Blueprint for Healthy Aging, Theoretical Models and Concepts Your written assignment this week is to develop a draft of the second section of your Blueprint fo

Multidomain Trajectories of Psychological Functioning in Old Age: A Longitudinal Perspective on (Uneven) Successful Aging

Jennifer Morack Pennsylvania State University

Nilam Ram Pennsylvania State University and Max Planck Institute for

Human Development, Berlin, Germany

Elizabeth B. Fauth Utah State University

Denis Gerstorf Pennsylvania State University and Humboldt University

Life-span developmentalists have long been interested in the nature of and the contributing factors to successful aging. Using variable-oriented approaches, research has revealed critical insights into the intricacies of human development and successful aging. In the present study, we opted instead for a more subgroup-oriented approach and examined multiple-indicator information of late-life change at the person level. We applied latent profile analysis to 8-year longitudinal data pooled together across 4 Swedish studies of the oldest old (N � 1,008; Mage � 81 years at Time 1; 61% women). Results revealed 4 psychosocial aging profiles with uneven patterns of successful (and less successful) aging characterized by distinct trajectories of change across indicators of depressive symptoms, social, and memory functions: a preserved system integrity group of participants who maintained functioning across very old age; an aging in isolation group with a persistent lack of social support, and 2 groups of people with average well-being and social functions but distinctive memory profiles. A compromised memory group was characterized by poor memory throughout late life, whereas participants in a memory failing group exhibited dramatic memory declines late in life. The subgroups were also differentiated by sociodemo- graphic characteristics, functional limitations, and mortality hazards, which may have served as ante- cedents, correlates, or consequents of profile trajectories. We discuss the promises and challenges of using subgroup-oriented approaches in the study of successful aging.

Keywords: successful aging, patterns of aging, old age, mortality, latent profile analysis

Life-span developmentalists are interested in identifying the key components that allow people to lead happy and successful lives (Baltes & Baltes, 1990; Lawton, 1983; Ryff & Singer, 1998). A prominent model advanced by Rowe and Kahn (1997) defined successful aging as a combination of low disease and disability, high levels of cognitive and physical function, and high social engagement. Empirical research testing these notions has primarily

used cross-sectional data to identify subgroups of people aging more or less successfully based on these predefined criteria (e.g., Andrews, Clark, & Luszcz, 2002; Berkman et al., 1993; Garfein & Herzog, 1995; Jorm et al., 1998). However, aging is a process that evolves over time. Conceptual and operational definitions that articulate the dynamic nature of the phenomenon may reveal more nuanced patterns and forms of successful aging. For example,

This article was published Online First March 25, 2013. Jennifer Morack, Department of Human Development and Family Stud-

ies, Pennsylvania State University; Nilam Ram, Department of Human Development and Family Studies, Pennsylvania State University, and Max Planck Institute for Human Development, Berlin, Germany; Elizabeth B. Fauth, Department of Family, Consumer, and Human Development, Utah State University; Denis Gerstorf, Department of Human Development and Family Studies, Pennsylvania State University, and Institute of Psychol- ogy, Humboldt University, Berlin, Germany.

We are grateful for the support provided by the National Institute on Aging (NIA; Grants RC1-AG035645, NIA R21-AG032379, and NIA R21-AG033109), the Max Planck Institute for Human Development, and the Social Science Research Institute at the Pennsylvania State University. We also acknowledge support for the original Swedish studies: National Institutes of Health/NIA Grant R03 AG028471-01, European Union project contract QLK6-CT-2001-02283; Research

Board in the County Council of Jönköping; FORSS; NIA Grant AG- 08861; MacArthur Foundation Research Network on Successful Aging; The Axel and Margaret Axson Johnson’s Foundation; Swedish Council for Social Research; Swedish Foundation for Health Care Sciences and Allergy Research; and NIA Grant T32 AG20500. The content of the article is solely the responsibility of the authors and does not neces- sarily represent the official views of the funding agencies. We also thank Stig Berg, Boo Johansson, Bo Malmberg, Gerald McClearn, Nancy Pedersen, Steven Zarit, and researchers at the Institute for Gerontology in Jönköping University, the Karolinska Institute, and the Pennsylvania State University, who conceived of and completed the original four studies.

Correspondence concerning this article should be addressed to Jennifer Morack, Department of Human Development and Family Studies, Penn- sylvania State University, 422 Biobehavioral Health Building, University Park, PA 16802. E-mail: [email protected]

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Developmental Psychology © 2013 American Psychological Association 2013, Vol. 49, No. 12, 2309 –2324 0012-1649/13/$12.00 DOI: 10.1037/a0032267

2309

individuals who are functioning well at one point in time may decline considerably afterward and no longer be defined as suc- cessfully aging according to the Rowe and Kahn criteria. Another concern is that using predefined criteria for successful aging may constrain our ability to identify naturally occurring subgroups— individuals who are aging well in distinct aspects of life. Such “uneven” successful aging subgroups, with high functioning in some but not all domains, are repeatedly identified with cross- sectional data (e.g., Garfein & Herzog, 1995; Ko, Berg, Butner, Uchino, & Smith, 2007; Smith & Baltes, 1997). These groups are very informative, in that their existence may provide us with suggestions for how interventions can be targeted for and tailored to specific population segments (e.g., aimed at improving social integration among individuals with overall preserved functioning but a distinct lack of social support).

In this study, we capitalize on the Rowe and Kahn (1997) definition of successful aging with a focus on different aspects of psychosocial functioning, and we go several steps ahead by ex- amining multiple profiles of successful aging as these evolve across old and very old age. In particular, we use a subgroup- oriented approach to understand whether and how we can distin- guish subgroups of persons who show similarities and differences in multidomain trajectories of psychological change. We are in- terested in identifying a set of profiles with uneven patterns of successful (and less successful) aging.

Approaches to the Study of Successful Aging

Research on interrelations among within-person changes has primarily been carried out from a variable-oriented perspective. Here studies describe developmental stability and change in a particular variable and examine how between-person differences in those changes are interrelated with other variables (e.g., how changes in health relate to changes in depressive symptoms). Relations among variables constitute the main focus of analysis, and persons derive their importance from their rank ordering within the overarching distribution of scores within the sample. Such variable-oriented research has provided invaluable insights into normative trajectories of change in a variety of different domains and identified possible mechanisms underlying these changes. For example, a myriad of empirical reports demonstrate that well-being, on average, exhibits relative stability across most of adulthood and old age, with larger decrements only observed with the experience of major social or health-related losses (Ger- storf et al., 2010; Lucas, 2007). In short, well-being appears to be maintained over time and is thus, from this perspective, considered a key component of successful aging.

A person- or subgroup-oriented approach provides a comple- mentary perspective on the study of interrelations among within- person changes (Magnusson, 1998). Here individuals or subgroups of individuals constitute the main focus of analysis, and variables derive their importance from the way they are embedded in the overarching configuration (i.e., profile) of variables within a given person or subgroup. For example, Aldwin, Spiro, Levenson, and Cupertino (2001) used data from the Normative Aging Study to classify men into groups with distinctively different age trajecto- ries in physical and mental health. A subgroup-oriented study may be particularly well suited to empirically test theories of successful aging according to which some groups of people maintain func-

tioning across key domains in old age, whereas other groups of people exhibit steep losses in a variety of different domains (Baltes & Baltes, 1990; Rowe & Kahn, 1997; Ryff & Singer, 1998).

Studies of Successful Aging From a Subgroup- Oriented Perspective

Aside from studies using theoretically or clinically relevant criteria to define groups a priori (e.g., Berkman et al., 1993; Jorm et al., 1998), many subgroup-oriented studies use exploratory approaches such as cluster analysis, latent class, or latent profile analysis (LPA) to identify subgroups within a given sample (e.g., Fiori & Jager, 2012; Gerstorf, Smith, & Baltes, 2006). A compre- hensive overview of subgroup-oriented studies in adult develop- ment and aging is given in the Appendix. It is important to note that although not all of the studies in the Appendix focus on successful aging specifically, each uses an approach that charac- terizes subgroups of individuals across a variety of domains, and the subgroups are often referred to by varying levels of aging well or successfully or are ranked from higher functioning (i.e., suc- cessful aging) to poorer functioning (i.e., less successful aging).

Several cross-sectional studies listed provided highly valuable information about various forms of successful aging. For example, Smith and Baltes (1997) applied cluster analysis to cross-sectional data from older adults (aged 70 –103) in the Berlin Aging Study and identified nine subgroups with distinctively different cross- domain psychological profiles. Although typical findings from variable-oriented studies suggest positive associations between cognitive and social variables (e.g., individuals with poor cogni- tion tend to have low levels of social embeddedness), one of Smith and Baltes’s subgroups was characterized by low cognitive func- tioning and high social embeddedness—an uneven successful ag- ing group counterintuitive to the variable-oriented pattern. That the subgroups were also differentiated by a multitude of variables not used in the group extraction (e.g., health, survival) demonstrated that the profiles provided valid and useful information that would have been missed by a purely variable-oriented perspective.

Although longitudinal subgroup-oriented studies exist (e.g., Lövdén, Bergman, Adolfsson, Lindenberger, & Nilsson, 2005; Maxson, Berg, & McClearn, 1996), very few directly examine the differential aging of subgroups of individuals. Instead, profile configurations at one point in time are often used to predict whether and how those configurations change over time. For example, Gerstorf et al. (2006) applied cluster analysis to data from each of the first three waves of the Berlin Aging Study. Analyses revealed highly similar subgroup profiles over time, and after matching the profiles longitudinally, some two thirds of the participants were found to exhibit stable profiles across time. The groups also showed distinct levels and time-related changes on cognitive, personality, and social domain indicators. Importantly, profile grouping was a robust predictor of long-term outcomes such as mortality, although the subgroup-defining variables were not.

A notable exception of a subgroup-oriented study using longi- tudinal data to examine age changes directly is the report from Aldwin et al. (2001), who first estimated individual growth curves for physical and mental health, and then entered the individual estimates into univariate cluster analyses, one for physical health and one for mental health. Our approach here is an extension of

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2310 MORACK, RAM, FAUTH, AND GERSTORF

this approach by simultaneously including changes occurring in three domains within the profile discovery. Note that this is a somewhat different approach than growth mixture modeling meth- ods (e.g., Maggs & Schulenberg, 2004/2005; Nagin & Tremblay, 1999), which typically define groups based on repeated measures of a univariate outcome. The approach used here explicitly iden- tifies subgroup profiles based on multidimensional change.

The Present Study

We have two major research questions. The first is whether we can identify different types of (un)successful aging using a bottom-up approach and studying naturally occurring subgroups characterized by multivariate trajectory profiles. Specifically, we operationally define successful (psychosocial) aging as sustained high-level functioning in three key domains: maintaining few depressive symptoms, preserving social integration, and maintain- ing good memory. These particular domains were selected because they broadly represent central characteristics of individual func- tioning and psychological development in old age. Depressive symptoms refer to signs of clinically diagnosable depression and provide an important indicator of mental health. Reporting no or very few depressive symptoms over time is one central component of happiness and quality of life (Diener & Seligman, 2002). Social integration revolves around how connected and supported people feel socially, including few feelings of loneliness, and represents a key indicator of the perception of and satisfaction with one’s social life (Cohen, 2004). Cognitive functioning refers to a broad array of mental processes and capacities, with higher functioning consid- ered a general purpose mechanism for adaptation and a resource people draw from to master the challenges of everyday life (Baltes, Lindenberger, & Staudinger, 2006). In particular, memory is the ability of individuals to learn and retain information and is an essential piece of everyday functioning and crucial for indepen- dence later in life.

In our second research question, we corroborate the viability of the profiles by examining how the grouping relates to a set of variables that may have served as antecedents (age, education, gender, marital status, and living arrangement), correlates (change in functional limitations), or consequences (survival time). On the basis of earlier studies (Gerstorf et al., 2006; Ko et al., 2007; Smith & Baltes, 1997), we hypothesize that a sizable group of successful agers indeed maintain psychosocial function throughout late life and that they differ from their less successful peers in key sociode- mographic and health factors. To examine these questions, we applied LPA to 8-year longitudinal data pooled together across four Swedish studies of the oldest old (N � 1,008; Mage � 81 years at Time 1; 61% women) that obtained repeated measures of depressive symptoms, social integration, and memory.

Method

Participants and Procedure

We make use of 8-year longitudinal data pooled across four Swedish longitudinal studies of aging: Sex Differences in Health and Aging study (GENDER; Gold, Malmberg, McClearn, Peder- sen, & Berg, 2002), Swedish Octogenarian study (OCTO; Johans- son & Zarit, 1995), Origins of Variance in the Oldest-Old: Octo-

genarian Twins study (OCTO-TWIN; McClearn et al., 1997), and Swedish Nonagenarian study (NONA; Fauth, Zarit, Malmberg, & Johansson, 2007). In the GENDER and OCTO-TWIN studies, beginning in 1995 and 1990, respectively, representative samples of twin-pairs in their 70s and 80s were recruited from the Swedish Twin Registry, a population-based registry of all multiple births in Sweden. In the OCTO and NONA studies, beginning in 1987 and 1999, respectively, participants in their 80s and 90s were recruited from the municipality of Jönköping’s population registry (which contains names and birth dates of all residents). At baseline as- sessment, OCTO participants were aged 84, 86, 88, and 90 years and NONA participants were aged 86, 90, and 94 years. All four studies followed individuals for three occasions (OCTO-TWIN for five occasions) at 2-year or 4-year (GENDER) intervals.

Our analyses used longitudinal data from the subsample of 1,008 participants who provided data on two or more occasions for the three profile-defining indicators of depressive symptoms, so- cial integration, and memory. These participants were aged be- tween 69 and 95 years at their initial assessment (M � 81.2, SD � 5.6), were 61% women, and provided an average of 3.1 occasions of data over 8 years. Relative to those not included here (N � 785 who did not contribute change information on either of the three profile measures), our participants were younger (M � 81.1, SD � 5.9 vs. M � 84.0, SD � 5.9), F(1, 3526) � 104.54, p � .001; more educated (M � 7.2, SD � 2.3 vs. M � 6.9, SD � 2.1), F(1, 48) � 9.98, p � .01; more likely married (41% vs. 32%), �2(1, N � 1,717) � 16.18, p � .001; and less likely to live in an institution (9% vs. 38%), �2(1, N � 1,774) � 219.88, p � .001; whereas no differences were found for gender. In addition, our subsample reported fewer depressive symptoms (M � 0.5, SD � 0.4 vs. M � 0.7, SD � 0.5), F(1, 10) � 43.32, p � .001; more social integra- tion (M � 3.4, SD � 0.5 vs. M � 3.2, SD � 0.6), F(1, 15) � 49.41, p � .001; greater recall (M � 6.8, SD � 2.5 vs. M � 4.6, SD � 3.4), F(1, 1667) � 200.79, p � .001; and fewer disabilities (M � 1.1, SD � 1.8 vs. M � 4.0, SD � 3.9), F(1, 3274) � 410.99, p � .001. Effect sizes for selectivity differences were in the small to medium range (R2 � .20 for all comparisons).

Relative to participants who provided below the minimum two waves of data for inclusion in our analysis (n � 165), participants who provided three or more waves of data (n � 843) were younger (M � 80.8, SD � 5.7 vs. M � 82.8, SD � 5.5), F(1, 535) � 16.41, p � .001; less likely to live in an institution at Time 1 (7% vs. 19%), �2(1, N � 1,008) � 27.36, p � .001; performed better on the recall test (M � 7.0, SD � 2.5 vs. M � 6.0, SD � 2.7), F(1, 112) � 17.66, p � .001; and had fewer disabilities (M � 0.9, SD � 1.7 vs. M � 1.9, SD � 2.4), F(1, 134) � 41.22, p � .001; but no differences were found for education, marital status, gender, re- porting of depressive symptoms, or social integration. Effect sizes were in the small range (R2 � .04 for all comparisons).

Measures

Profile-defining measures. Measures from three domains representing key areas of psychological functioning (depressive symptoms, social integration, and memory) were used to identify subgroups defined by multidimensional trajectory profiles. Each measure was administered in the same manner in all studies and at each wave, unless otherwise noted. Table 1 provides descriptive information for each measure.

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2311PSYCHOLOGICAL PROFILES AND SUCCESSFUL AGING

Depressive symptoms. An individuals’ level of depressive symptoms was indexed by the average of responses to the 10 items from the Center for Epidemiologic Studies Depression Scale (Rad- loff, 1977) that were common across all four pooled studies. Using a scale ranging from 0 (rarely or never) to 3 (most of the time), participants rated how often during the past week they had expe- rienced a variety of depressive symptoms (e.g., thought life had been a failure, felt fearful or depressed; Cronbach’s � � .79).

Social integration. The average of responses to five items measuring subjective support (three items) and loneliness (two items; reverse coded) was used to index social integration (Cron- bach’s � � .74). Participants were asked to rate four of these items (e.g., “Do you have someone you can talk with?”; “Do you feel you are part of a circle of friends?”) adapted from the UCLA Loneliness Scale (Russell, 1982) plus one additional (global) item (Malmberg, 1990) using a scale ranging from 1 (not at all) to 4 (nearly always; for details and measurement properties, see Femia, Zarit, & Johansson, 2001).

Memory. Individuals’ memory was measured with a recall subtest of the Memory in Reality Test (Johansson, 1988/1989). Participants were presented and asked to memorize a list of 10 common objects (keys, medicine, wrist watch, comb, pencil, matchbox, ring, eyeglasses, scissors, and glass). Memory was indexed by the number of words from the list participants recalled when prompted 30 min later (test–retest reliability � .73; for details and measurement properties, see Fiske & Gatz, 2007).

Correlates. We examined whether the groups differed on a variety of factors, including chronological age, years of education, gender (0 � men, 1 � women), marital status (0 � married, 1 � not married), and living arrangement (0 � ordinary housing, 1 � living in an institution). Functional limitations assessed individu- als’ ability to complete four personal activities of daily living (bathing, dressing, toileting, and feeding; Katz, Ford, Moskowitz, Jackson, & Jaffe, 1963) and four instrumental activities of daily living (house cleaning, cooking, shopping, and going places out of walking distance; Lawton, 1971). Participants were asked how much difficulty they had performing those activities from 0 (com- pletely independent) to 3 (unable to do the activity at all). Sum scores of personal activities of daily living and instrumental ac- tivities of daily living were calculated and then averaged to obtain an overall functional limitation score (Fauth, Zarit, & Malmberg,

2008; Cronbach’s � � .82). Finally, survival time was assessed with mortality information obtained from Swedish public health records and quantified as the number of years between an individ- ual’s last assessment and his or her date of death.

Statistical Analysis

In a preliminary step, growth models were used to derive inter- cepts and linear rate of change for depressive symptoms, social integration, and memory that quantified interindividual differences in intraindividual trajectories for the three aspects of psychological functioning. These six measures (level and rate of change for each of the three profile defining variables) were then used in the LPA to identify subgroups of individuals with distinct multidimensional developmental trajectory configurations.

Growth models. Using a multilevel modeling framework, growth curve models (e.g., McArdle & Nesselroade, 2003; Ram & Grimm, 2007; Singer & Willett, 2003) summarized and extracted information about initial levels and rates of change in depressive symptoms, social integration, and memory. Models took the fol- lowing form

Domainti � �0i � �1i�timeti� � eti, (1) where person i’s score in a particular domain at time t, Domainti, is a function of an individual-specific intercept parameter, �0i, and an individual-specific linear slope parameter, �1i, that captures the linear rate of change per year of time, and residual error, eti. Following standard growth curve modeling procedures, individual-specific intercepts, �0i, and linear slopes, �1i, (from the Level 1 model give in Equation 1) were modeled as

�0i � �00 � u0i,

�1i � �10 � u1i, (2)

(i.e., Level 2 model) where �00 and �01 are sample means and u0i and u1i are individual deviations from those means. Using SAS PROC MIXED with restricted maximum likelihood estimation and standard missing at random assumptions (Little & Rubin, 1987), we fitted the model separately for each of the three profile-defining measures. Using Bayes empirical estimates (see Littell, Milliken, Stroup, Wolfinger, & Schabenberger, 2006), we obtained level,

Table 1 Means, Standard Deviations, and Intercorrelations Among the Profile Defining Constructs and Correlates

Construct M SD 1 2 3 4 5 6

Profile defining 1. Depressive symptoms 0.5 0.4 — 2. Social integration 3.4 0.5 �.41� — 3. Memory 6.8 2.5 �.08� .25� —

Correlates 4. Age 81.1 5.8 �.45� .04 �.22� — 5. Years of education 7.2 2.3 .15� �.05 .08� �.14� — 6. Functional limitations 1.1 1.8 �.39� .20� �.31� .37� �.11� — 7. Gender (% women) 61.3 8. Marital status (% married) 41.2 9. Living arrangement (% institutionalized) 8.8

Note. N � 1,008. � p � .05.

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2312 MORACK, RAM, FAUTH, AND GERSTORF

�0i, and rate of change, �1i, scores for each domain for each individual. Our objective was to reduce the longitudinal (up to 8 years) data across the three dimensions (depressive symptoms, social integration, memory) down to six informative scores. Given the short length of the individual time series, we did not include a quadratic term in the models.

Latent profile analysis. The estimates of level and rate of change for depressive symptoms, social integration, and memory extracted above were then transformed to Z scores (to alleviate concerns of differential weighting) and used to obtain a set of subgroups defined by their multidimensional trajectory profiles. Specifically, LPA uses latent mixture models to identify latent classes based on mean differences in continuous, manifest vari- ables. We applied LPA to determine the optimal number of sub- groups in the data (Gibson, 1959; Ko et al., 2007; Lanza, Flaherty, & Collins, 2003; Muthén, 2002). Following the standard approach, we estimated profiles and group membership probabilities across a number of altern

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