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Volume 29 / Number 1 / 2016

GeroPsych

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The Journal of Gerontopsychology and Geriatric Psychiatry

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GeroPsych The Journal of Gerontopsychology and Geriatric Psychiatry

Volume 29, Issue 1, 2016 Editor-in-Chief Frieder R. Lang

Associate Editors Dieter Ferring Julia Haberstroh Eva-Marie Kessler Mike Martin Johannes Pantel Michael Rapp Peter Schoenknecht

GeroPsych 29 (1)

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Ken Laidlaw (Edinburgh) Giovanni Lamura (Ancona) Anja Leist (Walferdange) Becca Levy (New Haven) Ulman Lindenberger (Berlin) Jasminka Lu2anin (Zagreb) Frank Oswald (Frankfurt) Pasqualina Perrig-Chiello (Bern) Philippe Rast (Victoria BC) Christina Röcke (Zurich) Cornel C. Sieber (Erlangen-Nuremberg) Clemens Tesch-Römer (Berlin) Alan Thomas (Newcastle) Pieter J. Visser (Maastricht) Susanne Zank (Cologne) Daniel Zimprich (Ulm)

GeroPsych 29 (1)

Contents Full-Length Research Report

Education, Occupational Class, and Cognitive Decline in Preclinical Dementia Dorina Cadar, Andrea M. Piccinin, Scott M. Hofer, Boo Johansson, Graciela Muniz-Terrera

Substance Addiction in Old Age: A Cross-Sectional Study in a German Hospital Johanna Cristina Cossmann, Norbert Scherbaum, Udo Bonnet

Deficits in Selective Attention Alter Gait in Frail Older Adults Véronique Cornu, Jean-Paul Steinmetz, Carine Federspiel

Personality and Life Satisfaction Over 12 Years: Contrasting Midand Late Life Benjamin Tauber, Hans-Werner Wahl, Johannes Schröder

GeroPsych 29 (1)

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D. Cadar et al.: Co gnitive Reserve GeroPsych and (2016), Co© gnitive 2016 29 (1), Decline Hogrefe 5–15

Full-Length Research Report

Education, Occupational Class, and Cognitive Decline in Preclinical Dementia Dorina Cadar1, Andrea M. Piccinin2, Scott M. Hofer2, Boo Johansson3, and Graciela Muniz-Terrera1 1

MRC Unit for Lifelong Health and Ageing at University College London, UK

Department of Psychology, University of Victoria, Victoria, Canada Department of Psychology, University of Gothenburg, Sweden

Abstract. We investigated education and occupational influences as markers of cognitive reserve in relation to cognitive performance and decline on multiple fluid and crystallized abilities in preclinical dementia. From the total sample of 702 participants stemming from the OCTOTwin Study (Sweden), aged 80+ at baseline in 1992–1993, only those who developed dementia during the study period (N = 127) were included in these analyses. Random effects models were used to examine the level of performance at the time of dementia diagnosis and the rates of decline prior to diagnosis. The results demonstrated that both fluid and crystallized abilities decline in preclinical stages, and that education and occupational class have independent moderating roles on the cognitive performance at the time of diagnosis, but not on the rates of decline. Keywords: dementia, aging, preclinical, cognitive decline, fluid and crystallized abilities

Introduction Although most individuals likely experience a decline in their cognitive abilities as they age, greater cognitive impairment highlights the first signs of neuropathological damage and the potential progression to neurodegenerative conditions such as dementia or Alzheimer’s disease. Studies comparing the risk of developing late-onset Alzheimer’s disease in identical twins compared with nonidentical twins show a heritability of around 0.6, so the genetic component of late-onset Alzheimer’s disease lies at around 60%, with the other 40% coming from environmental factors (Bergem, Engedal, & Kringlen, 1997). The concordance of Alzheimer’s disease (AD) in monozygotic (MZ) twins is estimated to be 80%, suggesting high heritability patterns. However, the genetic patterns seem to diverge as MZ twins become older, so the exact significance of heritability remains unexplained. In this context, environmental influences such as education or lifestyle behaviors have been proposed to have a long-term influence on genetic alterations (Bjornsson, Fallin, & Feinberg, 2004; Sweatt, 2010). One proposed explanation is that cognitive reserves may moderate the effects of neocortical and limbic neuropathology (Stern, 2009). According to this hypothesis, complex mental activities that develop across the lifespan (through educational attainment, occupation or participation in leisure activities) could allow compensatory cognitive mechanisms to be employed, when confronted with underlying neurodegenerative © 2016 Hogrefe DOI 10.1024/1662-9647/a000138

dysfunction (Stern, 2002). Education and socioeconomic position are often considered proxies for cognitive reserve since they are key markers for the environmental experiences that have an effect on cognition (Albert et al., 1995), cognitive decline (Clouston, Kuh, Herd, Elliott, Richards, & Hofer, 2012; Hall, Derby, LeValley, Katz, Verghese, & Lipton, 2007; Richards & Deary, 2005; Wilson et al., 2004) and dementia incidence (Tervo et al., 2004). Cognitive reserve theory was supported by the results of a systematic review, emphasizing that complex patterns of mental activity across life stages are associated with a significant reduction in dementia incidence and less severe clinical or cognitive changes in the presence of agerelated neurodegeneration (Valenzuela & Sachdev, 2006). In addition, several prospective longitudinal studies showed that a number of individuals with no evidence of symptomatic cognitive impairment who met clinicopathologic criteria for Alzheimer’s disease could be identified (Mortimer, 1997). Although the existing evidence regarding protection against faster cognitive decline in healthy individuals is well documented, the evidence related to preclinical dementia remains unclear. It is not inconceivable that the difference in rate of neuropathological advancement balances the difference in the association between cognitive ability and neuropathology, which could lead to similar slopes in cognitive decline. According to the cognitive reserve hypothesis, a person’s cognitive test score depends on both the premorbid ability level and the amount of neuropathology that accumulates, which increases with age. However, after a certain inflection point or accumulation stage, cognitive ability starts to decline, and this decline could start GeroPsych (2016), 29 (1), 5–15

later and proceed more quickly for those with high cognitive reserve (Stern, 2012). This assumes that the rate of neuropathology progression is constant regardless of level of cognitive reserve. When the rate of neuropathological decline is slower in those with higher cognitive reserve, due to compensatory mechanisms (Valenzuela & Sachdev, 2006), the effective rate could be slower with more cognitive reserve. The discrepancy in findings could also be related to the methodology employed or to the type of neuropsychological tests examined (Hofer & Piccinin, 2010; Piccinin, Muniz, Matthews, & Johansson, 2011). Regarding the latter, according to the different type of cognitive abilities (fluid or crystallized), the aging process has a differential effect on cognitive functioning in later life. In general, fluid abilities such as processing speed, memory, visuospatial ability, and attention are considered to be age-sensitive, while crystallized knowledge such as verbal abilities (understanding written and spoken language), are less age-sensitive and tend to remain stable even into old age (Cattell, 1963; Crawford, Deary, Starr, & Whalley, 2001; Horn & Cattell, 1967). Consequently, these mental abilities tend to present different slopes of decline in relation to age (Anstey & Low, 2004). Several epidemiological studies showed that an accelerated decline was mostly observed with the fluid cognitive abilities, so that a steeper decline in these functions was associated with an increased risk of developing dementia (Fleisher et al., 2007; Petersen, 2004; Roberts, Karlawish, Uhlmann, Petersen, & Green, 2010). However, less is known about the rate of decline between fluid and crystallized functions prior to dementia diagnosis, when the process of neurodegeneration is considered to vary substantially from normal aging. In this context, it is essential that population samples be well defined, and that cognitive trajectories be investigated within samples of subpopulations (e.g., healthy individuals or dementia cases). Process-based approaches to analyze longitudinal data represent unique opportunities to evaluate theories of aging, such as by testing key predictions about the rates of cognitive decline in the period prior to dementia diagnosis and how cognitive performance (one variable) changes in relation to proximity to dementia (another variable) (Hofer & Piccinin, 2010; Sliwinski & Mogle, 2008). Modeling cognitive change in relation to the time prior to clinical diagnosis could provide a more informative-descriptive and explanatory modeling process, compared to the age-based models. It offers an improved indication of the rates of cognitive decline longitudinally, as there is an important non-age-graded process driving the cognitive change in the years prior to dementia diagnosis, reflecting the progression of the disease in older individuals. This study evaluates the associations of different cognitive reserve variables (e.g., education, occupational class) with fluid and crystallized abilities in the period prior to dementia. With this purpose in mind, we considered a process-based approach (Sliwinski & Mogle, 2008), in which we modeled the decline in all neuropsychological tests available in the Octogenarian GeroPsych (2016), 29 (1), 5–15

D. Cadar et al.: Cognitive Reserve and Cognitive Decline

Twins Study (also known as the OCTO-Twin), using random effects mixed models, where cognitive scores were aligned according to time to dementia diagnosis from study entry. We hypothesized that both higher education (more years) and higher occupational levels (intermediate or professional levels) may protect against the adverse effects of accumulating neuropathology, and could moderate the rate of cognitive decline measured in both fluid and crystallized abilities in the preclinical stages of dementia. We also hypothesized that persons with higher cognitive reserve will have better performance on both fluid and crystallized measures at the time of diagnosis.

Method Study Population Participants were drawn from the longitudinal Origins of Variance in the Old-Old: Octogenarian Twins (OCTO-Twin Study), based on the oldest cohort of the Swedish Twin Registry. The full sample includes 702 participants, with 351 complete twin pairs born 1913 and earlier, who thus became 80 years of age during the first wave of data collection (1991–1993). Participants have been reassessed every 2 years across the study period (1991–2001) on up to five occasions. The average rate of attrition from one testing wave to the next was 20% (10% per year), primarily due to death. Full details of the study population characteristics were published previously (McClearn et al., 1997; Pedersen, Lichtenstein, & Svedberg, 2002). We analyzed data from the subsamples of individuals who developed dementia during the course of the study and who were free of dementia at study entry. From the total sample of OCTO-Twin Study, 225 individuals had dementia, representing 32% of the total sample. Of these, 98 people had already been diagnosed with dementia at study entry and were excluded from the main analyses. The remaining 127 participants who developed dementia during the study period represent the subpopulation sample used for these analyses.

Procedure Ethical approval was received from the Ethics committee at Karolinska Institute in Stockholm and the Swedish Data Inspection Authority in Sweden. All participants signed informed consent forms (Johansson & Zarit, 1995). Dementia diagnosis was established by clinicians according to the revised third edition of the Diagnostic and Statistical Manual of Mental Disorders (3rd ed., DSM-III; American Psychiatric Association, 1980). During the interviews across the study period, participants were asked for permission to have their medical records reviewed, ordered from hospitals, outpatient clinics, district physicians, and primary healthcare. A mul© 2016 Hogrefe

D. Cadar et al.: Cognitive Reserve and Cognitive Decline

tidisciplinary team consisting of a physician and two neuropsychologists reviewed testing results and medical records, including medicine use and self-reported information about diseases at each new wave (van den Kommer et al., 2009).

Study Variables The OCTO-Twin Study encompasses a broad spectrum of cognitive and behavioral measures, assessed at each of the five waves during the study period. Neuropsychological measurements consisted of multiple tests of fluid abilities and crystallized abilities, as well as the Mini-Mental State Examination (MMSE) (Folstein, Folstein, & McHugh, 1975). Fluid abilities including visuospatial ability inductive reasoning, and memory were measured by Block Design, Figure Logic, Swedish Clock, Digit-Symbol, Prose Recall, Memory Recall, Memory Recognition and Memory Correspondence. Crystallized abilities such as knowledge ability and verbal meaning were measured by the Information and Synonym tests. Participants were tested in their home by medical research nurses who had been specially trained. A summary of these cognitive tests is provided as online material. Education was measured as the number of years the individual had gone to school. Occupational class was measured during the home interview. The question asked was: “What has been your main occupation for most of your working life (during the longest period)?”; the responses were coded into low (manual occupations), medium (nonmanual occupations), and high (intermediate and professional occupations).

Data Analyses and Analytic Approach We evaluated the whole range of neuropsychological tests. Random effects mixed models were fitted to each cognitive outcome as a function of time to dementia diagnosis, which was calculated for each wave of data collection in relation to the time of dementia diagnosis, which was set at zero to represent the intercept. Between-person differences in age and in time to dementia diagnosis were separated from within person changes by adjusting the level and rate of change by age at study entry and by time to dementia diagnosis from study entry (Piccinin et al., 2011). We examined education (years of formal education) and occupational class as markers of cognitive reserve as well as a number of common demographic covariates including sex, age at study entry, and time to dementia diagnosis from study entry. Male and low occupational histories were used as reference categories. Age and time to dementia diagnosis from study entry were mean centered. Given this model specification, the intercept represents performance at the time of dementia diagnosis for an individual with values of zero on all covariates (i.e., for an 83 years old man of low occupational class and 7 years of education who entered the study 5 years before dementia diagnosis) and the linear slopes represent annual rate of change (e.g., increase, decrease) per year closer to the time of dementia diagnosis. All analyses were performed using Stata software, Version 13 (StataCorp, 2013).

Results Table 1 provides a summary of participant characteristics. Information is provided for the full sample as well as for the de-

Table 1. Characteristics of study participants Total sample, N (%)

Total sample 702 (100%)

Nondementia 477 (68%)

Dementia cases 225 (32%) At study entry N = 98

In study period N = 127

p values

Sex: Female, N

468 (66%)

311 (65%)

78 (76%)

79 (62%)

≤ .01

Baseline age, mean (SD)

83.52 (3.2)

83.42 (3.2)

84.64 (3.4)

83.13 (2.6)

≤ .001

Education1, mean (SD) 7.13 (2.3)

7.28 (2.4)

6.23 (1.2)

6.91 (1.8)

≤ .01

Education, range

0 to 23 y

4 to 12 y

2 to 17

Low, N (%)

332 (49%)

215 (45%)

35 (58%)

72 (56%)

Medium, N (%)

256 (38%)

197 (41%)

19 (32%)

40 (32%)

High, N (%)

85 (13%)

64 (14%)

6 (10%)

15 (12%)

Occupation2

Years to diagnosis from study entry, range/mean

.09

–11 to 0.01 y –5 y

Note. 1Education was coded as the number of years. 2Occupational class was coded into low (manual occupations), medium (nonmanual occupations) and high (intermediate and professional occupations).

GeroPsych (2016), 29 (1), 5–15

D. Cadar et al.: Cognitive Reserve and Cognitive Decline

mentia-free at study entry, demented at study entry, and incident case groups for comparative purposes. Only 10% of those who had dementia at study entry and 12% of incident cases during the study period had a higher occupational class. Occupation and dementia incidence were found to be independent (χ²(2) = 8.01, p = .09). The relationship between sex and dementia incidence was significant (χ² with two degrees of freedom χ²(2) = 8.96, p = .01), suggesting that women were more likely to have already been diagnosed with dementia at baseline. There were significant differences in years of education and baseline age by dementia status (ANOVA, F(2, 655) = 5.58, p = .003 and F(2, 701) = 7.38, p = .001, respectively). Posthoc analyses using Tukey HSD pairwise comparisons for variable case studentized showed that the mean education was significantly lower in those with dementia at study entry (M = 6.23, SD = 1.22) than in those who were free of dementia during the study period (M = 7.28, SD = 2.46), though not significantly different from incident cases during the study period (M = 6.91, SD = 1.81). Baseline age also differed significantly between groups.

Cognitive Trajectories on Fluid and Crystallized Abilities in Preclinical Dementia Results from the multilevel analysis of the MMSE and fluid ability tests are displayed in Table 2. A significant rate of cognitive decline was observed in all tests, whether crystallized or fluid, except for Clock Test, Figure Logic, Prose Recall, and MIR Recognition. Figure 1 illustrates the expected trajectories of all neuropsychological tests investigated in these analyses from the time of study entry until the time of dementia diagnosis with the additional upper and lower bands of 5 years’ education. As illustrated in Figure 1 and listed in Table 2, mental status, as measured with the MMSE at the time of dementia diagnosis, for a male participant age 83, with 7 years of education (i.e., reference values), enrolling in the study 5 years before dementia diagnosis was estimated at 23.04 (SE = 0.90) with annual rate of decline of –0.83 (SE = 0.20) points. The visuospatial ability and speed measured with Block Design and Digit Symbol at the time of diagnosis were estimated at 5.53 (SE = 1.29) and 14.13 (SE = 2.05), respectively, while the rates of decline were estimated at –0.89 (SE = 0.25) and

Table 2. Mean, standard error of the estimates of the effect of risk factors on random effects of preclinical decline mixed model for the fluid cognitive abilities MMSE

Block Design

Figure Logic

Mental Status N = 126

Visuo-spatial ability N = 109

Inductive reasoning N = 91

Coef.

p value

Coef.

p value

23.04

0.90

<.001

0.00

0.17

.99

5.53

1.29

<.001

14.14

0.99

<.001

–0.36

0.24

.13

–0.21

0.19

–0.00

0.25

.99

–0.29

.26

0.39

.46

–0.50

0.34

.14

Coef.

Fixed effects Level of performance at dementia diagnosis Years to dementia diagnosis from study entry Education Occupation Medium

0.46

1.01

.64

5.66

1.42

<.001

1.68

1.09

.12

–1.41

1.48

.34

3.76

2.21

.08

3.96

2.04

.05

Female

–0.47

0.95

.61

–1.58

1.34

.24

–0.32

1.05

.75

Baseline age

–0.40

0.19

.03

–0.59

0.27

.03

0.06

0.24

.80

Rate of change

–0.83

0.20

<.001

–0.89

0.25

<.001

–0.23

0.25

.35

High

Years to diagnosis from study entry

–0.04

0.03

.26

–0.03

0.05

.57

0.01

0.05

.85

Education

–0.04

0.04

.35

0.02

0.07

.75

–0.04

0.09

.65

Occupation Medium

–0.06

0.20

.76

0.55

0.24

.02

0.29

0.25

.24

High

–0.32

0.32

.31

0.51

0.47

.27

0.13

0.48

.77

Female

–0.03

0.04

.44

–0.16

0.24

.49

0.14

0.24

.55

Baseline age

–0.03

0.04

.44

–0.15

0.06

.01

0.03

0.07

Random effects variance

95% CI 0.40–0.79 0.04

0.02

0.24–0.52 0.31

.63 95% CI

Level of performance

0.56

Rate of decline

3.84

0.40

3.12–4.72 5.15

0.43

4.37–6.07 2.73

0.40

2.04–3.63

Error

2.82

0.15

2.54–3.13 3.20

0.19

2.84–3.61 3.20

0.22

2.79–3.67

GeroPsych (2016), 29 (1), 5–15

0.09

95% CI 0.12

0.15–0.68

D. Cadar et al.: Cognitive Reserve and Cognitive Decline

Clock

Digit Symbol

Prose Recall

Visuo-constructive ability N = 120

Short-term memory N = 110

Verbal memory N = 110

Coef.

p value

Coef.

p value

Coef.

p value

Fixed effects Level of performance at dementia diagnosis

11.98

0.71

<.001

14.13

2.05

<.001

5.67

1.01

<.001

Years to dementia diagnosis from study entry –0.04

0.13

.75

–0.15

0.37

.67

–0.06

0.18

.72

Education

–0.22

0.25

.37

1.30

0.82

.11

0.19

0.32

.55

0.45

0.81

.58

4.01

2.30

.08

–0.29

1.06

.78

Occupation Medium

–0.45

1.19

.70

–0.04

3.31

.98

0.47

1.62

.76

Female

High

–0.53

0.73

.46

–0.98

2.05

.63

0.14

1.02

.89

Baseline age

–0.03

0.15

.81

–0.43

0.47

.36

–0.24

0.20

.23

Rate of change

–0.26

0.17

.12

–0.84

0.44

.05

–0.44

0.25

.07

Years to diagnosis from study entry

–0.02

0.03

.52

–0.02

0.08

.75

–0.01

0.04

.71

Education

–0.00

0.04

.96

0.05

0.13

.65

–0.00

0.06

.96

Occupation Medium High Female Baseline age

0.10

0.17

.56

–0.32

0.39

.42

–0.17

0.24

.48

–0.38

0.28

.18

–0.87

0.69

.21

–0.09

0.38

.80

–0.10

0.16

.54

–0.30

0.40

.45

–0.19

0.24

.42

0.02

0.04

.53

0.09

0.10

.37

–0.01

0.05

Random effects variance

95% CI 0.08

95% CI

0.27–0.59 0.17

0.21

.79 95% CI

Level of performance

0.40

0.01–1.90 0.56

0.16

0.32–0.99

Rate of decline

2.48

0.27

1.99–3.04 7.25

0.84

5.77–9.11 3.37

0.50

2.52–4.52

Error

2.50

0.14

2.23–2.80 4.89

0.32

4.30–5.56 2.59

0.20

2.22–3.02

Memory recall

Memory recognition

Memory correspondence

Short-term memory N = 112

Short-term memory N = 113

Short-term memory N = 110

Coef.

p value

Coef.

p value

Coef.

p value

<.001

Fixed effects Level of performance at dementia diagnosis

2.43

0.58

<.001

8.60

0.43

<.001

4.14

0.56

Years to dementia diagnosis from study entry –0.00

0.11

.95

–0.13

0.08

.09

0.09

0.10

.35

Education

0.21

.31

0.20

0.15

.19

0.29

0.21

.16

0.21

Occupation Medium High Female

0.52

0.65

.42

–0.69

0.47

.14

0.14

0.62

.81

–0.71

0.99

.47

–0.01

0.75

.98

–0.32

0.94

.73

0.41

0.60

.49

–0.19

0.44

.66

1.31

0.5

.02

Baseline age

–0.09

0.12

.43

–0.17

0.09

.07

–0.10

0.12

.38

Rate of change

–0.55

0.13

<.001

–0.13

0.10

.21

–0.35

0.14

.01

Years to diagnosis from study entry

–0.04

0.02

.10

–0.02

0.02

.34

–0.02

0.03

.48

0.01

0.03

.65

0.03

0.02

.27

0.02

0.04

.57

Education Occupation Medium

0.09

0.14

.48

–0.06

0.10

.56

–0.02

0.15

.85

High

0.02

0.23

.89

0.01

0.18

.92

–0.20

0.24

.41

–0.07

0.13

.60

–0.07

0.10

.50

–0.03

0.14

.83

0.05

0.03

.06

–0.02

0.02

.23

0.02

0.03

Female Baseline age Random effects variance

95% CI 0.17–0.50 0.33

0.05

.42 95% CI

Level of performance

0.30

Rate of decline

2.29

0.27

1.80–2.90 1.32

0.20

0.98–1.77 1.78

0.29

1.29–2.47

Error

1.54

0.10

1.34–1.77 0.67

0.03

1.32–1.71 1.72

0.12

1.48–1.99

0.07

95% CI 0.23–0.46 0.27

0.10

0.12–0.59

GeroPsych (2016), 29 (1), 5–15

D. Cadar et al.: Cognitive Reserve and Cognitive Decline

Figure 1. Estimated mean trajectories of cognitive decline prior to dementia diagnosis for a man aged 83 with 7 years of education entering the study at 5 years from dementia diagnosis (solid lines) and individuals with 2 and 12 years of education (the additional dotted upper and lower lines).

GeroPsych (2016), 29 (1), 5â&#x20AC;&#x201C;15

ÂŠ 2016 Hogrefe

D. Cadar et al.: Cognitive Reserve and Cognitive Decline

Table 3. Mean, standard error of the estimates of the effect of risk factors on random effects of preclinical decline mixed model for the crystallized cognitive abilities Synonym

Information

Verbal ability N = 117

Semantic knowledge N = 117

Coef.

p value

Level of performance at dementia diagnosis

10.88

1.15

<.001

Years to dementia diagnosis from study entry

–0.10

0.22

.63

0.80

0.35

.02

Medium

3.12

1.28

.01

High

p value

20.33

1.97

<.001

–0.22

0.39

.56

1.64

0.56

.004

3.57

2.25

.11

Coef.

Fixed effects

Education Occupation

3.09

2.17

.15

1.95

3.24

.55

Female

1.36

1.24

.27

–6.30

2.10

.003

Baseline age

0.19

0.26

.46

–0.75

0.43

.08

Rate of change

–0.87

0.26

<.001

–1.78

0.31

<.001

Years to diagnosis from study entry

–0.07

0.05

.18

–0.22

0.06

.001

Education

–0.07

0.07

.32

–0.09

0.08

.27

Medium

0.63

0.24

.01

–0.03

0.30

.91

High

Occupation

0.64

0.47

.18

–0.62

0.50

.21

Female

0.27

0.25

.27

0.27

0.29

.35

Baseline age

0.06

.35

–0.02

0.07

.76

0.34

0.19

0.23

0.49

Random effects variance Level of performance

95% CI 0.11–1.06

95% CI 0.00–15.05

Rate of decline

4.40

0.51

3.50–5.53

9.52

0.77

8.11–11.18

Error

2.36

0.21

1.98–2.82

3.81

0.28

3.29– 4.42

–0.84 (SE = 0.44) points per year, respectively. A significant change was also observed in tests of memory recall and memory correspondence (measured with the Memory in Reality Tests) with the corresponding linear slope estimates of –0.55 (SE = 0.13) and –0.35 (SE = 0.14) points per year, while the estimated mean intercepts were 2.43 (SE = 0.58) and 4.14 (SE = 0.56), respectively. Results from analysis of crystallized ability tests are displayed in Table 3. At dementia diagnosis, performance on Synonym test was estimated at 10.88 (SE = 0.90), while on the Information test was estimated at 20.33 (SE = 1.97). The corresponding rates of decline were estimated at –0.87 (SE = 0.26) and –1.78 (SE = 0.31) points per year.

Cognitive Reserve: Education Education was positively associated with performance at time of dementia diagnosis on both crystallized measures (Synonym and Information Test, see Table 3). An increase in education from the average of 7 years (per extra year), was associated with a better performance on the Synonyms test © 2016 Hogrefe

with 0.80 (SE = 0.35) and with 1.64 (SE = 0.56) in the Information test. In contrast, education did not have a significant association with the level of performance at the time of dementia diagnosis or with rate of change for any of the fluid or crystallized cognitive abilities examined, and this was not modified by excluding occupation from the existing models (see Tables 2 and 3).

Cognitive Reserve: Occupational Class At the time of dementia diagnosis, individuals with higher occupational class (medium and high) had better performance on two measures of fluid abilities, Figure Logic and Block Design (high occupational class only), and on one crystallized measure, Synonym tests (medium occupation class only), compared to the estimated scores for individuals with lower occupational class, independent of level of education. Regarding the rate of preclinical decline, individuals with a medium occupational class declined at a slower annual rate on only two measures, Block Design and Synonym tests, compared to those with a lower occupational class. GeroPsych (2016), 29 (1), 5–15

Other Covariates Distance from baseline to the time of dementia diagnosis was associated only with rate of change on the Information test, with individuals declining at a faster rate with –0.22 (SE = 0.06) points per year closer to the time of diagnosis. Women performed better than men at the time of dementia diagnosis, on memory correspondence 1.31 (SE = 0.57), but worse on the Information test –6.30 (SE = 2.10), and they did not change at a different rate compared to men with similar age, education level, and occupational level. Lastly, age at study entry was associated with performance at the time of dementia diagnosis for both MMSE and Block Design tests. Older age at study entry (per extra year increase from the age of 83 years) was associated with a lower performance on both MMSE and Block Design and a slightly faster decline in the Block Design test with –0.15 (SE = 0.06) points per year closer to dementia diagnosis.

Discussion In this study, we examined the association between different markers of cognitive reserve, such as education and occupational class with preclinical cognitive decline in multiple tests of fluid and crystallized abilities, using a process-based approach where cognitive scores were modeled as a function of time to dementia diagnosis. First, we found no support for our hypothesis postulating that there would be a distinction between the rates of decline with fluid and crystallized cognitive abilities prior to the time of diagnosis, as seen in previous findings with normal aging (Horn & Cattell, 1967). A significant rate of decline was observed for the MMSE and for both fluid and crystallized measures (Block Design, Digit Symbol, Memory Recall and Correspondence, Synonym and Information tests), even though the rates of decline varied by cognitive domain tapped by the various neuropsychological tests investigated. Second, our results consistently identified associations of education and performance among the crystallized abilities at the time of dementia diagnosis. However, education was found to be associated neither with performance on fluid abilities at the time of dementia diagnosis nor with the rate of decline in either fluid or crystallized measures as we had originally hypothesized. Third, we found that occupational class did not mirror the effect of education in these analyses, suggesting a slightly more independent pattern from education itself. Interestingly, our results showed that higher levels of occupation (medium or high) were associated with better performance at the time of dementia diagnosis and with slower decline on both measures of fluid and crystallized abilities (e.g., Block Design and Synonym test). This could reflect the fact that there are GeroPsych (2016), 29 (1), 5–15

D. Cadar et al.: Cognitive Reserve and Cognitive Decline

separate effects for higher education and occupational attainment within the cognitive reserve hypothesis framework. According to cognitive reserve, the neuroprotective effect is assumed to reflect increased neural networks and hemispheric activation, in those with a higher number of years of education or higher occupation class, which tend to mask the dementia pathology leading perhaps to a slower rate of decline on both fluid and crystallized functions. Despite the fact that the majority of studies investigating the brain-reserve hypothesis used education as a single measure, there is no overall consensus on how to measure cognitive reserve – which remains a matter of ongoing debate. Only a limited number of studies have estimated cognitive reserve from multiple autobiographical data such as occupational complexity and frequency of mentally stimulating lifestyle pursuits along with education (Mortimer, 1997). As a consequence, cognitive reserve advantage cannot be conveyed to education alone, as this could continue to be affected by other circumstances such as socioeconomic circumstances unfolding across the lifespan. This highlights a number of methodological issues related to the components measured, when interpreting the role of cognitive reserve across different studies (Batterham, Mackinnon, & Christensen, 2011; Carnero & Del, 2007). Furthermore, understanding the individual differences in cognitive decline prior to dementia diagnosis requires longer follow-up investigations, to be able to distinguish the severe neuropathological from the normal age-associated changes, which seem to be accelerated at least in some cases in later life (Hall, Lipton, Sliwinski, & Stewart, 2007; Schaie et al., 2005). In addition, it is essential to understand which cognitive abilities start to deteriorate faster with normal aging, and which show further sensitivity in preclinical dementia. From a neurobiological perspective, cognitive decline is thought to reflect the accumulation of degeneration lesions, which could predict a faster process of mental deterioration associated with normal aging or neurodegeneration (Stern, Albert, Tang, & Tsai, 1999; Wilson, Beck, Bienias, & Bennett, 2007). However, only detailed longitudinal neuroimaging data could clarify the nature of neuropathological changes, when only relatively small impairments are detected in neuropsychological testing, particularly in individuals with high cognitive reserve that could have the potential to maintain peak cognitive functioning levels for an extended period of time, when confronted with brain neuropathology (Stern, 2012).

Strengths and limitations In summary, the current findings provide rich and unique information regarding the rates of cognitive decline prior to dementia diagnosis as well as how we can detect evidence of variance across a wide range of cognitive abilities (both fluid and © 2016 Hogrefe

D. Cadar et al.: Cognitive Reserve and Cognitive Decline

crystallized) within a sensitive time period in an oldest-old population sample. The independent investigations of different markers of cognitive reserve (education and occupational class) on specific measures of cognitive abilities and their decline prior to dementia diagnosis have made salient contributions to the field of cognitive aging and dementia research. One strength of this paper is the adoption of a processbased approach to model cognitive decline documented as the optimal representation of intraindividual psychological change in individuals experiencing a common process that drives their cognitive changes, compared to the age-based models (Hall et al., 2000; Sliwinski, Hofer, & Hall, 2003). Using chronological age as the time metric in modeling cognitive change in older individuals at risk of dementia tends to obscure the true intraindividual trajectories and produce misleading results, since participants of the same age are unlikely to be at the same stage in their disease progression. This improved methodology tends to overcome the large heterogeneity seen in observational studies, where cognition is modeled as a function of age (Sliwinski et al., 2003). We are also aware of a number of limitations. The sample size available for these independent investigations was relatively small. However, the methodology employed compensated for the missing data aspect, since random effects mixed models can handle naturally uneven spacing of repeated measurements, whether intentional or unintentional. The power to detect variance and covariance in rates of change was calculated for this study elsewhere and was favorable for the sample size used here (Rast & Hofer, 2014). Cognitive reserve was evaluated in this study with measures of education and occupational class. However, these two measures alone may not be sufficient to fully capture an individual’s cognitive reserve accumulated across the life course. Furthermore, the results need to be considered within the historical context. This population sample was born in Sweden in 1913 and earlier, where the attendance in school was until 1960 compulsory for children only between 7–13 years of age (Erikson & Jonsson, 1996). Therefore, it can be considered a limitation of this study that the current sample had a rather reduced number of years of education and no great variability. Finally, we were unable to evaluate the cognitive changes within the monozygotic twins, because of the initially low number of cases at the study entry and the various rates of dropout within pairs. This prevented us from examining the genetic effects and the genetic environment interaction within this population across the entire study period. However, a previous investigation comparing the OCTO-Twin Study with a population based sample of nontwins from the OCTO-Twin Study indicated that, based on health status, cognitive and behavioral functions (Simmons et al., 1997), the older surviving twins were similar to a representative sample of nontwins, strengthening the case of generalizability of twin studies. © 2016 Hogrefe

Conclusions We presented results from a longitudinal study with repeated measures of a wide range of fluid and crystallized abilities in the preclinical stages of dementia in which we investigated the role of different markers of cognitive reserve. We found that both fluid and crystallized cognitive abilities tend to decline in the period prior to dementia diagnosis. Importantly, education was positively associated with the level of performance on the crystallized measures even at the time of dementia diagnosis, but did not protect against faster rates of preclinical decline. However, occupational class did have a moderating role on the rate of decline in two of the fluid abilities measured. Thus, whereas both abilities decline during the preclinical phase, different markers of cognitive reserve have different moderating effects on the level of performance or on the rates of decline prior to dementia diagnosis. Longer follow-up periods and closer intervals between measurements are needed for more detailed characterizations of the transition from normal cognitive aging to the time at which there is evidence for a clinical dementia diagnosis.

Funding This work was supported by the following funding agencies: Alzheimer’s Society [Grant number 144], the Medical Research Council [Unit Program number MC_UU_12019/1], the US National Institutes of Health National Institute of Aging [P01AG043362], the Swedish Research Council for Health, Working Life and Welfare, and by the National Institute on Aging of the National Institutes of Health under award number P01AG043362 for the Integrative Analysis of Longitudinal Studies of Aging (IALSA) research network. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or other funding bodies mentioned above.

Declaration of Conflicts of Interest The authors declare that no conflicts of interest exist.

Electronic Supplementary Material Electronic supplementary material for this article is available at http://dx.doi.org/10.1024/1662-9647/a000139. ESM 1. Full description of the neuropsychological tests used in the study analyses. GeroPsych (2016), 29 (1), 5–15

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Dorina Cadar MRC Unit for Lifelong Health and Ageing 33 Bedford Place London WC1B 5 JU United Kingdom d.cadar@ucl.ac.uk

GeroPsych (2016), 29 (1), 5–15

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Full-Length Research Report

Substance Addiction in Old Age A Cross-Sectional Study in a German Hospital Johanna Cristina Cossmann1, Norbert Scherbaum2, and Udo Bonnet1 1

Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, Evangelisches Krankenhaus Castrop-Rauxel, Academic Training

Hospital of the University of Duisburg-Essen, Germany 2 Department of Addictive Behavior and Addiction Medicine, LVR-Klinikum Essen, Hospital of the University of Duisburg-Essen, Germany

Abstract. There is a lack of studies using a structured face-to-face interview focusing on the full spectrum of substance addictions according to ICD or DSM classification systems in older patients. We therefore examined a cohort of 400 randomly selected, at least 65-year-old inpatients of a general hospital concerning addictive disorders using a well-tested structured clinical interview (DSM-IV-TR-based SKID-I). Nearly one third of this cohort was substance dependent: The 12-month prevalence rate for nicotine was 10.3% and for alcohol dependence 3%, with 24.4% and 66.7%, respectively, being severely dependent. These rates were similar to those found in the general German population of persons under 65 year of age. A fifth of the cohort was (mostly mildly) dependent on prescription drugs, remarkably including nonopioid analgesics. One case with a previous dependence on gabapentin was identified. Identification and management of addiction disorders should be considered as part of the basic geriatric assessment. Keywords: SKID-I, substance addiction, prescription drugs, nonopioid analgesics, older adults

Introduction Older adults are prone to the use of substances often because of age-related burdens such as physical complaints and pain, retirement and lack of daily structure, financial losses, changes of the living situation, or social losses. One may assume that the problem is aggravated by present demographic trends: The absolute number of older substance users will likely increase (Colleran, 2004; Han, Gfroerer, Colliver, & Penne, 2009; Wu & Blazer, 2011; Sacco, Kuerbis, Goge, & Bucholz, 2012; SAMHSA, 2012; Geppert & Taylor, 2015; Arora, O’Neill, Crome, & Martin, 2015), including the use of illegal drugs (Chapman & Wu, 2015a; Crome, Wu, Rao, & Crome, 2015; EMCDDA, 2008; Purvis, 2010) and analgesics (Maxwell, 2015; McLachlan et al., 2011; Mitchell, Hilmer, & McLachlan, 2009). Moreover, perceived cognitive decline may easily be attributed to old age, although the inappropriate use of substances may contribute to their deficits (Wolter, 2011). Comorbid mental health problems may also play a part (Samsi, 2015). The background to older people‘s impairments is complex, which makes it difficult to assess substance use disorders (Wilson, Jackson, Crome, & Rao, 2015). The German Epidemiological Survey of Substance Abuse (ESA) did not include the population of 65+-year-olds (Pabst, Kraus, Gomes de Matos, & Piontek, 2013). There are – to the best of our knowledge – six international prospective epidemiological studies that do take the 65+-year-old population into account (ABOS, 2008; Chen et al., 2006; Gum, King-Kallimanis, & Kohn, 2009; NLAES, 1998; Regier et al., 1990; SAM© 2016 Hogrefe DOI 10.1024/1662-9647/a000140

HSA, 2014). Among the subjects in question, illegal drugs do not play an important role yet. On the one hand, it seems that when people get older, their drug use shifts from illicit drugs to nonmedical pharmaceuticals (Chapman & Wu, 2015a; Maxwell, 2015). On the other hand, it can be expected that the use of illicit drugs will continue to rise in the future (Chapman & Wu, 2015a). At present, primarily tobacco, alcohol, and prescribed psychoactive substances play an essential part in substance misuse among older people (Storr & Green, 2015). Among adults aged 65 and older in the United States, 14.5% used tobacco products in the past year (Chapman & Wu, 2015b), and 84% thereof them are daily smokers – a figure that did not change from 2002 to 2011 (Dube & Wu, 2015). In 2000–2001, 4.0% fulfilled criteria for dependence during the past year (Pilowski & Wu, 2015). 1.5% of the seniors in the United States fulfilled the criteria for alcohol use disorder in the past year (Chapman & Wu, 2015b), and between 4% and 14% seem to have harmful drinking habits (Arndt & Schulz, 2015). The greatest portion of the data available so far refers to problems with alcohol (Hybels & Blazer, 2003; Moos, Schutte, Brennan, & Moos, 2009; Ondus, Hujer, Mann, & Mion, 1999; Weyerer et al., 2009; Wolter, 2011) and benzodiazepines (Johnell & Fastborn, 2011; Wetterling & Kugler, 2006; Wetterling & Schneider, 2012; Wolter, 2011) in old-age primary care (Hybels & Blazer, 2003; Moos et al., 2009; Wetterling & Kugler, 2006; Weyerer et al., 2009; Wolter, 2011) and hospitalized patients (Hybels & Blazer, 2003; Johnell & Fastborn, 2011; Ondus et al., 1999; Wetterling and Schneider, 2012; Weyerer et al., 2009; Wolter, 2011). Previous retrospective US American GeroPsych (2016), 29 (1), 17–27

studies, which did not consider analgesics, described the high prevalence of alcohol (8%) or benzodiazepine dependence (12%) in geriatric psychiatric inpatients (Holroyd & Duryee, 1997; Pourian, Finkel, & Lyons, 1995; Whitcup & Miller, 1987). These studies revealed an overall prevalence of current substance dependence of up to 25% in hospitalized seniors (Holroyd & Duryee, 1997; Ondus et al., 1999; Pourian et al., 1995; Whitcup & Miller, 1987). Several German collections of hospital data (acc. to ICD-10) of psychiatric patients aged 65+ years were analyzed retrospectively (Wetterling & Kugler, 2006; Wetterling & Schneider, 2012; Wetterling et al., 2002). Prevalences of current alcohol dependence of up to 8% (Wetterling et al., 2002) as well as an abuse of benzodiazepines (5.4%) and Z-drugs (1.7%) were found (Wetterling & Schneider, 2012). This is the only study that differentiated between opioid and nonopioid analgesics, with six cases of abuse for each substance class (of the 1266 cases overall) (Wetterling & Schneider, 2012). The authors are not aware of any work aimed at screening addiction in older hospital or primary care patients by a structured face-to-face interview focusing on the full spectrum of ICD-10 or DSM-IV-TR substance abuse and dependence and differentiating between opiod and nonopioid analgesics. In recent years some cases of addiction to nonopioid analgesics in younger people were published (Etcheverrigaray, Grall-Bronnec, Blanchet, Jolliet, & Victorri-Vigneau, 2014; Gahr, Freudenmann, Connemann, Hiemke, & Schönfeldt-Lecuona, 2013; Schifano, 2014). This type of addiction has not yet been systematically screened in an older population. Therefore, the present cross-sectional study on hospital patients employs explorative data analysis to address the following four questions: 1. Is the prevalence of substance abuse and dependence in the older German hospital population different from that found in the ESA (Pabst et al., 2013)? 2. How severe is the substance abuse or dependence? 3. Is there evidence that illegal drugs have already reached the older hospital population? 4. Is there evidence of older hospital patients being addicted to nonopioid analgesics?

Methods Setting and Patients This cross-sectional study was conducted from April 17 to October 17, 2013 at the Evangelisches Krankenhaus Castrop-Rauxel, an Academic Teaching Hospital of University of DuisburgEssen. It was approved by the Ethics Commission of the Medical Association Westfalen-Lippe, Northrhine-Westphalia, Germany, and the Medical Department of Westfälische Wilhelms University of Munster, Germany on March 5, 2013. Thus, the study was conducted in accordance with the guideGeroPsych (2016), 29 (1), 17–27

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lines laid down in the current version of the Declaration of Helsinki. A total of 400 patients aged 65 years and older from all departments – with the exception of the intensive care unit – voluntarily participated in a clinical interview. The patients were randomly visited after their third day in hospital on the assumption that the acute crisis that had provoked their admission would be in de-escalation at that time. They had to be able to understand the explanation of the study and voluntarily gave their written informed consent.

Data Collection and Material Data were collected prospectively by personal interview (J. C. C.), which took 30 min on average. Section E – Abuse and Dependence on Psychotropic Substances – of a structured clinical interview (SKID-I) (Saß, Wittchen, Zaudig, & Houben, 2003; Wittchen, Wunderlich, Gruschwitz, & Zaudig, 1997a) was applied. SKID-I is the German successor to SCID-I (First, Gibbon, Spitzer, & Williams, 1996), which was based on DSMIV (APA, 1994). The SKID-I proposes to identify psychiatric symptoms, syndromes, and diagnoses according to the diagnostic criteria of the DSM-IV-TR (APA, 2000) and estimates the severity, development, and course of psychiatric diseases (Wittchen et al., 1997a; Saß et al., 2003). It collects 12-month prevalence rates of dependencies which equal current dependencies (as stated in the Results section). Past dependencies are referred to as “dependencies in remission.” It is meant to be applied by clinically experienced examiners who have the appropriate specialist knowledge (Wittchen et al., 1997a). In Section E subjects are screened for the DSM-IV-TR criteria of substance abuse and substance dependence (Wittchen et al., 1997a; APA, 2000) (see Table 3). According to DSM-IV-TR, physical dependence was specified when (1) tolerance and/or (2) withdrawal symptoms occur (APA, 2000). Psychological dependence can be derived from the occurrence of the following symptoms: (1) The substance is often taken in larger amounts or over a longer period than intended and/or (2) persistent desire or unsuccessful efforts to cut down or control substance use. SKID-I was tested with satisfactory results as to applicability, reliability and efficiency (Wittchen, Zaudig, & Fydrich, 1997b). Objectivity with regard to realization and evaluation is given and can be improved if the detailed prescriptions are obeyed. Retest reliabilities lie largely in the area of r = .60 or higher. Interrater reliabilities, which can be regarded as an important quality standard here, lie between κ (Kappa) = .57 and 1.0 (Braehler, Holling, Leutner, & Petermann, 2002; Strauß & Schumacher, 2005). The SKID-I delivers the highest standard in the area of clinical interview diagnostics (Braehler et al., 2002; Strauß & Schumacher, 2005). (It was ensured that participants had an adequate knowledge of German.) In order to exclude patients suffering from dementia, we used the MiniMental Status Test (Folstein, Folstein, & McHugh, 1975; Kessler, Markowitsch, & Denzler, 2000), where the exclusion was © 2016 Hogrefe

J. C. Cossmann et al.: Substance Addiction of Senior Inpatients

< 25 points. Furthermore, the application of this test allowed the assessment of the participants’ command of German. In addition, the data gathered included information on demographic facts, the patients’ hospital departments, and the medication prescribed. With regard to sedatives and sleeping pills, patients were asked about their use of benzodiazepines and Z-drugs (zaleplon, zolpidem, and zopiclone). As for opioid analgesics, among others the following substances were taken into consideration: codeine, morphine, oxycodone, hydromorphone, hydrocodone, tramadol, tilidine, polamidone, methadone, and tapentadol. Since the drug list of the SKID-I (Wittchen et al., 1997a) does not contain nonopioid analgesics, this class of drugs was added (gabapentin, pregabalin, acetaminophen, metamizole, flupirtine, and nonsteroidal antiinflammatory drugs [NSAID] such as diclofenac and ibuprofen, or COX-2 inhibitors such as rofecoxib).

from April 17 to October 17, 2013 (period of data collection) in these hospital departments: Surgical Ward, Gynecology, Psychiatry, Psychotherapy and Psychosomatic Medicine, Neurology, Geriatrics, Internal Medicine and Gastroenterology. 671 of overall 2,108 inpatients (32%) were visited by chance (data were randomly supplied by central patient admission) and asked to participate. 224 (11%) of them refused, 447 (21%) agreed. One female (.05%) made use of her withdrawal right so that her data were eventually deleted. During data collection, 46 (2%) patients achieved less than 25 points in the Mini-Mental Status Test (Folstein et al., 1975; Kessler et al., 2000), so that they were excluded as well. Four of them did not have adequate knowledge of German. This produced the final sample size of N = 400 patients (63% females), that is, 19% of the population in question. The age spectrum ranged from 65 to 91 years, the average age being 75.45 years (SD = 6.4). Table 1 shows the distribution of sociodemographic variables and of hospital departments in the sample.

Statistical Analyses Results for abuse of and dependence on the different substances are given in form of absolute and relative frequencies. A correlation analysis was carried out using two-sided χ² tests (significance level: .05), i.e., differences in distribution as to categorical data were examined. For example, significant gender-related differences between men and women regarding substance abuse/dependence were analyzed. As for nicotine, 104 of 148 men (70.3%) were dependent, whereas only 68 of 252 women (27%) fulfilled the addiction criteria (χ² = 71.28, p = .000).

Results Sample Figure 1 shows the sample realization of the present cross-sectional study. 2,108 patients aged 65 and older who stayed for at least 4 days (100%, population in question) were treated

Current and Past Substance Addiction 31% (n = 124) of the sample were currently dependent on a substance. The participants who were not currently dependent (n = 276, 69%) can be divided into those who had never been dependent (n = 152, 55%) and those who had a dependence in remission (n = 124, 45%). The ranking of substances regarding 12-month prevalences of current dependency was as follows: At the top there were nicotine and opioid analgesics (n = 41, 10.3% respectively), followed by sedatives and sleeping pills (n = 34, 8.5%) and nonopioid analgesics (n = 24, 6%). Illegal drugs played a minor role. Only one person (.3%) had a current dependence on cannabis. A high percentage of persons with dependence on nicotine (32.5%) and on alcohol (3.8%), respectively, were currently in sustained full remission. There was no current abuse of any substance according to DSM-IV-TR. For abuse in full remission of alcohol (n = 31, 7.8%) and of hallucinogens (n = 1, .3%), see Table 2. Figure 1. Sample-realization of the present cross-sectional study

GeroPsych (2016), 29 (1), 17–27

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Table 1. Distribution of sociodemographic variables and of hospital departments in the sample Variable Gender Nationality

Male

148

37%

252

63%

German

397

99.3%

Unmarried Married Separated

Relationship Children Grandchildren Housing situation

Widowed

148

In relationship

240

60%

Single

160

40%

Yes

350

87.5%

12.5%

Yes

291

72.8%

109

27.3%

Independent in their own apartment

291

72.8%

24.8%

2.5%

317

79.3%

Dog

9.5%

Cat

5.5%

No pets

4.3%

2.3%

287

71.8%

Grade 10

Grade 13, university entrance qualification

No specialist job training

20.8%

295

73.8%

Housewife/-husband

19%

5.5%

114

28.5%

Blue-collar worker

15.8%

White-collar worker

186

46.5%

Civil servant

Self-employed

6.3%

< 1001 EUR

139

34.8%

1001–2000 EUR

139

34.8%

2001–3000 EUR

15.3%

3001–4000 EUR

4001–7000 EUR

3.8%

2 24

.5% 6%

Surgical departments

129

32%

Nonsurgical departments

271

68%

Surgical Ward

123

30.5%

1.5%

Gynecology

GeroPsych (2016), 29 (1), 17–27

1.5%

Without school qualification

No statement

Nonsurgical departments

Simple qualification grade 8

> 7000 EUR

Surgical departments

37%

University degree

Surgical/nonsurgical departments

53.3% .8%

Specialist job training without university qualification

Net income

213

5.3%

Other pets

Current/last vocational occupation

3.8%

Dog and cat

Vocational qualification

.8%

Living in a home

School qualification

Divorced

In their own apartment with supporting network Pets

% (N = 400)

Female Other Marital status

Psychiatry, Psychotherapy and Psychosomatic Medicine

Neurology

17.6%

Geriatrics

13.5%

Internal Medicine

23.6%

Gastroenterology

6.3%

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Table 2. Current (12-month prevalences) and past substance dependencies and substance abuses as absolute and relative portions of the total population (N = 400) Substance dependence

Substance abuse

Substance

Currently (12-month prevalence)

In sustained full remission*

Currently (12-month prevalence)

In sustained full remission*

Nicotine

41 (10.3%)

130 (32.5%)

0 (0%)

Alcohol

12 (3%)

15 (3.8%)

0 (0%)

31 (7.8%)

Sedatives and sleeping pills

34 (8.5%)

11 (2.8%)

0 (0%)

Opioid analgesics

41 (10.3%)

2 (.5%)

0 (0%)

Nonopioid analgesics

24 (6%)

2 (.5%)

0 (0%)

Cannabis

1 (.3%)

0 (0%)

Stimulants

0 (0%)

Opiates (Heroin)

0 (0%)

Cocaine

0 (0%)

Hallucinogens

0 (0%)

1 (.3%)

Inhalants

0 (0%)

Polytoxicomania

0 (0%)

Note. *None of the criteria for dependence or abuse were fulfilled at any time over a period of 12 months or longer (translated from Wittchen et al., 1997a, p. 51).

Nicotine 172 (43%) subjects were dependent on nicotine (41 currently dependent, 130 in sustained full remission, one in sustained partial remission). They had started smoking on average at an age of 20.73 years (SD = 6.93). Dependence duration averaged 33.55 years (SD = 16.22). Severity of dependence was as follows: 46 (26.7% of the nicotine dependents) had a mild form, 89 (51.7%) a moderate form, and 37 (21.5%) fulfill the criteria for severe dependence. Most of those who were currently dependent showed symptoms of physical as well as psychological dependence (see Table 3). Men (n = 104, 70.3%) were significantly more often dependent than women (n = 68, 27%) (χ² = 71.28, p = .000). There was a significant correlation between nicotine dependency and marital status (χ² = 11.22, p = .024); the relatively lowest number of dependent smokers was found among widowed seniors (n = 48, 32.4%). Those who live in a relationship (n = 116, 48.3%) were significantly more frequently affected by nicotine dependency than singles (n = 56, 35%) (χ² = 6.96, p = .008). There were also significant differences regarding the housing situation: Inpatients living in a nursing facility were relatively more often affected (n = 6, 60%) (χ² = 9.35, p = .009). There was a significant correlation between nicotine dependency and vocational qualification (χ² = 7.77, p = .021). Different from those who had no vocational qualification (n = 27, 32.5%) as well as those who had received specialist job training (n = 131, 44.4%), the majority of university graduates were dependent (n = 14, 63.6%). There was also a significant correlation between the current/last vocational occupation and nicotine dependency (χ² = 39.38, p = .000). While there were majorities of nicotine dependents among the selfemployed (n = 14, 56%), civil servants (n = 7, 58.3%), and bluecollar workers (n = 38, 60.3%), there were fewer smokers © 2016 Hogrefe

among white-collar workers (n = 91, 48.9%) and housewives (n = 22, 19.3%). Differences in the distribution regarding the affiliation to hospital departments were significant (χ² = 15.65, p = .016). In contrast to Geriatrics (n = 14, 25.9%) and Gynecology (n = 0, 0%), this revealed that the number of nicotine dependents was relatively high in the departments of Neurology (n = 38, 54.3%), Gastroenterology (n = 12, 48%), Psychiatry, Psychotherapy and Psychosomatic Medicine (n = 13, 46.4%), and Internal Medicine (n = 44, 46.8%).

Alcohol 31 persons (7.8%) fulfill the criteria for an abuse of alcohol in sustained full remission. On average abuse started at an age of 30.29 years (SD = 14.12), the duration averages 11.44 years (SD = 10.36). There are significant correlations between alcohol abuse and sex (χ² = 52.46, p = .000). Men (n = 30, 21.9%) are more often affected than women (n = 1, .4%). Furthermore, there is a significant correlation between alcohol abuse and vocational qualification (χ² = 9.23, p = .01). There is less alcohol abuse among seniors without vocational qualification (n = 1, 1.3%) than among those who are qualified (n = 30, 10.2%). As regards the current/last vocational occupation there is alcohol abuse only among blue (n = 10, 17.2%) and white collar workers (n = 17, 10.1%) as well as self-employed people (n = 4, 16.7%) (χ² = 20.01, p = .000). 27 subjects (6.8%) are dependent on alcohol (12 currently dependent, 15 in sustained full remission). Dependence onset averages at age 41.81 (SD = 11.76), mean duration is 14.17 years (SD = 11.76). Of all 27 subjects, 8 (29.6%) show a mild form, while 9 (33.3%) have a moderate one, and 10 (37%) have a severe dependence. Currently, 8 (67%) are physically dependGeroPsych (2016), 29 (1), 17–27

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Table 3. Profile and severity of current dependencies (12-month prevalences)* Symptom**

Nicotine (N = 41)

Alcohol (N = 12)

Sedatives/sleeping pills (N = 34)

Opioid analgesics (N = Nonopioid analgesics 41) (N = 24)

N = 32 (78%)

N = 8 (66.7%)

N = 18 (52.9%)

N = 31 (75.6%)

N = 21 (87.5%)

N = 13 (31.7%)

N = 8 (66.7%)

N = 31 (91.2%)

N = 30 (73.2%)

N = 10 (41.7%)

N = 41 (100%)

N = 11 (91.7%)

N = 23 (67.6%)

N = 21 (51.2%)

N = 19 (79.2%)

N = 40 (97.6%)

N = 12 (100%)

N = 32 (94.1%)

N = 37 (90.2%)

N = 22 (91.7%)

N = 33 (80.5%)

N = 12 (100%)

N = 9 (26.5%)

N = 16 (39%)

N = 7 (29.2%)

N = 0 (0%)

N = 6 (50%)

N = 2 (5.9%)

N = 6 (14.6%)

N = 4 (16.7%)

N = 25 (61%)

N = 10 (83.3%)

N = 2 (5.9%)

N = 13 (31.7%)

N = 6 (25%)

Mild

N = 11 (26.8%)

N = 3 (25%)

N = 25 (73.5%)

N = 28 (68.3%)

N = 19 (79.2%)

Moderate

N = 20 (48.8%)

N = 1 (8.3%)

N = 7 (20.6%)

N = 9 (22%)

N = 3 (12.5%)

Severe

N = 10 (24.4%)

N = 8 (66.7%)

N = 2 (5.9%)

N = 4 (9.8%)

N = 2 (8.3%)

Profile

Severity***

Note. *The numbers in parentheses specify the percentages of individuals who fulfill the given symptom of dependence. **according to DSM-IV-TR (APA, 2000): 1: tolerance; 2: withdrawal symptoms; 3: the substance is often taken in larger amounts or over a longer period than was intended; 4: persistent desire or unsuccessful efforts to cut down or control substance use; 5: a great deal of time is spent in activities necessary to obtain the substance, use the substance, or recover from its effects; 6: important social, occupational, or recreational activities are given up or reduced because of substance use; 7: the substance use is continued despite knowledge of having a persistent or recurrent physical or mental problem that is likely to have been caused or exacerbated by the substance. Substance dependence is diagnosed if three (or more) of these symptoms occur within a 12-month period. Physiological dependence is diagnosed if the symptoms 1 and/or 2 are present. Psychological dependence is derived from the symptoms 3 and/or 4. ***according to SKID-I (Wittchen et al., 1997a) mild: presence of 3 symptoms (or slightly above) as well as minor impairment of occupational performance, social relationship skills and activities; moderate: between mild and severe; severe: symptoms clearly above 3 as well as profound impairment of occupational performance, social relationship skills and activities.

ent, and all of them have the symptoms of psychological dependence (see Table 3). There are significant correlations between dependence on alcohol and affiliation to hospital departments (χ² = 57.01, p = .000). In the Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, a relatively high number of patients (n = 12, 42.9%) fulfill the dependency criteria. Accordingly, there are significant correlations between alcohol dependency and affiliation to nonsurgical and surgical hospital departments (χ² = 4.27, p = .039). Inpatients in nonsurgical departments (n = 23, 9.3%) are significantly more affected than those in surgical ones (n = 4, 3.3%).

Sedatives and Sleeping Pills 45 patients (11.3%) are dependent on sedatives and sleeping pills (34 currently dependent (Figure 2A), 11 in sustained full remission (Figure 2B)). The mean onset of dependence is 60.13 years (SD = 16.57), the duration averages 9.68 years (SD = 11.87). Of all 45 subjects, 30 persons (66.7%) show a mild form, whereas 12 (26.7%) have a moderate one, and 3 (6.7%) suffer from a severe dependence. The vast majority of current dependents show physical as well as psychological symptoms (see Table 3). Some 28 have a dependence on benzodiazepines (currently: 17, in sustained full remission: 11), 17 are dependent on Z-drugs (all currently dependent) (Figure 2). There are significant correlations between dependence on sedatives and sleeping pills, on the one hand, and sex on the other (χ² = 8.04, p = .005). Women (n = 37, 14.7%) are more often affected than men (n = 8, .7%). GeroPsych (2016), 29 (1), 17–27

Figure 2A. Current DSM-IV-TR (Diagnostic and Statistical Manual of Mental Disorders, Vol. 4, Text Revision) dependence on sedatives and sleeping pills. Number of respective dependencies is shown directly above the pie chart.

Figure 2B. DSM-IV-TR dependence (current and in remission) on sedatives and sleeping pills. Number of respective dependencies is shown directly above the pie chart.

J. C. Cossmann et al.: Substance Addiction of Senior Inpatients

Moreover there is a significant correlation between the dependency on these drugs and the affiliation to hospital departments (χ² = 24.35, p = .000). In the Departments of Gastroenterology (n = 7, 28%), Psychiatry, Psychotherapy and Psychosomatic Medicine (n = 7, 25%) as well as Internal Medicine (n = 16, 17%) there are significantly higher numbers of patients who fulfill the criteria for a dependency on sedatives and sleeping pills.

Opioid Analgesics 43 subjects (10.8%) have a dependence on opioid analgesics (41 currently dependent, 2 in sustained full remission). Average beginning of dependence was at age 70.53 (SD = 8.51), and the mean duration was 5.84 years (SD = 6.29). Severity of dependence is as follows: 28 patients (65.1%) have a mild form, 10 (23.3%) a moderate one, and 5 (11.6%) fulfill the criteria for a severe dependence. Most of them are currently physically as well as psychologically dependent (see Table 3). Female patients (n = 35, 13.9%) are more often dependent on these substances than men (n = 8, 5.4%) (χ² = 7, p = .008). Moreover significantly more patients in surgical hospital departments (n = 20, 15.5%) are affected than patients in nonsurgical ones (n = 23, 8.5%) (χ² = 4.49, p = .034).

Nonopioid Analgesics 28 subjects (7%) fulfill the criteria for a dependence on nonopioid analgesics (24 currently dependent (Figure 3A), 1 is in short full remission, 2 in sustained full remission, and 1 in sustained partial remission (Figure 3B). Dependence onset averages at age 65.11 (SD = 16.76), mean duration is 10.36 years (SD = 12.86). Of all 28 persons, 21 (75%) have a mild form, 5 (17.9%) a moderate one, and 2 (7.1%) have a severe dependence. They are all physically dependent, and most of them show symptoms of psychological dependence (see Table 3). There is a significant correlation between a dependence on these drugs and the possession of pets (χ² = 9.66, p = .047). Seniors who have pets (n = 4, 4.8%) are less often affected than those who live without pets (n = 24, 7.6%). However, this result may be based on a statistical artefact stem-

Figure 3B. Current and past (in remission) DSM-IV-TR dependencies on nonopioid analgesics: Single and combined substances ingested. Number of respective dependencies is shown directly above the pie chart.

ming from the small sample size. The distribution of the various nonopioids is shown in detail in Figure 3A and Figure 3B. Whereas one pure dependence on gabapentinoids as well as on acetaminophen was found, NSAID and metamizole were involved in the majority of cases (see Figure 3B).

Profile and Severity of Current Dependencies Table 3 shows the profile and severity of current dependencies according to SKID-I (Wittchen et al., 1997a). As expected, dependents on psychotropic substances (nicotine, alcohol, sedatives, and opioid analgesics) show a typical mixture of symptoms indicating both physical and psychological dependence features. This was not significantly different in older adults who were dependent on nonopioids (see Table 3). The social dimensions (symptoms 5 and 6 in Table 3) and harm (symptom 7 in Table 3) of dependence, however, differed considerably between nicotine/alcohol and prescription drugs (sedatives/opioid analgesics). The nonopioids were approximately level with prescription drugs regarding symptoms 5 to 7 (see Table 3). Dependence on alcohol and nicotine was severe to moderate in the majority of cases. The severity of the dependencies on prescription drugs and nonopioid analgesics was largely mild (see Table 3).

Discussion

Figure 3A. Current DSM-IV-TR dependence on nonopioid analgesics: Single and combined substances ingested. Number of respective dependencies is shown directly above the pie chart.

This cross-sectional study (63% females) reveals that a considerable portion (31%) of the older inpatients in a German general hospital is dependent on psychoactive substances (12month prevalence), in this sample mostly nicotine (10.3%) and opioid analgesics (10.3%). Notably, 6% of the sample is dependent on nonopioid analgesics. In most cases the severity of their dependence is mild to moderate according to SKID-I (Wittchen et al., 1997a). Illegal drugs have not yet reached the older hospital population. GeroPsych (2016), 29 (1), 17–27

Comparison with Similar Populations A chart review (2003–2005, N = 46911) of a German psychiatric hospital network revealed a substance use disorder according to ICD-10 in 50.7% of the inpatients, which declined to 22.3% in the group of people older than 64 years (Wetterling & Kugler, 2006). Alcohol dependence (3.9%) was followed by benzodiazepine dependence (2.3%) in the group > 64 years. Abuse of alcohol and benzodiazepines was found in 1.5% and .5% of this population. Benzodiazepine-induced disorders were significantly more frequent in female (3.5%) than male (1%) inpatients > 64 years; illegal drugs were negligible in this population (.4%) (Wetterling & Kugler, 2006). A more recent chart review of the same hospital network described an abuse of benzodiazepines (65.1%), Z-drugs (17.4%) and analgesics (19.5%) in 110 (8.7%) of 1266 patients > 64 years, who had been admitted to a mental hospital due to psychiatric morbidity (Wetterling & Schneider, 2012). Both, opioid analgesics (N = 6) and, remarkably, NSAID (N = 6, most often ibuprofen) were described as being abused (Wetterling & Schneider, 2012). Although these studies were carried out in a more homogeneous population (psychiatric inpatients), they support the occurrence of a significant portion of addicted older people, mostly addicted to benzodiazepines and analgesics (Wetterling & Kugler, 2006; Wetterling & Schneider, 2012). In adult patients < 65 years old who were treated in a German general hospital, current incidence rates for dependencies on benzodiazepines/their equivalents and painkillers were found to be 1.3% to 2.6% (Fach, Bischof, Schmidt, & Rumpf, 2007). Most likely, the increasing chronic morbidity and functional impairment with aging may be the factors that account for the larger prevalence rates of these dependencies found in the present study (Kalapatapu & Sullivan, 2010; Simoni-Wastila & Yank, 2006; Wolter, 2011).

Profile and Severity Table 3 shows that most people who are dependent on a substance confirm the symptoms of physical and psychological dependence. Alcohol and nicotine dependencies were most often rated to be moderate to severe, whereas the dependence on prescription drugs was generally mild. If we take a closer look at nonopioid analgesics, these drugs to may have the potential for physical and psychological dependency (many of the surveyed dependent seniors show symptoms no. 3 (79.2%) and 4 (91.7%), see Table 3). Most of those dependent on these substances have a mild form (79.2%). We are not aware of animal experiments that demonstrate any rewarding or reinforcing features of nonopioid analgesics. In humans, an addictive potential of ibuprofen (Etcheverrigaray et al., 2014), flupirtine (Gahr et al., 2013) and gabapentinoids (Schifano, 2014) is currently a matter of debate. We found only one case of dependence on gabapentinoids. In most cases, GeroPsych (2016), 29 (1), 17–27

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NSAID and metamizole were involved, which, surprisingly, also produced features of psychological dependence, although these substances are known to be nonpsychotropic. Of course, reinforcing properties of analgesics can be assumed to arise generally from a compensation for pain. Further studies should analyze the role of “pseudodependence” (due to undertreatment of pain, Schnoll & Weaver, 2003) and overprescription of painkillers in patients dependent on nonopioids. With regard to metamizole prescriptions, an increase of 266% was observed from 2011 to 2012 in Germany (Glaeske & Hoffmann, 2014). Unfortunately, metamizole and NSAID were not included in the only study that focused on the inappropriate prescription of medication in old age (Leikola, Dimitrow, Lyles, Pitkälä, & Airaksinen, 2011).

What Does this Study Add? This study provides novel insights into the 12-month prevalence and severity of the full spectrum of DSM-IV-TR substance abuse and dependence among older hospitalized patients using a structured face-to-face interview. It draws attention to the 12month prevalence of dependencies of seniors not only on opioid analgesics, but also on nonopioid analgesics. It confirms the gender effects in older patients dependent on alcohol (Holroyd & Duryee, 1997; Moos et al., 2009; Weyerer et al., 2009; Simoni-Wastila & Yank, 2006) or benzodiazepines (Holroyd & Duryee, 1997; Simoni-Wastila & Yank, 2006; Wetterling & Kugler, 2006; Wetterling & Schneider, 2012). It provides further information about the course of addiction into advanced age. Thus, dependencies on sedatives/sleeping pills and analgesics are demonstrated to increase in late life. Some 75% of the older adults with dependence on sedatives/sleeping pills were dependent at the time of data collection. Of the older adults with dependence on opioid and nonopioid analgesics 95% and 86% were currently dependent, respectively. The “earlier” dependencies on nicotine and alcohol had been overcome in the majority of the affected cases (76% and 56% of the patients with dependence on nicotine and alcohol live in full remission, respectively). It is noteworthy that the 12-month prevalence of nicotine dependence in German older hospital inpatients is high and no different from that in the overall German population (Pabst et al., 2013). The high 12-month prevalences of dependencies on analgesics and sedatives/sleeping pills in older hospital patients deserve further detailed investigation.

Representativeness of the Study The Evangelisches Krankenhaus Castrop-Rauxel is an Academic Teaching Hospital of the University of Duisburg-Essen which provides medical care for a provincial town in the Ruhr Area in Northrhine-Westphalia. Its departments (Surgical Ward, Gynecology, Psychiatry, Psychotherapy and Psychosomatic Medi© 2016 Hogrefe

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cine, Neurology, Geriatrics, Internal Medicine, Gastroenterology) address the basal requirements of general healthcare. Most of the participants were inpatients from the Surgical Ward (n = 123, 30.5%) and Internal Medicine (n = 94, 23.6%). It can be expected that the distribution pattern of patients here is representative of small-town hospitals. Nearly every third inpatient of the population in question could be randomly selected to participate in this study pointing to a moderate representativeness of the results for seniors staying at a German general hospital. Remarkably, 12-month prevalence rates of dependence on nonprescription drugs (alcohol and nicotine) were in the range of those determined in the general representative German adult population (Pabst et al., 2013). The rates of dependence on prescription drugs were considerably higher than those in the general German adult population (Casati, Sedefov, & Pfeiffer-Gerschel, 2012; Pabst et al., 2013) as could be expected from the concomitant functional impairment and morbidity of older hospitalized patients. It is difficult to further assess the generalizability of the study findings since, to the best of our knowledge, this is the first structured face-to-face interview (basing on an operationalized diagnostic classification system) of older patients concerning substance addiction. Thus, a valid statement about the constancy of our hospital population with other elderlies across Germany or outside of Germany cannot be given. However, the 12-month rates of nicotine and alcohol dependence of the presented hospital population were in the ranges of those estimated for elderlies in large international epidemiological studies (ABOS, 2008; Arndt & Schulz, 2015Chapman & Wu, 2015a; Chen et al., 2006; Dube & Wu, 2015; Gum et al., 2009; NLAES, 1998; Pilowski & Wu, 2015; Regier et al., 1990; SAMHSA, 2014; Storr et al., 2015).

not be generalized with regard to the entire age group of the population. A first trend, however, is demonstrated.

Limitations

Declaration of Conflicts of Interest

The study does not contain data on diagnoses at admission. Furthermore, a considerable number of patients (n = 224) refused to participate in the study. Since no informed consent is given, no specific data about these patients are available. The same applies to persons in the population in question who were not asked to take part. Presumably, some of those who refused to participate suffered from the problems in question, to that the term “substance-induced psychiatric diseases” may have had a deterring effect here, because they were afraid of moral devaluation. Likewise, a number of the people interviewed (which cannot be quantified) may perhaps have given some of their answers according to social desirability, leading to an underestimation of overall prevalence. It can also be regarded as a serious limitation that the study depends exclusively on participants’ oral statements. Objective measures such as blood and/or urine screenings were not applied. Furthermore, since the participants of this study are hospital inpatients, results can© 2016 Hogrefe

Future Perspectives The inclusion of the older adult population is strongly recommended for further nationwide surveys on substance use disorders. The operationalized addiction criteria and symptoms of ICD-10 (Dilling, Mombour, & Schmidt, 2004), DSM-IV-TR (APA, 2000), and DSM-V (APA, 2013) await further study to test their validity on diagnosing and estimating the severity of substance use disorders in older adults, given their special biopsycho-social factors including multiple comorbidities and other functional impairments (Arndt & Schulz 2015; Han et al., 2009; Kalapatapu & Sullivan, 2010; Simoni-Wastila & Yank 2006; Wilson et al., 2015; Wolter, 2011).

Conclusion Approximately one third of the older inpatients of an urban German general hospital seem to be dependent on substances. Their 12-month prevalence rates of nicotine and alcohol dependence were found to be in the range of those of the German people in general as known for the under 65-year-old age group. A fifth of the older inpatients were found to be dependent on prescription drugs (analgesics and sedatives), surprisingly also on nonopioid analgesics. The identification and management of addiction disorders should be considered in the basic geriatric assessment.

The authors declare that no conflicts of interest exist.

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Wetterling, T., & Schneider, B. (2012). Prescription drug abuse in elderly patients. Psychiatrische Praxis, 39, 275–279. Weyerer, S., Schäufele, M., Eifflaender-Gorfer, S., Köhler, L., Maier, W., Haller, F., & Riedel-Heller, S. G. (2009). At-risk alcohol drinking in primary care patients aged 75 years and older. International Journal of Geriatric Psychiatry, 24, 1376–1385. Whitcup, S. M., & Miller, F. (1987). Unrecognized drug dependence in psychiatrically hospitalized elderly patients. Journal of the American Geriatrics Society, 35, 297–301. Wilson, D., Jackson, S., Crome, I. B., & Rao, R. T. (2015). Comprehensive geriatric assessment and special needs of older people. In I. B. Crome, L.-T. Wu, R. T. Rao, & P. Crome (Eds.), Substance use and older people (pp. 173–191). Chichester, UK: Wiley-Blackwell. Wittchen, H.-U., Wunderlich, U., Gruschwitz, S., & Zaudig, M. (1997a). SKID-I Strukturiertes Klinisches Interview für DSM-IV. Achse I: Psychische Störungen. Interviewheft [SKIK-I structural clinical interview for DSM-IV. Axis I: Mental disorders. Interview booklet]. Göttingen: Hogrefe. Wittchen, H.-U., Zaudig, M., & Fydrich, T. (1997b). SKID Strukturiertes Klinisches Interview für DSM-IV Achse I und II: Handanweisung [SKID structural clinical interview for DSM-IV Axis I and II: Manual]. Göttingen: Hogrefe. Wolter, D. K. (2011). Sucht im Alter – Altern und Sucht. Grundlagen, Klinik, Verlauf und Therapie [Addiction in the elderly – Aging and addiction. Basics, clinical findings, process, and therapy]. Stuttgart: Kohlhammer. Wu, L.-T., & Blazer, D. G. (2011). Illicit and nonmedical drug use among older adults: A review. Journal of Aging and Health, 223, 481–504. Manuscript submitted: 04.08.2015 Manuscript accepted after revision: 08.10.2015 Udo Bonnet Department of Psychiatry Psychotherapy and Psychosomatic Medicine Evangelisches Krankenhaus Castrop-Rauxel Grutholzallee 21 44577 Castrop-Rauxel Germany Tel. +49 2305/1022858 Fax +49 2305/1022860 udo.bonnet@uni-due.de

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Recommendations and clinical tools for assessing and treating heart disease

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Full-Length Research Report

Deficits in Selective Attention Alter Gait in Frail Older Adults Véronique Cornu1,2, Jean-Paul Steinmetz1,3, and Carine Federspiel1,3 1

Department of Research and Development, ZithaSenior Luxembourg

Luxembourg Centre for Educational Testing (LUCET), University of Luxembourg

Center for Memory and Mobility (CeM2), Michel Rodange Luxembourg

Abstract. A growing body of research demonstrates an association between gait disorders, falls, and attentional capacities in older adults. The present work empirically analyzes differences in gait parameters in frail institutionalized older adults as a function of selective attention. Gait analysis under single- and dual-task conditions as well as selective attention measures were collected from a total of 33 nursing-home residents. We found that differences in selective attention performances were related to the investigated gait parameters. Poorer selective attention performances were associated with higher stride-to-stride variabilities and a slowing of gait speed under dual-task conditions. The present findings suggest a contribution of selective attention to a safe gait. Implications for gait rehabilitation programs are discussed. Keywords: aging, selective attention, gait, falls

Introduction The risk of falling is likely to be increased in older adults, given the amplification of gait abnormalities as people grow older (Bridenbaugh & Kressig 2011; Rubenstein, 2006; Verghese, Holtzer, Lipton, & Wang, 2009). Several gait parameters have been identified as playing a crucial role in predicting falls and the occurrence of concurrent negative health outcomes and mortality (Rubenstein, 2006; Van Kan et al., 2009; Yogev-Seligmann, Hausdorff, & Giladi, 2008). Gait speed has been shown to reliably predict falls (Van Kan et al., 2009) and figures as a potential biomarker for frailty in general (Van Kan et al., 2008). In addition to gait speed, gait variabilities have proved to be instabilities in the gait cycle (i.e., strides) in dual-task situations and have been related to the occurrence of falls in older adults (Beauchet & Berrut, 2006; Hausdorff, Rios, & Edelberg, 2001; Springer et al., 2006; Hausdorff, 2005). The dual-task paradigm (DT) is an important experimental method to study the concurrent sharing of attentional resources among two competing tasks (i.e., capacity sharing; see Pashler & Johnston, 1998). The DT paradigm provides a window to observe the magnitude of attentional capacities required during walking and represents a method for assessing the role of cognition in controlling posture and gait (Beauchet & Berrut, 2006; Borel & Alescio-Lautier, 2014). The DT method was previously suggested as being sensitive enough to detect gait disorders and to predict future falls in older adults (Lundin-Olsson, Nyberg, & Gustafson, 1997; Beauchet et al., 2009). More generally, the association between noncognitive and cognitive functions in older adults may result from the fact that sensory processes © 2016 Hogrefe DOI 10.1024/1662-9647/a000137

decline with age (Zanto & Gazzaley, 2014) requiring additional attentional resources (see Baltes & Lindenberger, 1997). This results in seemingly simple sensory tasks increasing the cognitive demands in older adults (Baltes & Lindenberger, 1997). Interestingly, this increased cognitive demand does not seem to be restricted to simple sensory processes (hearing, vision), but has been found to relate to walking as well. Although walking has long been considered an automatic motor task, today it is generally acknowledged that walking is a complex task requiring high-level cognitive input (e.g., Yogev-Seligmann et al. 2008; Allali et al. 2008; Amboni, Barone & Hausdorff, 2013). Data from several research groups (Borel & Alescio-Lautier, 2014; Giordani & Persad, 2005) suggest that subtle changes in motor control and sensory feedback reduce the level of automaticity of the walking process and engender by this a need for cognitive supervision. Hausdorff and colleagues demonstrated that executive functions are critical for walking (Hausdorff, Yogev, Springer, Simon, & Giladi, 2005), while Yogev-Seligman and colleagues (2008) amplified more precisely the role of attentional resources during walking. It can thus be concluded that, in older adults, walking requires more attentional resources than in younger adults, with older adults being more likely to reach their attentional capacities in DT situations resulting in worse performances in at least one of the two tasks (Borel & Alescio-Lautier, 2014). Relatively little is known about potentially different attentional components implicated in the walking process, although selective attention was previously reported to be less effective in older subjects (Borel & Alescio-Lautier, 2014). In the present research, we therefore more thoroughly investigate selective attention as one specific attentional component implicated in GeroPsych (2016), 29 (1), 29–36

V. Cornu et al.: Deficits in Selective Attention and Gait

the walking process. Selective attention requires selecting and focusing on relevant information while inhibiting distracting irrelevant information. This component is suggested to be negatively affected by the aging process and thus less effective in older adults compared to younger adults, given compromised inhibition in the old group (McDowd & Filion, 1992; Persad, Abeles, Zachs, & Denburg, 2002; Zanto & Gazzaley, 2014). We thus hypothesize that, with the chosen DT paradigm, participants with more proficient selective attention performances concurrently demonstrate (1) a higher gait speed and (2) lower stride time and stride length variabilities under dual-task situations compared to participants with lower selective attention performances. With the present research, we thus aim at increasing the amount of data available on the influence of cognitive variables on well-defined gait parameters previously shown to be critical for a safe and stable gait.

Methods Participants A total number of 33 nursing home residents (24 females) with a mean age of 83.7 years (SD = 6.2) participated in the study. Eligibility criteria were a Mini Mental State Examination score of 23 (a similar cutoff score has been used by other research teams, e.g., Dubost et al., 2006; Kressig et al., 2004; O’Halloran et al., 2011), ability to walk without an ambulatory aid for more than 10 meters, and no substantial visual or hearing impairments unless corrected. Our participants were functionally independent (see Barthel Index, Table 1), whereas 69% of participants made regular use of an ambulatory walking aid in everyday life. The study was approved by the National Research Ethics Committee and written consent was obtained from all participants. Table 1. Sample characteristics, N = 33 Characteristics Age (years)

83.7 ± 6.2

MMSE (max. 30)

27.6 ± 1.9 a

Grip strength maximal (kg ) Barthel Index (max. 100)

18.3 ± 7.4 92.2 ± 11.0

Get Up and Go Test (s)

19.7 ± 7.3

Falls during the last 6 months (no.)

0.65 ± 1.3

GDS-4

0.69 ± 0.9

Walking aid in everyday life (in%)

68.8

Fear of falling (in %)

40.6

Note. All values are means ± SD unless otherwise stated. GDS-4 = Geriatric Depression Scale, 4 item version [25]. agrip strength available for 30 participants; bBarthel index available for 32 participants.

GeroPsych (2016), 29 (1), 29–36

Test of Selective Attention Selective attention was assessed using a nonverbal paper-pencil test developed to measure attention performances in geriatric settings (Alterskonzentrationstest [AKT]; Gatterer, 2007). The test material consists of 55 upturned and downturned semicircles in black ink randomly arranged on a white sheet of paper, with one target cue presented at the top of the same sheet. Of the 55 semicircles, 20 are target cues with the remaining 35 representing distracters. Subjects are instructed to identify and cross out as quickly as possible and without errors all semicircles similar to the target. Prior the testing situation, participants completed a sample trial to ensure that each participant had fully understood the instructions and the handling of the material. Participants were tested in a quiet and well-lit room with the experimenter present in the same room. The AKT has excellent psychometric properties with Spearman-Brown split-half reliabilities ranging from r = .89 to .99 (Gatterer, 2007). Dependent variables are the Percentage of Errors (percentage of distracters erroneously crossed out) and the Time to Complete the test (in s). Both outcome variables were included in the present study as deficits in selective attention may result in (1) an increased percentage of errors and/or (2) longer times to complete the test. In our sample, both variables Percentage of Errors and the Time to Complete were correlated significantly (ρ = .40, p = .02).

Gait Analyses Gait analyses were performed according to the European Guidelines (Kressig & Beauchet, 2006) using a 518 cm long GAITRite® walkway (CIR Systems Inc., Clifton, NJ, USA). The GAITRite® system allows a rapid, reliable, and valid assessment of spatio-temporal gait parameters (Bilney, Morris, & Webster, 2003; Webster, Wittwer, & Feller, 2005). Two gait conditions were assessed: (1) normal walking, where participants were instructed to walk at their habitual speed (single task [ST] walking); and (2) normal walking, where participants were instructed to count backwards by twos out loud starting from a predefined number (DT walking) (condition adopted from the Basel motor-cognition dual-task paradigm; Theill, Martin, Schumacher, Bridenbaugh & Kressig, 2011). Under the DT condition no specific instructions on prioritization were given. Participants were instructed to perform two walks per condition, aiming at increasing the precision of the measure. Gait assessment was performed without a walking aid (Schwenk, Schmidt, Pfister, Oster, & Hauer, 2011), whereas participants were free to sit down and rest in between walks at their own convenience. The selection of gait parameters was limited to parameters that have previously been associated with gait dysfunctions and falls among old adults (Taylor, Delbaere, Mikolaizak, Lord, & Close, 2013; © 2016 Hogrefe

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Hausdorff, 2007), with three gait parameters retained for further analysis: velocity (cm/s), coefficient of variation (CoV) of stride time (in%) and CoV of stride length (in%). Stride-tostride variabilities are measured by the coefficient of variation [CoV = (standard deviation/mean) × 100]. Influences of a secondary task (counting backwards) on the performance of a primary task (normal walking) are considered as costs and are quantified by computing the percentage of change in performance between the ST condition and the DT condition ((DT walking – ST walking/ST walking) × 100)). The resulting score is referred to as dual-task costs (DTC).

Table 2. Final group assignment according to individual performances on either one of the two attention variables, percentage of errors and time to complete Attention variables Percentage of errors Time to complete (%) (sec) Individual scores ≤ group median (“Good performers”)

To assess gait differences as a function of selective attention, subjects were assigned either to a group of high selective attention performers or a group of low selective attention performers depending on their individual performances on the AKT. Two different measures of attentional performances are distinguished: the Percentage of Errors variable and the Time to Complete variable. Good attention performances are either reflected by a low percentage of errors and/or by short times to complete the task (Gatterer, 2007). To account for this, we created two groups of attention performances for each of these two variables. Group assignment was performed using the median-split method. Medians were computed for the Percentage of Errors variable and the Time to Complete variable, with Mdn = 0% and Mdn = 60 s, respectively. Participants with performances on the variable Time to Complete lower or equal to the group median were assigned to the AttentionT+ group (good performers), whereas participants with scores higher than the group median were assigned to the AttentionT– group (low performers). A similar approach was chosen to assign participants according to their performance on the Percentage of Errors variable. Participants with performances on the variable Percentage of Errors lower or equal to the median were assigned to the AttentionE+ group (good performers), whereas participants with scores higher than the median were assigned to the AttentionE– group (low performers). Refer to Table 2 for a detailed overview of the assignment of participants to the groups depending on their individual performances on both dependent variables. With regard to the norms of the AKT for the entire standardization sample, our high-performing group regroups people of the 100th percentile rank, whereas people who are low performers on this indicator and commit at least one error are part of percentile rank 43 or lower. Regarding the time variable, the median is 60 s, people who took more than 1 mim to complete the task were grouped as low performers on this indicator. According to the norms, the value of 60 s corresponds to a percentile rank of 48.3. Being this close to 50, the median found for our sample © 2016 Hogrefe

AttentionT+ n = 18

Group characteristics Mdn ≤ 0.0

Individual scores > group median (“Low performers”)

Statistical Analyses

AttentionE+ n = 20

M ≤ 0.0 (SD = 0.0)

Mdn = 60.0

M = 46.7 (SD = 8.4)

AttentionE– n = 13

AttentionT– n = 15

Group characteristics Mdn > 0.01

M = 16.4 (SD = 10.6)

Mdn > 60.0

M = 98.5 (SD = 36.1)

Table 3. Frequency of fallers on nonfallers according to attentional performancea Nonfallers Fallers High performance on both indicator variables 10

High performance on one, low performance on other indicator variable

Low performance on both indicator variables

Note. Information of self-reported falls is missing for one subject.

seems to match the percentile rank of the standardization sample quite well. In addition to the information provided in Table 2, it should be noted that, of the 33 subjects of the present study, 13 showed good performance on both indicators, 8 showed low performance on both indicators, and 12 subjects performed well on one indicator but low on the other. Of these 12 subjects, 7 performed well on the error indicator but took more time, whereas the other 5 subjects worked fast but committed a higher number of mistakes. Further information on the frequency of fallers versus nonfallers per group assignment is shown in Table 3. Prior to analyzing the data, we conducted outlier detection on the entire sample for attention and gait variables using the boxplot labeling rule (Hoaglin & Iglewicz 1987; Hoaglin et al. 1986). Distribution assumptions of gait data were verified by examining values of skewness (range: –0.30 to 2.58) and kurtosis (range: 0.23 to 8.73) and by the KolmogorovSmirnov test. Because of the nonnormal distribution of the data, differences in gait parameters for each group were calculated using the nonparametric Mann-Whitney U test, with p-values < .05 considered as significant. We furthermore report nonparametric effect sizes r (Rosenthal, 1991) for DTC to quantify differences between the respective attention groups. Guidelines for interpreting an effect size are 0.2 for small, 0.5 for moderate, and 0.8 for large changes (Cohen, 1988). GeroPsych (2016), 29 (1), 29–36

V. Cornu et al.: Deficits in Selective Attention and Gait

Table 4. Correlation table of gait and selective attention measures 1 1. AKT – error percentage 2. AKT – time to complete 3. Gait speed – normal walking

.40*

–

.14

–.11

4. Gait speed – dual tasking

–.02

–.29

.81**

5. CoV stride length – normal walking

–.05

.28

–.65**

– – –.54**

–

6. CoV stride length – dual-tasking

.33

.39*

–.41*

–.66**

.39*

7. CoV stride time – normal walking

.17

.10

–.62**

–.55**

.62**

8. CoV stride time – dual-tasking

–

– .40*

.25

.27

–.29

–.64**

.14

.30

–

–.15

–.28

–.21

.33

.21

–.35*

.20

–.52**

10. DTC CoV stride time

.12

.34

.03

–.45**

.28

–.59**

.52**

–.65**

11. DTC CoV stride length

.38*

.07

.29

–.06

–.61**

.45**

–.26

.54**

–.55**

9. DTC gait speed

.76**

–

– – .72**

Note. Values correspond to Spearman rho coefficient. *significant at the .05 significance level, **significant at the .01 significance level.

Results Bivariate Relations Among the Measures of Gait and Selective Attention Correlations between gait parameters and measures of selective attention (percentage of errors, time to complete) were computed (Table 4). We observe a positive, tentatively significant, correlation between the attention variable Percentage of Errors and the stride length variability under the dual-task situation (ρ = .33, p = .06), suggesting that patients with higher stride variabilities demonstrate more attention-related errors. Accordingly, DTC in stride length variability correlate positively and significantly with the Percentage of Errors variable in the attention task (ρ = .38, p = .03). This finding suggests that more attention-related errors are related to a higher relative increase in variabilities under the dual-task situation, compared to the single walking condition. In addition, we observe a positive correlation between the attention variable Time to Complete and the stride length variability under the dual-task situation (ρ = .39, p = .03), suggesting that patients requiring more time to complete the attention task demonstrate accordingly higher variabilities in their stride length. In contrast, no substantial relationship is observed between the variable time to complete and dual-task costs in stride length variability (ρ = .07, p = .70).

Differences in Gait as a Function of Attentional Performances Considering differences on the respective gait parameters between the two attention groups AttentionT+ and the AttentionT–, we observe a significant difference in DTC for gait speed (U = 76, p = .03, r = .37 ). This suggests that patients GeroPsych (2016), 29 (1), 29–36

Figure 1. Dual task costs (DTC) in gait parameters for the good and the low attention performers groups. Figure 1a. Differences in DTC of gait speed. Figure 1b. Differences in DTC of CoV stride time. Figure 1c. Differences in DTC of CoV stride length.

V. Cornu et al.: Deficits in Selective Attention and Gait

Table 5. Means, standard deviations, medians and interquartile range of gait parameters in groups of attentional performance Complete sample

Grouping variable – Time to complete

Grouping variable – Percentage of errors

AttentionT+

AttentionT–

p-value

AttentionE+

AttentionE–

p-value

Gait speed (cm/s) ST

M ± SD Mdn (IQR)

65.9 ± 23.1 61.2 (30.6)

68.0 ± 27.0 68.8 (38.6)

63.3 ± 17.9 57.2 (17.1)

.76

65.3 ± 28.0 56.4 (40.2)

66.8 ± 13.4 63.0 (24.5)

.46

M ± SD Mdn (IQR)

55.9 ± 21.5 52.9 (30.2)

62.1 ± 24.5 61.2 (41.5)

48.4 ± 14.8 45.0 (23.2)

.10

56.8 ± 23.5 47.7 (31.2)

54.5 ± 18.8 54.9 (34.2)

.96

DTC

M ± SD Mdn (IQR)

–13.8 ± 19.0 –15.3 (26.9)

–7.4 ± 15.6 –10.7 (23.4)

–21.5 ± 20.3 –24.4 (29.8)

.03*

–10.3 ± 18.6 –11.0 (30.9)

–19.2 ± 18.9 –18.4 (25.7)

.30

CoV stride time (%) ST

M ± SD Mdn (IQR)

4.9 ± 2.8 4.0 (3.0)

5.1 ± 3.1 4.3 (5.2)

4.7 ± 2.5 4.0 (2.8)

.85

5.0 ± 3.4 3.8 (4.9)

4.7 ± 1.7 4.9 (2.5)

.57

M ± SD Mdn (IQR)

7.6 ± 4.6 6.6 (4.7)

6.3 ± 3.4 6.5 (4.2)

9.1 ± 5.6 6.6 (4.7)

.11

6.5 ± 3.8 6.5 (4.7)

9.3 ± 5.5 6.6 (8.3)

.13

DTC

M ± SD Mdn (IQR)

92.1 ± 153.5 45.1 (164.8)

57.6 ± 108.8 18.9 (116.6)

133.5 ± 190.1 74.1 (216.5)

.15

60.6 ± 91.8 44.3 (153.7)

140.5 ± 212.9 59.4 (180.8)

.28

CoV stride length (%) ST

M ± SD Mdn (IQR)

6.2 ± 4.9 5.2 (4.8)

5.8 ± 4.5 4.0 (5.2)

6.6 ± 4.1 5.6 (4.6)

.26

6.8 ± 4.9 5.4 (5.6)

5.2 ± 2.9 4.1 (4.6)

.68

M ± SD Mdn (IQR)

8.0 ± 4.8 7.1 (5.8)

6.5 ± 4.2 4.9 (5.1)

9.8 ± 5.0 8.3 (7.0)

.02*

7.1 ± 5.0 5.3 (4.6)

9.3 ± 4.4 8.7 (6.6)

.07

DTC

M ± SD Mdn (IQR)

76.1 ± 154.5 41.0 (90.0)

70.3 ± 179.9 –0.1 (101.2)

82.9 ± 123.0 49.1 (187.7)

.29

36.7 ± 108.2 –7.8 (92.8)

136.6 ± 196.4 52.8 (209.5)

.02*

Note. p values based on the Mann-Whitney-U test for independent samples. *significant at the .05 significance level

with reduced attentional capacities demonstrate a greater slowing under the dual-task walking situation compared to a single walking situation (refer to Table 5 for means and medians, respectively). Concerning gait variabilities under dual-task situations, a significant difference between the groups AttentionT+ and AttentionT– may be observed for stride length (U = 200, p = .02, r = .41 ), with the AttentionT+ group demonstrating a lower stride length variability (Mdn = 4.9) than the AttentionT– group (Mdn = 8.3). This suggests that high-attention performers (AttentionT+ group) demonstrate a more stable stride length (lower CoV). Considering differences on the respective gait parameters between the two remaining attention groups, AttentionE+ and AttentionE–, we observe a significant difference in DTC for stride length variability (U = 192, p = .02, r = .40), suggesting that high-attention performers (AttentionE+ group) demonstrate a lower increase in variability during the dual-task walking situation than during the single walking situation. Accordingly, increased stride length variabilities under the dual-task situation are observed in the AttentionE– group (U = 180, p = .07, r = .32). Interestingly, negative median values are observed for both groups of high-attention performers (AttentionT+ and AttentionE+) in DTC of stride length, reflecting lower variabilities in stride lengths during dual-task walking situations compared to the single-task walking situation (Table 4). This finding means that at least half of the subjects with a high-attention perfor© 2016 Hogrefe

mance decrease their variability in the stride length parameter when simultaneously performing a cognitive task. Possible explanations for this finding are discussed in the following section. Please refer to Figure 1 for a graphic illustration of the group differences on the respective variables.

Discussion Gait speed and gait variabilities are important physical parameters that have repeatedly been shown to be related to cognition (Bridenbaugh & Kressig, 2011; Hausdorff et al., 2005; Springer et al., 2006; Theill et al., 2011; Yogev-Seligmann et al., 2008). In the present research, we studied the relationship between one specific cognitive function (selective attention) on the one hand, and three important gait parameters, gait speed, stride length, and stride time variability, on the other hand. We hypothesized that the detrimental influence of a secondary task performed during walking is more pronounced in patients with reduced attentional reserves. This contention was confirmed for some variables, but did not hold true for others. When studying attention by performance-based assessments, two types of scores can generally be distinguished, namely, time to complete the task and percentage of errors performed during the completion of the task. In the present sample, both scores are significantly correlated (p < .02), whereas the size of the correlation (ρ = .40) is indicative for two GeroPsych (2016), 29 (1), 29–36

different subcomponents of selective attention playing a role during the completion of the test. This contention is supported by a differential pattern of relationships of both variables with the respective gait parameters. We observe a significant difference with a moderate sized effect in DTC for stride length variability between subjects with no errors on the attention task (AttentionE+ group) compared to subjects with a mean error score of 16.4 errors (AttentionE– group). No difference between both groups is found for DTC in the remaining two gait parameters, gait speed and CoV stride time. When considering the AKT variable Time to Complete, we furthermore observe a significant difference between both attention groups AttentionT+ and AttentionT–, with high performers (AttentionT+) characterized with lower DTC in gait speed compared to the group of low-attention performers (AttentionT–). Moreover, stride length variabilities (CoV) differ significantly between both groups, with high performers demonstrating a more stable gait under a dual-task condition compared to low performers (Mdn = 4.9 vs. Mdn = 8.3, respectively). In sum, worse selective attention performances are consistently found to be suggestive of increased dual-task costs in all gait parameters. Generally, this suggests that patients with a reduced selective attention performance concurrently demonstrate an increased negative influence of a secondary (cognitive) task performed during walking compared to a single walking condition alone. These effects on DTC differ in size as a function of the grouping variable used (Percentage of Errors, Time to Complete). More precisely and on the one hand, we observed an increased variability in stride length under a dualtask situation in the AttentionE– group (low performers). This suggests that attention assessed by the quality of the performance (correct versus erroneous answers) relates to spatial gait parameters. On the other hand, however, patients characterized by a more slow performance on the attention task (AttentionT– group) demonstrated a lower gait speed under the dual-task walking condition. These findings are indicative that different attentional subcomponents support some gait components more than others. For example, the latter finding is indicative that the speed component of selective attention relates more strongly to temporal gait parameters (gait speed). Moreover, and because gait speed can be considered a valuable indicator of activities of daily living in geriatric patients (Potter, Duncan, & Evans, 1995), this present finding is consistent with previous studies reporting a strong relationship between the Time to Complete variable of the AKT and the activity level of the patient (Gatterer, 2007). To confirm this contention, however, data from larger samples are required. If confirmed, the reported findings would be important to informing cognitive rehabilitative programs. For example, rehabilitation programs focusing on improving selective attention and concentration in older adults would be required to train both speed and quality components of attentional functions, as both are likely to relate to different aspects of functional abilities (gait). Moreover, and no less important, these differential effects between gait paramGeroPsych (2016), 29 (1), 29–36

V. Cornu et al.: Deficits in Selective Attention and Gait

eters and selective attention components underline the important role of the assessment instruments used to study (selective) attention. We found that patients with better attentional performances tentatively have a more stable gait under dual-task situations compared to single walking situations. A possible explanation for this finding might be prioritization. Simultaneous performance of two tasks requiring attention (walking and counting backwards) causes a competition in the cognitive system for the available attentional resources, with healthy older adults unconsciously choosing a “posture first” strategy (Yogev-Seligmann et al., 2008). That is, patients with intact attention capacities allocate more of these resources to the stability of the walking process (avoiding hazards) than to the cognitive task, resulting in lower DTC, less variability, and hence, a safer gait. In line with these findings, another research group report reduced cognitive performances in intact older adults during dual-tasking, with gait speed remaining constant (Theill et al., 2011). The present study has a major limitation. Our sample was relatively small and consisted of old (pre)frail nursing-home residents. Furthermore, we had a vast inclusion criterion regarding the MMST cutoff score (23 and above). It is thus possible that our sample included subjects with mild cognitive impairment. This cutoff score should nevertheless allow for controlling that no subjects with moderate to severe dementia were part of the present sample. This results in a limited generalizability of the present findings to a larger population of (nonfrail) older adults. However, and importantly, an investigation of cognitive and functional abilities in old (pre)frail adults is crucial for designing rehabilitative programs to prevent falls and a further functional decline, with a negative impact on quality of life (Rubenstein, Josephson, & Robbins, 1994). Findings from a previous study investigating the influence of cognitive training on gait are encouraging and warrant these kinds of detailed investigations (Smith-Ray et al., 2015; Steinmetz & Federspiel, 2014).

Conclusion The present findings suggest that selective attention is related to gait stability and is consequently associated to the risk of falling in old (pre)frail nursing-home residents. We demonstrated that DTC in gait speed, stride time variability, and stride length variability differed between subjects with lower attentional performances compared to those with better attentional performances. It could thus be hypothesized that improving selective attention in older adults leads to reductions in gait instabilities and contributes by this to a safer gait with a lower fall risk. The present data extends previous findings on the relationship between gait and cognition by focusing on the role of selective attention in (pre)frail old adults. This is critical as this © 2016 Hogrefe

V. Cornu et al.: Deficits in Selective Attention and Gait

group of old adults is especially vulnerable in developing further negative health outcomes as a consequence of a fall. The present findings are furthermore important in the development of cognitive intervention programs designed to reduce the fall risk in institutional and community-dwelling older adults.

Acknowledgments The authors are grateful to Nissrine Benabad, MSc, for her support during data collection. The authors would like to thank Danielle Lodhi, BA, for proofreading the present manuscript. Furthermore, we would like to thank the participants who kindly agreed to take part in the present study.

Declaration of Conflicts of Interest The authors declare that no conflicts of interest exist.

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people an International Academy on Nutrition and Aging (IANA) Task Force. The Journal of Nutrition, Health & Aging, 13, 881–889. doi 10.1007/s12603-009-0246-z Van Kan, G. A., Rolland, Y., Bergman, H., Morley, J. E., Kritchevsky, S. B., & Vellas, B. (2008). The I. A. N. A. task force on frailty assessment of older people in clinical practice. The Journal of Nutrition Health and Aging, 12(1), 29–37. doi 10.1007/BF02982161 Verghese, J., Holtzer, R., Lipton, R. B., & Wang, C. (2009). Quantitative Gait Markers and Incident Fall Risk in Older Adults. Journal of Gerontology Series A Biological Sciences and Medical Sciences, 64A, 896–901. doi 10.1093/gerona/glp033 Webster, K. E., Wittwer, J. E., & Feller, J. A. (2005). Validity of the GAITRite walkway system for the measurement of averaged and individual step parameters of gait. Gait Posture, 22, 317–321. doi 10.1016/j.gaitpost.2004.10.005 Yogev-Seligmann, G., Hausdorff, J. M., & Giladi, N. (2008). The role of executive function and attention in gait. Movement Disorders, 23, 329–242. doi 10.1002/mds.21720 Zanto, T. P., & Gazzaley, A. (2014). Attention and Aging. In The Oxford handbook of aging (pp. 927–971). Oxford, UK: Oxford University Press. Manuscript received: 21.07.2015 Manuscript accepted after revision: 15.09.2015

Véronique Cornu University of Luxembourg Campus Belval Maison des Sciences Humaines 11 Porte des Sciences 4366 Esch-sur-Alzette Luxembourg veronique.cornu@uni.lu

Full-Length Research Report

Personality and Life Satisfaction Over 12 Years Contrasting Mid- and Late Life Benjamin Tauber1, Hans-Werner Wahl1, and Johannes Schröder2 1

Department of Psychological Aging Research, Heidelberg University, Germany

Department of Geriatric-Psychiatric Research, University Hospital Heidelberg, Germany

Abstract. Both theoretical reasoning and empirical data suggest that personality and well-being have substantial interrelationships. However, more longitudinal evidence is required, and the relationship lacks research attention from a lifespan perspective. We examined the mid-term and long-term interrelations of Neuroticism and Extraversion with life satisfaction in two cohorts from middle and late adulthood, using data from the “Interdisciplinary Longitudinal Study of Adult Development (ILSE).” Multigroup, cross-lagged models reveal personality to be more predictive of life satisfaction than vice versa. Furthermore, an aging effect occurs regarding the relationships between life satisfaction and personality, with life satisfaction being predictive of personality only in the old cohort. Controlling for health weakens the interrelationship. Results add to the understanding of lifespan dynamics among personality and life satisfaction. Keywords: personality, life satisfaction, longitudinal, age effect, adulthood

A close interrelationship of personality and subjective wellbeing seems theoretically plausible and in fact is empirically well documented (e.g., meta-analysis of Steel, Schmidt, & Schultz, 2008). However, to date most of the evidence has been restricted to cross-sectional data; longitudinal evidence typically has not exceeded 4-year observational intervals. Hence, it is still unclear how long-term personality development in adulthood affects change in well-being and vice versa. In particular, longitudinal data targeting the interrelation between personality and life satisfaction in middle versus late adulthood remain scarce. This study contributes to filling this void by analyzing 12 years of personality and life satisfaction change data of the Interdisciplinary Longitudinal Study on Adult Development and Aging (ILSE) (Sattler et al., in press). We restrict the treatment of personality to the traits Neuroticism and Extraversion for two reasons: (1) These traits have been generally revealed at the meta-analytical level (Steel et al., 2008) as highly influential on life satisfaction (Neuroticism: ρ = –.45; Extraversion: ρ = .35) and have also been found to be more strongly associated to life satisfaction than other traits, such as Openness (ρ = .04), Conscientiousness (ρ = .27), or Agreeableness (ρ = .19). (2) Neuroticism and Extraversion have most frequently been considered in the previous literature, when it comes to links between personality and life satisfaction (see again Steel et al., 2008). Hence, when we use the omnibus term personality in what follows, we always mean Neuroticism and Extraversion. © 2016 Hogrefe DOI 10.1024/1662-9647/a000141

Rationale Behind the Personality and Life Satisfaction Intertwine In order to better understand possible links between personality and well-being, we refer to a theoretical framework based on considerations of person-environment fit which also draws on the sociogenomic model suggested by Roberts and Jackson (2008). This perspective predicts that, if a person perceives an enhanced personality-environment fit (e.g., adequate living conditions, etc.), higher life satisfaction will result. More importantly, a person who experiences higher levels of life satisfaction might, in a second step, enhance their endeavor to maximize the congruence of personality and environment demands. Theoretically, these changes might manifest at first in short-term momentary thoughts, feelings, and behaviors; later, they may transition into deep-seated, long-term personality and life-satisfaction developments. Throughout the course of their lives, individuals are confronted with hardships, changing social roles, and other demands, such as establishing and maintaining success at work or mastering family roles. The argument is that coping with stressful events and maintaining a coherent identity and personality requires constant adjustment. Achievement of successful fit is awarded with higher levels of life satisfaction, while an unsuccessful fit corresponds to lower levels, which again may trigger subsequent future processes of personality change. Supporting empirical evidence for this framework GeroPsych (2016), 29 (1), 37–48

comes from studies on self-regulation and adaptation factors: For instance, high Extraversion and low Neuroticism are related to less stress-sensitivity (e.g., Bolger & Schilling, 1991), the application of more adaptive coping styles (Cosway, Endler, Sadler & Deary, 2000), and available opportunities to recruit more and better social support (Russell, Booth, Reed & Laughlin, 1997). Such constellations might foster subsequent positive developments in long-term well-being and, in turn, be perceived as a motivation to readjust one’s personality. High well-being supports positive personality developments by increasing social investment and increased efforts to maximize person-environment fit (e.g., Roberts & Wood, 2006; Specht, Egloff, & Schmukle, 2013).

Empirical Evidence on the Interrelationship of Personality and Life Satisfaction The interrelationship of personality and well-being has been the target of two meta-analyses by DeNeve and Cooper (1998) and Steel et al. (2008). While, in light of what has been said above, DeNeve and Cooper (1998) found surprisingly weak connections (overall correlation = .19), 10 years later, based on newly generated data and more refined analyses, Steel et al. reported approximately twofold higher relationships. Therefore, substantial correlations between personality and life satisfaction are considered to have been established, but unfortunately there are currently few longitudinal studies that target the interrelationship of personality and life satisfaction – and even fewer target at cross-lagged effects. Scollon and Diener (2006) conducted cross-lagged analyses on work and relationship satisfaction with Extraversion and Neuroticism in 1,130 participants over an 8-year interval. Results revealed both traits to be significant longitudinal predictors of work satisfaction (Extraversion: β = .10, p < .001; Neuroticism: β = –.09, p < .01), while work satisfaction had only significant cross-effects with Extraversion (β = .09, p < .01) and no significant role-to-trait effect with Neuroticism. On the other hand, only Neuroticism was a significant predictor of relationship satisfaction (β = –.06, p < .05), while Extraversion was not. Role satisfaction was longitudinally only marginally predictive for Extraversion (β = .05, p = .08) and was not significant in predicting Neuroticism. Specht et al. (2013) used data from the German Socioeconomic Panel (SOEP) to investigate the relationship of 14,718 participants (M = 47.21; SD = 16.28) across 4 years. Combined dual latent change models, comprised of a latent change approach and a latent growth approach, revealed the change correlations of personality and well-being to be moderate to high in magnitude. Furthermore, life satisfaction was more influential for personality change than the other way around. Going further, similar to the Specht GeroPsych (2016), 29 (1), 37–48

B. Tauber et al.: Personality and Life Satisfaction Longitudinally

et al. study, Soto (2014) analyzed data from a large representative sample (n = 16,367, M = 40.39, SD = 18.88). Soto’s latent growth curve models on the interrelationship revealed subjective well-being to also predict personality changes better than the other way round. Soto conducted completely prospective cross-lagged analysis as well and found well-being and personality to predict each other equally well. All in all, longitudinal evidence points to both effects of personality on life satisfaction and vice versa. However, taken as a whole, the current state of evidence is limited, for two primary reasons: First, both the studies by Specht et al. (2013) and Soto (2014) – thus far the most ambitious studies in terms of sophisticated data analysis – were restricted to 4-year observational intervals, which may be a too short period to address the linkage between personality and life satisfaction; in particular, as suggested by the environment-fit and self-regulation perspective introduced above (e.g., Roberts & Jackson, 2008), underlying change processes may operate more slowly and thus only surface across longer time intervals. Moreover, Soto (2014) argues that the mutual prospective effects of personality and life satisfaction on each other might accumulate over time, which also supports the assumption that stronger relationships will be found in longer time intervals (e.g., a decade or longer) compared to rather short observational periods such as observational periods of less than 5 years. Thus, it remains largely an open question of how personality and life satisfaction are crossrelated under the condition of such longer time intervals. Second, to our knowledge, no previous study has addressed the issue of age effects related to the adult lifespan, particularly the later window from mid-adulthood to late adulthood. Addressing this question is important, because old age comes with a range of significant changes in day-to-day life, such as no longer being in the labor force ecosystem and undergoing health and functioning challenges.

Personality, Life Satisfaction, and the Transition from Middle Adulthood to Old Age Here, we rely on a set of elements primarily derived from lifespan concepts of various origins. First, classic theories of human development, such as those of Erikson (1950), as well as more recent theories, such as socioemotional selectivity theory (Carstensen, 2006), all have the common fundamental premise that individuals undergo important motivational changes as they age. More specifically, most lifespan models assume a transition from an outward orientation to a more inward orientation, such as an increased focus on one’s own life story and self-narrative as well as on values such as intimacy. Second, prominent lifespan developmental models, such as the motivational theory of lifespan development (Heckhausen, Wrosch, © 2016 Hogrefe

B. Tauber et al.: Personality and Life Satisfaction Longitudinally

& Schulz, 2010), point to the importance of circumscribed “windows” for goal engagement opportunities as people age. In particular, the opportunity to be engaged in the workforce as a major and decade-long developmental context ends in many countries at around 65 years of age, so that it can no longer serve as a source for life satisfaction and purpose in life considerations. Combined with the increasing aging-related inward focus described above, this may result in stronger referral to “what we have in us,” i.e., what has been wired into our personality. Third, established models on emotional development, such as the strength and vulnerability integration model (SAVI; Charles, 2011), support the notion that older individuals are highly efficient in selecting and maintaining ecologies that best fit their personalities. As SAVI argues, by doing so, older adults maintain and secure positive affect and avoid negative affect. Hence, older adults may be seen as ideal candidates for the ecology-fit perspective as suggested by Roberts and Jackson (2008). Taken together, we assume that the link between personality and life satisfaction would increase as we move from middle adulthood to old age. However, it may be asked whether this assumption proves to be true when health is also considered. Across the lifespan, major and minor health problems emerge and accumulate. This new source of life stress is particularly important, because, when comparing midlife with old age, the probability of facing bodily decline and illness continually increases, limiting developmental possibilities and straining people’s adaptation capacities. Regarding Neuroticism, in accordance with reinforcement sensitivity theory (Gray, 1987), highly neurotic individuals are especially prone to experiencing accumulated health burdens and related stress, and perceive health problems as more severe than emotionally stable people (Matthews, Deary & Whiteman, 2009), which – combined with the age-related aggregation of health burdens – leads to high Neuroticism being a risk factor for life satisfaction in late life (e.g., Wahl, Heyl, & Schilling, 2013). Extraversion, proved to be important for stress buffering, is viewed as a protective factor against influences of life stress and is theoretically related to the dopaminergic system and positive affect (e.g., Gray, 1987; Matthews et al., 2009); people scoring high on Extraversion are better at choosing and using effective coping strategies and have better mental health, making them overall superior at enduring harmful influences of health burdens (e.g., Matthews et al., 2009). Empirically, health proved to be a longitudinal predictor of life satisfaction across many studies (e.g., Gana et al., 2013), but there are also studies that present a somewhat mixed picture of the relationship of personality and health. For example, one study (Chapman, Roberts, Lyness, & Duberstein, 2013) shows Neuroticism and Conscientiousness to be important predictors of subsequent health, while Turiano et al. (2012) found that each of the Big Five traits, except Openness, are important predictors for subsequent self-rated health. Focusing on the reciprocal relationship between health and subsequent personality impacts, Sutin, Zonderman, Ferrucci, and Terracciano © 2016 Hogrefe

(2013) found no evidence for the predictive power of health for personality, while Jokela, Hakulinen, Singh-Manoux, and Kivimäki (2014) observed a rather consistent role of lowered health, related to decreases in Extraversion and increases in Neuroticism. In conclusion, we arrive at the following predictions regarding the relationship of Neuroticism and Extraversion with life satisfaction. First, we expect that the relationship becomes stronger in old age compared to mid-life, because, in light of the arguments described above as well as the person-environment fit model at large, older adults are better to select and maintain best-fitting ecologies. Second, because the need to cope with increasing health burdens becomes increasingly important over the course of one’s life – and particularly in old age – controlling for health should reduce the effect of age on the interrelationship between personality and well-being.

The Present Study The present study examines the longitudinal interrelationships of Neuroticism and Extraversion with life satisfaction. We aim to extend previous insights with three elements: (1) We are in a position to lengthen the time interval of 4 years, commonly used in prior research and also available in our data, to 12 years, which allows us to compare rather short and long-term relationships. Based on this larger dataset, we expect personality and life satisfaction to be more strongly interrelated in their crosspaths in the 12-year interval compared to the 4-year interval. (2) Regarding the impact of the transition from middle to old age for the relationship between personality and life satisfaction, we expect a closer relationship in the period of old age compared to mid-age. (3) Based on the previous findings, we expect that the consideration of health will weaken possible differences in the strength of relationships between personality and life satisfaction from middle adulthood to old age.

Method Study Population and Sample Description Data stem from the Interdisciplinary Longitudinal Study of Adult Development (ILSE) (Sattler et al., in press). The ILSEStudy consists of three finished times of measurement: 1993–1996 (n = 1002), 1997–2000 (n = 896), and 2005–2007 (n = 789). There is an approximately 4-year time interval between measurement occasions 1 and 2 and a 12-year time interval between measurement occasions 1 and 3. The ILSE-participants can be divided in two cohorts by age, born either before WWII (1930–1932; older cohort) or afterward (1950–1952; younger cohort) and two cohorts by residence GeroPsych (2016), 29 (1), 37–48

(Heidelberg/Leipzig, Germany). Data collection was conducted by questionnaires, testing of cognitive abilities, and an extensive medical assessment executed by the study’s trained geriatricians. Further information, such as additional sample characteristics and attrition analyses, have already been compiled and reported (e.g., Sattler et al., in press; Allemand, Zimprich, & Hertzog, 2007).

B. Tauber et al.: Personality and Life Satisfaction Longitudinally

widely recognized as a well-normed, robust, reliable, and valid measure (e.g., Lang & Lüdtke, 2005). Internal consistencies (Cronbach) regarding Neuroticism are .79/.82/.84 at t1/t2/t3, respectively. The respective internal consistencies for Extraversion are .71/.71/.77.

Health

Measures Life Satisfaction Life satisfaction is measured using a 1-item questionnaire. The question targets general satisfaction with life itself at the “precise moment.” Answer options range from 1 = not at all satisfied to 5 = totally satisfied. Life satisfaction provides good longitudinal convergent validity and moderate-to-good discriminant validity (Lucas, Diener, & Suh, 1996). Moreover, single-item measures of life satisfaction are found to cross the frequently cited heuristic of 0.70, indicating acceptable reliability (Lucas & Donnellan, 2012).

Health at t1 was assessed via two separate ratings. First, a selfrating for subjective health was conducted, and participants were asked to rate their personal health perception “at the precise moment.” Answer options on the 6-point Likert scale ranged from 1 = very bad to 6 = very good. Second, an objective health assessment – comprising an anamnesis, a blood analysis, a geropsychiatric assessment, and a medical checkup, conducted by one to two trained study geriatricians – was also available (see Miche, Elsässer, Schilling, & Wahl, 2014, for more details). The professionals aggregated the data and rated the participants’ state of health on 6-point Likert scales. Answer options ranged from 1 = very bad; professional health care is urgent to 6 = very good.

Neuroticism and Extraversion

Statistical Analyses

Neuroticism and Extraversion were assessed using the corresponding subscales of the NEO-Five Factor Inventory (NEOFFI; Costa & McCrae, 1992). The NEO-FFI consists of 60 items (12 per subscale), worded as defining statements that are rated on personal accordance by the participant on 5-grade scales ranging from 1 = strongly disagree to 5 = strongly agree. The questionnaire has proven to have internal and temporal reliability (e.g., Murray, Rawlings, Allen & Trinder, 2003) and is

Utilizing Mplus (Version 6, Muthén, & Muthén, 2011), we constructed multigroup, cross-lagged path models as depicted in Figure 1. The grouping variable was cohort (mid-adulthood/old age). The different personality factors at t1 and t2 (4-year time interval) or t1 and t3 (12-year time interval) were measured latently, using three indicator-parcels of 4 items for each parcel. Item parceling was conducted due to reasons of parsimony. Life satisfaction, being a single-item measurement, was entered Figure 1. Multigroup, cross-lagged path model with two latent variables for personality (N/E = Neuroticism/Extraversion) measured by respectively three item parcels (e.g., par_1) and two manifest variables modeling life satisfaction (LS) (Model 1). Objective and subjective health variables are added as controls (Model 2; with dashed lines). Grouping variable is cohort. Model is either defined by t2 or t3.

GeroPsych (2016), 29 (1), 37–48

B. Tauber et al.: Personality and Life Satisfaction Longitudinally

Table 1. The basic descriptive statistics of the study variables t1

Older cohort

500 (¢ = 47.9%)

449

381

Younger cohort

502 (¢ = 48.2%)

447

408

Older cohort

3.94 (SD = .798)

4.11 (SD = .644)

3.94 (SD = .814)

Younger cohort

3.82 (SD = .837)

3.82 (SD = .790)

3.55 (SD = 1.051)

Population

Life satisfaction

Neuroticism Older cohort

18.63 (SD = 6.88)

18.17 (SD = 6.84)

17.43 (SD = 6.59)

Younger cohort

17.74 (SD = 6.99)

16.27 (SD = 7.14)

16.97 (SD = 7.46)

Extraversion Older cohort

26.51 (SD = 5.67)

26.23 (SD = 5.40)

25.43 (SD = 6.00)

Younger cohort

28.50 (SD = 5.71)

28.27 (SD = 5.56)

27.68 (SD = 5.83)

Health Older cohort, obj.

3.53 (SD = .876)

Older cohort, subj.

3.45 (SD = .912)

Younger cohort, obj.

3.73 (SD = .782)

Younger cohort, subj.

3.53 (SD = .813)

Note. The table displays the participant count in the first two rows and mean values with their associated standard deviations (SD) in the following rows. Measurement times 1, 2, and 3 are abbreviated as t1, t2, and t3. Older cohort = cohort born 1930–1932; younger cohort = cohort born 1950–1952; ¢ = female participants; obj. = objective health; subj. = subjective health.

as a manifest variable. In a following step, the model (Model 1) was enhanced by adding objective and subjective health at t1 as control variables (Model 2) to predict t2 (4-year interval) or, respectively, t3 (12-year interval) of personality and life satisfaction. The logic behind including the health variables only at t1 was to operate with prospective predictors of equal time intervals. Strong measurement invariance was assessed following recommendations by van de Schoot, Lugtig, and Hox (2012). Cutoff criteria of model-fit indices were used following Hu and Bentler (1999).

Results Descriptive Data and Examination of Basic Model-Fit The descriptive statistics of the study variables are given in Table 1. 500 participants of the older cohort and 502 participants of the younger cohort were investigated at t1. At t3, only 381 of the older cohort population remained, while the younger cohort population still consisted of 408 participants. The sex ratio in both cohorts at t1 amounts to roughly 48% women to 52% men. The overall means depict only small changes. Life satisfaction remained stable for the older cohort, but decreased slightly for the younger cohort. The means for both Neuroticism and Extraversion were decreasing, for both cohorts, respectively. There were no remarkable differences in partici© 2016 Hogrefe

pant’s self- and externally rated health (i.e., subjective health, objective health) at t1. The model-fit indices of the multigroup cross-lagged models are shown in Table 2, separated for Model 1 (without health) and Model 2 (with health). The fit indices of the submodels of Model 1 (E/N, 4/12) ranged from .027 to .055 for the RMSEA, from .975 to .992 for the CFI, and from .042 to .052 for the SRMR, indicating good–excellent fit. The four implementations of Model 2 likewise revealed good–excellent model fits. The RMSEA values ranged from .025 to .052, the CFI values from .971 to .981, and the SRMR values from .038 to .046. The model paths of Model 1 and Model 2 for Neuroticism and Extraversion are extensively reported below. Tables 3 and 4 illustrate the respective correlations and prospective paths.

Examination of Stability, Cross-Sectional Correlations, and Longitudinal Interrelationships There was high stability for Neuroticism and Extraversion across all models regardless of cohort, time interval, or the inclusion of health as a predictor. The path coefficients of Neuroticism at t1 to Neuroticism at t2, or respectively, t3, ranged from β = .683 to β = .870 (all ps < .001). The path coefficients of Extraversion at t1 predicting Extraversion at t2 or t3 ranged from β = .685 to β = .890 (all ps < .001). Life satisfaction revealed mild to moderate stability across all models. Overall, the GeroPsych (2016), 29 (1), 37–48

B. Tauber et al.: Personality and Life Satisfaction Longitudinally

Table 2. Model-fit indices of the multigroup and, cross-lagged models χ²

RMSEA

CFI

SRMR

Model 1

128.315

.000

.055

.975

.042

Model 1

N 12

109.786

.000

.048

.975

.047

Model 1

69.172

.045

.027

.992

.049

Model 1

E 12

78.851

.007

.033

.984

.052

Model 2

158.394

.000

.052

.971

.038

Model 2

N 12

135.001

.000

.045

.973

.043

Model 2

87.200

.049

.025

.991

.043

Model 2

E 12

98.289

.007

.031

.983

.046

Model-fit indices

Note. N = Neuroticism; E = Extraversion; 4 = 4-year time interval; 12 = 12-year time interval; Model 1 = Model without health; Model 2 = Model with health; χ² = chi-square value; df = degrees of freedom; p = probability value; RMSEA = root mean squared error of approximation; CFI = comparative fit index; SRMR = standardized root mean square residual.

Table 3. Correlations and standardized prospective paths of Model 1 (without health), separated for personality factor, time interval, and cohort Paths

Neuroticism

Extraversion

Older cohort

–.299***

–.304***

.219***

.201***

Younger cohort

–.369***

–.370***

.246***

.245***

Older cohort

–.217**

–.316***

.394***

.386***

Younger cohort

–.465***

–.383***

.255***

.277***

Older cohort

.849***

.744***

.890***

.850***

Younger cohort

.748***

.709***

.841***

.706***

Older cohort

.362***

.290***

.356***

.327***

Younger cohort

.294***

.342***

.311***

.397***

Base correlations (P t1 ↔ LS t1)

Subsequent correlations (P t2/t3 ↔ LS t2/t3)

Trait stability (P t1 → P t2/t3)

Life satisfaction stability (LS t1 → LS t2/t3)

Cross-lagged trait effects (P t1 → LS t2/t3) Older cohort

–.142**

–.125†

.137**

.003

Younger cohort

–.126*

–.164**

.120*

.045

Older cohort

–.018*

–.030**

Younger cohort

–.002

–.010

Cross-lagged life satisfaction effects (LS t1 → P t2/t3) –.008 .017

–.042*** .041

Note. Values represent standardized model parameters. P = personality factor; LS = life satisfaction; t1/t2/t3 = measurement times 1/2/3; 4 = 4-year time interval; 12 = 12-year time interval; older cohort = cohort born 1930–1932; younger cohort = cohort born 1950–1952; ↔ = correlation; → = directed path. *p < .05, **p < .01, ***p < .001, †p < .1.

coefficients of life satisfaction at t1 predicting life satisfaction at t2 and t3 ranged from β = .248 to β = .397 (all ps < .001). Looking at the cross-section, both Neuroticism and Extraversion correlated significantly with life satisfaction, revealing mild to moderate relationships (Neuroticism: r = –.207 to r = –.465; Extraversion: r = .196 to r = .394) across all measurement occasions and both cohorts (all ps < .001). In Model 1, Neuroticism significantly predicted life satisfaction 4 years later (older cohort: β = –.142, p = .004; younger cohort: β = –.126, p = .016). Respectively, Extraversion’s ability to predict life satisfaction 4 years later was significant for both cohorts (older cohort: β = .137, p = .009; younger cohort: β = .120, p = .025). The opposing prediction of life satisfaction for GeroPsych (2016), 29 (1), 37–48

personality (Neuroticism, Extraversion) 4-years-later, showed a different picture. Here, only in the older cohort, life satisfaction significantly predicted Neuroticism 4 years later (β = –.018, p = .019). Briefly summarizing, the present data support personality significantly predicting life satisfaction 4 years later, but contradict the opposing idea that life satisfaction predicts personality factors 4 years later. Extending the time interval from 4 to 12 years has significant impact on the interrelationship of Extraversion with life satisfaction, while revealing almost no effect considering the interrelationship with Neuroticism. The path from Neuroticism to life satisfaction 12 years later barely missed significance for the older cohort (β = –.125, p = .054), but remained highly signif© 2016 Hogrefe

B. Tauber et al.: Personality and Life Satisfaction Longitudinally

Table 4. Correlations and standardized prospective paths of Model 2 (with health), separated for personality factor, time interval, and cohort Paths

Neuroticism

Extraversion

Older cohort

–.302***

.209***

.196***

Younger cohort

–.369***

–.370***

.246***

.245***

Older cohort

–.207**

–.294***

.382***

Younger cohort

–.464***

–.351***

.256***

.257***

Base correlations (P t1 → LS t1)

Subsequent correlations (P t2/t3 → LS t2/t3)

Trait stability (P t1 ↔ P t2/t3) Older cohort

.870***

.736***

.877***

.850***

Younger cohort

.736***

.683***

.839***

.685***

Life satisfaction stability (LS t1 ↔ LS t2/t3) Older cohort

.339***

.248***

.334***

.271***

Younger cohort

.288***

.330***

.305***

.382***

Cross-lagged trait effects (P t1 ↔ LS t2/t3) Older cohort

–.112*

–.073

.115*

–.049

Younger cohort

–.117*

–.131*

.109†

–.007

–.003

–.046

.020

–.022

.006

.000

.015

.037

Cross-lagged life satisfaction effects (LS t1 ↔ P t2/t3) Older cohort Younger cohort Base correlations of health at t1 Subj. health ↔ Obj. health Older cohort

.495***

.494***

.495***

Younger cohort

.453***

.452***

.453***

.452***

Subj. health ↔ LS t1 Older cohort

.222***

.221***

.222***

.221***

Younger cohort

.178***

.181***

.177***

.181***

Older cohort

–.362***

–.354***

.199***

.215***

Younger cohort

–.271***

–.269***

.257***

.261***

Older cohort

.166***

.165***

.166***

.165***

Younger cohort

.044

.043

.044

Subj. health → P t1

Obj. health → LS t1

Obj. health → P t1 Older cohort

–.230***

–.223***

.120*

.133*

Younger cohort

–.142**

–.138**

.112*

.114*

Prospective health effects on LS/P Subj. health ↔ LS t2/t3 Older cohort

.056

.165**

.076

.196**

Younger cohort

.050

.079

.043

.104†

.052

.017

–.011

–.087

–.038

–.075

.023

.039

Subj. health ↔ P t2/t3 Older cohort Younger cohort Obj. health ↔ LS t2/t3 Older cohort Younger cohort

.093† –.003

.131† .208***

.103* –.001

.135* .208***

Obj. health ↔ P t2/t3 Older cohort

–.025

–.050

.064

.132*

Younger cohort

–.047

–.097†

–.043

.068

GeroPsych (2016), 29 (1), 37–48

icant for the younger cohort (β = –.164, p = .007). The relationships between life satisfaction to Neuroticism 12 years later were significant for the older cohort (β = –.030, p = .006) but not significant for the younger cohort (β = –.010, p = .855). Regarding Extraversion, both relationships between personality and life satisfaction 12 years later were not significant (older cohort: β = .003, p = .964; younger cohort: β = .045, p = .468). Surprisingly, however, the relationship between life satisfaction to later Extraversion became significant for the older cohort (β = –.042, p < .001). The corresponding relationship for the younger cohort failed to reach significance (β = .041, p = .441).

Examination of Differences in the Relationship of Personality and Life satisfaction between Middle Adulthood and Old Age While the magnitudes and significances of the relationships between both personality factors and life satisfaction indicated almost no differences between the cohorts – with the exception of the relationship between Neuroticism and life satisfaction 12 years later, which missed the statistical significance level in the old cohort (see Table 3) – the opposing relationships between life satisfaction and personality differed systematically. In 3 out of 4 cases life satisfaction was a significant predictor for subsequent personality in the older cohort (N 4: β = –.018, p = .019; N 12: β = –.030, p = .006; E 4: β = –.008, p = .307; E 12: β = –.042, p < .001); however, in the younger cohort, life satisfaction was on no account a significant longitudinal predictor (N 4: β = –.002, p = .969; N 12: β = –.010, p = .855; E 4: β = .017, p = .678; E 12: β = .041, p = .441). All in all, there is evidence for an aging effect that is concerned with life satisfaction being predictive of subsequent Neuroticism (both intervals) and Extraversion (only long-term interval).

The Role of Health The inclusion of health (Table 4) did not remarkably change the stability effects and cross-sectional relations, though the longitudinal interrelationships were weakened, and of the former eight significant longitudinal cross-effects, only four remain. In the short-term interval, the relationships between Neuroticism and subsequent life satisfaction were significant for both cohorts (older cohort: β = –.112, p = .034; younger cohort: β = –.117, p = .028). Furthermore, the relationships between Extraversion and life satisfaction 4 years later were significant for the older cohort (β = .115, p = .028) and barely missed significance for the younger cohort (β = .109, p = .052). In the longterm interval, only the relationship between Neuroticism and later-life satisfaction in the younger cohort showed significance (β = –.131, p = .027). GeroPsych (2016), 29 (1), 37–48

B. Tauber et al.: Personality and Life Satisfaction Longitudinally

In conclusion, the results hint at three findings. First, in the short-term interval and controlled for health, personality remains a predictor of later-life satisfaction, while life satisfaction does not significantly predict later Neuroticism or Extraversion. Second, in the long-term interval, when controlled for health, the interrelationship becomes slightly weakened, and only one effect remains significant. Third, it is particularly noteworthy that all life satisfaction to later personality factor effects became insignificant after entering health into the models, indicating that the life satisfaction to personality age effect might vanish when controlled for health. As expected, objective and subjective health were highly correlated with each other (rs ranging from r = .452 to r = .495; all ps < .001). Cross-sectional, subjective health was mild to moderately correlated to Neuroticism (rs ranging from r = –.269 to r = –.362; all ps < .001), Extraversion (rs ranging from r = .199 to r = .261; all ps < .001), and life satisfaction (rs ranging from r = .177 to r = .222; all ps < .001), across both time intervals and cohorts. Longitudinally, subjective health was not significantly predictive of Neuroticism, Extraversion, or life satisfaction, with one exception. The relationship between subjective health at t1 to life satisfaction 12 years later was significant in the older cohort (β model with Neuroticism = .165, p = .009; β model with Extraversion = .196; p = .002). Objective health showed a more complex cross-sectional and longitudinal relationship pattern, being mildly correlated with Neuroticism (rs ranging from r = –.138 to r = –.230; all ps < .01) and Extraversion (rs ranging from r = .112 to r = .133; all ps < .05) at t1 across all cohorts and in all calculated models. Moreover, objective health was mildly correlated to life satisfaction at t1 in the older cohort (r = .166, p < .001), but not in the younger cohort (r = .044, p = .324). Objective health at t1 revealed no significant longitudinal relations to the personality factors at t2 or t3, with one exception: In the older cohort, a significant longitudinal effect of objective health to 12-year-later Extraversion reached significance (β = .132, p = .026). In the models with Neuroticism, the paths of objective health predicting life satisfaction in the older cohort barely missed significance (4: β = .093, p = .066; 12: β = .131, p = .052), which was reached in the long-term interval of the younger cohort (12: β = .208, p < .001). In the model with Extraversion, these paths emerged as significant longitudinal predictors (older cohort 4: β = .103, p = .042; older cohort 12: β = .135, p = .046; younger cohort 12: β = .208, p = < .001). Therefore, the longitudinal influences of subjective and objective health showed relevance, especially for life satisfaction and the long-term intervals.

Discussion The present study had the goal of adding long-term evidence to the relationship between personality (Neuroticism, Extraversion) and life satisfaction. Additionally, and for the first time, © 2016 Hogrefe

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two very important enhancements were made to the present body of research. First, in contrast to the usually rather shortterm observation intervals (e.g., 2–6 years), we offered longterm, longitudinal, cross-lagged data on the relationship between personality and life satisfaction, amounting to 12 years. Second, we contrasted for the first time mid-adulthood and old age, based on theoretical reasoning regarding the link between personality and life satisfaction. The overall results of stability and cross-sectional correlations of the cross-lagged models were in line with previous research in terms of confirming high stability for both personality factors across the 4- and 12-year time intervals (e.g., Roberts & DelVecchio, 2000); we also found smaller yet substantial stability coefficients for life satisfaction. Moreover, our cross-sectional correlations of Neuroticism and Extraversion with life satisfaction accord well with the meta-analytical results of DeNeve and Cooper (1998) and Steel et al. (2008), revealing mild to moderate relationships. Across all our models – meaning both those controlled and uncontrolled for health – the relationships between the personality factors predicting life satisfaction 4 years later reach significance in 7 out of 8 cases, with one relationship barely missing significance (p = .052). On the other hand, 4 years later only one prospective relationship between life satisfaction and Neuroticism reached significance (older cohort). While the standardized betas of personality predicting later-life satisfaction were small but statistically meaningful, their counterparts from life satisfaction to personality were practically in the zero range. These results are quite surprising compared to Specht et al.’s (2013) and Soto’s (2014) findings, who argued that wellbeing effects on traits are equally predictive or even stronger than vice versa. Our data do not support this notion. We found personality clearly predicting life satisfaction 4 years later, but almost no effect for the reversed cross-lagged relationships. When interpreting these results, some key differences between the present study and Specht et al.’s (2013) and Soto’s (2014) analyses must be addressed. First, there is a wide gap between sample sizes. They investigated very large samples of 14,718 and 16,367 participants, while we were restricted to only 1,002 participants at t1. Second, their study samples covered the full adulthood period (i.e., early, middle, and late), while we concentrated only on mid-adulthood versus old age. Third, they investigated the whole set of personality factors, whereas we restricted ourselves to Neuroticism and Extraversion alone1. A comparison of the results of the present study with Soto’s (2014) results revealed the same pattern of significant relationships regarding life satisfaction and Neuroticism/Extraversion with one remarkable difference: The stan-

dardized trait effects in the present study are far larger in magnitude (Soto’s Neuroticism trait effect: β = –.084; Extraversion trait effect: β = .045). Fourth, Soto (2014) included measurements of positive and negative affect in addition to life satisfaction and, fifth, modeled life satisfaction latently. All these reasons might account for the differences in results. It is plausible that life satisfaction might predict later-life personality, specifically in its early stages. It is also possible that Conscientiousness, Agreeableness, and Openness may be more reactive to earlier life satisfaction than Neuroticism and Extraversion. All in all, we were partly able to replicate previous research regarding the short-term interrelationship of personality and life satisfaction. Our study, however, does not support the importance of life satisfaction as a predictor for personality change across 4 years. Turning to our long-term observations – covering 12 years with a focus on Neuroticism – the long-term, cross-lagged paths were of equal magnitude and thus different from the short-term observations. This, however, was not true for Extraversion. Extraversion was well predictive for life satisfaction 4 years later, but in the long-term models, these relationships became insignificant and, surprisingly, very small. Although it appears hypothetical, it is possible to derive from our results that Neuroticism might address the more long-term-oriented components (e.g., health behavior, tendency for social isolation) and Extraversion the more short-term-oriented components (e.g., participation in social activities) of life satisfaction, which is an open question for future studies. It is of additional interest that the relationship between life satisfaction and Extraversion became significant in the long-term interval (older cohort). As Soto (2014) argued, the prospective effects of life satisfaction and personality might grow larger when allowed to accumulate in longer time intervals. Overall, our data do not consistently support the theoretically assumed stronger interrelations of longer time intervals compared to shorter time intervals. Obviously, more research is needed to further clarify this issue and observations even longer than 12 years may be helpful in this regard. In terms of possible age effects regarding the relationship between personality and life satisfaction, based on theoretical reasoning we also expected that the longitudinal interrelationship might be stronger in old age compared to mid-adulthood. We also assumed that health may be an important control variable for such possible differences in cross-lagged effects. According to our findings, there was no difference between the two age cohorts when it comes to the cross-paths from Neuroticism or Extraversion to later life satisfaction, which supports the rejection of the assumed age effect. However, our data also revealed that the corresponding cross-lagged effects from life

1 In response to one reviewer’s comments, we conducted additional analyses on the remaining three personality traits of the Big-Five, namely, Conscientiousness, Agreeableness and Openness. Overall, only two out of 24 possible cross-lagged paths reached significance (old/young cohort, 4/12 years, cross-lagged trait/life satisfaction effect). One significant relationship is between Conscientiousness at t1 and life satisfaction at t2, while the other significant relationship is between life satisfaction at t1 and Agreeableness at t3. The two effects remained unaffected by the inclusion of health into the models.

GeroPsych (2016), 29 (1), 37–48

satisfaction to Neuroticism were predictive for the old cohort, but not for the mid-adulthood cohort. Regarding Extraversion, in the long-term interval and the old cohort, the same pattern is observed. Taken together, the relationship between life satisfaction and subsequent personality change seems – at least for Neuroticism and partly for Extraversion – to be systematically different between the two age groups, supporting the existence of an age effect. It thus seems that a differential argument is needed to better understand possible differences in the relationships between personality and life satisfaction in midadulthood versus old age. Finally, the inclusion of health weakened the cross-lagged relationships between personality and life satisfaction and vice versa, leaving only four of the former eight effects as significant and erasing the partial age effect as described above. Thus, health seems to have an important influence on life satisfaction predicting later personality, when middle adulthood and old age are contrasted. Furthermore, in line with our theoretical argument, we cautiously interpret that health is partly responsible for the cross-relationships of Neuroticism and Extraversion with life satisfaction as indicated by the differences in results after health has been included. It, however, remains unclear how health unfolds its influence on the interrelationship. We therefore recommend future studies on the interrelationship to include health as a time varying covariate. Furthermore, the two health variables revealed the expected cross-sectional results with each other, life satisfaction, Neuroticism, and Extraversion, with only one exception: In the younger cohort, objective health and life satisfaction were not significantly correlated. We interpret this to mean that in mid-adulthood, objective health constraints are seemingly superimposed by other sources of life satisfaction, for example, success at work and striving in family roles, which changes when people grow older and bodily decline becomes a more frequent issue. The longitudinal results of health were surprising to us, because subjective health scarcely showed a significant relationship to life satisfaction or personality. Objective health, in contrast, showed a couple of interesting prospective relationships. According to our long-term data, objective health seems indeed to be more important than subjective health, which questions to some extent the now-classic but mostly cross-sectional based priority of subjective health compared to objective health, when it comes to the prediction of life satisfaction and well-being in general. Surprisingly, personality was hardly affected by objective health.

Limitations and Future Research Needs The present study analyzed data from the German Interdisciplinary Longitudinal Study of Adult Development (ILSEStudy), which has numerous important strengths: its long study interval, multidisciplinary data gathering, and refined cohort design to explicitly cover mid- and late adulthood. GeroPsych (2016), 29 (1), 37–48

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However, the study has also considerable limitations. First, the present investigation as well those of Specht et al. (2013) and Soto (2014) heavily relied on very short assessment formats (and, indeed, to some extent 1-item formats). Although such short assessments have been found to be acceptable in terms of reliability and validity (e.g., Diener, Suh, Lucas & Smith, 1999; Lucas & Donnellan, 2012; Diener, Inglehart & Tay, 2013), such findings need to be replicated and extended, driven by the spirit of a multitrait, multimethod analysis (Campbell & Fiske, 1959). Second, the measurements used were mostly self-reports. Even though self-reports are stateof-the-art in psychological panel studies, external assessments might shed further light on the subject. Third, the modeling became asymmetrical because personality was measured latently and life satisfaction was implemented into the models as a manifest variable. More indicators for life satisfaction than one item would certainly improve the plausibility of the construct and help equalize the relationship. The lacking predictability of life satisfaction for later-life personality changes might be partly due to the chosen approach. Fourth, a different modeling – compared to the multigroup, cross-lagged models – could be promising. Specht et al. (2013) and Soto (2014) in part used latent-growth curve models to describe the interrelationship. Even though this approach is not fully prospective, change correlations and the cross level to change effects might be affected by the implementation of health and the enlarged time intervals. Fifth, in order to better understand the impact of health on the interrelationship, a more refined measurement approach and modeling of health seems promising. We included health only at t1 to align with the prospective cross-lagged effects, but recommend future studies to include health as a dynamic change variable.

Conclusion Despite the limitations of the present study, a number of conclusions were supported by our data. First, Neuroticism and Extraversion were shown in our data to predict subsequent life satisfaction in the short-term interval. In the long-term interval, however, a mixed picture appeared and only Neuroticism was able to predict life satisfaction longitudinally. Furthermore, a (partial) age effect regarding the intertwining of personality and life satisfaction emerged. Life satisfaction was predictive for Neuroticism in old age, but not in midadulthood, which was also true for Extraversion (but only in the long-term interval). This age effect appeared nevertheless as health dependent. Overall, our data support taking a differential perspective to understand better developmental trajectories and links of multidimensional constructs like personality and life satisfaction. © 2016 Hogrefe

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Acknowledgments This article reports data from the Interdisciplinary Longitudinal Study of Adult Development (ILSE), currently funded by the Dietmar Hopp Stiftung and previously funded by the German Federal Ministry of Family Affairs, Senior Citizens, Women, and Youth (AZ: 301-1720-295/2 and 301-6084/035). We thank all project members for their contribution. In particular, thanks are extended to the remaining project leadership: Prof. Dr. Ute Kunzmann, Prof. Dr. Peter Rammelsberg, Dr. Christine Sattler, and Prof. Dr. Peter Schönknecht.

Declaration of Conflicts of Interest The authors declare that no conflicts of interest exist.

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Manuscript submitted: 04.09.2015 Manuscript accepted after revision: 16.12.2015

Benjamin Tauber Department of Psychological Aging Research Heidelberg University Bergheimer Straße 20 69115 Heidelberg Germany Tel. +49 6221-54 81 14 Fax +49 6221-54 81 12 benjamin.tauber@psychologie.uni-heidelberg.de

GeroPsych – The Journal of Gerontopsychology and Geriatric Psychiatry publishes innovative, original, high-quality research articles on normal, optimal, or pathological human aging. The scope of the journal is defined by empirical and evidence-based methods, including clinical, experimental, and applied research. The journal welcomes manuscripts from all areas of fundamental and applied research related to adult development and human aging in the behavioral as well as the clinical context. To name just a few typical fields and domains of inquiry, GeroPsych considers manuscripts dealing with prevention and intervention, early diagnosis and treatment of dementia, depression, mental health, cognitive aging, risk and protective effects of social environments, motivation and personality across adulthood, including clinical, neurological, and methodological aspects of these topics. A criterion for publication is that research papers should link their findings to theoretical research questions, that is, for reports and papers that are purely descriptive or speculative GeroPsych is not the appropriate outlet.

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