.7.1.25. [DOI] [PubMed] [Google Scholar]
  5. Barnes LL, Mendes de Leon CF, Wilson RS, Bienias JL, Evans DA. Social resources and cognitive decline in a population of older African Americans and Whites. Neurology. 2004;63:2322–2326. doi: 10.1212/01.wnl.0000147473.04043.b3. [DOI] [PubMed] [Google Scholar]
  6. Barnes LL, Wilson RS, Bienias JL, Mendes de Leon CF, Kim HJ, Buchman AS, Bennett DA. Correlates of lifespace in a volunteer cohort of older adults. Experimental Aging Research. 2007;33:77–93. doi: 10.1080/03610730601006420. [DOI] [PubMed] [Google Scholar]
  7. Barnes LL, Wilson RS, Mendes de Leon CF, Bennett DA. The relation of lifetime cognitive activity and lifetime access to resources to late-life cognitive function in older African Americans. Aging Neuropsychology and Cognition. 2006;13:516–528. doi: 10.1080/138255890969519. [DOI] [PubMed] [Google Scholar]
  8. Bassuk SS, Glass TA, Berkman LF. Social disengagement and incident cognitive decline in community-dwelling elderly persons. Annals of Internal Medicine. 1999;131:165–173. doi: 10.7326/0003-4819-131-3-199908030-00002. [DOI] [PubMed] [Google Scholar]
  9. Bennett DA, Schneider JA, Buchman AS, Mendes de Leon CF, Bienias JL, Wilson RS. The Rush Memory and Aging Project: Study design and baseline characteristics of the study cohort. Neuroepidemiology. 2005;25:163–175. doi: 10.1159/000087446. [DOI] [PubMed] [Google Scholar]
  10. Benton AL, Sivan AB, Hamsher KDS, Varney NR, Spreen O. Contributions to neuropsychological assessment. 2nd Ed. Oxford University Press; New York: 1994. [Google Scholar]
  11. Cheng S-T, Chan ACM. The multidimensional scale of perceived social support: Dimensionality and age and gender differences in adolescents. Personality and Individual Differences. 2004;37:1359–1369. [Google Scholar]
  12. Christensen H, Mackinnon A. The association between mental, social, and physical activity and cognitive performance in young and old subjects. Age and Aging. 1993;22:175–182. doi: 10.1093/ageing/22.3.175. [DOI] [PubMed] [Google Scholar]
  13. Cooper JA, Sager HJ. Incidental and intentional recall in Parkinson's disease: An account based on diminished attentional resources. Journal of Clinical and Experimental Neuropsychology. 1993;15:713–731. doi: 10.1080/01688639308402591. [DOI] [PubMed] [Google Scholar]
  14. Cornoni-Huntley J, Brock DB, Ostfeld A, Taylor JO, Wallace RB. Established populations for epidemiologic studies of the elderly resource data book. U.S. Department of Health and Human Services; Washington, DC: 1986. NIH Publication No. 86−2443. [Google Scholar]
  15. Costa PT, McCrae RR. Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI) professional manual. Psychological Assessment Resources; Odessa, FL: 1992. [Google Scholar]
  16. Dustman R, Emmerson R, Shearer D. Physical activity, age and cognitive neuropsychological function. Journal of Aging and Physical Activity. 1994;2:143–181. [Google Scholar]
  17. Ekstrom RB, French JW, Harman HH, Kermen D. Manual for kit of factor-referenced cognitive tests. Educational Testing Service; Princeton, NJ: 1976. [Google Scholar]
  18. Folstein M, Folstein S, McHugh P. Mental-Mental State: A practical method for grading the mental state of patients for the clinician. Journal of Psychiatric Research. 1975;12:189–198. doi: 10.1016/0022-3956(75)90026-6. [DOI] [PubMed] [Google Scholar]
  19. Fratiglioni L, Wang HX, Ericsson K, Maytan M, Winblad B. Influence of social network on occurrence of dementia: A community-based longitudinal study. Lancet. 2000;355:1315–1319. doi: 10.1016/S0140-6736(00)02113-9. [DOI] [PubMed] [Google Scholar]
  20. Hilleras PK, Jorm AF, Herlitz A, Winblad B. Activity patterns in very old people: A survey of cognitively intact subjects aged 90 years or older. Age and Ageing. 1999;28:147–152. doi: 10.1093/ageing/28.2.147. [DOI] [PubMed] [Google Scholar]
  21. Holtzman RE, Rebok GW, Saczynski JS, Kouzis AC, Doyle KW, Eaton WW. Social network characteristics and cognition in middle-aged and older adults. Journal of Gerontology: Psychological Sciences. 2004;59B:P278–P284. doi: 10.1093/geronb/59.6.p278. [DOI] [PubMed] [Google Scholar]
  22. Kaplan EF, Goodglass H, Weintraub S. The Boston Naming Test. Lea & Febiger; Philadelphia: 1983. [Google Scholar]
  23. Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW. Studies of illness in the aged. The index of ADL: A standardized measure of biological and psychosocial function. Journal of the American Medical Association. 1963;185:914–923. doi: 10.1001/jama.1963.03060120024016. [DOI] [PubMed] [Google Scholar]
  24. Kohout R, Berman L, Evans D, Cornoni-Huntley J. Two shorter forms of the CES-D (Center for Epidemiological Studies Depression) depression symptoms index. Journal of Aging Health. 1993;5:179–193. doi: 10.1177/089826439300500202. [DOI] [PubMed] [Google Scholar]
  25. Lee Y, Shinkai S. Correlates of cognitive impairment and depressive symptoms among older adults in Korea and Japan. International Journal of Geriatric Psychiatry. 2005;20:576–586. doi: 10.1002/gps.1313. [DOI] [PubMed] [Google Scholar]
  26. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan E. Clinical diagnosis of Alzheimer's disease: Report of the NINCDS/ADRDA work group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology. 1984;34:939–944. doi: 10.1212/wnl.34.7.939. [DOI] [PubMed] [Google Scholar]
  27. McPhillips JB, Pellettera KM, Barrett-Connor E, Wingard DL, Criqui MH. Exercise patterns in a population of older adults. American Journal of Preventive Medicine. 1989;5:65–72. [PubMed] [Google Scholar]
  28. Mendes de Leon CF, Glass TA, Berkman LF. Social engagement and disability in a community population of older adults: The New Haven EPESE. American Journal of Epidemiology. 2003;157:633–642. doi: 10.1093/aje/kwg028. [DOI] [PubMed] [Google Scholar]
  29. Morris J, Heyman A, Mohs R, Hughes J, van Belle G, Fillenbaum G, et al. The consortium to establish a registry for Alzheimer's disease (CERAD). Part I. Clinical and neuropsychological assessment of Alzheimer's disease. Neurology. 1989;39:1159–1165. doi: 10.1212/wnl.39.9.1159. [DOI] [PubMed] [Google Scholar]
  30. Nelson HE. National Adult Reading Test (NART): Test manual. NFER Nelson; Windsor, UK: 1982. [Google Scholar]
  31. Radloff L. The CES-D Scale: A self-report depression scale for research in the general population. Applied Psychological Measurement. 1977;1:385–401. [Google Scholar]
  32. Raven JC, Court JH, Raven J. Manual for Raven's progressive matrices and vocabulary: Standard progressive matrices. Oxford Psychologists Press; Oxford, UK: 1992. [Google Scholar]
  33. SAS Institute Inc. SAS/STAT® user's guide, Version 8. SAS Institute Inc.; Cary, NC: 2000. [Google Scholar]
  34. Smith A. Symbol Digit Modalities Test Manual—Revised. Western Psychological Services; Los Angeles: 1982. [Google Scholar]
  35. Trenerry MR, Crosson B, DeBoe J, Leber WR. The Stroop Neuropsychological Screening Test. Psychological Assessment Resources; Odessa, FL: 1989. [Google Scholar]
  36. Wechsler D. Wechsler Memory Scale—Revised Manual. Psychological Corporation; San Antonio, TX: 1987. [Google Scholar]
  37. Weuve J, Kang JH, Manson JE, Breteler MM, Ware JH, Grodstein F. Physical activity, including walking, and cognitive function in older women. Journal of the American Medical Association. 2004;12:1454–1461. doi: 10.1001/jama.292.12.1454. [DOI] [PubMed] [Google Scholar]
  38. Wilson RS, Barnes LL, Bennett DA. Assessment of lifetime participation in cognitively stimulating activities. Journal of Clinical and Experimental Neuropsychology. 2003;25:634–642. doi: 10.1076/jcen.25.5.634.14572. [DOI] [PubMed] [Google Scholar]
  39. Wilson RS, Barnes LL, Krueger KR, Hoganson G, Bienias JL, Bennett DA. Early and late life cognitive activity and cognitive systems in old age. Journal of the International Neuropsychological Society. 2005;11:400–407. [PubMed] [Google Scholar]
  40. Wilson RS, Barnes LL, Mendes de Leon CF, Aggarwal NT, Schneider JS, Bach J, et al. Depressive symptoms, cognitive decline, and risk of AD in older persons. Neurology. 2002;59:364–370. doi: 10.1212/wnl.59.3.364. [DOI] [PubMed] [Google Scholar]
  41. Wilson RS, Beckett LA, Barnes LL, Schneider JA, Bach J, Evans DA, Bennett DA. Individual differences in rates of change in cognitive abilities of older persons. Psychology and Aging. 2002;17:179–193. [PubMed] [Google Scholar]
  42. Wilson RS, Beckett LA, Bienias JL, Evans DA, Bennett DA. Terminal decline in cognitive function. Neurology. 2003;60:1782–1787. doi: 10.1212/01.wnl.0000068019.60901.c1. [DOI] [PubMed] [Google Scholar]
  43. Wilson RS, Bennett DA, Beckett LA, Morris MC, Gilley DW, Bienias JL, et al. Cognitive activity in older persons from a geographically defined population. Journal of Gerontology: Psychological Sciences. 1999;54B:P155–P160. doi: 10.1093/geronb/54b.3.p155. [DOI] [PubMed] [Google Scholar]
  44. Wilson RS, Bienias JL, Evans DA, Bennett DA. Religious Orders Study: Overview and change in cognitive and motor speed. Aging Neuropsychology and Cognition. 2004;11:280–303. [Google Scholar]
  45. Wilson RS, Bienias JL, Mendes de Leon CF, Evans DA, Bennett DA. Negative affect and mortality in older persons. American Journal of Epidemiology. 2003;158:827–835. doi: 10.1093/aje/kwg224. [DOI] [PubMed] [Google Scholar]
  46. Wilson RS, Mendes de Leon CF, Barnes LL, Schneider JA, Bienias JL, Evans DA, Bennett DA. Participation in cognitively stimulating activities and risk of incident Alzheimer disease. Journal of the American Medical Association. 2002;287:742–748. doi: 10.1001/jama.287.6.742. [DOI] [PubMed] [Google Scholar]
  47. Wilson RS, Mendes de Leon CF, Bennett DA, Bienias JL, Evans DA. Depressive symptoms and cognitive decline in a community population of older persons. Journal of Neurology, Neurosurgery, and Psychiatry. 2004;75:126–129. [PMC free article] [PubMed] [Google Scholar]
  48. Wilson RS, Scherr PA, Schneider JA, Tang Y, Bennett DA. Relation of cognitive activity to risk of developing Alzheimer's disease. Neurology. 2007;20:1911–1920. doi: 10.1212/01.wnl.0000271087.67782.cb. [DOI] [PubMed] [Google Scholar]
  49. Yeh SCJ, Liu YY. Influence of social support on cognitive function in the elderly. BMC Health Service Research. 2003;3:1–9. doi: 10.1186/1472-6963-3-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Zimet GD, Dahlem NW, Zimet SG, Farley GK. The Multidimensional Scale of Perceived Social Support. Journal of Personality Assessment. 1988;52:30–41. doi: 10.1080/00223891.1990.9674095. [DOI] [PubMed] [Google Scholar]
  51. Zimet GD, Powell SS, Farley GK, Werkman S, Berkoff KA. Psychometric characteristics of the Multidimensional Scale of Perceived Social Support. Journal of Personality Assessment. 1990;55:610–617. doi: 10.1080/00223891.1990.9674095. [DOI] [PubMed] [Google Scholar]
  52. Zunzunegui MV, Alvarado BE, Del Ser T, Otero A. Social networks, social integration, and social engagement determine cognitive decline in community-dwelling Spanish older adults. Journal of Gerontology: Social Sciences. 2003;58B:S93–S100. doi: 10.1093/geronb/58.2.s93. [DOI] [PMC free article] [PubMed] [Google Scholar]

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