geronb      J Gerontol B Psychol Sci Soc Scigeronb      The Journals of Gerontology Series B: Psychological Sciences and Social Sciences      J Gerontol B Psychol Sci Soc Sci      1079-5014      1758-5368              Oxford University Press                    P4110.1093/geronb/63.1.P41                        Journal of Gerontology: Psychological Sciences                            Spousal Social Activity Trajectories in the Australian Longitudinal Study of Ageing in the Context of Cognitive, Physical, and Affective Resources                                          Hoppmann            Christiane A.                                                                    Gerstorf            Denis                                                          Luszcz            Mary                                    1School of Psychology, Georgia Institute of Technology, Atlanta.        2Department of Human Development and Family Studies, The Pennsylvania State University, University Park.        3Centre for Ageing Studies, Flinders University, Adelaide, Australia.                    Address correspondence to Christiane A. Hoppmann, Georgia Institute of Technology, School of Psychology, 654 Cherry Street, Atlanta, GA 30332. E-mail: ch295@mail.gatech.edu                    1        2008            63      1      P41      P50                        6          9          2007                          21          12          2006                            Copyright 2008 by The Gerontological Society of America        2008                          We examined the dyadic interdependence of spousal social activity trajectories over 11 years by using longitudinal data on 565 couples from the Australian Longitudinal Study of Ageing (Mage = 76 years at Time 1). Social activity trajectories were interrelated in elderly couples, and they depended not only on individual but also on spousal cognitive, physical, and affective resources at baseline. Most associations examined were similar in husbands and wives. However, wives performed more social activities and displayed different depression–social activity associations than did husbands. We found stronger within-couple associations in the domain of social activities than for cognition. Our findings illustrate the important role of social relationships for late-life development and suggest that the mechanisms involved in dyadic interdependencies may be domain and gender specific.                    Successful aging        Social activities        Couples        Growth curve modeling                              hwp-legacy-fpage          P41                          hwp-legacy-dochead          RESEARCH ARTICLE                                      ENGAGEMENT in social activities is key to successful aging and has attracted considerable interest regarding its association with cognition, physical functioning, and affect (Andrews, Clark, & Luszcz, 2002; Bath & Deeg, 2005; Ghisletta, Bickel, & Lövdén, 2006; Hultsch, Hertzog, Small, & Dixon, 1999; Lang & Baltes, 1997; Lövdén, Ghisletta, & Lindenberger, 2005; Rowe & Kahn, 1997; Ryff & Singer, 2000). Social activities comprise a person's activities that are performed for the purpose of direct interaction with others (Herzog, Ofstedal, & Wheeler, 2002). An important benefit of social activities is that they can be implemented by older adults despite physical limitations that may restrict other activities. Hence, engagement in social activities may continue to contribute to a person's sense of competence in a way not afforded by other types of activities (Herzog et al.).      Studies on social activities have primarily been based on unrelated people. Because social activities inherently depend on others, marriage partners are prime social targets, particularly in late life. Couples comprise a unique social unit in old age that is often characterized by a long relational history, frequent interaction, and considerable closeness (Antonucci & Akiyama, 1991; M. M. Baltes & Carstensen, 1998; Dixon, 1999; Lang, 2001). Simply being married may be socially advantageous, given the high rates of bereavement in old age. However, past research shows that the positive potential of marriage has to be qualified by spousal behaviors (Coombs, 1991; Hagedoorn et al., 2006). Investigating social activities in couples therefore addresses one specific process that occurs within marital contexts. Changes in, and entrainment of, social activity trajectories over 11 years in couples from the Australian Longitudinal Study of Ageing (ALSA; Luszcz, 1998) is the central focus of this article.      Spousal development is interdependent in many central life domains (Carstensen, Gottman, & Levenson, 1995; Gruber-Baldini, Schaie, & Willis, 1995; Townsend, Miller, & Guo, 2001). The social activities of elderly couples may be interrelated for at least two reasons. First, social networks are dominated by joint kin and become smaller but increasingly interdependent throughout adulthood and into old age (M. M. Baltes & Carstensen, 1998; Kahn & Antonucci, 1980; Lang, 2001; Milardo & Helms-Erikson, 2000). Second, elderly couples typically have long marital histories with many shared experiences. Because each partner has developed an in-depth knowledge of the other's strengths and weaknesses (Dixon, 1999; Meegan & Berg, 2002), each partner's expertise and skills can be invoked for the sake of both partners. For example, if one spouse initiates a social activity (e.g., inviting friends for dinner), the other spouse also will have a social engagement opportunity. We therefore expect social activity trajectories of elderly couples to be closely interrelated.      Our second major objective is to examine associations among social engagement by spouses and resources in other domains, namely cognitive, physical, and affective resources. Associations between social activities and cognitive resources may in part be due to the complex nature of social activities that place high demands on cognitive skills, thereby potentially protecting against cognitive decline (Fratiglioni, Paillard-Borg, & Winblad, 2004; Ghisletta et al., 2006; Lövdén et al., 2005; Pushkar et al., 1999). Alternatively, cognitive fitness may be a prerequisite for social activities (Hultsch et al., 1999). We also expect social activities to be positively associated with physical resources. Social activities modulate stress reactivity and promote psychobiological recovery processes that are associated with age-related illnesses (Seeman & McEwen, 1996). Finally, social activities may be linked to affective resources (e.g., resistance to depression) because they fulfill an inner need for affiliation (Cantor & Sanderson, 1999; Herzog et al., 2002). We therefore expect social activities to be positively associated with cognitive, physical, and affective resources.      Some of these links with resources may be shared with other activity categories. Cognitive resources in particular are likely to shape a broad range of activities, for example, crossword puzzling (Ghisletta et al., 2006). In contrast to crossword puzzling, we expect that additional resource domains impinge on social activity. This makes social activities particularly interesting for researchers interested in successful aging, and it also highlights the special role of cognitive resources for activities. Hence we also explore dyadic associations in longitudinal trajectories of cognitive functioning (cf. Anstey, Hofer & Luszcz, 2003). This enables us to investigate the domain specificity of entrainment of partner's functioning and to assess whether social activity and cognitive trajectories wax and wane together. Thoroughly examining the relative contributions of different categories of activities or domains of functioning is beyond the scope of this article; rather, we target entrainment and change in social activities as particularly salient in the context of long-term marriage.      One feature of this study is the capacity to identify associations of social activities to functioning in other domains, not only at the individual level but also at the couple level. For example, if one spouse experiences serious cognitive decline, resources of the partner may compensate, at the expense of social activities (Clark & Bond, 2000; Tremont, Davis, & Bishop, 2006). Hence, we assume that one spouse's cognition, physical functioning, and affect not only relate to his or her own but also to spousal social activities. Following the terminology of the dyadic literature (Cook & Kenny, 2005), we refer to associations of an individual's characteristics with his or her own score on a given variable as actor effects and with the partner's score on a given variable as partner effects. Applying this nomenclature, we hypothesize that a spouse's cognitive, physical, or affective resources relate not only to his or her own social activities (actor effects) but also to those of the other spouse (partner effects).      Finally, the expected relationships between social activities and cognitive, physical, and affective resources may not be symmetric in husbands and wives. A large body of literature indicates that women take a more active part in their social networks than do men, playing an important role in initiating and organizing social activities (Antonucci, 1994, 2001; Moen, 2001). Additionally, relationship maintenance is often seen as obligatory by women whereas men's social activities are more voluntary (Antonucci, 1994). Hence, husbands may not enter all social engagement opportunities that are provided by their wives. We therefore expect wives to report more social activities than husbands. Being more socially invested than men (Moen, 2001) may have both positive and negative implications for wives, because they may not only benefit more from their social activities but also be more vulnerable when circumstances necessitate a change in them (Rook, 1998). We therefore expect that social activities are more strongly linked with both wives' own and their spouses' resources. Hence, wives' social activities are expected to be more closely related to both actor and partner resources than husbands' social activities are.      In summary, we examine three major questions by using 11-year longitudinal couple data. First, we examine dyadic interdependencies in level and overall change in social activities in couples. Second, we examine how social activity trajectories are related to individual and spousal resources. We further explore dyadic interdependencies of longitudinal changes in social activities and cognition. Third, we investigate potential gender-based (a) symmetries of the associations examined.              Methods      The ALSA is a broad biopsychosocial and behavioral panel study. On the advice of the third author (M. Luszcz), we selected variables on an a priori basis as the best available markers of the constructs targeted for investigation. Detailed descriptions of variables and procedures are published elsewhere (Andrews et al., 2002; Anstey et al., 2003; Luszcz, Bryan, & Kent, 1997).              Participants and Procedure        The baseline ALSA sample (N = 2,087) was stratified by age and sex into four cohorts (70–74 years, 75–79 years, 80–84 years, and 85+ years); men and those over the age of 85 years were oversampled. The sample (55% response rate) was obtained from the South Australian Electoral Roll that identified households with residents over 70 years of age (Hugo, Healy, & Luszcz, 1987). Randomly sampled individuals within these households were invited to volunteer for ALSA; spouses over age 65 were also invited to participate.        We included all couples with valid data on the variables of interest (N = 565 or 1,130 people). Average marriage duration was 46.33 years (SD = 11.23; range = 0–70 years); number of children was 2.7 (SD = 1.63). Husbands were about 3 years older than their wives, at 77.7 versus 74.3 years; F (1, 1128) = 101.1, p <.001. Responding to a 5-point Likert-scale, couples were “extremely” to “very” satisfied with their marriage (M = 1.69, SD = 0.78) and family life (M = 1.97, SD = 0.81), but slightly less satisfied with their friendships (M = 2.37, SD = 0.76). As compared with the remaining 957 participants from the total ALSA sample, participants in the ALSA couple subsample did not differ in number of social activities, education, or health constraints (all p >.10). However, they were younger [M = 76.66, SD = 5.87 vs M = 80.72, SD = 6.70; F (1, 2085) = 295.6, p <.001], reported fewer depressive symptoms [M = 7.27, SD = 6.90 vs M = 9.36, SD = 7.85; F (1, 1991) = 40.0, p <.001], and performed better on the Digit Symbol Substitution task [M = 30.59, SD = 10.89 vs M = 27.20, SD = 11.20; F (1, 1241) = 28.6, p <.001].        Data were collected in two 1.5- to 2-hour sessions at participants' home (98.4% private household or 1.6% institution). Session 1 assessed demographics, social activities, depression, and health. The Session 2 functional assessment occurred approximately 2 weeks later and included tests of perceptual speed for a subsample of, on average, 70% of Session 1 participants (see Table 1).        We use four waves of longitudinal data spanning 11 years: baseline in 1992–1993 (Time 1 or T1; n = 1,130), 1994–1995 (Time 3 or T3; n = 929), 2000–2001 (Time 6 or T6; n = 487), and 2003–2004 (Time 7 or T7; n = 315). Assessments at Times 2, 4, and 5 did not provide data on the targeted domains; we excluded them. On average, T3 occurred 1.53 years (SD = 0.50), T6 occurred 7.43 years (SD = 0.50), and T7 occurred 10.57 years (SD = 0.50), respectively, after T1. By T3, T6, and T7, 9.9%, 38.4%, and 51.7%, respectively, of participants had become deceased (this corresponds closely to the incidence of widowhood, i.e., 10%, 37%, and 52%); whereas in the residual sample of 987 participants, there were 12.9%, 52.9%, and 65.1% of the sample who became deceased across the same period.                    Measures                  Social activities          We selected four items from the Adelaide Activity Profile (Clark & Bond, 1995) that specifically assessed social-life aspects in which both partners can potentially participate. These concerned the frequency of (a) having invited other people to one's home, (b) making phone calls to friends or family, (c) attending social activities at a center such as a club, a church, or a community center, and (d) participating in outdoor social activities. Participants responded by means of a 4-point Likert-scale, with higher scores indicating more activity. A structural model at T1 indicated that all factor loadings for husbands and wives were above.60 (M =.79), suggesting a reliable social activity factor.                          Covariates          We considered education, T1 health constraints and depression, and processing speed at T1 and over time. We dichotomized years of education by contrasting participants who left school at age 15 plus (higher education) with those who left under age 15. Health constraints were self-reports of the number of chronic medical conditions from a comprehensive list of 61 (e.g., stroke, diabetes, arthritis). For depressive symptoms, we used the Center for Epidemiological Studies–Depression scale (Radloff, 1977); 20 items asked participants how often over the past week they felt symptoms such as lack of energy or sad feelings. For a parsimonious indicator of cognition, we selected perceptual speed because it is highly reliable and sensitive to change throughout adulthood (Anstey et al., 2003; Salthouse & Madden, in press), and, as a cognitive primitive (Luszcz & Bryan, 1999), is conceptually closer to a resource than other abilities. In contrast, decline in verbal ability, for example, occurs very late in life (Anstey et al., 2003), when it may be too late to engage in compensatory behaviors (Ghisletta et al., 2006), and memory change is fundamentally limited by change in perceptual speed. We assessed perceptual speed by using the Digit Symbol Substitution subscale of the revised Wechsler Adult Intelligence Scale (Wechsler, 1981; for details, see Luszcz et al., 1997), at T1, T3, T6, and T7. Participants substituted symbols corresponding to the numbers 1–9 as rapidly as possible into a randomly ordered array of 93 digits. Symbols were presented throughout the task. We used the number of correct substitutions in 90 seconds.                            Data Preparation        We standardized social activities and perceptual speed to a T metric (M = 50; SD = 10), with the T1 ALSA couples providing the reference. This transformation ensured a common metric while maintaining the psychometric properties of the scores and the longitudinal changes in means and variances. Missing data for social activities amounted to 7% (206 out of 2,861 attainable data points over four occasions). Missing data for perceptual speed amounted to 6% of Session 2 participants and were primarily due to poor vision.1 No data imputation procedure was applied. The average longitudinal observation interval was 3.68 years (SD = 3.63). Table 1 presents, separately for husbands and wives, the age at assessment as well as means and standard deviations for the variables under study. Participants were tested, on average, in their late seventies and early eighties. Husbands and wives did not differ in education or health problems (p >.10), but wives reported more depressive symptoms than did husbands, F (1, 1084) = 4.5, p <.05. The effects of social activities and perceptual speed were our main focus and are presented in the Results section.                    Statistical Procedures        We applied two-variable and four-variable extensions of the latent growth model over time (McArdle, 1988) that estimates fixed effects (average level and change) and random effects (interindividual differences in level and change). The two-variable growth curve is a straightforward extension of a univariate growth curve. Specifically, we consider the couple as the unit of analysis and model separate growth processes for social activities of husbands and wives. A graphical representation of the model is given in Figure 1. The diagram shows observed variables as squares, latent variables as circles, and the required constant as a triangle as well as fixed model parameters as one-headed arrows and random parameters as two-headed arrows. Unlabeled paths are fixed to 1. The four repeated measures of X (husband) and Y (wife) have three sources: the latent intercept, X0, Y0, with unit loadings; the latent slope, Xs, Ys, with linear loadings (0, 1.5, 7.4, 10.6); and the time-specific residual, ex[t], ey[t]. Intercepts and slopes are estimated at the population level and are allowed to vary and to covary. Time-specific residuals ex[t], ey[t] have a mean of zero, a single variance (σ2ex, σ2ey), and are allowed to covary with each other within occasion (σ2ex,ey).        Because of the age-heterogeneous sample, all models reported include husbands' and wives' ages as covariates to adjust otherwise inflated covariances among variables that are due to their common association with chronological age. In Figure 1, covariates are represented by variables Zx and Zy with their own mean (μZx, μZy), variance (σ2Zx, σ2Zy), and covariance (σ2Zx, Zy), as well as regression effects on intercepts and slopes both as actor effects (βZx,X0, βZx,Xs, βZy,Y0, βZy,Ys) and partner effects (βZx,Y0, βZx,Ys, βZy,X0, βZy,Xs). We used the full-information maximum likelihood estimation algorithm and included all data points available, which allowed us to accommodate for unbalanced data structures and incomplete data under the missing-at-random assumption (McArdle, 1994). We estimated the models by using the Mplus program, Version 4 (Muthén & Muthén, 1998–2006).        We carried out three sets of analyses. First, we examined level and overall change of social activities among husbands and wives as well as associations among these factors at the couple level. Second, we investigated whether level and overall change in social activities were related to T1 measures of education, perceptual speed, health constraints, and depression, both at the individual and couple level. We did this by including additional Z variables for each covariate in the model shown in Figure 1, allowing for intercorrelations among all covariates. In follow-up analyses, we explored covariations among longitudinal changes in social activities and perceptual speed, both at the individual and couple level. To do so, we extended the two-variable growth model shown in Figure 1 to a four-variable model that also included linear and quadratic slopes of perceptual speed for both partners. Third, we evaluated the (a) symmetry of covariate–growth factor associations between spouses by means of statistically nested model comparisons of models that either freely estimated the respective parameters or set it invariant across spouses.                    Results              Level and Change of Social Activities in Older Couples        Results for spousal social activity trajectories (Table 2) indicate that the model fit the data well (e.g., root mean square error of approximation or RMSEA =.023). Consistent with our expectation, fixed effects for the levels indicate that wives reported more activities than did husbands, which was corroborated by a substantial loss of model fit when we set the levels invariant (Δχ2/df = 34.1/1, p <.001). Fixed effects for both slopes were not significantly different from zero, suggesting that social activities did not show much change over time on average. Significant estimates for all random effects among husbands as well as wives revealed, however, that individuals reliably differed from one another in both their activity levels and change over time. We also note the significant and negative level–slope covariance among wives, but not among husbands, indicating that wives who initially reported more social activities tended to show an overall subsequent decline.        Of particular interest were the three significant partner effects. Social activities between partners were strongly correlated, and more activities among wives were associated with activity decline for husbands. In addition, associations between the time-specific residuals indicate that, after we accounted for individual activity changes, activities of both partners were still related with one another. In line with our expectations, these findings suggest that social activity trajectories were interrelated not only at the individual level but also at the couple level.                    Relations of Level and Change in Social Activities to Individual and Spousal Correlates        To examine associations between level and change in social activities with individual and spousal correlates (Table 3), we introduced husbands' and wives' T1 measures of education, perceptual speed, health constraints, and depressive symptoms as covariates. Regarding actor effects, husbands' activity levels were negatively associated with husbands' age and health constraints and positively with husbands' perceptual speed. Similarly, wives' activity levels related negatively to wives' age and depression and positively to wives' education and perceptual speed. The only significant correlate of change in activity was husbands' depression, and the negative sign indicates that more baseline depression related to more activity decline. Again, partner effects were of primary interest. The only statistically significant association at the partner level was that depression of wives related positively to husbands' activity change, suggesting that husbands of depressed wives showed an increase in activities.        In follow-up analyses, we examined a four-variable growth model of longitudinal changes in social activities and perceptual speed among husbands and wives.2 This model again provided reasonable fit to the data (e.g., RMSEA =.040). Fixed effects for social activities were very similar to the aforementioned two-variable model (e.g., wives reported more activities than husbands); for perceptual speed, we found no spousal differences at any time, but both husbands and wives showed significant decline over time. The fixed effects from this model are shown in Figure 2. <--CO?1-->What is most important for our question is that both husbands and wives showed significant interindividual differences in their linear change components on social activities and perceptual speed (whereas no interindividual differences were found in the quadratic change component on perceptual speed). This allowed for a close inspection of the covariances across level and linear slope factors at the individual and couple level (Table 4). These intercorrelations did not provide evidence that activity changes either at the individual or couple level were accompanied by cognitive changes, but showed three significant actor effects. Analogous to the two-variable model, level–slope intercorrelations for social activities were negative among wives, but not among husbands. Linking domains of functioning, we found that levels of activities and perceptual speed were associated among both husbands and wives. We also found four significant partner effects: Levels of activities and perceptual speed were positively related, as were husbands' activity levels and wives' level of perceptual speed. In addition, level of activity for wives was strongly associated with activity decline for husbands. Finally, examining asymmetrical effects in the four-variable model revealed that activity levels showed stronger associations among both husbands and wives (r =.59) than did levels of perceptual speed (r =.13; Δχ2/df = 5.4/1, p <.01). This suggests that mutual engagement in activity is stronger than entrainment of cognitive capacity. In sum, our results suggest that social activity trajectories were not only associated with one's own resources but also with the resources of one's partner.3                    Asymmetry of Associations Between Husbands and Wives        Finally, we conducted a series of statistically nested model comparisons to evaluate whether social activity trajectories show differential associations among husbands and wives. To begin with, the nominal difference in level–slope associations among spouses (−.26 vs −.41; see Table 2) was not statistically different, given that a model with these parameters set invariant across spouses did not result in loss of model fit (Δχ2/df = 0.2/1, p >.10). Regarding asymmetrical actor effects in covariate–growth factor associations (see Table 3), we found no effects for age, education, perceptual speed, and health (all p >.10). However, level and slope effects of depression differed between spouses, suggesting that depression was associated with fewer activities among wives but not among husbands (Δχ2/df = 6.5/1, p <.01); and in husbands, but not wives, depression related to activity decline (Δχ2/df = 5.7/1, p <.01).        Asymmetrical partner effects indicated that the effects of wives' depression on husbands' activity slope were not mirrored in the effects of husbands' depression on wives' activity slope (Δχ2/df = 3.2/1, p <.05).4 In sum, affective resources of husbands and wives are differentially associated with social activity trajectories of the spouse.                    Discussion      On the basis of 11-year longitudinal couple data, we demonstrated that level of, and change in, social activities are closely interrelated among elderly spouses and that wives perform more social activities than do husbands. We also showed that spousal social activity trajectories were related to both individual cognitive, physical, and affective resources, and to spousal resources. Specifically, significant partner effects emerged linking depression with social activity changes and levels of cognition and social activities. Longitudinally, significant actor effects concerning cognition–social activity associations emerged. Partner effects were stronger in social activities than perceptual speed. Finally, several social activity–depression associations were gender specific.              Level and Change of Social Activities in Older Couples        Our findings concerning dyadic interdependencies in social activities show that developmental trajectories of spouses are closely intertwined. As expected (Antonucci, 2001; Moen, 2001), wives reported more social activities than husbands. We can only speculate as to the mechanisms linking social activity trajectories in couples. In keeping with socioemotional selectivity theory, our findings may reflect an increased focus on emotionally meaningful relationships in old age as manifested in high mutual engagement in social activities among spouses (Carstensen, 1995). More work is needed, however, to differentiate whether such interdependencies are due to similar background characteristics, overlapping networks, or a reciprocal activity involvement (Milardo & Helms-Erikson, 2000).                    Relations of Level and Change in Social Activities to Individual and Spousal Resources        Our findings that social activity trajectories are related to individual cognitive, physical, and affective resources are in line with past research on unrelated individuals. Within our sample, poorer physical functioning at baseline was related to fewer social activities (Zunzunegui et al., 2005), and spouses with more educational and cognitive resources reported more social activities (Hultsch et al., 1999). Although we found correlation coefficients of different sizes across spouses (e.g., linking physical functioning and social activities), we note that statistically nested model comparisons indicated that these relationships were similar among husbands and wives. Over and above these actor affects, we also found partner effects linking depression and social activities. These findings are discussed in the context of gender-specific associations.        We also explored longitudinal associations between social activities and perceptual speed. In line with previous research, participants living socially active lifestyles showed better cognitive performance than did socially inactive participants (Ghisletta et al., 2006; Lövdén et al., 2005). We also found partner effects, and these were much stronger in social activities relative to cognition, thereby highlighting the important role of spouses for social lifestyles. This difference in relative strength of dyadic associations illustrates the increased need for cultural support at advanced ages (P. B. Baltes, 1997). Culture is likely to be more efficacious in the social domain than in the cognitive domain, because spouses can provide input that directly helps their partner to remain socially engaged. Perceptual speed is a fluid ability and more reliant on biological propensities than on cultural resources. Hence, older adults are likely to encounter loss-based cognitive limitations that may be harder for marriage partners to influence. When conducting analyses of cognition and social activities including both partners' age, education, health, and depression as covariates, we found that only partner effects in social activities remained. In sum, our findings suggest that taking into account long-term relationships as an important contextual factor may help researchers to better understand the processes underlying successful aging.                    Asymmetry of Associations Between Husbands and Wives        Our findings of gender-specific actor and partner effects in depression–social activity associations illustrate that husbands and wives are both vulnerable to the effects of depression on social activities. However, the underlying processes may arise at different times. Whereas for wives the negative depression–social activity associations may result from processes that occurred earlier in life, husbands' negative social activity slopes suggest that higher depression scores are followed by social activity decline over the study period. This interpretation is in line with research showing that women report more depression than men, but the relative difference is larger in midlife than in old age (Anstey, van Sanden, Cox, & Luszcz, in press; Barefoot, Mortensen, Helms, Avlund, & Schroll, 2001). Hence, the study period may coincide with a point in time when husbands' depression-related processes unfold, whereas those processes for wives have already emerged.        Over and above these actor effects, we also found evidence that wives' depressive symptoms were associated with an increase in husbands' social activities. These results can be interpreted in the context of substantial positive correlations between spousal depression scores (Townsend et al., 2001) and suggest that husbands assume responsibility for social activities when their wives are emotionally challenged. These findings demonstrate that associations of depression and social activity trajectories in elderly couples are best studied in dyads, rather than unrelated individuals.                    Strengths and Limitations        We examined a unique longitudinal couple data set that allowed us to explore actor and partner effects in the relationship between social activities and cognitive, physical, and affective resources. Despite the longitudinal data, most findings refer to mean level results. Like other studies (Ghisletta et al., 2006; Hultsch et al., 1999; but see Lövdén et al., 2005), our study indicated that social activities showed relatively little average change over time (but reliable individual differences therein), which may impose limits on detecting relationships of longitudinal changes to other individual and partner variables. One promising route for future research could thus be to explore whether other (social) activity indicators that are more sensitive to normative change (e.g., loss of bridge partners or closure of a favorite social club) reveal different patterns of longitudinal associations.        In addition, our focus on only genuinely social activities, such as inviting friends, contrasts with previous reports from the ALSA (Luszcz et al., 1997; Newson & Kemps, 2005) and other studies (Ghisletta et al., 2006; Hultsch et al., 1999; Lövdén et al., 2005; Pushkar et al., 1999), which used much broader activity measures covering a wide range of physical, leisure, and work activities. We selected social activities that both partners could potentially participate in, but we cannot differentiate whether social activities were performed jointly or individually. Follow-up analyses found that accounting for variability in marriage duration did not change our results, but we could not determine how other relationship characteristics such as mutual affection shape social activities. Together, our results highlight the necessity to distinguish the nature of “activity” in order to accurately understand interrelationships between activities and resources.        We acknowledge that our growth curve models produced estimates of average within-person change independent of whether or not an individual (or couple) stayed in the sample over time (i.e., missing-at-random assumption). Nonetheless, we corroborated our main results in a restricted sample of longitudinal participants.<5 Furthermore, these models consider concurrent associations between level and overall change on a given set of variables but not their causal ordering, such as whether one spouse's social activities affect subsequent change in the other spouse's perceptual speed (for an overview of alternative approaches, see Ghisletta et al., 2006). Future research may apply “dynamic” modeling techniques such as bivariate dual change score models (McArdle & Hamagami, 2001) to examine potential lead–lag associations.                    Conclusions        We showed that social activity trajectories are interrelated in elderly married couples and associated with not only individual, but also spousal, characteristics. This attests to the importance of social relationships for late-life development and some domain specificity in underlying dynamics. Together, our results suggest that although physical and cognitive resources provide an environment that is conducive to social activities, the affective resources of one marital partner are crucial to variations in both level and change in social activities of the other and hence to that of the couple as a unit. Our findings are heuristic in posing new questions about the dynamics of long-term spousal relationships for successful aging. For example, is the codependence in social activities due to a priori similarities or does it develop over time (Luo & Klohnen, 2005)? Does the codependence originate in shared environmental characteristics or is it similar across different environmental contexts? Is the relationship between social activities and resources driven by social activity performance or by social embeddedness?        Future research may substantiate these findings by going beyond individual differences variables and addressing the underlying dyadic processes. This may get at issues concerning social reserve capacities and vulnerabilities in late life.                                            Decision Editor: Thomas M. Hess, PhD                          1Because of the specific study design, sample attrition between Session 1 and Session 2 over the 2-week period was substantial. As discussed extensively by >nstey and Luszcz (2002), attrition primarily reflected self-selection as a result of poor health or physical or cognitive functioning. Using the sample in Session 1 as a reference, we found that missing data for the Digit Symbol task amounted to 31%.                          2Results from a preliminary two-variable latent growth model for perceptual speed revealed that both husbands and wives showed significant decline over time (husbands, −0.74, SE = 0.09; wives, −0.55, SE = 0.09; both p < .001) as well as interindividual differences in decline (husbands, 0.25, SE = 0.08, p < .01; wives, 0.21, SE = 0.09, p < .05).                          3To examine whether or not our main results concerning partner effects for social activity trajectories were contingent upon physical activities, we included a covariate in our models that comprised a unit-weighted composite of the following items: doing heavy housework, light and heavy gardening, or household or car maintenance; engaging in sports; and walking outside for more than 15 minutes. These analyses revealed substantively identical results to those reported here. In addition, we examined the relationship between physical activities and spousal cognitive, physical, and affective resources. Results showed that the pattern of relationships concerning physical activities differs from the reported findings concerning social activities. In addition, we found no association between physical activity performances of both spouses. Hence, our findings appear to be specific to social activities and suggest that social activities do not represent a proxy for activities in general.                          4When introducing the covariates, we found that the reported individual (actor) and spousal (partner) covariances among level and change factors of social activities remained virtually the same.                          5For our major findings at the couple level (husband's activity decline relates to wives' level of activities and depression at T1; correlated residuals), analyses of a restricted sample of those who remained in the study up to T6 or longer (n = 164) revealed substantively identical results to those reported here.                          Figure 1.                      Graphical representation of the two-variable latent growth curve model (McArdle, 1988) as applied in the present study. Observed variables are represented by squares, latent variables by circles, regression weights by one-headed arrows, and variances and covariances by two-headed arrows; the triangle represents a constant indicating means and intercepts. All unlabeled paths are set to 1. There are four repeated measures of social activities for husbands (X) and wives (Y), and covariates are represented by variables Zx (husbands) and Zy (wives)                                              Figure 2.                      Model-implied means over time in study from a four-variable latent growth curve model of social activities (solid lines) and perceptual speed (dashed lines) among husbands and wives. We standardized scores to the T metric by using the Time 1 Australian Longitudinal Study of Ageing couple sample (N = 1,130), M = 50, SD = 10                                              Table 1.                      Age at Assessment and Descriptive Statistics for Measures Used in the Present Study.                                                                              Husbands                Wives                                            Measure                                  n                                Age                                  M                                                  SD                                                  n                                Age                                  M                                                  SD                                                                                    Social activities                                                                                                                                                                                T1                563                77.71                48.37                9.88                561                74.30                51.63                9.86                                                T3                415                78.79                49.11                8.89                452                75.88                52.89                9.19                                                T6                164                83.38                50.00                9.89                242                80.70                52.87                9.20                                                T7                96                85.08                52.50                9.71                170                82.85                55.01                9.72                                            Perceptual speed                                                                                                                                                                                T1                371                77.23                49.40                9.79                357                74.03                50.64                10.19                                                T3                339                78.45                49.69                10.06                350                75.55                52.43                10.03                                                T6                128                82.60                48.88                9.70                180                79.92                51.45                9.20                                                T7                85                84.94                47.17                9.66                158                82.60                50.01                9.66                                            Health constraints                                                                                                                                                                                T1                508                77.44                5.43                3.00                508                74.11                5.33                3.13                                            Depression                                                                                                                                                                                T1                489                77.39                6.83                6.51                497                74.05                7.71                7.25                                                                                            Percent                                                Percent                                            High education                                                                                                                                                                                T1                242                77.85                43%                267                74.35                47%                                                                        Note: T1, T3, T6, and T7 = Times 1, 3, 6, and 7, respectively. We standardized scores of social activities and perceptual speed to the T metric by using the T1 Australian Longitudinal Study of Ageing couple sample (N = 1,130), M = 50, SD = 10. We collected data on perceptual speed in Session 2, which was attended by two thirds to three fourths of Session 1 participants. The average age of Session 1 and Session 2 participants is highly similar. High education signifies that the participants were 15 years of age or older when they left school. For depression, scores >16 correspond to the cutoff for depression according to the Center for Epidemiological Studies–Depression scale.                                                Table 2.                      Estimates of a Two-Variable Latent Growth Model for Social Activities of Husbands and Wives.                                                                                              Actor Effect                                                                            Husbands                Wives                Partner Effect                                            Social Activities                Estimate                                  SE                                Estimate                                  SE                                Estimate                                                                    Fixed effects                                                                                                                                Level μ0                49.12***                0.44                51.54***                0.45                                                                Slope μs                0.01                0.10                0.03                0.08                                                            Random effects                                                                                                                                Variance of level σ20                39.02***                4.71                44.14***                4.69                                                                Variance of slope σ2s                0.28*                0.13                0.21*                0.09                                                                Correlation level σ20, slope σ2s                −0.26                                −.41*                                                                                Residual variance σ2e                47.33***                3.01                46.35***                2.62                                                            Correlations                                                                                                                                Husband level σ20 − Wife level σ20                                                                                .60***                                                Husband slope σ2s − Wife slope σ2s                                                                                .14                                                Husband level σ20 − Wife slope σ2s                                                                                −.23                                                Wife level σ20 − Husband slope σ2s                                                                                −.43*                                                Residual variance σ2e Husband − Wife                                                                                .26***                                                                        Notes: All noncorrelation estimates are unstandardized. The significance tests assigned to the correlations refer to the corresponding covariances. Model includes age of husbands and wives as covariates. We standardized social activities to the T metric by using the Time 1 Australian Longitudinal Study of Ageing couple sample (N = 1,130), M = 50, SD = 10. Model fit statistics: χ2 (df) = 45.0 (35); Comparative Fit Index, CFI = 0.988; and root mean square error of approximation, RMSEA = 0.023.                                      * p <.05, **p <.01, ***p <.001.                                                Table 3.                      Estimates of a Two-Variable Latent Growth Model for Social Activities of Husbands and Wives, Including the Effects of Individual Difference Variables Assessed at Time 1.                                                                              Husbands                Wives                                             Parameter or Covariate                 Estimate                                  SE                                Actor Effect                Partner Effect                 Estimate                                  SE                                Actor Effect                Partner Effect                                                                    Parameter                                                                                                                Level μ0                48.51***                0.59                                                51.44***                0.60                                                                                Slope μs                −0.10                0.13                                                −0.07                0.11                                                                                Variance of level σ20                35.45***                4.52                                                38.69***                4.43                                                                                Variance of slope σ2s                0.12                0.12                                                0.19                0.09                                                                                Residual variance σ2e                47.51***                3.00                                                46.06***                2.60                                                                            Covariate                                                                                Age                                                −.23**                .01                                                −.15*                −.06                                                Age × Slope                                                −.30                −.22                                                −.16                −.12                                                Education                                                .04                .08                                                .14*                −.09                                                Education × Slope                                                −.18                .22                                                −.15                .24                                                Perceptual speeda                                                .14                .06                                                .21**                −.02                                                Perceptual Speed × Slope                                                −.02                .04                                                −.16                −.17                                                Health constraints                                                −.18**                −.07                                                −.06                −.06                                                Health Constraints × Slope                                                .11                .27                                                −.11                .11                                                Depression                                                .11                −.04                                                −.16*                .13                                                Depression × Slope                                                −.50**                .39*                                                .07                −.05                                                                        Notes: All noncorrelation estimates are unstandardized. The significance tests assigned to the correlations refer to the corresponding covariances. We standardized social activities to the T metric by using the Time 1 Australian Longitudinal Study of Ageing couple sample (N = 1,130), M = 50, SD = 10. Age, health constraints, and depression were centered at zero. Education: 0 = 0–14 years of age when the participant left school; 1 = 15 years of age or older when the participant left school. Model fit statistics: χ2 (df) = 84.1 (67); Comparative Fit Index, CFI = 0.981; root mean square error of approximation, RMSEA = 0.021. Variance in social activities accounted for level of husbands (R2 =.170), linear slope of husbands (R2 =.601), level of wives (R2 =.190), and linear slope of wives (R2 =.131).                                      a For perceptual speed, p =.06.                                      * p <.05, **p <.01, ***p <.001.                                                Table 4.                      Intercorrelations Among Level and Slope Factors of Social Activities and Perceptual Speed Among Husbands and Wives (Obtained From a Four-Variable Latent Growth Model).                                                                              1                2                3                4                5                6                7                                            Husbands                Actor effect                                                                                                1. Activity level σ20                —                                                                                                                                                2. Activity slope σ2s                −.25                —                                                                                                                                3. Speed level σ20                .16**                −.03                —                                                                                                                4. Speed slope σ2s                −.06                .39                −.10                —                                                                                            Wives                Partner effect                Actor effect                                                5. Activity level σ20                .59***                −.43**                .00                .07                —                                                                                6. Activity slope σ2s                −.22                .15                −.07                −.16                −.39*                —                                                                7. Speed level σ20                .16*                −.07                .13*                −.07                .28***                −.14                —                                                8. Speed slope σ2s                .17                −.09                −.02                −.11                .01                .36                −.22                                                                        Notes: Model includes age of husbands and wives as covariates. The significance tests assigned to the correlations refer to the corresponding covariances.                                      Model fit statistics: χ2 (df) = 226.5 (116); Comparative Fit Index, CFI = 0.940; root mean square error of approximation, RMSEA = 0.041.                                      * p <.05, **p <.01, ***p <.001.                                                      Preparation for this manuscript was supported by a Research Fellowship awarded by the German Research Foundation (DFG) to Christiane Hoppmann. Large parts of this article were prepared while Denis Gerstorf was at the Department of Psychology, University of Virginia on a Research Fellowship awarded by the German Research Foundation (DFG).      The ALSA was conducted by the Centre for Ageing Studies, Flinders University, Adelaide, Australia. ALSA was financially supported by grants from the U.S. National Institute on Aging (Grant AG08523), the South Australian Health Commission, Australian Rotary Health Research Fund, National Health and Medical Research Council (Grant 229922), the Australian Research Council, the South Australian Department of Human Services, Flinders Medical Centre Foundation, Elderly Citizens Homes PL (ECH), and the Flinders Research Grants Scheme.      We thank the ALSA participants, who have given their time over many years, and without whom the present study would not have been possible. We dedicate this article to the memory of the late Professor Gary Andrews (May 2, 1938–May 18, 2006), Director of the Centre for Ageing Studies and founding Chief Investigator.              References              Andrews, G., Clark, M., Luszcz, M. (2002). Successful aging in the Australian Longitudinal Study of Ageing: Appling the MacArthur Model cross-nationally. Journal of Social Issues, 58,749-765.                    Anstey, K. J., Hofer, S. M., Luszcz, M. A. (2003). A latent growth curve analysis of late-life sensory and cognitive function over 8 years: Evidence for specific and common factors underlying change. Psychology and Aging, 18,714-726.                    Anstey, K. J., Luszcz, M. A. (2002). Selective non-response to clinical assessment in the Longitudinal Study of Ageing: Implications for estimating population levels of cognitive function and dementia. International Journal of Geriatric Psychiatry, 17,704-709.                    Anstey, K. J., van Sanden, C., Cox, K. S., Luszcz, M. A., in press Prevalence and risk factors for depression in a longitudinal, population-based study including individuals in the community and residential care. Australian Journal of Geriatric Psychiatry..                    Antonucci, T. C. (1994). A life-span view of women's social relations. In B. F. Turner & L. Troll (Eds.), Women growing older: Psychological perspectives (pp. 239–269). Thousand Oaks, CA: Sage.                    Antonucci, T. C. (2001). Social relations: An examination of social networks, social support, and sense of control. In J. E. Birren (Ed.), Handbook of the psychology of aging (pp. 427–453). San Diego, CA: Academic Press.                    Antonucci, T. C., Akiyama, H. (1991). Convoys of social support: Generational issues. Marriage & Family Review, 16,103-123.                    Baltes, P. B. (1997). On the incomplete architecture of human ontogeny: Selection, optimization, and compensation as foundation of developmental theory. American Psychologist, 52,366-380.                    Baltes, M. M., Carstensen, L. L. (1998). Social-psychological theories and their applications to aging: From individual to collective. In V. L. Bengtson & K. W. Schaie (Eds.), Handbook of theories of aging (pp. 209–226). New York: Springer.                    Baltes, P. B., Lindenberger, U. (1997). Emergence of a powerful connection between sensory and cognitive functions across the adult life span: A new window to the study of cognitive aging? Psychology and Aging, 12,12-21.                    Baltes, P. B., Staudinger, U. M. (1996). Interactive minds in a life-span prologue. In P. B. Baltes & U. M. Staudinger (Eds.), Interactive minds: Life-span perspectives on the social foundation of cognition (pp. 1–32). New York: Cambridge University Press.                    Barefoot, J. C., Mortensen, E. L., Helms, M. J., Avlund, K., Schroll, M. (2001). A longitudinal study of gender differences in depressive symptoms from age 50 to 80. Psychology and Aging, 16,342-345.                    Bath, P. A., Deeg, D. (2005). Social engagement and health outcomes among older people: Introduction to a special section. European Journal of Ageing, 2,24-30.                    Bryk, A. S., Raudenbush, S. W. (1992). Hierarchical linear models in social and behavioral research: Applications and data analysis methods. Newbury Park, CA: Sage.                    Cantor, N., Sanderson, C. A. (1999). Life task participation and well-being: The importance of taking part in daily life. In D. Kahneman & E. Diener (Eds.), Well-being: The foundations of hedonic psychology (pp. 230–243). New York: Russell Sage Foundation.                    Carstensen, L. L. (1995). Evidence for a life-span theory of socioemotional selectivity. Current Directions in Psychological Science, 4,151-156.                    Carstensen, L. L., Gottman, J. M., Levenson, R. W. (1995). Emotional behavior in long-term marriage. Psychology & Aging, 10,140-149.                    Clark, M. S., Bond, M. J. (1995). The Adelaide Activities Profile: A measure of the lifestyle activities of elderly people. Aging Clinical and Experimental Research, 7,174-184.                    Clark, M. S., Bond, M. J. (2000). The effect on lifestyle activities of caring for a person with dementia. Psychology, Health & Medicine, 5,13-27.                    Cook, W. L., Kenny, D. A. (2005). The Actor–Partner Interdependence Model: A model of bidirectional effects in developmental studies. International Journal of Behavioral Development, 29,101-109.                    Coombs, R. H. (1991). Marital status and personal well-being: A literature review. Family Relations, 40,97-102.                    Dixon, R. A. (1999). Exploring cognition in interactive situations: The aging of N + 1 minds. In T. M. Hess & F. Blanchard-Fields (Eds.), Social cognition and aging (pp. 267–290). San Diego, CA: Academic Press.                    Fratiglioni, L., Paillard-Borg, S., Winblad, B. (2004). An active and socially integrated lifestyle in late life might protect against dementia. The Lancet, 3,343-353.                    Ghisletta, P., Bickel, J.-F., Lövdén, M. (2006). Does activity engagement protect against cognitive decline in old age? Methodological and analytical considerations. Journal of Gerontology: Psychological Sciences, 61B,P253-P261.                    Gruber-Baldini, A. L., Schaie, K. W., Willis, S. L. (1995). Similarity in married couples: A longitudinal study of mental abilities and rigidity–flexibility. Journal of Personality and Social Psychology, 69,191-203.                    Hagedoorn, M., Yperen, N. V., Coyne, J. C., Jaarsfeld, C. H. M., Ranchor, A. V., Sonderen, E., et al. (2006). Does marriage protect older people from distress? The role of equity and recency of bereavement. Psychology and Aging, 21,611-620.                    Herzog, A. R., Ofstedal, M. B., Wheeler. L. M. (2002). Social engagement and its relationship to health. Clinics in Geriatric Medicine, 18,593-609.                    Hertzog, C., Hultsch, D. F., Dixon, R. A. (1999). On the problem of detecting effects of lifestyle on cognitive change in adulthood: Reply to Pushkar et al. (1999). Psychology and Aging, 14,528-534.                    Hugo, G., Healy, J., Luszcz, M. (1987). Surveying the aged in Australia. Australian Journal of Ageing, 6,14-19.                    Hultsch, D. F., Hertzog, C., Small, B. J., Dixon, R. A. (1999). Use it or lose it: Engaged lifestyle as a buffer of cognitive decline in aging? Psychology and Aging, 14,245-263.                    Jang, Y., Mortimer, J. A., Haley, W. E., Small, B. J., Chisolm, T. E. H., Graves, A. B. (2003). The role of vision and hearing in physical, social, and emotional functioning among older adults. Research on Aging, 25,172-191.                    Kahn, R. L., Antonucci, T. C. (1980). Convoys over the life course: Attachment, roles, and social support. In P. B. Baltes & O. G. Brim (Eds.), Life-span development and behavior (Vol. 3, pp. 254–283). New York: Academic Press.                    Lang, F. R. (2001). Regulation of social relationships in later adulthood. Journal of Gerontology: Psychological Sciences, 56B,P321-P326.                    Lang, F. R., Baltes, M. M. (1997). Being with people and being alone in late life: Costs and benefits for everyday functioning. International Journal of Behavioral Development, 21,729-746.                    Lövdén, M., Ghisletta, P., Lindenberger, U. (2005). Social participation attenuates decline in perceptual speed in old and very old age. Psychology and Aging, 20,423-434.                    Luo, S., Klohnen, E. C. (2005). Assortative mating and marital quality in newlyweds: A couple-centered approach. Journal of Personality and Social Psychology, 88,304-326.                    Luszcz, M. A. (1998). A longitudinal study of psychological changes in cognition and self in late life. The Australian Developmental and Educational Psychologist, 15,39-61.                    Luszcz, M. A., Bryan, J. (1999). Toward understanding age-related memory loss in late adulthood. Gerontology, 45,2-9.                    Luszcz, M., Bryan, J., Kent, P. (1997). Predicting memory performance of very old men and women: Contributions from age, depression, activity, cognitive ability and perceptual speed. Psychology and Aging, 12,340-351.                    McArdle, J. J. (1988). Dynamic but structural equation modeling of repeated measures data. In J. R. Nesselroade & R. B. Cattell (Eds.), Handbook of multivariate experimental psychology (Vol. 2, pp. 561–614). New York: Plenum Press.                    McArdle, J. J. (1994). Structural factor analysis experiments with incomplete data. Multivariate Behavioral Research, 29,409-454.                    McArdle, J. J., Hamagami, F. (2001). Latent difference score structural models for linear dynamic analyses with incomplete longitudinal data. In L. M. Collins & A. G. Sayer (Eds.), New methods for the analysis of change (pp. 137–176). Washington, DC: American Psychological Association.                    Meegan, S. P., Berg, C. A. (2002). Contexts, functions, forms, and processes of collaborative everyday problem solving in older adulthood. International Journal of Behavioral Development, 26,6-15.                    Milardo, R. M., Helms-Erikson, H. (2000). Network overlap and third-party influence in close relationships. In C. Hendrick & S. S. Hendrick (Eds.), Close relationships: A sourcebook (pp. 33–45). Thousand Oaks, CA: Sage.                    Moen, P. (2001). The gendered life course. In R. H. Binstock & L. K. George (Eds.), Handbook of aging and the social sciences (Vol. 5, pp. 179–196). San Diego, CA: Academic Press.                    Muthén, L. K., Muthén, B. O. (1998). Mplus user's guide, the comprehensive modeling program for applied researchers (4th ed.). Los Angeles, CA: Author.                    Newson, R., Kemps, E. (2005). General lifestyle activities as a predictor of current cognition and cognitive change in older adults. Journal of Gerontology: Psychological Sciences, 60B,P113-P120.                    Pushkar, D., Etezadi, J., Andres, D., Arbuckle, T., Schwartzman, A. E., Chaikelson, J. (1999). Models of intelligence in late life: Comment on Hultsch et al. (1999). Psychology and Aging, 14,520-527.                    Radloff, L. W. (1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1,385-401.                    Rook, K. S. (1998). Investigating the positive and negative sides of personal relationships: Through a lens darkly? In B. H. Spitzberg & W. R. Cupach (Eds.), The dark side of close relationships (pp. 369–393). Mahwah, NJ: Erlbaum.                    Rowe, J. W., Kahn, R. L. (1997). Successful aging. The Gerontologist, 37,433-440.                    Ryff, C. D., Singer, B. (2000). Interpersonal flourishing: A positive health agenda for the new millennium. Personality and Social Psychology Review, 4,30-44.                    Salthouse, T. A., Madden, D. J., in press Information processing speed and aging. In J. DeLuca & J. Kalmar (Eds.), Information processing speed in clinical populations. New York: Psychology Press.                    Schwarzer, R., Leppin, A. (1989). Social support and health: Meta-analysis. Psychology and Health, 3,1-15.                    Seeman, T. E., McEwen, B. S. (1996). Impact of social environment characteristics on neuroendocrine regulation. Psychosomatic Medicine, 53,459-471.                    Townsend, A. L., Miller, B., Guo, S. (2001). Depressive symptomatology in middle-aged and older married couples: A dyadic analysis. Journal of Gerontology: Social Sciences, 56B,S352-S364.                    Tremont, G., Davis, J. D., Bishop, D. S. (2006). Unique contribution of family functioning in caregivers of patients with mild to moderate dementia. Dementia and Geriatric Cognitive Disorders, 21,170-174.                    Wechsler, D. (1981). Wechsler Adult Intelligence Scale—Revised manual. New York: Psychological Corporation.                    Zunzunegui, M. V., Rodriguez-Laso, A., Otero, A., Pluijm, S. M. F., Nikula, S., Blumstein, T., Jylhae, M, Minicuci, N., Deeg, D. J. H. (2005). Disability and social ties: Comparative findings of the CLESA study. European Journal of Ageing, 2,40-47.