Leseprobe GeroPsych 2018

Volume 30 / Number 1 / 2017

GeroPsych

Editor-in-Chief Frieder R. Lang Associate Chief Dieter Ferring Julia Haberstroh Eva-Marie Kessler Mike Martin Johannes Pantel Michael Rapp Peter Schoenknecht

The Journal of Gerontopsychology and Geriatric Psychiatry

Treatment of migraine and headache “For clinicians and others who seek a single authoritative, clearly presented, and fully referenced discussion of how to manage tension-type headache and migraine, this book is what they are looking for!”

John F. Rothrock, MD, Director, Renown Institute for the Neurosciences, Reno, NV Professor and Chair, Department of Clinical Neurosciences, University of Nevada School of Medicine, Reno, NV

Todd A. Smitherman / Donald B. Penzien / Jeanetta C. Rains / Robert A. Nicholson / Timothy T. Houle

Headache (Series: Advances in Psychotherapy – Evidence-Based Practice – Vol. 30) 2015, xiv + 112 pp.

US $29.80 / € 24.95 ISBN 978-0-88937-328-0 Also available as eBook This book describes the conceptualization, assessment, and evidence-based behavioral treatment of migraine and tension-type headache – two of the world’s most common medical conditions, and also frequent, highly disabling comorbidities among psychiatric patients.

Headache disorders at their core are neurobiological phenomena, but numerous behavioral factors play an integral role in their onset and maintenance – and many providers are unfamiliar with how to work effectively with these patients to ensure optimal outcomes.

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This book, the first major work on behavioral treatment of headache in over 20 years, provides much-needed help: An overview of relevant psychological factors and the behavioral conceptualization of headache is followed by a step-by-step, manualtype guide to implementing behavioral interventions within clinical practice settings. Mental health practitioners and trainees and other healthcare professionals who want to improve their headache patients’ outcomes by supplementing routine medical treatment with empirically supported behavioral strategies will find this book invaluable.

GeroPsych The Journal of Gerontopsychology and Geriatric Psychiatry

Volume 30, Issue 1, 2017 Editor-in-Chief Frieder R. Lang

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

Editor-in-Chief

Frieder R. Lang, Institute of Psychogerontology University of Erlangen-Nuremberg Kobergerstr. 62 90408 Nuremberg Germany Tel. +49 911 5302-96100, Fax +49 911 5302-96101 geropsych@fau.de

Associate Editors

Dieter Ferring, Esch-sur-Alzette Julia Haberstroh, Frankfurt Eva-Marie Kessler, Berlin Mike Martin, Zurich Johannes Pantel, Frankfurt Michael Rapp, Potsdam Peter Schoenknecht, Leipzig

Editorial Board

Isabelle Albert, Esch-sur-Alzette Noel Ballentine, Pennsylvania Sube Banerjee, Brighton Thomas Boll, Esch-sur-Alzette Sheung-Tak Cheng, Hong Kong Helen F. K. Chiu, Hong Kong Barry A. Edelstein, Morgantown Ulrike Ehlert, Zurich Simon Forstmeier, Siegen Alexandra Freund, Zurich Helene H. Fung, Hong Kong Denis Gerstorf, Berlin Vjera Holthoff-Detto, Berlin Frank Jessen, Bonn Boo Johansson, Gothenburg Reto W. Kressig, Basel Ken Laidlaw, Norwich

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1662-9647 (ISSN-L) 1662-9647 (Print) 1662-971X (Online)

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Giovanni Lamura, Ancona Anja Leist, Esch-sur-Alzette 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 Katja Werheid, Berlin Claire Wolfs, Maastricht Susanne Wurm, Erlangen-Nuremberg Susanne Zank, Cologne Daniel Zimprich, Ulm

© 2017 Hogrefe

GeroPsych 30 (1)

© 2017 Hogrefe

Contents Full-Length Research Reports

© 2017 Hogrefe

12-Year Associations of Health with Personality in the Second Half of Life – Being versus Feeling Healthy Markus Wettstein, Benjamin Tauber, Hans-Werner Wahl, and Claudia Frankenberg

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Cross-Validation of the Newly-Normed SKT for the Detection of MCI and Dementia Johannes Baltasar Hessler, Mark Stemmler, and Horst Bickel

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Routine Data Indicators of Treatment for Dementia and Old-Age Depression Frank Godemann, Claus Wolff-Menzler, Michael Löhr, and Hauke Wiegand

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Cognitive Performance in Patients with Chronic Schizophrenia across the Lifespan Christina Josefa Herold, Lena Anna Schmid, Marc Montgomery Lässer, Ulrich Seidl, and Johannes Schröder

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GeroPsych 30 (1)

The first structured resource for psychologists that combines mindfulness with character strenghts Ryan M. Niemiec

Mindfulness and Character Strengths A Practical Guide to Flourishing

2014, xx + 274 pp. + CD with meditation exercises US $39.80 / € 27.95 ISBN 978-0-88937-376-1 Also available as eBook At the core of this hands-on resource for psychologists and other practitioners, including educators, coaches, and consultants, is MindfulnessBased Strengths Practice (MBSP), the first structured program to combine mindfulness with the character strengths laid out in the VIA Institute’s classification developed by Drs. Martin E. P. Seligman and Christopher Peterson. This 8-session program systematically boosts awareness and application of character strengths – and so helps people flourish and lead more fulfilling lives.

The author’s vast experience working with both mindfulness and character strengths is revealed in his sensitive and clear presentation of the conceptual, practical, and scientific elements of this unique combined approach. It is not only those who are new to mindfulness or to character strengths who will appreciate the detailed primers on these topics in the first

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section of the book. And the deep discussions about the integration of mindfulness and character strengths in the second section will benefit not just intermediate and advanced practitioners. The third section then leads readers step-by-step through each of the 8 MBSP sessions, including details of session structure and content, suggested homework, 30 practical handouts, as well as inspiring quotes and stories and useful practitioner tips. An additional chapter discusses the adaption of MBSP to different settings and populations (e.g., business, education, individuals, couples). The mindfulness and character strengths meditations on the accompanying CD support growth and development.

This highly accessible book, while primarily conceived for psychologists, educators, coaches, and consultants, is suitable for anyone who is interested in living a flourishing life.

M. Wettstein GeroPsych et al.: Personality (2017), © 2017 30 and(1), Hogrefe Health 5–17

Full-Length Research Report

12-Year Associations of Health with Personality in the Second Half of Life Being versus Feeling Healthy Markus Wettstein1,*, Benjamin Tauber1,*, Hans-Werner Wahl1, and Claudia Frankenberg2 1

Department of Psychological Aging Research, Heidelberg University, Germany

2

Section for Geriatric Psychiatry, University Hospital Heidelberg, Germany

Abstract: We examined longitudinal associations between personality, objective (physician-rated) and self-rated health over 12 years in two German cohorts (midlife cohort, born 1950/52, nT0 = 502; late-life cohort, born 1930/32, nT0 = 500) from the Interdisciplinary Longitudinal Study of Adult Development (ILSE). Based on cross-lagged panel design analyses controlling for sex, education, depression, and cognitive abilities, we found that after 12 years better baseline objective health predicted lower Neuroticism and higher Agreeableness, whereas baseline Extraversion and Conscientiousness were positive predictors of later self-rated health. Our findings thus illustrate that the direction of longitudinal personality-health associations is dependent on whether objective or self-rated health is considered, whereas relations do not seem to be considerably different in midlife vs. in old age.

Keywords: personality, health, middle adulthood, old age, Big Five

Both personality and health change across the entire lifespan (Morack, Infurna, Ram, & Gerstorf, 2013; Wagner, Ram, Smith, & Gerstorf, 2015). This study examines reciprocal longitudinal associations between the Big Five personality traits (Costa & McCrae, 1992a) and health over 12 years in a midlife (baseline age 43–46 years) and a late-life cohort (baseline age 61–65 years).

Given the multidimensionality of health (Spiro, 2001), we chose to investigate the interplay of both objective health (rated by a physician) and self-rated health with personality. Rather than focusing on specific diseases, we are interested in how the position of individuals on a general health continuum, as rated by themselves as well as by physicians, interacts with personality traits. We argue that most of previous research has (1) considered personality only as a predictor, rather than as both an antecedent and an outcome, of health, and (2) neglected the potentially differential role of objective vs. self-rated health regarding the longitudinal personality-health interplay. Moreover, we investigate whether personality-health association patterns are different in midlife vs. late life.

Personality as a Predictor of Health Most existing research on the longitudinal personality-health interplay has been dedicated to the question whether person-

ality traits predict health outcomes (Friedman & Kern, 2014; Smith & MacKenzie, 2006). Various interacting mechanisms may underlie such associations, including behavioral (e.g., Lodi-Smith et al., 2010), physiological (Luchetti, Barkley, Stephan, Terracciano, & Sutin, 2014), and social pathways (Hill, Nickel, & Roberts, 2014).

Regarding empirical evidence, the role of personality, particularly Neuroticism, as a predictor of health has frequently been investigated. High Neuroticism seems to play a consistent role as a risk factor for poor health. For instance, Sutin, Zonderman, Ferrucci, and Terracciano (2013) found that Neuroticism (and particularly its subfacet impulsiveness) was associated with a higher risk of developing disease or of getting more ill. Moreover, apart from predicting future self-rated health (Human et al., 2013; Magee, Heaven, & Miller, 2013; Turiano et al., 2012) and decline in self-rated health (Löckenhoff, Terracciano, Ferrucci, & Costa, 2012), Neuroticism also acts as an important prospective risk factor when an individual’s health is rated by a physician rather than being self-reported (Chapman, Roberts, Lyness, & Duberstein, 2013). Furthermore, Extraversion seems to be a protective trait regarding health outcomes, with higher scores and increases in Extraversion over time being associated with better prospective self-rated health (Magee et al., 2013; Turiano et al., 2012). Regarding Openness to Experience, most studies found no meaningful relationship between baseline Openness and prospective health outcomes

* Both authors contributed equally to this manuscript and should be considered as co-first authors. © 2017 Hogrefe

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or health changes (Magee et al., 2013; Morack et al., 2013; Tolea, Costa et al., 2012; Turiano et al., 2012). In contrast, Agreeableness may play a more meaningful role with regard to health. For instance, lower Agreeableness was found to be associated with faster accumulation of morbidity, as assessed by a physician, over time (Chapman et al., 2013). However, with respect to subjective health outcomes, Turiano et al. (2012) reported that higher Agreeableness scores as well as increases in Agreeableness over time were prospectively associated with lower self-rated health. Together with Neuroticism, Conscientiousness may be the most important health predictor (Reiss, Eccles, & Nielsen, 2014; Shanahan, Hill, Roberts, Eccles, & Friedman, 2014). Specifically, higher Conscientiousness was found to be associated with a lower prospective risk of “getting sicker” as well as with lower future disease burden (Sutin et al., 2013). Additionally, higher Conscientiousness also predicts lower physician-assessed illness burden accumulation over time (Chapman et al., 2013). Moreover, individuals with higher baseline Conscientiousness as well as with increases in Conscientiousness over time were found to report better subsequent self-rated health (Human et al., 2013; Magee et al., 2013; Turiano et al., 2012).

Personality as an Outcome of Health However, a one-sided perspective, focusing on personality as a determinant of health, may not fully capture the complexity of the longitudinal personality-health interface. Health deterioration and the onset of an illness may considerably challenge personality stability. For instance, the experience of health restrictions may upset individuals and complicate the engagement in social and other activities, consequently leading to tendencies of social withdrawal and avoidance of new experiences which may be too exhausting once health is compromised. Thus, poor health may result in an increase in Neuroticism as well as a decrease in Extraversion and Openness to Experience. In addition, severe health problems may also challenge an individual’s capacity to maintain a certain level of Agreeableness, e.g., due to feelings of envy with regard to others’ (better) health.

Although the overwhelming majority of empirical studies has considered personality an antecedent of health, there is also some evidence in favor of meaningful health effects on later personality. For instance, the onset of chronic disease was found to be associated with an increase in Neuroticism and a decrease in Extraversion, Openness, and Conscientiousness (Jokela, Hakulinen, Singh-Manoux, & Kivimäki, 2014). Sutin et al. (2013) observed that an increase in illness burden was associated with a decrease in Openness and – though only marginally significantly – with a decline in Extraversion. Similarly, in a sample of very old adults, higher self-reported disability emerged as a risk factor for declines in Extraversion and OpenGeroPsych (2017), 30 (1), 5–17

M. Wettstein et al.: Personality and Health

ness (Wagner et al., 2015). Finally, turning to specific health conditions, the experience of late-life sensory impairment (i.e., vision or hearing loss) seems to be associated with an increase in Neuroticism (Lißmann, 2003), and hearing impairment is related with steeper declines in Extraversion (Berg & Johansson, 2014; Lißmann, 2003).

To summarize, personality (particularly Neuroticism and Conscientiousness) seems to meaningfully predict health outcomes, but there is also some evidence pointing at the role of health as a predictor of personality change. However, there is still a lack of studies that simultaneously investigate both directions of the personality-health interface instead of (or in addition to) considering personality as only a determinant of health.

Are Objective and Self-Rated Health Differentially Related with Personality?

So far, many studies investigating personality-health associations have focused either on self-rated or on objective/physician-rated health. However, the strength and direction of associations may vary according to whether self-rated or objective health is considered. It seems that Neuroticism, for instance, is more strongly related with self-rated than with objective health (Israel et al., 2014), and the relationship between Neuroticism and self-rated health also holds when controlling for objective health indicators (Duberstein et al., 2003). As another example, Agreeableness was found to be a protective factor for later objective health as rated by a physician in one study (Chapman et al., 2013), but in another study in which health was assessed by self-reports, Agreeableness turned out to be a negative predictor (Turiano et al., 2012).

Generally, correlations between indicators of self-rated and objective health are far from deterministic (French, SargentCox, & Luszcz, 2012; Pinquart, 2001), which implies that selfrated and objective health represent empirically distinguishable constructs. The discrepancy between these both health modalities seems to increase with advancing age (French et al., 2012; Pinquart, 2001; Schnittker, 2005). A reason for this discrepancy could be that different factors predict self-rated vs. objective health, and personality might be one of these factors. Specifically, self-rated health may be more strongly influenced and predicted by personality than objective health. Indeed, most of the studies which examined personality effects on later health considered self-rated rather than objective health (e.g., Löckenhoff et al., 2012; Magee et al., 2013). In contrast, though evidence regarding the effects of health on personality change may still be too scarce for firm conclusions, personality may rather change in reaction to objective health conditions (such as sen© 2017 Hogrefe

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sory impairment or chronic disease; Berg & Johansson, 2014; Jokela et al., 2014; Lißmann, 2003) than to self-rated health.

To summarize, we assume that objective health predicts personality change to a larger extent than it is predicted by personality. In contrast, self-rated health may be rather predicted by personality traits than acting as a prospective predictor of personality.

and particularly based on longitudinal and reciprocal personality-health relations, we do not derive specific predictions for this study regarding differences between middle-aged and older adults in the strength of personality-health associations.

Research Questions and Expectations The Personality-Health Interplay in Midlife and in Late Life Finally, associations between personality and health may be different during different life phases. Health is generally good in middle adulthood (Lachman, 2004; Lachman, Teshale, & Agrigoroaei, 2014), whereas old age is usually associated with declining health (Jacobs et al., 2012; Morack et al., 2013). This could imply that health affects personality to a larger extent in later life than in midlife: As long as health is good (i.e., in midlife), it should not have a major impact on personality.

However, the opposite scenario is also plausible: Considering that health decline in old age is rather “normative” (Moser et al., 2013; Sprangers & Schwartz, 1999) and can be anticipated by aging individuals, health restrictions in old age may not represent a severe challenge for personality stability. In contrast, experiencing health problems in middle adulthood is a rather nonnormative, “off-time” experience, because “biologically based changes are typically not as dramatic in midlife as in other periods of the lifespan” (Lachman, 2004, p. 325), so that the impact of health restrictions on personality could as well be stronger in midlife than in late life.

Turning to self-rated health, empirical evidence is inconclusive.

Some studies report stronger associations between personality and self-rated health with advancing age (Canada, Stephan, Jaconelli, & Duberstein, 2016; Duberstein et al., 2003), whereas other studies state the opposite effect (Magee et al., 2013), and still other studies found no age trend at all (Morack et al., 2013).

Given the paucity of empirical findings comparing personality-health associations across different age groups in general,

In this study, we examine 12-year longitudinal relationships between personality traits and self-rated as well as objective health in middle-aged and older adults. Our predictions are: (1) Generally, associations are reciprocal, i.e., personality is not only a determinant of health, but also predicted by health. (2) Self-rated and objective (physician-assessed) health are differentially related with personality [for objective health, stronger effects are expected for the direction “health → personality”; for selfrated health, stronger effects are expected for the direction “personality → health”]. (3) Moreover, we investigate whether cohort membership (midlife vs. late-life) moderates longitudinal personality-health associations as an exploratory research question.

Methods Study Population and Sample Description The data of the present study were obtained from the “Interdisciplinary Longitudinal Study of Adult Development” (ILSE; Sattler et al., 2015), a German population-based study that started in the early 1990s. The ILSE sample was stratified by sex and region (with one subsample drawn in the cities Heidelberg, Mannheim, and Ludwigshafen, and the other subsample recruited in Leipzig) and consists of two cohorts, a late-life cohort born 1930–1932 and a midlife cohort born 1950–1952.

The sample was drawn with the help from city registries (for further information on the sampling procedures, see Martin &

Table 1. Sample description Variable N

Total 1002

Midlife Cohort 502

Late-Life Cohort 500

Age (M, SD)

53.50 (9.40)

44.17 (0.91)

62.87 (0.89)

Sex: Male (%)

520 (51.9%)

260 (51.8%)

260 (52.0%)

Statistical Testa

χ² (1) = 0.00, ns

Education (years) (M, SD)

13.48 (2.70)

14.07 (2.50)

12.89 (2.76)

t (980) = –7.05, p < .001

Self-rated health1 (M, SD)

3.74 (0.97)

3.77 (0.96)

3.72 (0.98)

t (983) = –0.83, ns

Physician-rated health1 (M, SD)

4.63 (0.84)

4.73 (0.78)

4.53 (0.88)

t (966.19) = –4.71, p < .001

Depression (M, SD)

1.67 (0.36)

1.61 (0.35)

1.73 (0.36)

t (984) = 5.31, p < .000

Cognitive abilities (M, SD)

–0.00 (0.60)

0.19 (0.54)

–0.20 (0.59)

t (1000) = –10.96, p < .000

Note. at-test for continuous variables and χ² test for categorical variables. ns = not significant. 1Higher values indicate better health.

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Martin, 2000) and was representative for the two regions in which the sampling took place. The overall participation rate at the first measurement occasion was 42.3%. The study comprises three completed measurement waves (i.e., first wave: 1993–1996, n = 1002, second wave: 1997–2000, n = 896, third wave: 2005–2008, n = 769; response rate at third wave: total sample 76.7%, late-life cohort 74.84%, midlife cohort 81.45%).

For the following analyses, we focused on the entire study interval of 12 years, because we were interested in the longitudinal personality-health interplay over an extended time period; investigating this interplay over only 4 years may not adequately capture the reciprocal associations because very high rank-order stability of personality traits and health across this rather short time interval can be expected. In the following, the first measurement occasion will be denoted “T0” and the third measurement occasion (which took place 12 years later) will be denoted “TFU” (FU = follow-up).

Basic descriptive data of the sample is shown in Table 1.

Significant group differences favoring the younger cohort were found for education, physician-rated health, cognitive abilities, and depressive symptoms. Correlations between study variables are shown in Table 2.

In order to investigate potential effects of selective dropout as well as to compare the size of these dropout effects between both cohorts, we computed 2 × 2 analyses of variance with the factors TFU Participation (yes vs. no) and Cohort (midlife vs. late-life) as well their interaction. Regarding education, TFU study participants had significantly more years of education compared to the dropout sample (F[1, 946] = 15.64, p < .001), but this difference corresponded to a small effect size (partial η² = .016). Similarly, there was a statistically significant interaction effect of cohort and TFU study participation (F[1, 946] = 5.14, p = .024), with the difference between dropouts and nondropouts in mean years of education being more pronounced in the midlife cohort compared to the late-life cohort, but the effect size of this interaction effect was also small (partial η² = .005). The sex distribution of dropouts vs. nondropouts was not significantly different in both cohorts. Regarding depression, there was no significant difference between the dropout and the nondropout sample, and the interaction of cohort and TFU study participation also did not reach significance. General cognitive abilities were significantly higher in TFU study participants compared to the dropouts (F[1, 965] = 34.61, p < .000), though this effect was small (partial η² = .16), and there was no significant interaction with cohort. Regarding differences in our target variables, both self-rated health (F[1, 948] = 19.07, p < .000, partial η² = .020) and physicianrated health (F[1, 951] = 24.80, p < .000, partial η² = .025) were significantly poorer in the dropout sample than in nondropouts. Regarding self-rated health, this difference between dropouts and nondropouts was significantly larger in the late-life sample than in the midlife sample (F[1, 951] = 7.42, p = .007, partial η² = 0.008). Remarkably, TFU partici© 2017 Hogrefe

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pants and dropouts did not significantly differ in any of the Big Five personality traits, and interaction effects with cohort were consistently not significant.

To summarize, selective dropout occurred regarding some, but not all of the study variables, and all of the selective dropout effects that reached statistical significance were of small effect size. The midlife and late-life samples differed only in the size of two selective dropout effects (education and self-related health), and these differences were also of small effect size.

Measures Personality Traits Personality traits were measured by the German version of the NEO-Five-Factor Inventory (NEO-FFI; Borkenau & Ostendorf, 1993; Costa & McCrae, 1992b). The NEO-FFI consists of 60 items, i.e., 12 items per personality trait (Cronbach’s α at both measurement occasions: Neuroticism .79/.84, Extraversion .71/.77, Openness .54/.61, Agreeableness .62/.71, and Conscientiousness .75/.79).

Health Self-rated health was assessed using a single-item measurement.

Participants were asked to rate how satisfied they were with their health. The answer options ranged from 1 = very dissatisfied to 5 = very satisfied. Second, physician-rated health was based on the judgment of one to two trained study geriatricians (for a detailed description, see Miche, Elsässer, Schilling, & Wahl, 2014). Overall, the clinical health ratings of the physicians were a summary score based on four in-depth examinations, namely, an anamnesis, a medical check-up, a laboratory blood test and a geriatric assessment. Each of the clinical examinations consisted of several subtests (such as hearing and vision assessment or blood pressure measurement). Geriatricians integrated the results from the four examinations into one rating of each study participant’s overall health, with the response scale ranging from 1 = participant exhibits a serious medical condition, which is immediately life-threatening; professional healthcare is urgent to 6 = participant exhibits very good health (i.e., no chronic disease, no chronic pain, all clinical assessments conducted led to nonpathological findings).

Covariates Analogous to other studies addressing relationships between personality traits and health measures, we controlled for sex and education (Chapman et al., 2013; Jaconelli, Stephan, Canada, & Chapman, 2013; Löckenhoff et al., 2012; Sutin et al., 2013; Turiano et al., 2012). In addition, some previous studies addressing the personality-health interplay also controlled for depressive symptoms (e.g., Tolea, Ferrucci et al., 2012) and cognitive abilities (e.g., Israel et al., 2014; Tolea, Costa et al., 2012). As both these variables are indeed meaningfully associGeroPsych (2017), 30 (1), 5–17

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Figure 1. Illustration of a cross-lagged panel design with one personality trait and one health indicator at two measurement occasions lying 12 years apart (here: T0 and TFU).

Note. The two measurement occasion are abbreviated with T0 and TFU, respectively. a and b: auto-regression path coefficients; c and d: cross-lagged path coefficients; e and f: concurrent correlations at both measurement occasions.

ated with personality (Curtis, Windsor, & Soubelet, 2014; Klein, Kotov, & Bufferd, 2011) and health (Schilling, Wahl, & Reidick, 2013; Tolea, Morris, & Galvin, 2015), we also decided to additionally include baseline depression and cognitive abilities as covariates. Education was measured in years (of attending school and university). Depression was measured by the 20-item self-rated Zung depression scale (SDS; Zung, 1965; α = .42). To control for cognitive abilities, we included a composite score of global cognitive ability based on a set of well-established and widely used cognitive measures implemented in the regular data protocol of the ILSE (for a detailed description of the cognitive assessment which took place as part of the ILSE study and of each single cognitive test that was conducted, see Zimprich, Allemand, & Dellenbach, 2009; Zimprich & Mascherek, 2010). Specifically, we included tests of information processing speed (Number-Connection Test and Digit Span Substitution task; Oswald & Roth, 1987; Tewes, 1991), crystallized abilities (Information and Similarities tests from the Wechsler Adult Intelligence Scale; Tewes, 1991), memory (Picture Recall, Delayed Picture Recall and Word List Recall from the Nuremberg Inventory of Old Age; Oswald & Fleischmann, 1995), and working memory capacity (Digit Span Forward and Digit Span Backward from the Nuremberg Inventory of Old Age; Oswald & Fleischmann, 1995). The scores of these different cognitive tests were z-transformed and then averaged, resulting in a composite score of global cognitive ability.

scores ≤ .08 indicate an acceptable model fit, and a good model fit is indicated by RMSEA values ≤ .05 (Hu & Bentler, 1999).

IBM SPSS Statistics 20 and IBM SPSS Amos 22 (Arbuckle, 2013) were used for statistical analyses.

Parameter estimation was done via full information maximum likelihood (FIML). With regard to missing data treatment, FIML has been recommended as state-of-the art approach, using the full data information available and relying on less restrictive “missingness pattern” assumptions compared to approaches such as list-wise deletion (Schafer & Graham, 2002).

Regarding model specification, a stepwise approach was chosen by successively testing additional restrictions: First, the (unstandardized) autoregressive paths of the personality and health indicators, denoted a and b in Figure 1, were constrained to be equal across groups (i.e., between the midlife and the late-life cohort). If this restricted model led to a significant misfit as indicated by the χ² difference test, the unrestricted model was accepted. If no significant misfit resulted, the cross-sectional personality-health correlations (e and f in Figure 1) and both cross-lagged paths (c and d in Figure 1) were additionally set equal across groups. If this “more restricted” model did not reveal a significantly worse fit than the “less restricted” model, it was accepted. Otherwise the alternative model (with only autocorrelations set equal between groups) was selected.

Statistical Analyses

Results

The 12-year longitudinal interplay between personality traits and health indicators as well as the role of cohort (midlife vs. late-life) as potential moderator were investigated by multigroup cross-lagged panel analyses (Kenny, 2005), which are illustrated in Figure 1 and can be considered as “best possible option for investigating causal directionality when experiments are not available” (Newsom, 2015).

For the evaluation of goodness of fit in our models, we relied on established recommendations (McDonald & Ho, 2002).

Specifically, we took both the Comparative Fit Index (CFI) and the root mean squared error of approximation (RMSEA) into account. A CFI score ≥ .90 or above indicates an acceptable model fit, and scores ≥ .95 indicate a good model fit. RMSEA GeroPsych (2017), 30 (1), 5–17

Table 3 shows the results of the multigroup cross-lagged panel analyses. Goodness-of-fit of all models was very good, with RMSEA scores ranging between .00 and .08 (all RMSEA values not significantly deviating from the cut-off criterion of .05) and CFI values ranging between .99 and 1.

Regarding potential differences between cohorts in terms of rank-order stabilities, we found that all autocorrelations of the personality traits as well as of the health indicators could be set equal across cohorts without a significant loss in model fit. Stability coefficients of the Big Five personality traits ranged from .45 to .70, indicating high, but not perfect rank-order consistency. The rank-order stability estimates for the health indica© 2017 Hogrefe

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tors were generally lower than the ones of personality, ranging between .21 and .49.

Notably, most cross-lagged path coefficients could also be set equal between both cohorts. Only the cross-lagged paths between Neuroticism and self-rated health could not be constrained to be equal between groups when controlling for covariates, though in the adjusted models these cross-lagged paths were not significant in both groups. Therefore, all cross-lagged effects that we describe in the following refer to both cohorts.

Regarding Neuroticism, better baseline objective health was significantly associated with lower Neuroticism 12 years later.

This association remained significant in the adjusted model.

Higher Neuroticism was significantly related with worse health after 12 years in the unadjusted model, but this association did not remain significant when controlling for covariates.

Higher TFU Extraversion was significantly predicted by better objective health at T0; however, this relationship was no longer significant when covariates were taken into account. In contrast, higher baseline Extraversion was a significant positive predictor of later self-rated health, and this association remained significant in the adjusted model.

Higher Openness at T0 was a significant predictor of better objective health 12 years later, but this effect was no longer significant after controlling for the covariates. Moreover, the adjusted cross-lagged relations between Openness and self-rated health were all not significant.

Agreeableness at TFU was significantly associated with T0 objective health, with better baseline health predicting higher subsequent Agreeableness. This association remained significant in the adjusted model. Better self-rated health at baseline was also associated with higher Agreeableness scores after 12 years, but this relationship did not remain significant when including the covariates.

Higher baseline Conscientiousness was significantly associated with both better objective and self-rated health after 12 years. However, only the relationship with self-rated health remained significant when adjusting for the covariates. Moreover, better self-rated health at T0 was significantly related with higher Conscientiousness 12 years later, but this association did not remain significant when adjusting for the covariates.

Regarding the effects of the covariates included, we observed the following significant effects (in both cohorts): In the models including Neuroticism, sex was significantly related to Neuroticism (higher Neuroticism scores in women). Moreover, higher depression was significantly related to higher T0 and TFU Neuroticism as well as to poorer TFU physician-rated health and T0 self-rated health. In the models containing Extraversion, depression was also a significant negative predictor of baseline

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Extraversion as well as of T0 and TFU physician-rated health and T0 self-rated health. In the models with Openness to Experience, education was a significant predictor of both baseline and follow-up Openness. Moreover, the relationship between depression and baseline Openness was negative and significant.

Depression was also significantly related with baseline physician-rated and self-rated health, and cognitive abilities were a significant positive predictor of T0 physician rated health. Regarding the models with Agreeableness, sex was significantly associated with baseline Agreeableness (higher Agreeableness scores in women), and depressive symptoms were a significant negative predictor of baseline Agreeableness, T0 self-rated health, and both T0 and TFU physician-rated health. Finally, in the models with Conscientiousness included, depressive symptoms were a significant negative predictor of baseline Conscientiousness as well as of baseline physician-rated and self-rated health. It thus seems that – also in line with the bivariate correlation pattern among the variables (see again Table 2) – depression was the covariate with strongest and most consistent associations with both personality traits and health variables1.

Discussion Our expectation of reciprocal personality-health relationships in the second half of life could be confirmed, implying that personality is not only a predictor of health as reported in multiple studies and reviews (Friedman & Kern, 2014; Smith & MacKenzie, 2006), but may as well be an outcome of health. Specifically, we found that better physician-rated health at baseline was significantly associated with lower Neuroticism and higher Agreeableness after 12 years in both cohorts. These associations remained significant when controlling for sex, education, depression, and cognitive abilities. Meaningful relations between health and later Neuroticism have also been reported by other studies (Jokela et al., 2014; Lißmann, 2003). Better physician-rated health was also significantly related with higher subsequent Extraversion in both cohorts (for comparable findings, see Berg & Johansson, 2014; Jokela et al., 2014; Sutin et al., 2013; Wagner et al., 2015), but this association did not remain statistically significant after adjusting for covariates. Aspects of physical and functional health may thus not be major and robust predictors of change in Extraversion, which has also been reported by other studies (Mõttus, Johnson, Starr, & Deary, 2012). Regarding self-rated health, some associations between T0 self-rated health and TFU personality (Agreeable-

1 Following the suggestion of one reviewer, we further investigated the role of the different covariates by first including only the demographic variables (sex, education) as covariates before additionally controlling for cognitive abilities and depression. Most of the cross-lagged paths that were significant in the unadjusted models remained significant when controlling only for the demographic covariates; only the path from baseline Openness to TFU physician-rated health was no longer significant, and the path from baseline self-rated health to TFU Agreeableness was slightly above the significance threshold (p = .055). It thus seems that adjusting for demographic variables hardly altered the cross-lagged relations between personality and health, whereas additional adjustment for cognitive abilities and particularly for depression did.

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ness and Conscientiousness) reached significance in both cohorts, but were reduced to nonsignificance when adjusting for covariates. It thus seems that – in line with our assumptions – physician-rated health challenges personality stability to a larger extent than self-rated health. Specifically, having the objective confirmation that one’s health is poor may worry individuals, resulting in an increase in Neuroticism. Suffering from poor (objective) health may also complicate the maintenance of Agreeableness; for instance, realizing that many peers are in better health may provoke feelings of envy or hostility. In contrast, regarding self-rated health, the mere feeling that one’s health gets poorer may affect personality to a lesser extent than having objective evidence via a physician. Moreover, given that the relationships between T0 self-rated health and TFU Agreeableness and Conscientiousness did not remain significant when controlling for covariates, it seems that the relationship between self-rated health and later personality is – unlike the one between physician-rated health and personality – to some extent spurious. Among the covariates, particularly depression may have acted as a “common cause” by influencing both selfrated health (Despot Lucanin & Lucanin, 2012; Pinquart, 2001; Schnittker, 2005; Spuling, Wurm, Tesch-Römer, & Huxhold, 2015) as well as personality change (Klein et al., 2011) over time. Indeed, we found that depression was the covariate which exhibited strongest associations with both personality traits and health measures, and adjusting only for demographic variables attenuated the personality-health cross-lagged relationships to a lesser extent than additionally adjusting for depression and cognitive abilities. However, more research is needed to investigate the role of potential third variables in the personality-health interplay. Some of these third variables may actually be important mediators or moderators of personalityhealth associations. For instance, personality-health associations have been found to vary according to sex (Chapman, Fiscella, Duberstein, Coletta, & Kawachi, 2009) and education (Jaconelli et al., 2013). The identification of such mediating and moderating factors requires further research.

Our finding of significant associations between objective health and subsequent personality may imply that the wellknown effect of health on quality of life and well-being (e.g., Kunzmann, Little, & Smith, 2000) is mediated by personality.

That is, poorer objective health seems to predict unfavorable personality levels 12 years later which could in turn affect wellbeing. Indeed, previous research has shown that personality traits act as meaningful predictors of well-being (Charles, Reynolds, & Gatz, 2001; Mroczek & Spiro, 2005; Tauber, Wahl, & Schröder, 2016).

Considering the opposite paths, from baseline personality to later health, both higher baseline Openness and Conscientiousness were significantly related with better subsequent physician-rated health in both cohorts which is in line with other findings (Chapman et al., 2013; Sutin et al., 2013; Tolea, Costa et al., 2012; Tolea, Ferrucci et al., 2012); however, associations were no longer significant when controlling for covariates. In © 2017 Hogrefe

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line with our assumption, associations between personality and later self-rated health were stronger and more robust compared to the associations between personality and later physician-rated health. Specifically, higher Extraversion and Conscientiousness scores at T0 were significantly related with better self-rated health 12 years later in both cohorts, and relationships remained significant when controlling for covariates. This potentially protective role of both Extraversion and Conscientiousness for self-rated health has also been found in other studies (Human et al., 2013; Magee et al., 2013; Sutin et al., 2013; Turiano et al., 2012) and could be due to social and lifestyle factors (such as health-related behaviors; Friedman, Kern, Hampson, & Duckworth, 2014; Shanahan et al., 2014).

Moreover, in analogy to other reported findings (Human et al., 2013; Löckenhoff et al., 2012; Sutin et al., 2013; Turiano et al., 2012), the relationship between baseline Neuroticism and later self-rated health was also significant in both cohorts, with lower Neuroticism predicting better health. However, when adjusting for covariates, this association did not remain significant.

Our finding that some personality traits (Extraversion and Conscientiousness) are significant and robust predictors of selfrated health may have meaningful implications regarding interventions. As self-rated health is a meaningful marker of functioning, significantly predicting mortality above and beyond objective health (DeSalvo, Bloser, Reynolds, He, & Muntner, 2006; Idler & Benyamini, 1997), interventions to improve selfrated health could be beneficial for “distal” health outcomes such as longevity. Specifically, following our findings, interventions targeting at personality change could also affect self-rated health, and there is indeed first promising evidence for the changeability of personality traits via interventions (Chapman, Hampson, & Clarkin, 2014; Magidson, Roberts, Collado-Rodriguez, & Lejuez, 2014). Moreover, an interesting question for future research could be whether the well-established effect of personality on mortality (e.g., Turiano, Chapman, Gruenewald, & Mroczek, 2015) is mediated by self-rated health.

Notably, unlike other studies (Canada et al., 2016; Duberstein et al., 2003; Magee et al., 2013), we found weak evidence for the moderating role of age/birth cohort regarding longitudinal personality-health associations: apart from the covariateadjusted model relating Neuroticism with later self-rated health, all other associations between personality and health could be set equal between groups. However, most of the research reporting age differences in personality-health associations has been based on cross-sectional study designs. It may thus be that when considered longitudinally, associations between personality and health do not (or only negligibly) change from middle adulthood to old age. Alternatively, the cohorts in our study may still have been too similar regarding mean age, with our late-life sample representing “young-old age” rather than “old-old age”; the age-associated increase in associations between self-rated health and personality, as reported in other studies (Duberstein et al., 2003), may not occur before the onGeroPsych (2017), 30 (1), 5–17

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set of very old age. Further research including additional age cohorts (particularly old-old samples) will be needed to investigate this assumption.

The present study has several strengths, including the extensive measurement of physician-rated health, the long measurement interval, and the low attrition rate of the study sample. However, there are also some limitations. First, the present study investigated longitudinal interrelationships of (admittedly) broad concepts of personality and health. Investigations of associations between personality trait subfacets (such as impulsiveness; Sutin et al., 2013) – or even single personality scale items (Murray & Booth, 2015) – and health, and more refined, multidimensional assessments of health based on additional health indicators might offer deeper, “micro-level” insights in the personality-health interplay.

Second, possible mechanisms underlying the personalityhealth interplay were not investigated in the present study. For instance, it seems that the combination of (daily) stress (Neupert, Mroczek, & Spiro, 2008; Rickenbach, Almeida, Seeman, & Lachman, 2014) or sensory impairment (Gaynes, Shah, Leurgans, & Bennett, 2013; Wettstein, KuUma, Wahl, & Heyl, 2015) with high Neuroticism is particularly detrimental for cognitive functioning.

Whether this is also true for health in general (and not only for cognitive health), remains unclear and an important issue for future investigations. Moreover, personality-health associations may be stronger when considering trait interactions (e.g., Tolea, Terracciano, Milaneschi, Metter, & Ferrucci, 2012; Turiano, Mroczek, Moynihan, & Chapman, 2013) instead of isolated, single personality traits only which requires further research. Further, associations of personality traits with health (or health-related behaviors; Armon & Toker, 2013) could be nonlinear which also deserves future empirical investigation.

Third, regarding measurement issues, self-rated health was measured by one single item only so that psychometric properties of this variable may be questionable. However, single-item measures of self-rated health are commonly utilized, parsimonious, and exhibit consistent and meaningful correlates with objective health parameters (DeSalvo et al., 2006; Idler & Benyamini, 1997; Pinquart, 2001). Therefore such single-item measures can be considered as valid.

Fourth, the issue of sample selectivity must be addressed. Overall, participants who continuously take part and remain in longitudinal studies are healthier (Vestergaard et al., 2015), which can be subsumed under the term “healthy volunteer bias.” Similarly, personality traits have been found to be systematically related with both study participation (Walsh & Nash, 1978) and missing data patterns (Jerant, Chapman, Duberstein, & Franks, 2009).

This, of course, limits the extent to which the results can be transferred to the general population. Regarding the ILSE-sample, there is a similar selective dropout dynamic as in other longitudinal studies (Sattler et al., 2015), but the very low attrition rates which we observed for both sample cohorts may counteract this problem to some extent. We also want to point out that our selectivity analyses suggest that there is no evidence for selective GeroPsych (2017), 30 (1), 5–17

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dropout with regard to personality traits in our sample. Regarding health and the covariates, some selective dropout effects were significant, but all of them were of minor effect size. This is also true regarding differences between cohorts regarding the size of selective dropout effects. Therefore, it is rather unlikely that our findings were severely biased due to selective attrition. Moreover, sample selectivity might have contributed to lower interindividual variability in health and personality traits so that the results of the present study may have actually underestimated the “true” size of personality-health associations.

Fifth, it is important to point out that our rather restrictive adjustment for covariates, which has resulted in several attenuated (and no longer significant) longitudinal relationships, may to some extent reflect an “overadjustment.” For instance, depressive symptoms are (both conceptually and empirically) closely related to personality traits such as Neuroticism (Klein et al., 2011; Matthews, Deary, & Whiteman, 2009), and depressive symptoms and cognitive abilities (i.e., as a marker for mild cognitive impairment (MCI) or dementia) were also an explicit criterion for the rating of participants’ health by the study physicians. Therefore, the “true” strength of associations between personality and health may lie somewhere in between the coefficients from the unadjusted models and the ones estimated in the adjusted models.

Sixth, the availability of more measurement occasions would have been desirable. According to Kenny (2005), the unfolding of processes and their interrelations over time may not be sufficiently informed (and described) based on two assessments only (see also Johnson et al., 2012). Therefore, future studies including more measurement occasions are necessary.

Our study provides important conclusions regarding the general longitudinal interplay of personality and health in the second half of life. First, our findings suggest that the longitudinal relationships of personality and health are indeed reciprocal. Second, we found objective health to be a stronger predictor of later personality, particularly of Neuroticism and Agreeableness, than selfrated health. Third, considering the opposite direction, personality (particularly Extraversion and Conscientiousness) seems to be more strongly related to later self-rated than to objective health.

Fourth and finally, no differences regarding the interrelationships according to cohort were found, implying that longitudinal associations are similar in midlife and early late life.

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

Acknowledgments This publication is based on data from the Interdisciplinary Longitudinal Study of Adult Development (ILSE), currently © 2017 Hogrefe

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funded by the Dietmar Hopp Stiftung and previously funded by the Federal Ministry of Family Affairs, Senior Citizens, Women and Youth (Grant Ref.: 301-1720-295/2 and 3016084/035). We thank all project members for their contribution. In particular, we extend our appreciation to Prof. Dr. Johannes Schröder and Dr. Christine Sattler as well as to our study participants, who invested a great deal of time and energy to serve our research.

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Turiano, N. A., Pitzer, L., Armour, C., Karlamangla, A., Ryff, C. D., & Mroczek, D. K. (2012). Personality trait level and change as predictors of health outcomes: Findings from a National Study of Americans (MIDUS). Gerontology Series B: Psychological Sciences and Social Sciences, 67B, 4–12. doi 10.1093/geronb/gbr072 Vestergaard, S., Andersen-Ranberg, K., Skytthe, A., Christensen, K., Robine, J.-M., & Jeune, B. (2015). Health and function assessments in two adjacent Danish birth cohorts of centenarians: Impact of design and methodology. European Journal of Ageing, 13, 15–23. doi 10.1007/s10433-015-0354-z Wagner, J., Ram, N., Smith, J., & Gerstorf, D. (2015). Personality trait development at the end of life: Antecedents and correlates of mean-level trajectories. Journal of Personality and Social Psychology, 111, 411–429. doi 10.1037/pspp0000071 Walsh, J. A., & Nash, M. M. (1978). Personality characteristics of volunteers for medical research. Criminal Justice and Behavior, 5, 99–116. doi 10.1177/009385487800500201 Wettstein, M., KuUma, E., Wahl, H.-W., & Heyl, V. (2015). Cross-sectional and longitudinal relationship between neuroticism and cognitive ability in advanced old age: The moderating role of severe sensory impairment. Aging & Mental Health, 20, 918–929. doi 10.1080/13607863.2015.1049119 Zimprich, D., Allemand, M., & Dellenbach, M. (2009). Openness to Experience, fluid intelligence, and crystallized intelligence in middle-aged and old adults. Journal of Research in Personality, 43, 444–454. doi 10.1016/j.jrp.2009.01.018 Zimprich, D., & Mascherek, A. (2010). Five views of a secret: Does cognition change during middle adulthood? European Journal of Ageing, 7, 135–146. doi 10.1007/s10433-010-0161-5 Zung, W. K. (1965). A self-rating depression scale. Archives of General Psychiatry, 12, 63–70. doi 10.1001/archpsyc.1965.

01720310065008 Manuscript received: 16.08.2016 Manuscript accepted after revision: 03.11.2016 Markus Wettstein, PhD Department of General Internal Medicine and Psychosomatics Medical Hospital Im Neuenheimer Feld 410 69120 Heidelberg Germany markus.wettstein@med.uni-heidelberg.de

GeroPsych (2017), 30 (1), 5–17

Using the LIM “The book fulfills its promise to explain the dynamics of autobiographical memory.”

Francine Conway, in PsycCRITIQUES, Vol. 55 (44)

Marian Assink / Johannes J. F. Schroots

The Dynamics of Autobiographical Memory Using the LIM | Life-line Interview Method 2010, x + 162 pp.

US $69.00 / € 49.95 ISBN 978-0-88937-370-9 Also available as eBook Does autobiographical memory change through the lifespan? Which events do we remember and why?

Do the memories that are important differ between men and women, between younger and older people?

These are just some of the fundamental questions examined in this state-of-the art book about the course of autobiographical memory throughout life – a topic that is of increasing importance as people are living ever longer.

Based upon a 5-year longitudinal research study using the LIM | Life-line Interview Method, in which young, middle-aged, and older men and women were interviewed three times, this book provides a completely new perspective on the dynamics of both retrospective and prospective memory.

What is recalled, how it is evaluated, and the relationships between gender, age, and memory over the course of

www.hogrefe.com

life are reported, as is the “bump effect” demonstrated using the LIM, with older adults recalling a disproportionate number of events from adolescence and early adulthood, thus contradicting classical views of life-long memory.

This volume also presents the first authorized version of the Schroots’ LIM | Life-line Interview Method, which asks people to draw their life-line and tell their own life-story for both past and future. Results obtained using the LIM should be interpreted, qualitatively and quantitatively, in the light of the longitudinal data presented here.

Dynamics of Autobiographical Memory is a “must” for faculty, graduate students, and professionals engaged in the study of development and aging, and in the construction and interpretation of individual life histories and expectations for the future.

J. B. Hessler et al.: Cro ss-Validation GeroPsych (2017), of New © 2017 30SKT (1), Hogrefe 19–25 Norm

Cross-Validation of the Newly-Normed SKT for the Detection of MCI and Dementia Johannes Baltasar Hessler1, Mark Stemmler2, and Horst Bickel1 1 2

Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, Technical University of Munich, Germany Chair of Psychological Assessment, Quantitative Methods, and Forensic Psychology, Friedrich-Alexander University of Erlangen-Nuremberg, Germany

Abstract. New regression-based norms for the SKT Short Cognitive Performance Test were introduced but have not been cross-validated for the detection of mild cognitive impairment (MCI) and dementia. We examined 562 (59.6% female) community-dwelling persons (mean age = 75.8, SD = 5.5) at baseline and followed up with up to three annual visits. Participants were classified as being healthy, with MCI, or with dementia according to the Clinical Dementia Rating (CDR) and the SKT. Overall congruency between the ratings was 57.8%. The correlation between SKT and MMSE scores reached r = –0.67. Sensitivity and specificity for MCI and dementia were 0.89 and 0.60 as well as 0.83 and 0.84, respectively. The SKT detected cognitive decline at early stages but produced increased rates of false positives.

Keywords: SKT short cognitive performance test, regression-based norming, mild cognitive impairment, dementia, MCI, sensitivity, specificity

Introduction The Short Cognitive Performance Test (SKT; Erzigkeit, 2001) is an internationally employed instrument for the measurement of memory and attention (Lehfeld & Erzigkeit, 1997) that is available in five parallel forms. The SKT is suited for detecting mild cognitive impairment (MCI) and dementia as well as for grading the severity of cognitive impairment (Lehfeld & Erzigkeit, 2005).

The old norms of the SKT date from 2001 (Erzigkeit, 2001) and had to be renewed to conform to regulations for the actuality of test norms in Germany (DIN 33430, 2002). Following reports from clinicians about a certain vagueness of the old norms in early stages of cognitive decline, the new, regressionbased norming approach (Crawford & Garthwaite, 2006) was introduced with the aim of increasing the sensitivity of the SKT for MCI (Stemmler, Lehfeld, & Horn, 2015). A detailed description of the new norming procedure is published (Stemmler, Lehfeld, Siebert & Horn, in press). In general, the norming included two steps. First, multiple regression analyses were used to predict expected SKT raw scores for each subtest from age, sex, intelligence, and their interactions in a large sample of older healthy persons. Based on the magnitude of the difference between observed and expected scores, either 0, 1, or 2 points were given for each subtest with more points indicating lower performance than expected. The resulting total scores range from 0 to 18. Second, in a sample of memory clinic patients who were either cognitively healthy or had MCI or dementia, a traffic-light system was developed based on optimal cutoffs for the discrimination between three diagnostic groups. © 2017 Hogrefe

Total scores between 0 and 4 suggest cognitive health (green), scores between 5 and 10 MCI (yellow), and scores between 11 and 18 suspected dementia (red). However, the regressionnormed SKT scores and the traffic-light system have not yet been validated in an independent sample.

The present study applied the new regression-based normings to SKT raw scores obtained in a sample of older persons with and without cognitive impairment before the new norming procedure was introduced. We examined the concurrent validity of regression-normed SKT scores and the traffic light system with clinical criteria for MCI and dementia as well as with the Mini Mental Status Examination (MMSE; Folstein, Folstein, & McHugh, 1975).

Method Participants The sample for the cross-validation of the new norming of the SKT consisted of community-dwelling persons aged 65 and older who were recruited during treatment in three general hospitals in Munich, Germany. After discharge, they were visited in their homes in up to four follow-ups. Persons with dementia in the hospital were excluded from further examinations. The present analyses are based exclusively on data collected at the follow-up examination. A detailed description of the sample and the screening procedure in the hospital is published elsewhere (Bickel, Mösch, Seigerschmidt, Siemen, & Förstl, 2006).

GeroPsych (2017), 30 (1), 19–25 DOI 10.1024/1662-9647/a000154

20

Study Protocol and Materials The study protocol was approved by the institutional review board of the Faculty of Medicine at the Technical University of Munich. The patients were examined by trained psychiatrists and psychologists. An initial screening was conducted in the hospitals (T0). Approximately 3 months after all participants had been discharged from the hospital and their acute illness had subsided, they were visited at their homes for a first followup (T1). Three further examinations with identical procedures took place with intervals of one year (T2, T3, and T4).

Dementia at the screening in the hospital (T0) was diagnosed with the Structured Interview for the Diagnosis of Dementia of the Alzheimer Type, Multiinfarct Type, and Dementia of other Etiology according to DSM-III-R, DSM-IV and ICD-10 (SIDAM; Zaudig & Hiller, 1996). At the follow-ups (T1–T4) a specially compiled test battery was administered, including the MMSE, a clock drawing test (Manos & Wu, 1994), a verbal fluency test (number of animals within 60 s), and the SKT (Erzigkeit, 2001). At each examination a different parallel form of the SKT was used in order to avoid learning effects. Subjective memory impairment was assessed with items of the Cambridge Examination for Mental Disorders of the Elderly (CAMDEX; Roth et al., 1986). The participants’ functional level of daily activities was established in interviews with knowledgeable informants by using the Bayer Activities of Daily Living Scale (B-ADL; Erzigkeit et al., 2001; Erzigkeit & Lehfeld, 2010) and the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE; Jorm, 1994). Depressive symptoms were assessed with the 15-item version of the Geriatric Depression Scale (Yesavage & Sheikh, 1986). Based on all the above information, each participant’s cognitive status was rated on the Clinical Dementia Rating Scale (CDR; Hughes, Berg, Danziger, Coben, & Martin, 1982). The CDR discriminates between five stages of cognitive impairment (with corresponding numerical indices): none (0), very mild (0.5), mild (1), moderate (2), and severe (3). While a score of 0 indicates cognitive health and scores between 1 and 3 indicate dementia, a score of 0.5 corresponds to an intermediate state that resembles MCI.

Statistical Analysis SKT raw scores were converted into normed scores for all examinations (T1–T4). No direct estimator of intelligence was obtained in the cross-validation sample. As suggested by the authors (Stemmler et al., 2015), we substituted values for IQ according to the highest educational attainment (i.e., no degree, compulsory secondary degree, and higher degrees) with the mean values of the respective groups in the norming sample (Stemmler et al., in press).

Concurrent validity was examined with bivariate correlations (Pearson’s r) between SKT and MMSE total scores at all examinations. Further, the traffic light classification of the SKT GeroPsych (2017), 30 (1), 19–25

J. B. Hessler et al.: Cross-Validation of New SKT Norm

was compared with a trichotomized rating on the CDR, indicating cognitive health (0), MCI (0.5), and dementia (1–3). To summarize the results and to increase the sizes of the MCI and dementia groups, the cells of the SKT × CDR crosstabs were added up for all examinations.

Analysis of variance (ANOVA) was employed to compare the mean SKT scores between the above CDR groups at all four examinations. We tested the hypothesis that persons classified by the CDR as either healthy, having MCI, or having suspected dementia differed in their mean SKT scores.

The effect sizes for the main effect of CDR are given as partial η2, which describes the amount of variance explained by the predictor. According to conventional interpretation values between 0.02 and 0.12 are considered as small, values between 0.13 and 0.25 as moderate, and values above 0.26 as large effects. Significant main effects of cognitive status according to CDR were followed up with Bonferroni posthoc tests.

A receiver operator characteristics (ROC) analysis was employed to calculate the areas under the curve (AUC) for SKT total scores. The AUC indicates the overall classification accuracy of a test with a value of 1 suggesting perfect accuracy.

AUCs were separately calculated for each examination and for MCI (CDR 0 versus 0.5, 1–3 excluded), dementia (CDR 0–0.5 versus 1–3), and the discrimination MCI (CDR 0.5) versus dementia (CDR 1–3) with persons without cognitive impairment (CDR 0) excluded. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and odds ratios were calculated for the three diagnostic comparisons, using the above CDR groups. Also, odds ratios and 95% confidence intervals were calculated. Following the traffic light system of the SKT, a cutoff of 4/5 was employed for MCI and a cutoff of 10/11 for dementia. Case classifications were added up across all examinations to increase the group sizes and for the validity statistics to reflect the overall ability of the SKT to detect MCI and dementia.

ANOVAs were conducted to investigate the validity of the traffic light system in discriminating different stages of cognitive decline at each of the four examinations. We tested the hypothesis that persons classified by the SKT as either healthy, having MCI, or having suspected dementia differed in their mean MMSE scores. Bonferroni posthoc tests were performed for significant main effects of the SKT classification.

Data analysis was performed with SPSS 23.

Results At T1, 562 (59.6% female) persons were examined. Their mean age was 75.8 (SD = 5.5). A total of 350 (62.3%) persons completed lower secondary education and 212 (37.7%) had higher degrees. Table 1 displays the number of included participants and the CDR ratings at the four examinations. Because all pa© 2017 Hogrefe

J. B. Hessler et al.: Cross-Validation of New SKT Norm

21

Table 1. Characteristics of the verification sample at the four examinations Severity of cognitive impairment

Examination T1

T2

T3

T4

555/562

457/467

391/413

309/351

Healthy (0)

368 (66.3)

343 (75.0)

275 (70.3)

216 (69.9)

Mild cognitive impairment (0.5)

172 (31.0)

74 (16.2)

62 (15.9)

49 (15.9)

SKT completed/persons examined, N/N Cognitive impairment (CDR), N (%*)

Mild dementia (1)

14 (2.5)

35 (7.7)

35 (8.9)

24 (7.8)

Moderate dementia (2)

1 (0.2)

5 (1.1)

18 (4.6)

19 (6.1)

Severe dementia (3)

0

0

1 (0.3)

1 (0.3)

Note. CDR = Clinical Dementia Rating. *Percentages apply to the N of SKT completed.

Table 2. Congruence of Clinical Dementia Rating and SKT Short Cognitive Performance Test traffic light system summarized for all four examinations SKT Short Cognitive Performance Test CDR

Healthy (green) 0–4 points N (%a)

MCI (yellow) 5–10 points N (%a)

Healthy (0)

716 (59.5)

402 (33.4)

85 (7.1)

1203

41 (11.5)

146 (40.9)

170 (47.6)

357

2 (1.3)

24 (15.6)

128 (83.1)

154

MCI (0.5) Dementia (1–3) Total

759

572

Suspected dementia (red) 11–18 points N (%a)

383

Total

1714

a

Note. Percentages apply to horizontal marginal cell totals, i.e., the classifications of the Clinical Dementia Rating (CDR). MCI = mild cognitive impairment.

Table 3. Differences in SKT scores between cognitively healthy, mild cognitive impairment (MCI), and dementia according to the Clinical Dementia Rating (CDR). Results of the analyses of variance Examination

Main effect CDR F (df)a

partial η2

T1

11.54 (2, 552)

0.29

Posthoc testb CDR Healthy MCI Dementia

T2

T3

T4

206.26 (2, 454)

209.29 (2, 389)

196.55 (2, 307)

0.48

0.52

0.56

Healthy

SKT score M (SD) 5.06 (3.77) 9.80 (4.47) 14.53 (3.34) 4.28 (3.53)

MCI

10.66 (3.74)

Dementia

14.08 (3.42)

Healthy

4.21 (3.43)

MCI

10.31 (3.56)

Dementia

13.67 (3.74)

Healthy

3.97 (3.63)

MCI

8.82 (4.16)

Dementia

14.60 (3.86)

Note. aAll main effects significant at p < .001. bAll between groups comparisons significant at p < .001 with Bonferroni correction.

tients with dementia were excluded at the screening, most cases of dementia that developed over the course of the study were mild or moderate.

Correlations between SKT and MMSE total scores were moderately large and statistically significant at T1, r = –0.49, N = 550, p < .001, at T2, r = –0.63, N = 457, p < .001, at T3, r = –0.65, N = 391, p < .001, and at T4, r = –0.67, N = 310, p < .001. The congruence of the SKT traffic light system and the © 2017 Hogrefe

trichotomized CDR-ratings is displayed in Table 2 with the results of all examinations summed up. The overall classification congruency was 990/1,714 (57.8%). With the CDR as reference, the SKT had the best classification rate in the dementia group and the worst in the MCI group. The marginal cell totals of Table 2 suggest that the SKT is less forgiving than the CDR with more cases being classified as cognitively impaired. For example, 85 cases with a CDR rating of 0 (healthy) were conGeroPsych (2017), 30 (1), 19–25

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J. B. Hessler et al.: Cross-Validation of New SKT Norm

Table 4. Classification accuracy of the SKT and the Mini Mental State Examination (MMSE) in mild cognitive impairment (MCI) and dementia with test scores as continuous variables: results of the receiver operator characteristics analysis Validity

Area under the curve (95% confidence interval) T1

T2

T3

T4

Healthy vs. MCIa

0.79 (0.75–0.83)

0.88 (0.84–0.92)

0.88 (0.84–0.92)

0.83 (0.78–0.89)

No dementia vs. dementiab

0.91 (0.86–0.96)

0.93 (0.89–0.96)

0.92 (0.88–0.96)

0.96 (0.94–0.98)

MCI vs. dementiac

0.80 (0.69–0.90

0.76 (0.67–0.86)

0.75 (0.66–0.84)

0.87 (0.81–0.94)

Healthy vs. MCIa

0.79 (0.75–0.83)

0.86 (0.81–0.90)

0.91 (0.87–0.95)

0.82 (0.77–0.88)

No dementia vs. dementiab

0.89 (0.81–0.98)

0.94 (0.90–0.97)

0.95 (0.92–0.97)

0.95 (0.92–0.97)

MCI vs. dementiac

0.78 (0.65–0.92)

0.81 (0.72–0.89)

0.80 (0.72–0.88)

0.85 (0.78–0.93)

SKT

MMSE

Note. aClinical Dementia Rating 0 versus 0.5. bClinical Dementia Rating 0–0.5 versus 1–3. cClinical Dementia Rating 0.5 versus 1–3. AUC = area under the curve. 95% CI = 95% confidence interval.

Table 5. Validity of the SKT to detect mild cognitive impairment (MCI) with the cutoff 4/5 and dementia with the cutoff 10/11. The results are based on the cumulated classifications across all examinations Criterion

Validity

Healthy vs. MCIa No dementia vs. dementia MCI vs. dementia

b

c

Accuracy

Sensitivity

Specificity

PPV

NPV

Odds ratio (95% CI)

0.66

0.89*

0.60*

0.39*

0.95

6.34 (4.39–9.16)

0.84

0.83

0.84

0.33

0.98

25.19 (16.19–39.21)

0.62

0.83

0.52

0.42

0.88

5.42 (3.39–8.66)

Note. aClinical Dementia Rating 0 versus 0.5. bClinical Dementia Rating 0–0.5 versus 1–3. cClinical Dementia Rating 0.5 versus 1–3. PPV = positive predictive value. NPV = negative predictive value. 95% CI = 95% confidence interval. *In a previous version of this article that was published online first, the values for Sensitivity (0.78), Specificity (0.64), and PPV (0.27) were false and have been corrected in this version. The amendment has no effect on the interpretation of the values.

Table 6. Differences in Mini Mental Status Examination (MMSE) scores between cognitively healthy, mild cognitive impairment (MCI), and dementia according to the SKT traffic light system). Results of the analyses of variance Examination

Main effect SKT F (df)a

partial η2

T1

65.47 (2, 551)

0.19

T2

127.34 (2, 454)

T3

0.36

113.87 (2, 387)

T4

0.37

92.74 (2, 306)

a

0.38

Posthoc testb SKT

MMSE score M (SD)

Healthy (green)

27.58 (1.60)

MCI (yellow)

26.59 (2.49)

Suspected dementia (red)

24.65 (2.93)

Healthy (green)

27.97 (1.73)

MCI (yellow)

26.64 (2.16)

Suspected dementia (red)

23.62 (3.25)

Healthy (green)

27.95 (1.66)

MCI (yellow)

26.49 (2.34)

Suspected dementia (red)

22.57 (4.45)

Healthy (green)

27.88 (1.72)

MCI (yellow)

26.66 (2.24)

Suspected dementia (red)

22.18 (4.84)

b

Note. All main effects significant at p < .001. All between groups comparisons significant at p < .001 with Bonferroni correction.

GeroPsych (2017), 30 (1), 19–25

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J. B. Hessler et al.: Cross-Validation of New SKT Norm

sidered as having suspected dementia by the SKT, while the reverse occurred for only 2 persons.

ANOVAs revealed significant main effects of CDR group (healthy, MCI, dementia) on SKT scores at all four examinations with large effect sizes (Table 3). Bonferroni posthoc tests indicated that healthy persons had significantly better scores than persons with MCI who in turn had significantly better scores than persons with dementia.

Table 4 displays the results of the ROC analysis, examining the validity of the SKT and the MMSE to detect MCI and dementia. The AUCs for the detection of MCI ranged between 0.79 and 0.88 for the SKT and between 0.79 and 0.91 for the MMSE. For dementia, the AUCs were high and ranged between 0.91 and 0.96 for the SKT and between 0.89 and 0.95 for the MMSE. For the discrimination between MCI and dementia the values ranged between 0.75 and 0.87 for the SKT and between 0.78 and 0.85 for the MMSE. Table 5 displays further validity statistics based on the cumulated classifications across the four examinations. Accuracy with the cutoff 4/5 for MCI was moderate, resulting from good sensitivity at the cost of low specificity. Accordingly, the PPV was also low while the NPV was high. Accuracy with the 10/11 cutoff for dementia was good. The relationship between sensitivity and specificity was more balanced with relatively high values for both statistics. PPV, however, was low and NPV accordingly high. The odds ratios indicated an increased risk of having MCI or dementia when scoring above the respective cutoff. The SKT was only moderately able to discriminate between MCI and dementia with an accuracy of 0.62. While sensitivity and NPV were good, specificity and PPV were low. The odds ratios indicated an increased risk of having dementia when scoring above the cutoff.

ANOVAs revealed significant main effects of the SKT group (healthy, MCI, suspected dementia) on MMSE scores at all four examinations with moderate to large effect sizes (Table 6). Bonferroni posthoc tests indicated that healthy persons had significantly higher scores than persons with MCI who had, in turn, significantly higher scores than persons with suspected dementia.

Discussion We crossvalidated the regression-normed SKT scores for the detection of MCI and dementia in a large sample of older persons and at four different points in time. In general, the classification ability of the SKT was good, as indicated by the high AUCs. The proposed traffic light scheme was sensitive to MCI and dementia, and the derived groups differed in their level of cognitive functioning as measured by the MMSE. The cutoffs, however, also produced increased rates of false positives, especially in MCI and were only partly able to differentiate between MCI and dementia. © 2017 Hogrefe

23

The aim of the regression-based norming of the SKT was to increase the ability of the test to detect cognitive decline even at early stages, and the traffic light scheme was introduced to facilitate the evaluation of the patients’ cognitive status for clinicians (Stemmler et al., 2015; Stemmler et al., in press). The results of the present study suggest that the new approach was successful in that most cases with MCI were identified and the false negative rates were rather low. Considering the CDR as gold standard suggested that the traffic light classification of the SKT yields elevated rates of false positives. This tendency might reduce the economy of the SKT, since positive screening results should always be followed by more extensive examinations and high rates of false positive would lead to unnecessary referrals. However, it can be argued that it is preferable to initiate unnecessary referrals instead of failing to detect cognitive impairment. Also, suspicious results might be followed up with additional brief tests before initiating a referral for extensive examination to investigate whether a referral is actually warranted.

The high classification accuracy of the continuous SKT scores in the ROC analysis and the differences in mean SKT scores between CDR groups suggest that the SKT total score contains important diagnostic information. While the traffic light system has practical value, it inevitably reduces this information by obscuring more detailed differences between patients. It is possible that a person who scores at the upper end of the MCI group might differ more from a person at the lower end of the same group than from a person at the lower end of the dementia group. For clinicians it might, therefore, be of value to also consider the total score and not fully rely on the traffic light classification.

The SKT is a brief screening instrument with a test duration of approximately 10 minutes. In comparison to other short tests like the MMSE or the Montreal Cognitive Assessment (MoCA; Nasreddine et al., 2005) the validity of the newly normed SKT appears to be quite competitive. In the present sample, the AUCs of the SKT and the MMSE were similar for the detection of MCI and dementia as well as for the discrimination between MCI and dementia. Importantly, validity statistics always depend on the sample they were calculated for. Therefore, the results of the studies examining other tests cannot be directly compared to our findings for the SKT. With its new norm, however, the SKT seems to be a reasonable alternative in the context of MCI and dementia when compared with established tests like MoCA (Nasreddine et al., 2005; Trzepacz et al., 2015) and MMSE (Abdel-Aziz & Larner, 2015).

The tendency of the SKT found in our sample to classify even minor impairments in test performance as conspicuous may also result from a lack of sensitivity in the reference instruments. It would be necessary to investigate whether persons classified with MCI by the SKT but not by the CDR have an increased risk of developing dementia, which would justify the diagnostic strictness of the SKT. Also, the moderate correlation with the MMSE may result from the known ceiling effects of the MMSE that prevent GeroPsych (2017), 30 (1), 19–25

24

a more detailed discrimination in stages of mild impairment (de Jager, Milwain, & Budge, 2002). The cutoffs for the traffic light system were obtained in a sample of memory clinic patients, who might represent a population of people who might be particularly sensitive to changes in their cognitive functioning and show high effort during the neuropsychological assessment. As a consequence, a person with MCI from the norming sample might perform better than a person with MCI from the present sample. As a consequence, the SKT norms would be more punishing and produce higher rates of false positives.

Strengths and Limitations Strengths of the present study include its ability to crossvalidate the new norm of the SKT in a large sample of community-dwelling older persons who were examined at four points in time and for whom clinical diagnoses of cognitive impairment were available. The study is limited by the fact that the SKT scores were known to the researchers that assigned the CDR ratings, which might have inflated the associations between SKT and CDR. However, no SKT cutoffs were used and the ratings were based on many sources of information, of which the SKT was only one. Also, the ratings were made before the new norm was introduced.

Conclusion In cross-sectional examinations, the regression-based norm of the SKT produced scores that were sensitive to MCI and dementia but also lead to increased rates of false positives when compared to the CDR. Importantly, longitudinal studies are needed to investigate the actual ability of the SKT to detect persons at early stages of cognitive decline, who might further progress to dementia.

Acknowledgments This study was supported by Dr. Willmar Schwabe Arzneimittel, Karlsruhe, Germany. We thank Rainer Diebow (Dr. Willmar Schwabe) for converting the SKT raw scores into regression-normed scores.

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

GeroPsych (2017), 30 (1), 19–25

J. B. Hessler et al.: Cross-Validation of New SKT Norm

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Erzigkeit, H. (2001). Kurztest zur Erfassung von Gedächtnis- und Aufmerksamkeitsstörungen, SKT-Manual [A short test for the assesment of deficits of cognition and attention – SKT manual].

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Erzigkeit, H., & Lehfeld H. (2010). Die Bayer ADL-Skala (B-ADL), ein Screening-Instrument für Demenz-Erkrankungen sowie zur Erfassung von Beeinträchtigungen der Alltagskompetenz [The Bayer ADL-Scale, a screening instrument for dementia as well as for impairments in activities of daily living]. Frankfurt am Main, Germany: Pearson.

Erzigkeit, H., Lehfeld, H., Peña-Casanova, J., Bieber, F., YekrangiHartmann, C., Rupp, M., . . . Hindmarch, I. (2001). The Bayer-Activities of Daily Living Scale: Results from a validation study in three European countries. Dementia and Geriatric Cognitive Disorders, 12, 348–358.

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Lehfeld, H., & Erzigkeit, H. (2005). Die Störungsgradsensitivität des SKT: Ein Literaturüberblick [The sensitivity to cognitive impairment of the SKT Short Test – A literature survey]. Zeitschrift für Gerontopsychologie & -psychiatrie, 18, 131–141.

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Nasreddine, Z. S., Philips, N. A., Bédirian, V., Charbonneau, S., Whitehead, V., Collin, I., . . . Chertkow, H. (2005). The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. Journal of the American Geriatrics Society, 53, 695–699.

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cial reference to the early detection of dementia. British Journal of Psychiatry, 149, 698–709.

Stemmler, M., Lehfeld, H., & Horn, R. (2015). SKT nach Erzigkeit – SKT Manual Edition 2015 [SKT according to Erzigkeit – SKT manual edition 2015]. Erlangen, Germany: Geromed.

Stemmler, M., Lehfeld, H., Siebert, J., & Horn, R. (in press). Ein kurzer Leistungstest zur Erfassung von Störungen des Gedächtnisse und der Aufmerksamkeit – SKT Manual Edition 2015 – und der regressionsbasierte Ansatz [A short performance test for the assessment of impairments in cognitive performance and information processing – SKT manual edition 2015 – and the continuous norming approach]. Diagnostica.

Trzepacz, P. T., Hochstetler, H., Wang, S., Walker, B., & Saykin, A. J. for the Alzheimer’s Disease Neuroimaging Initiative. (2015). Relationship between the Montreal Cognitive Assessment and Mini-Mental State Examination for assessment of mild cognitive impairment in older adults. BMC Geriatrics, 15, 107–116.

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Zaudig, M. & Hiller, W. (1996). SIDAM-Handbuch. Strukturiertes Interview für die Diagnose einer Demenz vom Alzheimer Typ, der Multi-Infarkt- (oder vaskulären) Demenz und Demenzen anderer Ätiologien nach DSM-III-R, DSM-IV und ICD-10 [SIDAM. A structured interview for the diagnosis of dementia of the Alzheimer type, multi-infarct dementia and dementias of other aetiology according to ICD-10 and DSM-III-R]. Bern: Huber.

Manuscript submitted: 30.03.2016 Manuscript accepted after revision: 11.07.2016 Published online: 31.08.2016

Johannes Baltasar Hessler Ismaninger Strasse 22 81675 München Germany Tel. +49 89 4140 6183 Fax +49 89 4140 6379 johannes.hessler@tum.de

GeroPsych (2017), 30 (1), 19–25

Chaos theory and principles of dynamic complex systems “This is an exciting new way of assessing and understanding the psychotherapeutic process that will be of great interest to clinicians and researchers alike.”

Adele M. Hayes, PhD, Professor, Department of Psychological and Brain Sciences, University of Delaware, Newark, DE

Günter Schiepek / Heiko Eckert / Benjamin Aas / Sebastian Wallot / Anna Wallot

Integrative Psychotherapy

A Feedback-Driven Dynamic Systems Approach 2015, x + 100 pp.

US $39.80 / € 27.95 ISBN 978-0-88937-472-0 Also available as eBook This book introduces a new, integrative, systemic approach to psychotherapy and counseling and shows how the principles of dynamic complex systems can guide everyday clinical work.

Our mental, interpersonal, and biological (e.g., neuronal) systems are complex and nonlinear, and allow spontaneous pattern formation and chaotic dynamics. Their self-organizing nature sometimes maneuvers the systems into pathological states.

However, the very same principles can be utilized therapeutically to encourage change for the better. The feedback-driven nonlinear dynamic systems approach described here basically attempts to facilitate positive self-organizing processes, such as order transitions, healthy patterns of behavior, and learning processes.

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In addition to describing the theory and evidence supporting the feedbackdriven nonlinear dynamic systems approach, the authors use an extensive case study to illustrate how the principles of dynamic complex systems can guide everyday clinical work. They show how modeling and monitoring of the client‘s systems and an empirical description of its patterns allows the therapist to individually fine-tune therapeutic techniques to support the client’s progress. Fine-meshed feedback based on real-time data and time-series analysis is at the core of the approach, and so an internetbased monitoring system – the Synergetic Navigation System (SNS) – that helps capture dynamic processes and guide practitioners’ therapeutic decisions is also described.

F. Godemann et al.: Inpatient Treatment GeroPsychQuality (2017), ©for 2017 30 Dementia (1), Hogrefe 27–33

Full-Length Research Report

Routine Data Indicators of Treatment for Dementia and Old-Age Depression Frank Godemann1, Claus Wolff-Menzler2, Michael Löhr3, and Hauke Wiegand1 1

Klinik für Psychiatrie und verhaltenstherapeutische Medizin, Alexianer St. Joseph Berlin-Weißensee Berlin, Germany

2

Abteilung Allgemeinpsychiatrie, Alexianer Krankenhaus Aachen, Germany

3

Fachbereich Psychiatrische Pflege und Psychische Gesundheit, Fachhochschule der Diakonie Bielefeld, Germany

Abstract: Complications in the course of dementia are one of the leading reasons for treatment in German psychiatric hospitals. One way to assess treatment quality with a moderate effort is to analyze existing routine data. A large routine dataset exists for psychiatric hospitals in Germany. This work reports on the indicators of inpatient treatment of patients with dementia and compares them to those found with old-age depression. Among other results it was shown that no specific dementia diagnosis was defined in more than 15% of all cases, and that the readmission rate within 30 days was more than 25%. Depressed people, on the other hand, showed lower readmission rates: They got more specific diagnoses and more therapeutic contacts. In conclusion, several aspects of diagnosis and treatment demand improvement among patients with dementia.

Keywords: quality indicators, routine data, dementia, depression in the elderly

The changing demographic structure of society is leading to an increase in age-related psychiatric disorders. Particularly challenging is the rising number of cases of dementia. The recent World Health Organization (WHO) report “Dementia” estimates that in 2010 globally 35.6 million people were living with dementia. The numbers are projected to nearly double every 20 years, and the WHO estimates an increase of 7.7 million new cases each year (WHO, 2012). In Germany, about 7% of all people above the age of 65 suffer from dementia (Bickel, 2012). In 2002, the number of persons with dementia lay at about 1 million (Bickel, 2012). Two-thirds of all dementia patients are being cared for at home in Germany (Ärzteblatt, 2014). However, because of an aggravation of a patient’s mental situation (for example, unfamiliar behavior), caring for people with dementia can be temporarily a major burden for families and nursing facilities (Morgan, Semchuk, Stewart, & D’arcy, 2002). Therefore, psychiatric (and somatic) hospitals have seen a considerable increase in dementiarelated symptoms like delirium, depressive and delusional syndromes, aggression, tendency to walk off, and reversal of daynight rhythm (Azermai, 2015; Kolbeinson & Jónson, 1993; Lundberg, Gustafsson, Meagher, & Munk-Jørgensen, 2014). A recent study found a period prevalence of 18.6% of psychiatric inpatient cases (> 64 years) with dementia as their main diagnosis (F00, F01, F03) based on routine data from a sample of 35 specialist hospitals, university hospitals, and psychiatric departments (Löhr, 2014). In somatic inpatient settings, patients with dementia play an important role, too, with prevalence rates between 12.3–48.8%. (Erkinjuntti, 1986; Kolbeinson & Jónson, 1993; Sampson, Blanchard, Jones, Tookman, & King, 2009). © 2017 Hogrefe

Psychiatric hospitals have to prepare for the rising number of people with dementia and dementia-associated syndromes by offering adequate capacities and by adapting their treatment concepts. Along with these changes, the quality of diagnostic and treatment procedures for people with dementia is becoming increasingly important. This encompasses aspects like adequate differential diagnosis, a sufficient number of skilled staff and adequate resources for psychosocial interventions, for example, to reduce agitation (Livingston et al., 2014) or to execute cognition-based and exercise interventions (Wang, Yu, Wang, Tan, Meng, & Tan, 2014). An adapted pharmacological treatment that minimizes the use and side effects of psychotropic drugs is also important (Atti et al., 2014; Tampi & Tampi, 2014). Currently, it is being controversially discussed in Germany how best to measure and how to improve the diagnostic and treatment quality in psychiatric hospitals. New laws are being drawn up to install a system of payment for performance. The Federal Joint Committee (Gemeinsamer Bundesausschuss – G-BA), a decisionmaking body of the joint self-government of physicians, hospitals, and health insurance funds, was commissioned by the government (according to §137a SGB V) to develop quality indicators while respecting the principle of data economy by using mainly routine records.

This study describes indicators of inpatient dementia diagnosis and treatment in a large routine dataset, compares them to the same indicators in inpatient depression diagnosis and treatment in the same age group, and examines their adequacy as quality indicators. Such a comparison of the diagGeroPsych (2017), 30 (1), 27–33 DOI 10.1024/1662-9647/a000163

28

F. Godemann et al.: Inpatient Treatment Quality for Dementia

nostic groups is considered to inform about the usefulness of quality indicators for dementia. This comparison should serve as a guiding principle for further research.

includes 179,538 patients, 18,625 thereof in persons older than 65 years of age and 10,301 patients in persons older than 65 years of age and a hospital stay of more than 8 days (Table 1).

Method

Characteristics of the Hospitals Included in the Study

In 2011, the German Association for Psychiatry, Psychotherapy, and Psychosomatics (Deutsche Gesellschaft für Psychiatrie und Psychotherapie, Psychosomatik und Nervenheilkunde – DGPPN), together with the Union of Medical Directors of Psychiatric and Psychotherapeutic Hospitals (Bundesdirektorenkonferenz – BDK) and the Working Committee of Head Physicians in Departments of Psychiatry and Psychotherapy in General Hospitals in Germany (Arbeitskreis der Chefärztinnen und Chefärzte der Kliniken für Psychiatrie und Psychotherapie an Allgemeinkrankenhäusern – ackpa) launched a research project to identify indicators of patient care in psychiatric and psychosomatic facilities (VIPP) (Wolff-Menzler et al., 2014).

The VIPP project serves to establish a data pool on the basis of routine records from voluntarily participating psychiatric and psychosomatic departments and specialized hospitals, which helps to perform analyses for answering treatment-related questions. The routine records in question are the so-called §21 dataset, which, according to the KHEntgG (“Krankenhausentgeltgesetz,” Hospital Reimbursement Ac), all hospitals in Germany are required to transmit once a year to the Institute for the Hospital Remuneration System (Institut für das Entgeltsystem im Krankenhaus – InEK). This work is based on the §21 data from 2013.

The study group includes 72 psychiatric hospitals in Germany.

The total number of patients included in the VIPP database contains approximately 10% of all patients treated in the psychiatric hospitals of Germany. The hospitals were recruited by contacting the head of the psychiatric departments. The study sample includes 12 specialized psychiatric departments in hospitals and 60 specialized psychiatric hospitals.

Random Sample The analysis includes cases of inpatient treatment in adult psychiatry with dementia (> 18 years) and with depression (F32–F33) (> 64 years), derived from the §21 dataset from 2013. The following diagnoses were included in the analysis: F00.1–F3 and F05.1 in dementia and F32.0–F33.9 in depression. Most of the patients with dementia were treated because of complications in the course of the disease, e.g., aggression, hallucinations, or delirium. The sample includes 8,886 cases of depression and 7,605 cases of dementia with a hospital stay of at least 8 days. 947 cases that were diagnosed with both dementia and depression are excluded from the following analysis. Only cases with the main diagnosis of depression are included in the depression category. Altogether 7,939 cases with depression and 6,658 with dementia were used for the following statistics. In 1,689 cases, dementia was diagnosed as the main diagnosis, which means that the behavioral disturbances were recorded as the secondary diagnosis. The complete sample of the VIPP database without depression and/or dementia GeroPsych (2017), 30 (1), 27–33

Indicators The indicators are selected on the basis of plausibility and the availability of the so-called §21 dataset. Using routine data means limiting the validity and reliability of the data. Indicators used for comparison to dementia patients with depression are marked with an asterisk (*).

General Indicators The change of environment alone makes inpatient stays a stressful time for patients with dementia. Hence it is advisable to keep inpatient stays as short as possible (indicator: length of stay). At the same time, premature discharge should not lead to subsequent readmission (indicator: readmission rate within 30 or 60 days, respectively).

Diagnostic Indicators The particular dementia syndromes should be diagnosed specifically (e.g., F00.1 = Alzheimer’s dementia). Unspecific diagnoses should be avoided (e.g., F03 = Unspecified dementia). (indicator: share of unspecified diagnoses with length of stay of more than eight days).

Whenever imaging is necessary (the indication cannot be assessed via the dataset), an MRI is the procedure of choice.

This screening should be pursued soon after admission (indicator 1: share of cerebral imaging MRT/cCT; indicator 2: number of days between admission and imaging).

Therapeutic Indicators People with dementia should be offered therapeutic treatment.

Central to the therapeutic endeavors are efforts on the part of the nursing staff and specialist therapists (e.g., ergotherapists) (indicator 1: therapy intensity = number of therapy units per week; indicator 2: number of patients without therapy units during their inpatient stay). © 2017 Hogrefe

F. Godemann et al.: Inpatient Treatment Quality for Dementia

Community-based services should be preferred (indicator: average distances between hospital and home).

29

Results General Indicators

Statistics The descriptive comparison of both study groups is done with SPPS 15.01 for Windows. χ² and T-tests were used to proof the significance of differences between the indicators.

Limitations Information about pharmacotherapy was not part of the §21 routine data set. This article has the goal of informing about the possibility of representing indicators. The observation time of data to be available (1 year) is short. Routine data are usually collected largely for accounting purposes. Thus, data collection might be influenced by a reporting bias. Therefore, results of the current study have to be interpreted with caution. Moreover, the results of the current study found only limited caring indicators. Although this perspective in the context of quality indicators is very important, no further conclusion can be drawn with respect to caring. In conclusion, the extraction of quality indicators is limited by the limitations of the present dataset. Therefore, no statements are possible concerning the “pathway perspective,” e.g., are the right people with dementia being admitted to the unit and are they being treated properly.

6,698 cases with dementia (3,763 female, 2,935 male) spent an average of 26.0 ± 16.1 days and 7,939 depressive patients elder than 64 years (5,539 female, 2,400 male) an average of 38.7 ± 28.2 days in the hospital. It was possible to analyze readmission rates in 65 of the 72 clinics and in 6,226 patients with dementia (7,164 with depression in 68 of 72 clinics). The necessary prerequisite was the possibility of identifying the patients from year to year by a unique patient number. In 735 of the 6,226 cases of dementia (12.2%), readmission was necessary within 30 days (in depression 993 of 7,939; 12.5%); in 856 of these cases with dementia (14.4%) patients were readmitted within 60 days (in depression: 1,224 of 7,939; 15.4%). This difference is not significant. These figures do not identify the share of patients treated in somatic hospitals in the meantime.

Patients with dementia were on average 80.9 ± 6.8 years older and thus older than depressive patients (74.9 ± 6.3 years) (Table 1).

Diagnostic Indicators Psychiatric hospitals should strive to find specific diagnoses in order to be able to initiate guideline-based treatments. In 24.3% of all cases (1,514), this is not the case (ICD codes F00.8, F00.9, F01.8, F01.9, and F03 were included in the category of unspecific diagnosis). With depression (F32.8, F32.9, F33.8, F33.9), the number of unspecific diagnosis is 0.5% (38 cases) (Table 2).

With dementia, cerebral imaging was performed in 3,486 cases. This represents 52.6% of all cases. The ratio of cCT to cMRI was about 4:1 (2,835 cCT to 651 cMRT). The average

Table 1. General indicators of dementia and depression Indicator

Dementia

Depression

Significance

Age

80.9 ± 6.8 years

74.9 ± 6.3 years

p < .001

Days in hospital

25.9 ± 14.7

38.7 ± 28.2

p < .001

Readmission rate 30 days

735/6.226 = 11.8%

993/7.939 = 14.4%

ns

856/6.226 = 13.7%

1.224/7.939 = 15.4%

ns

23.4 ± 32.8

24.1 ± 36.2

ns

60 days Average distance to the hospital in km

Table 2. Therapeutic and diagnostic quality indicators Therapy units (TU) per day

Dementia

Depression

Significance

TU Doctor/psychologist

0.05 ± 0.1

0.14 ± 0.16

p < .001

TU Nurses/special therapists

0.91 ± 1.31

0.65 ± 0.78

p < .001 p < .001

Patients without therapy unit

274 (4.4%)

70 (0.9%)

MRT average of all patients

651 (10.5%)

1230 (15.2%)

cCT/MRT

2.835/651

1907/1243

* < .05

Unspecific diagnoses

1.514 (24.3%)

38 (0.5%)

* < .05

© 2017 Hogrefe

GeroPsych (2017), 30 (1), 27–33

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F. Godemann et al.: Inpatient Treatment Quality for Dementia

Table 3. Distribution of dementia diagnoses in the inpatient treatment ICD-10

Regional Supply

Absolute abundances (%)

F00.0

31 (0.6%)

F00.1

1140 (20.7%)

F00.8

969 (17.7%)

F00.82

59 (1.1%)

G00.9

57 (1.0%)

F01.0

114 (2.1%)

F01.1

201 (3.6%)

F01.2

607 (11.0%)

F01.3

1122 (20.3%)

F01.8

188 (3.4%)

F01.9

230 (4.2%)

F03

1227 (22.2%)

time for the imaging to be carried out was 12 days. With depression, cerebral imaging was performed 2,285 times, but in contrast the ratio was about 1.5:1 (1,907 cCT to 1,243 cMRI) (Table 2).

Therapeutic Indicators On average, dementia patients received 0.05 therapy units with doctors or psychologists per day as well as 0.95 units with nursing staff and special therapists. This corresponds to about 2.5 minutes per day spent with doctors or psychologists in individual contact (or 15 minutes in a group of 6 patients) and 23 minutes per day with nurses and special therapists in individual and 138 minutes in a contact as a group. With depression, the therapy units with doctors and psychologists are higher (0.14), units with nursing stuff and special therapists lower (0.65). In this context, it should be noted that therapy units of less than 25 minutes are not registered. Also, intensive individual treatment (1:1 treatment) is not included in these figures. The times mentioned therefore provide an underestimation of the actual time available for caring for people with dementia and depression. 234 cases (4.4%) of cases with dementia (0.9% in depression) failed to get any therapy units while being treated in the hospital (Table 3).

Table 4. Locally available treatment – distance between hospital and patient’s home Distance (km)

Dementia

Depression

0– < 5

15.03%

16.30%

5– < 10

18.96%

18.57%

10– < 15

13.24%

14.03%

15– < 20

9.72%

9.32%

20– < 25

10.34%

10.13%

25– < 30

8.58%

7.29%

30– < 35

6.71%

5.72%

35– < 50

8.42%

8.62%

>50

9.00%

10.01%

GeroPsych (2017), 30 (1), 27–33

The average distance to a hospital was 23.4 ± 32.8 km (24.1 ± 36.2 km in depressed patients). This difference is not significant. More than 75% of all cases with both diseases lived within 30 km of the hospital (Table 4).

Discussion This analysis of routine data has three main goals: First, to describe inpatient dementia diagnosis and treatment; second, to compare the results to inpatient depression diagnosis and treatment in the same age; and third, to examine the adequacy of the indicators as quality indicators. The indicators show that – on average, patients with dementia are older then patients with depression; – the average duration of stay in patients with dementia is lower (nearly 4 weeks) than for elderly patients with a depression main diagnosis; – the overall moderate readmission rates are lower in patients with dementia than in patients with depression; – the distance to hospital is similar in the two groups; – therapy unit levels (0.05 therapy units per day) are much lower in patients with dementia than in patients with depression; – the rate of unspecific diagnosis is much higher in patients with dementia than in patients with depression; – patients with dementia less often get the more specific MRI cerebral imaging (in relation to cCT imaging) than patients with depression.

Advantages, Limitations, and Interpretation Difficulties One of the most critical limitations of the current study has to be taken into consideration. With respect to evaluating the quality of inpatient care and viewing the perspective on quality, one might ask: “Are people with dementia who are admitted to a psychiatric unit being cared for appropriately?” (the socalled “internal perspective”). The current study considered this perspective. However, the opposite perspective might look at another question: “Are the right people with dementia being admitted to the unit and are they being treated properly?” (the so-called “pathway perspective”). The latter, although of great interest for further research and clinical practice, cannot be answered within the current study.

Assessing the diagnostic and therapeutic quality of inpatient treatment for dementia patients is a complex issue. Relationship building, the quality of therapeutic interventions by welltrained staff, and good sociotherapeutic care are central. How© 2017 Hogrefe

F. Godemann et al.: Inpatient Treatment Quality for Dementia

ever, these aspects are difficult to evaluate in routine clinical practice. Assessing the quality of psychopharmacological treatment proves to be difficult, too, and has so far only been successful (to some degree) within complex studies (e.g., the Berlin Age Study) (Baltes & Mayer, 1999) or by using records of outpatient treatment of health insurance institutions (Wiegand, Sievers, Schillinger, & Godemann, 2016).

In addition to these general disadvantages, routine data can have certain specific disadvantages (Swart et al., 2015): The usefulness of quality indicators largely depends on the quality of the raw data; more informative results could be achieved particularly by improving the coding quality. Additionally, it has to be kept in mind that the data are primarily collected for accounting purposes; consequently a reporting bias cannot be excluded. On the other hand, only in this way is it possible to get big datasets without a selection bias.

Do the Presented Indicators Qualify as Quality Indicators?

The §21 dataset used in this study comprises a wealth of information, although its interpretation proves to be difficult.

Readmission Rates The necessity of a hospitalization always implies either a need for immediate medical or psychosocial action or an insufficient outpatient treatment [21: §39]. Key objectives are a continuous improvement of care quality going hand in hand with a reduction of admission rates. It would be generally possible to realize a representation of these objectives, if the number of admissions in a certain region could be related to the number of inhabitants. This criterion, however, quickly reaches its limits; not only because the area of competence of regional mandatory care clinics is not always clearly defined in all German regions, but also because people with dementia disorders are often admitted to other departments, especially internal medicine and neurological departments (Ärzteblatt, 2014; Kolbeinson & Jónson, 1993). A quality indicator measuring the share of necessary inpatient treatments for dementia patients in the population (with dementia as main diagnosis) would allude to the capacity and quality of the outpatient psychiatric and psychosocial treatment. Nevertheless, the admission rate is a more objective parameter. And it gains in value when used to compare different disease conditions and to evaluate the level of supply in a longitudinal perspective.

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duced before admission. The so-called cumulative length of stay, which can also be deducted from routine records (provided the figures can be analyzed from year to year), is presumably a useful indicator (Brantervik, Jacobsson, Grimby, Wallén, & Bosaeus, 2005; Han et al., 2011; Nobili et al., 2011). Just like the number of readmissions in a predefined period of time, this indicator shows whether the therapy led to a sufficient stability of treatment and care. As useful as these indicators may be, they do not allow us to attribute a large share or shortage of quality to the different institutions such as a hospital. It is to be assumed that regions with a shortage of resident doctors experience a higher readmission rate.

Community-Based Treatment In order to address the issue of the regional mandatory care system (which could be considered as an indicator for structural quality, if unambiguous nationwide criteria existed), it should be noted that the routine records of the §21 dataset allow analysis, at least roughly, of the distance between the patient’s home and the treating hospital.

Cerebral Imaging Every patient with a dementia syndrome should at least once receive diagnostic cerebral imaging (DGPPN & DGN, 2009).

The absolute number of cerebral images performed in an inpatient setting can be deducted from the routine records. This number, however, does not permit drawing a reliable conclusion about the increase or decrease (in %) of the diagnostic quality. Routine records also fail to provide information about whether the individual imaging was in fact justified – this would be only conceivable to some extent with cross-sectional records.

It can clearly be said that an MRI is the superior method for brain imaging compared to a cCT. It is therefore surprising that only one in four images was carried out through an MRI. Even more useful as a quality indicator would be the share of cMRI in cerebral diagnostic imaging, if routine records included information about the indication. The promptness of diagnostic imaging can be another reference to the organizational capacity of a psychiatric hospital: It is often not possible to realize diagnostic imaging immediately, especially during the acute stage.

Over the course of time, however, the promptness of diagnostic imaging can be a descriptive indicator.

Diagnostic Specificity Length of Stay Another possible quality indicator is the length of stay. However, this is difficult to interpret, too. A short length of stay could mean that the patient was discharged from hospital prematurely, putting the outpatient surroundings once again into a situation they cannot cope with, since the necessary treatment intensity of an inpatient treatment had not been sufficiently re© 2017 Hogrefe

The criteria for the subtypes of dementia are clearly defined and are generally based on clinical criteria and additional examinations (such as MRT). Specialized hospitals should therefore be able to diagnose a specific type of dementia. A specific diagnosis is essential for subsequent treatment decisions.

Astonishingly, this does not seem to be possible in 15% of all patients.

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Time Spent with the Patient The amount of time patients spend with their therapists is one of the main factors for a psychiatric treatment to be successful. That said, it is alarming to learn how many dementia patients have not received even one therapy session of 25 minutes at a time. The average of therapy units per day spent with doctors or psychologists equally suggests that there are not enough resources available for dementia patients. In another study, focusing explicitly on the therapy units for dementia patients, only those patients who had actually received a therapy unit were considered for calculating the average time of the therapy units. In that study, the range was found to lie between 1.7 and 1 therapy units per week with doctors or psychologists, and between 3.2 and 3.8 therapy units per week with nurses and special therapists, depending on the main diagnosis (Löhr et al., 2014). Also, it must be considered that a value beneath 1 therapy unit per week implies that the patient received a therapy unit of approximately 25 minutes every 1.5 weeks in an individual therapy by a doctor or a psychologist. Often this is compensated for by group therapy (about 3 h in a group with 8 members). To date, it was not possible to differentiate between individual and group therapy. It must be emphasized time and again that treatments of less than 25 minutes are not taken into account, which causes a significant restriction, considering that especially people with dementia might not endure longer times of contact or might even avoid them. The question remains whether this circumstance alone can explain the low values found. The two indicators “share of patients without therapy unit during their entire stay” and “average number of therapy units” can be used as progression parameters for assessing treatment quality. A third indicator could be the relationship between the number of patients and nursing professionals. In this respect, the National Institute for Health and Care Excellence (NICE) in England recently determined staffing regulations for acute clinics. Aim of these guidelines is the reduction of mortality rates that might be connected to low qualification and insufficient staffing of the nursing personnel (NICE, 2014).

The quality of diagnosis and treatment in dementia is currently a central topic in European health systems. Many publications try to define process and outcome indicators. For example, the German Association for Psychiatry and Psychotherapy (DGPPN) defined 10 indicators in the diagnostic and treatment of dementia (Großimlinghaus et al., 2013). These include the differentiation of the subtypes of dementia and the necessity of brain imaging, but in contrast to our study they are connected to time criteria. [A] The exploration of these quality criteria in hospitals is limited due to the immense expenditure – mainly in longitudinal studies. [B] This point can only be solved by studies using routine data.

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F. Godemann et al.: Inpatient Treatment Quality for Dementia

Conclusions As in other psychiatric settings, the examined indicators from the §21 routine dataset proved to be difficult to interpret regarding the evaluation of the present treatment quality of inpatient dementia treatment in Germany. Important aspects that define a qualitative treatment like well-trained staff, adequate sociotherapeutic interventions, and adequate psycho-pharmacological treatment cannot be evaluated with the available data.

If misunderstood or linked to financial incentives (as certain political concepts suggest), they bear even the risk of promoting adaptation solely to their secondary goals, instead of encouraging improvement of real treatment quality. It is evident that the VIPP database was not exhaustively evaluated in the present paper to inform about the quality of care in psychiatric units for people with dementia.

However, the available indicators might be of value both for longitudinal analyses of the impact of changes in the health systems structure (e.g., change in therapy units, length of stay, effects of more or less community-based treatment, allocation of resources) and comparisons between disease groups.

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

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Azermai, M. (2015). Dealing with behavioral and psychological symptoms of dementia: A general overview. Psychology Research and Behavior Management, 8, 181–185.

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Aging from 70 to 100. A research project of the Berlin-Brandenburg Academy of Sciences. Cambridge: Cambridge University Press.

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Brantervik, Å. M., Jacobsson, I. E., Grimby, A., Wallén, T. C., & Bosaeus, I. G. (2005). Older hospitalized patients at risk of malnutrition: correlation with quality of life, aid from the social welfare system and length of stay?. Age and Aging, 34, 444–449.

DGPPN, DGN. (2009). Diagnose- und Behandlungsleitlinie Demenz [Diagnostic and treatment guideline in dementia]. Retrieved from http://www.awmf.org/uploads/tx_szleitlinien/038–013_S3_De menzen_lang_11–2009_11–2011.pdf

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Erkinjuntti, T., Wikström, J., Palo, J., & Autio, L. (1986). Dementia among medical inpatients: Evaluation of 2000 consecutive admissions. Archives of Internal Medicine, 146, 1923–1926.

Großimlinghaus, I., Falkai, P., Gaebel, W., Janssen, B., Reich-Erkelenz, D., Wobrock, T., & Zielasek, J. (2013). Entwicklungsprozess der DGPPN-Qualitätsindikatoren [Developmental process of DGPPN quality indicators]. Der Nervenarzt, 84, 350–365.

Han, J. H., Eden, S., Shintani, A., Morandi, A., Schnelle, J., Dittus, R. S., . . . & Ely, E. (2011). Delirium in older emergency department patients is an independent predictor of hospital length of stay. Academic Emergency Medicine, 18, 451–457.

Kolbeinson, H., Jónson, A. (1993). Delirium and dementia in acute medical admissions of elderly patients in Iceland. Acta Psychiatrica Scandinavica, 87, 123–127.

Livingston, G., Kelly, L., Lewis-Holmes, E., Baio, G., Morris, S., Patel, N., . . . & Cooper, C. (2014). Nonpharmacological interventions for agitation in dementia: Systematic review of randomized controlled trials. The British Journal of Psychiatry, 205, 436–442.

Löhr, M. (2014). Leistungsdokumentation der Therapieeinheiten bei stationär behandelten Menschen mit dementieller Erkrankung – erlaubt sie tatsächlich Rückschlüsse auf das Therapiegeschehen? Eine explorative Sekundärdatenanalyse von 5.111 Fällen aus 35 Fachkrankenhäusern, psychiatrischen Abteilungen und Universitätskliniken in Deutschland [Management documentation of therapeutic units on inpatient treatment for people with dementia – Does it allow conclusions to be drawn about inpatient services? An exploratory analysis of 5111 cases in 35 psychiatric hospitals, departments, and university hospitals in Germany] (PhD dissertation). University of Halle-Saale, Germany.

Löhr, M., Nitschke, R., Schulz, M., Wolter, A., Hennings, A., WolffMenzler, C., & Behrens, J. (2014). Leistungsdokumentation durch Therapieeinheiten bei stationär behandelten Menschen mit dementieller Erkrankung – erlauben sie Rückschlüsse auf das Leistungsgeschehen? Eine explorative Analyse [Management documentation of therapeutic units on inpatient treatment for people with dementia – Does it allow conclusions to be drawn about inpatient services? An exploratory analysis]. Gesundheitswesen, 76, 479–485.

Löhr, M., Schulz, M. & Behrens, J. (2014) Menschen mit Demenz im Krankenhaus [People with dementia in hospitals]. Psychiatrische Pflege Heute, 20, 189–195.

Lundberg, A. S., Gustafsson, L. N., Meagher, D., & Munk-Jørgensen, P. (2014). Delirium during psychiatric admission increases mortality in psychiatric patients during and after hospitalization: A nationwide study from 1995 through 2012. Journal of Psychosomatic Research, 77, 226–231.

Morgan, D. G., Semchuk, K. M., Stewart, N. J., & D’arcy, C. (2002).

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Nobili, A., Licata, G., Salerno, F., Pasina, L., Tettamanti, M., Franchi, C., . . . & Marcucci, M. (2011). Polypharmacy, length of hospital stay, and in-hospital mortality among elderly patients in internal medicine wards. The REPOSI study. European Journal of Clinical Pharmacology, 67, 507–519.

Sampson, E. L., Blanchard, M. R., Jones, L., Tookman, A., & King, M. (2009). Dementia in the acute hospital: Prospective cohort study of prevalence and mortality. The British Journal of Psychiatry, 195(1), 61–66.

SGB V, (1988). Legal mandate to collect quality indicators: Retrieved from http://www.gesetze-im-internet.de/sgb_5/__137a.html Swart, E., Gothe, H., Geyer, S., Jauzeme, J., Maier, B., Grobe T. G., Ihle, P. (2015). Good practice of secondary data analysis (GPS): Guidelines and recommendations (3rd revision 2012/2014). Gesundheitswesen, 77, 120–126.

Tampi, R. R., & Tampi, D. J. (2014). Efficacy and tolerability of benzodiazepines for the treatment of behavioral and psychological symptoms of dementia a systematic review of randomized controlled trials. American Journal of Alzheimer’s Disease and Other Dementias, 29, 565–574.

Wang, C., Yu, J. T., Wang, H. F., Tan, C. C., Meng, X. F., & Tan, L. (2014). Nonpharmacological interventions for patients with mild cognitive impairment: a meta-analysis of randomized controlled trials of cognition-based and exercise interventions. Journal of Alzheimer’s Disease, 42, 663–678.

WHO (2012). Dementia: A public health priority. Retrieved from http://www.who.int/mental_health/publications/dementia_re port_2012/en/index.html Wiegand, H. F., Sievers, C., Schillinger, M., & Godemann, F. (2016).

Major depression treatment in Germany: Descriptive analysis of health insurance fund routine data and assessment of guideline-adherence. Journal of Affective Disorders, 189, 246–253.

Wolff-Menzler, C., Maier, B., June, F., Löhr, M., Große, C., Falkai, P., . . . Godemann, F. (2014). Versorgungsindikatoren in der Psychiatrie und Psychosomatik (VIPP) – Ein Datenbank-Projekt [Indicators of patient care in psychiatric and psychosomatic facilities (VIPP Project) – A database project]. Fortschritte Neurologie und Psychiatrie, 82, 394–400.

Manuscript received: 16.01.2015 Manuscript accepted after revision: 25.07.2016

Prof. Dr. med. Frank Godemann Klinik für Psychiatrie und verhaltenstherapeutische Medizin Alexianer St. Joseph Berlin-Weißensee 13088 Berlin Germany f.godemann@alexianer.de

GeroPsych (2017), 30 (1), 27–33

An innovative and highly effective brief therapy for suicidal patients “ASSIP is perhaps the most significant innovation we have seen in the assessment and treatment of suicidal risk...”

David A. Jobes, PhD, Professor of Psychology, The Catholic University of America, Washington, DC, USA Past President, American Association of Suicidology

Konrad Michel / Anja Gysin-Maillart

ASSIP – Attempted Suicide Short Intervention Program A Manual for Clinicians

2015, x + 114 pp.

US $59.00 / € 41.95 ISBN 978-0-88937-476-8 Also available as eBook Attempted suicide is the main risk factor for suicide. The Attempted Suicide Short Intervention Program (ASSIP) described in this manual is an innovative brief therapy that has proven in published clinical trials to be highly effective in reducing the risk of further attempts. ASSIP is the result of the authors’ extensive practical experience in the treatment of suicidal individuals. The emphasis is on the therapeutic alliance with the suicidal patient, based on an initial patientoriented narrative interview.

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The four therapy sessions are followed by continuing contact with patients by means of regular letters.

This clearly structured manual starts with an overview of suicide and suicide prevention, followed by a practical, step-by-step description of this highly structured treatment. It includes numerous checklists, handouts, and standardized letters for use by health professionals in various clinical settings.

C. J. Herold et al.: Co gnitive GeroPsych Performance (2017), in©Schizophren 2017 30 (1), Hogrefe 35–44 ia

Full-Length Research Report

Cognitive Performance in Patients with Chronic Schizophrenia Across the Lifespan Christina Josefa Herold1, Lena Anna Schmid1, Marc Montgomery Lässer1, Ulrich Seidl2, and Johannes Schröder1,3 1

Section of Geriatric Psychiatry, Department of General Psychiatry, University of Heidelberg, Heidelberg, Germany

2

Center for Mental Health, Klinikum Stuttgart, Stuttgart, Germany

3

Institute of Gerontology, University of Heidelberg, Heidelberg, Germany

Abstract: Chronic schizophrenia involves neuropsychological deficits that primarily strike executive functions and episodic memory. Our study investigated these deficits throughout the lifespan in patients with chronic schizophrenia and in healthy controls. Important neuropsychological functions were tested in 94 patients and 66 healthy controls, who were assigned to three age groups. Compared with the healthy controls, patients performed significantly poorer on all tests applied. Significant age effects occurred on all tests except the digit span forward, with older subjects scoring well below the younger ones. With respect to cognitive flexibility, age effects were more pronounced in the patients.

These findings underline the importance of cognitive deficits in chronic schizophrenia and indicate that diminished cognitive flexibility shows age-associated differences.

Keywords: schizophrenia, cognition, aging, executive functions, memory

Cognitive impairment is a hallmark of schizophrenia. The pattern of deficits and their relationship to psychosocial functioning have been illustrated in a large number of neuropsychological studies over the past few decades (Dickinson & Gold, 2008; Green, 1996; Schröder, Tittel, Stockert, & Karr, 1996). It is generally assumed that cognitive function is often already below average in premorbid periods (Reichenberg et al., 2006; Woodberry, Giuliano, & Seidman, 2008) and decreases with manifestation of the disease (Bilder et al., 2000; Mesholam-Gately, Giuliano, Goff, Faraone, & Seidman, 2009). The respective deficits continue in patients with chronic schizophrenia, including those in whom symptoms have partially remitted (Barbarotto, Castignoli, Pasetti, & Laiacona, 2001; Heinrichs & Zakzanis, 1998).

Cognitive and functional losses occur with normal aging in the entire population. The frontal-lobe hypothesis (West, 1996) posits that the frontal lobe is particularly susceptible to age-related deterioration in healthy adults. This assumption is supported by neuroimaging data that demonstrate both structural and functional changes in the frontal lobe with aging (Hazlett et al., 1998; Raz et al., 1997; Salat et al., 2004). In addition, neuropsychological studies describe a worsening of frontal executive functions with aging in healthy adults (Salthouse, Atkinson, & Berish, 2003; Sorel & Pennequin, 2008).

The question of the extent to which this decline of frontal functions with age also applies to patients with chronic schizophrenia remains unresolved. While some studies indicate that © 2017 Hogrefe

certain cognitive domains such as information processing and executive functioning might bear a greater risk of worsening with age (Bowie, Reichenberg, McClure, Leung, & Harvey, 2008; Fucetola et al., 2000; Irani et al., 2012; Loewenstein, Czaja, Bowie, & Harvey, 2012), others did not find any differential aging effects (Heaton et al., 2001; Hijman, Hulshoff Pol, Sitskoorn, & Kahn, 2003; Mockler, Riordan, & Sharma, 1997). These divergent findings may reflect methodological differences between the studies, which are detailed in the Discussion section below. Recently, Kirkpatrick et al. (2008) established the hypothesis that schizophrenia is a syndrome of accelerated aging – as already conceptualized by the term “dementia praecox” (Kraepelin, 1913) – since cognitive deficits in chronic schizophrenia primarily strike those domains that are typically affected in the physiological aging process. This hypothesis also conforms to the frontal cortex changes frequently described in patients with schizophrenia in neuroimaging studies (Bachmann et al., 2004; Buchsbaum et al., 1982; DeLisi, Szulc, Bertisch, Majcher, & Brown, 2006; Schröder, Buchsbaum et al., 1996). Despite the renewed interest in cognition in old age schizophrenia, considerable controversy still lingers over this topic.

The current study examines the association between age and cognitive performance in chronic schizophrenia. We concentrate specifically on patients and psychiatrically healthy controls ranging in age from young adulthood to old age.

GeroPsych (2017), 30 (1), 35–44 DOI 10.1024/1662-9647/a000164

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We hypothesized that patients with chronic schizophrenia of all ages show substantial cognitive deficits. In addition, we expected these deficits to worsen with age. This effect should primarily involve executive functions, while episodic memory deficits should remain more stable.

Methods Subjects and Procedures Cognitive performance was assessed in healthy subjects and patients with chronic schizophrenia in the age range 18 to 82 years. The patients and healthy controls were each subdivided into three age groups (“young” ≤ 34 years, “middle” 35–49 years, and “older” ≥ 50 years).

94 patients with chronic or subchronic schizophrenia according to DSM-IV (American Psychiatric Association, 2000) were recruited from three psychiatric long-term units (n = 40) and a mental state hospital (n = 54). All patients were in a stable condition and had received antipsychotic therapy; dosage was evaluated in mg chlorpromazine (CPZ) equivalents (Woods, 2003). The diagnosis was established by experienced psychiatrists. Inclusion criteria for patients were (1) a diagnosis of schizophrenia according to DSM-IV (American Psychiatric Association, 2000), (2) German as the primary language, and (3) a minimum of 8 years school education. Patients with late onset schizophrenia with a manifestation of the disease after age 45 were not included as this condition may have involved a different etiology (Howard, Rabins, Seeman, & Jeste, 2000; Schmid, Lässer, & Schröder, 2011). Further exclusion criteria included a history of any neurological condition affecting the central nervous system, head injury, or substance abuse.

Healthy controls (n = 66) were recruited among the hospital staff and through advertisements in a newspaper. The Mini International Neuropsychiatric Interview (interrater and retestreliability Cohen’s κ > 0.75, Sheehan et al., 1998) and the Beck Depression Inventory II (Cronbach’s α = 0.89, retest-reliability r = 0.78, Hautzinger, Keller, & Kühner, 2006) were performed to screen controls for current psychopathology. They were carefully matched to patients with respect to age and sex (main effect “diagnosis,” p > .30).

Informed consent was obtained from all participants after the study had been fully explained. The study was approved by the local ethics committee.

C. J. Herold et al.: Cognitive Performance in Schizophrenia

Krausz, 1997; Overall & Gorham, 1962), the Scale for the Assessment of Positive Symptoms (SAPS) and the Scale for the Assessment of Negative Symptoms (SANS) (34 and 25 items respectively, maximum global score = 20 and 25 respectively, interrater-reliability r = 0.63 and r = 0.52 for SAPS and SANS respectively, Cronbach’s α SAPS = 0.77–0.91 and SANS = 0.83–0.92; Andreasen & Olsen, 1982; Norman, Malla, Cortese, & Diaz, 1996).

Important neuropsychological domains typically involved in chronic schizophrenia were assessed by using a comprehensive test battery. Therefore, verbal learning and memory, short-term and working memory, processing speed, and cognitive flexibility were taken into account; the Mini-Mental State Examination (MMSE, maximum score = 30, retest-reliability r = 0.80, Cronbach’s α = 0.91) was used as a screening instrument for cognitive ability (Folstein, Folstein, & McHugh, 1975; Marioni, Chatfield, Brayne, & Matthews, 2011). All subjects completed the logical memory subtests of the Wechsler Memory Scale (Härting et al., 2000) to assess verbal learning and memory (logical memory I and logical memory II, maximum score each = 50, retest-reliability r = 0.79, interrater-reliability r = 0.99), and the digit span forward and backward subtests, assessing short-term and working memory (each maximum score = 12, retest-reliability r = 0.83). As an index of processing speed and cognitive flexibility, we used the scores of the Trail Making Test (TMT A – max. 180 s, TMT B – max. 240 s, retest-reliability r = 0.74 and r = 0.43 for TMT A and B, respectively (Conway Greig, Nicholls, Wexler, & Bell, 2004; Reitan, 1992)).

Statistical Analyses The effects of diagnosis and age were examined using multivariate analyses of variance (MANOVA) with diagnosis (patients, controls) and age group (young, middle, older) as the betweengroup factors, and the different demographical/clinical characteristics and the cognitive parameters as the dependent variables, while controlling for years of education in the latter.

These analyses were followed by Bonferroni posthoc tests. An α level of 0.05 (two-tailed) was used for all statistical tests.

Analyses were conducted by means of the Predictive Analysis Soft Ware (PASW/SPSS 18.0).

Results Sample Characteristics

Measures Symptoms were assessed using the Brief Psychiatric Rating Scale (BPRS, 18 items, maximum score = 108, interrater-reliability r = 0.8; Ligon & Thyer, 2000; Mass, Burmeister, & GeroPsych (2017), 30 (1), 35–44

In a first step, demographic and clinical characteristics of the three age groups were tested for significant group differences (Table 1). Patient and control groups showed only minor, nonsignificant differences with regard to age and sex (main effect © 2017 Hogrefe

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C. J. Herold et al.: Cognitive Performance in Schizophrenia

Figure 1. Neuropsychological profiles of patients (black lines) and healthy controls (gray lines). Raw test scores of all cognitive parameters were transformed to z-scores, based on the norm values of the specific test.

“diagnosis,” p > .30), while the healthy subjects had received a significantly longer school education than the patients (mean of years of education M = 12.47 (SD = 2.78) vs. M = 13.58 (SD = 2.30), F(1, 154) = 6.755, p = .010, η2 = 0.042).

Further analysis of the patient group revealed that the three age groups did not differ in dosage of antipsychotic medication (CPZ equivalents), negative and positive symptoms, with a trend-level significant effect for SAPS global score, indicating more distinctive positive symptoms in the young patient group, F(2, 91) = 2.870, p = .062. With respect to BPRS sum score a significant effect for “age,” F(2, 91) = 5.238, p = .007, shows additionally evidence for a more severe psychopathology in younger patient groups. Posthoc tests revealed significant differences between the older patients and both patient groups of middle (p = .015) and young (p = .032) age.

As expected, significant differences were noticeable with regard to illness duration, F(2, 91) = 64.016, p < .001, the patient groups differed each with p < .001, and age at onset of the illness, F(2, 91) = 5.631, p = .005. Posthoc tests showed a significant difference between young and old patients (p = .004), whereas other comparisons failed to reach significance (p > .09).

There was a significant age cohort effect for dwelling status, χ² = 9.542, p = .008, with middle and older patients being more GeroPsych (2017), 30 (1), 35–44

often hospitalized in comparison to young patients at the time point of study.

Age Effects on Cognitive Performance In a second step it was shown that, compared with the healthy controls, patients performed lower on all tests applied (Figure 1). Test performance tended to be lower in the oldest than the young and middle-aged groups. With respect to TMT B, this effect was more pronounced in the patient groups in whom a sharper decline of performance with age became evident (Figure 2).

These findings were confirmed by a MANOVA (Table 2) which yielded a significant main effect for “diagnosis,” F(7, 147) = 19.227, p < .001, η2 = 0.478. Further comparisons revealed significant differences between patients and healthy controls for all neuropsychological tests applied, thus indicating a pronounced performance deficit of the patients (0.04 > p = .000).

The main effect for “age” reached significance level too, F(14, 296) = 4.280, p < .001, η2 = 0.168, with older subjects being more impaired (0.03 > p = .000), except for digit span forward (p = .145). © 2017 Hogrefe

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Figure 2. TMT A (above) and TMT B (below) performance as a function of age for patients (black lines) and healthy subjects (gray lines).

Posthoc tests revealed that MMSE performance was significantly lower for old than for young subjects (p = .007), while the difference between groups of old and middle-aged subjects failed to reach significance (p = .062). In case of logical memory I, the old subjects had significant impairments in contrast to the young subjects (p = .044); in the case of logical memory II, the old subjects showed marked deficits in comparison to both younger groups (p < .02). Working memory performance, as indicated by digit span backward, was significantly reduced in the old subject group in contrast to the young group (p = .003). Š 2017 Hogrefe

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Information processing speed, assessed via TMT A, was also significantly impaired in the older subjects compared to both younger groups (p < .001). With respect to cognitive flexibility, as assessed using TMT B, significant differences between each age group and the other groups were evident (.05 > p = .000).

The interaction “diagnosis × age” showed a significant effect for TMT B, F(2, 153) = 4.869, p = .009, with trend-level significance for TMT A, F(2, 153) = 2.716, p = .069. A trend-level significant “diagnosis × age” interaction with respect to MMSE also appeared, additionally indicating a cognitive deterioration in older patients, F(2, 153) = 2.774, p = .066.

Discussion The present study revealed three major findings regarding cognitive impairment of patients with chronic schizophrenia: (1) a confirmation of broad deficits in a variety of important neuropsychological domains which (2) apply to all life periods from young adulthood to early age; and (3) evidence that cognitive flexibility is particularly affected in the older patients.

The poorer test performance of patients with chronic schizophrenia in comparison to healthy subjects covers a wide range of cognitive domains. This was particularly evident with regard to verbal learning and memory, where z-scores nearly reached the mark of z = –1.5 for all age groups. Information processing speed and cognitive flexibility were impaired to a comparable extent with a considerable stronger dip in the older patients.

In contrast, short-term memory remained rather spared with performance still ranging in low average levels.

These results corroborate findings from previous studies on cognitive deficits in young and middle-aged patients with chronic schizophrenia (Heinrichs & Zakzanis, 1998; Irani et al., 2012) and extend them for an older group. One of the studies investigating cognition in schizophrenia over a wide age range was conducted by Fucetola et al. (2000), who examined 87 patients and 94 healthy controls assigned to three groups with an average age of M = 30.0 (SD = 3.6), M = 41.1 (SD = 4.2) and M = 58.3 (SD = 5.6) years in the patient groups and M = 28.5 (SD = 4.4), M = 41.3 (SD = 3.8) and M = 62.5 (SD = 7.2) years in the control groups, respectively. Cognitive deficits in the patient group involved verbal memory, perceptual motor skills, and abstraction, with z-scores below –1 throughout the three age groups. As in the present study, performance in memory and learning, information processing, and cognitive flexibility was well within the range of that typically obtained in older patients with a diagnosis of mild cognitive impairment (Sattler, 2012).

At this point it should be emphasized that even the marked deficits typically observed in older patients with chronic schizophrenia are not directly comparable to the impairments characteristic of neurodegenerative illnesses such as Alzheimer’s GeroPsych (2017), 30 (1), 35–44

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disease (AD), since declarative memory remains relatively spared and does not further deteriorate with progression of the disease. As in the present study, a consistent pattern of neuropsychological deficits was already described by McBride et al.

(2002) and Ting et al. (2009). The MMSE scores of our patient group were – though reduced and at trend-level deteriorating with increasing age – not comparable to that of patients with AD (Barth, Schönknecht, Pantel, & Schröder, 2005; Dos Santos et al., 2011). These findings parallel results from a review of neuropathological studies, which concluded that AD pathology does not occur more frequently among patients with schizophrenia than in the general population (Niizato, Genda, Nakamura, Iritani, & Ikeda, 2001).

While a wealth of studies investigated cognitive performance in schizophrenia in general, only few authors focused on the potential interaction effects between age and illness with regard to cognitive functioning. The present study demonstrated that older patients showed a significantly poorer performance in cognitive flexibility compared to their younger counterparts.

Along with this, a trend toward significant interaction of diagnosis with age was found for information processing. In contrast, none of the other cognitive domains examined showed such a differential effect of aging in the patient group compared to the healthy controls. In the study cited above, Fucetola et al.

(2000) found similar age-related performance differences between patients and controls across various domains, while a significant interaction was restricted to abstract thinking as assessed on the Wisconsin Card Sorting Test. In a recent study, Irani et al. (2012) tested two groups of 624 patients with schizophrenia and healthy controls on a computerized version of the Continuous Performance Test and on a Letter-N-Back Test and came to similar conclusions. Compared with the healthy controls, the patients showed significantly lower values regardless of age in most indices of cognitive performance. However, the older group under investigation showed a reduced speed but not accuracy in the N-back task compared to the younger patients. This indicates that the executive component of working memory performance was predominantly affected. Loewenstein et al. (2012) analyzed age-associated cognitive differences in a sample of 226 patients with chronic schizophrenia and 834 healthy controls, which were compiled from different databases. All participants were older than 40 years; the clinical course of the disorder was not further specified. The study yielded greater age effects for patients than for controls on measures of information processing i.e., the TMT A, the Stroop and the Digit Symbol Test, which also assess at least to a certain extent cognitive flexibility.

The results of our study indicate that patients with chronic schizophrenia show a slope of cognitive decline with advancing age similar to controls in all cognitive domains except for cognitive flexibility as a typical executive function. Although this effect was rather small, it clearly refers to progressive cerebral changes in normal aging, which particularly strike the frontal lobes (DeCarli et al., 2005; Raz et al., 1997; Salat et al., 2004). © 2017 Hogrefe

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Longitudinally, the extent of progressive brain tissue decrease in patients with schizophrenia is found to be twice that of healthy subjects and particularly affects frontal areas (Hulshoff Pol & Kahn, 2008; Olabi et al., 2011). Similar significant reductions in superior frontal gyrus and orbitofrontal regions were observed in a small male sample of young patients with schizophrenia and older healthy subjects in comparison to a young healthy control group (Convit et al., 2001). Moreover, gray matter decreases in frontal cortex were greater in chronic than in first-episode schizophrenia (Chan, Di, McAlonan, & Gong, 2011; Ellison-Wright, Glahn, Laird, Thelen, & Bullmore, 2008).

Except for cognitive flexibility, our pattern of findings with rather stable deficits across different groups is consistent with the results of previous studies. Mockler et al. (1997) confirmed widespread cognitive deficits, but did not report any significant age effects on cognitive functioning in 62 patients with chronic schizophrenia between 18 and 69 years of age. However, the majority of patients were below 50 years, and just 6 patients formed the oldest group (60 to 69 years). Moreover, executive functions were not specifically addressed. Similarly, Hijman et al. (2003) who compared performance on four subtests of the Wechsler Adult Intelligence Test between 112 patients with chronic schizophrenia and 70 healthy controls (age range: 16 to 56 years) did not describe a significant interaction effect of age with group, while patients performed worse on all subtests.

The oldest group (46 to 56 years) comprised 17 patients; the majority of patients were below 46 years of age. Performance on the subtest picture arrangement, which shares aspects of executive functioning, decreased with age, a process which appeared to be slightly more pronounced in the patient group.

Bowie and colleagues (2008) also reported deficits in a number of important neuropsychological domains including psychomotor speed and cognitive flexibility. Performance levels compare to the “middle-aged” and “older” patient subgroups investigated in the present study. However, Bowie et al. (2008) recruited a group of old patients (50–85 years), but did not include younger patients with chronic schizophrenia. In light of the reduced life expectancy of patients with chronic schizophrenia (Laursen, 2011), the subgroup of old patients (70–85 years) may represent a number of survivors who either had a more favorable course of the disorder or were less vulnerable to its consequences during the aging process. The study showed evidence for age-associated cognitive decline on the more complex components of an information-processing test, which Bowie et al. (2008) alternatively referred to “the course of illness and the processing demands of the cognitive measure of interest.” However, their results mirror our findings because they did not only show a significant age-associated decline in the TMT A, but also a similar although nonsignificant trend toward for the TMT B in the patients.

In the present study, executive functions were only addressed by using the TMT, while other tests such as the Wisconsin Card Sorting Test were not applied. Because of reduced © 2017 Hogrefe

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cognitive capacity of especially the older patients, we restricted our cognitive assessment to a few tests.

While groups were carefully matched for age and sex, in the patients years of education were significantly reduced, which may be expected in a group of patients with a chronic course of the disease, of whom 20–60% were hospitalized. For this reason years of education were controlled for in the MANOVA. Negative symptoms differed nonsignificantly between the patient groups, while positive symptoms (trend-level only) and BPRS total score were lower in older than younger patients.

These differences correspond to the amelioration of acute schizophrenic symptoms with increasing age (Schmid et al., 2011), already described by Bleuler (1949). That the older patients are nonetheless severely affected is indicated by their dwelling status, illustrating that older patients are more often institutionalized.

Given that age and duration of illness coincide because of onset of the disease in early adulthood and the exclusion of patients with late onset schizophrenia, the three age groups differed significantly with respect to illness duration. The marginal, albeit significant group difference of age at illness onset, determined on basis of the patients’ history and case notes, may well be explained by the fact that the youngest group per definitionem does not comprise patients with a later onset, which is also reflected by the respective standard deviations.

Data concerning the predominant treatment of the patients in the past were unfortunately not available. At the time of assessment the majority of the patients were receiving atypical antipsychotics only or typical and atypical antipsychotic medication in combination. Potential medication effects cannot be entirely excluded as patients were examined cross-sectionally, although the three patient groups showed only marginally, nonsignificant differences with respect to CPZ equivalents. Similarly, significant medication effects were not identified in the large meta-analysis by Irani and colleagues (2011). In contrast, other studies indicate a beneficial impact of atypical (Guilera, Pino, Gómez-Benito, & Rojo, 2009; Thornton, Van Snellenberg, Sepehry, & Honer, 2006; Woodward, Purdon, Meltzer, & Zald, 2005) and typical (Davidson et al., 2009; Mishara & Goldberg, 2004; Schröder, Tittel et al., 1996) antipsychotic medication on cognition in schizophrenia. Especially the latter findings are important given that particularly the older patients of our sample might have received mainly classical antipsychotics in the past.

Additional factors other than age are likely to affect cognitive flexibility: The large meta-analysis cited above (Irani et al., 2011) revealed a significant role for both demographic (age, sex, education, race) and clinical factors (living status, age of onset, duration of illness, clinical symptoms). From a clinical standpoint, co-morbid somatic conditions and other life-style factors should also be added in longitudinal studies, as physical illnesses like the metabolic syndrome, which increases in incidence with rising age and is associated with cognitive deterioration (Schröder & Pantel, 2011), are more common in patients GeroPsych (2017), 30 (1), 35–44

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with schizophrenia (Oud & Meyboom-de Jong, 2009; Sebastian & Beer, 2007).

The results of the present cross-sectional study underline the importance of cognitive deficits in chronic schizophrenia and indicate that diminished cognitive flexibility undergoes age-associated differences, which can be assigned to frontal lobe changes. This pattern of cognitive deficits facilitates the differentiation from neurodegenerative diseases such as mild cognitive impairment and AD and underlines the need for appropriate training programs for elderly patients with chronic schizophrenia.

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

Acknowledgments The study was supported by the Dietmar Hopp Foundation, Germany.

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A meta-analysis of neuropsychological change to clozapine, olanzapine, quetiapine, and risperidone in schizophrenia. International Journal of Neuropsychopharmacology, 8, 457–472. doi 10.1017/S146114570500516X Manuscript received: 17.07.2015 Manuscript accepted after revision: 17.12.2015 Dipl.-Psych. Dr. Christina Herold Section of Geriatric Psychiatry University of Heidelberg Voßstr. 4 69115 Heidelberg Germany christina-j.herold@med.uni-heidelberg.de

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Anonymous Review Anonymous review is available on request. This should be indicated on the cover letter and designated in the submission process. In this case manuscripts should be prepared so as to conceal the identity of the author(s). The cover page and footnotes that identify author(s) should be omitted. Manuscripts not prepared in this manner will receive open review.

Offprints The publisher will send the corresponding author of each accepted paper an e-offprint (PDF) of the published version of the paper when it is first released online. This e-offprint is provided for the author’s personal use, including for sharing with coauthors (see also “Online Rights for Journal Articles” on the publisher’s website at http://www.hogrefe.ch/produkte/zeitschriften/gro).

Copyright The first author confirms and guarantees on behalf of himself or herself and any coauthors that he or she holds all copyright in and titles to the submitted contribution, including any figures, photographs, line drawings, plans, maps, sketches, and tables, and that the article and its contents do not infringe in any way on the rights of third parties. Upon acceptance of the article for publication, the author agrees to transfer to the publisher the exclusive right to reproduce and distribute the article and its contents, both physically and in nonphysical, electronic, or other form, in the Journal to which it has been submitted and in other independent publications, with no limitations on the number of copies or on the form or the extent of distribution. These rights are transferred for the duration of copyright as defined by Swiss law. Furthermore, the author transfers to the publisher the following exclusive rights to the article and its contents: a) The rights to produce advance copies, reprints or offprints of the article, in full or in part, to undertake or allow translations into other languages, to distribute other forms or modified versions of the article, and to produce and distribute summaries or abstracts. b) The rights to microfilm and microfiche editions or similar, to the use of the article and its contents in videotext, teletext, and similar systems, to recordings or reproduction on other media, digital or analog, including electronic, magnetic, and optical media, and in multimedia form, as well as for public broadcasting in radio, television, or other forms of broadcast. c) The rights to store the article and its contents in machine-readable or electronic form on all media (such as computer disks, compact disks, magnetic tape), to store the article and its contents in online databases belonging to the publisher or to third parties for viewing or for downloading by third parties, and to present or reproduce the article or its contents on visual display screens, monitors, and similar devices, either directly or via data transmission. d) The rights to reproduce and distribute the article and its contents by all other means, including photomechanical andsimilar processes(such asphotocopying or facsimile), and as part of so-called document delivery services. e) The right to transfer any or all of the rights mentioned in this agreement as well as the rights retained by the Verwertungsgesellschaft «WORT» including the corresponding royalty rights to third parties within or outside Switzerland.

Use movies to learn about positive psychology “This is the most important book about movies of our times.”

Frank Farley, PhD, L. H. Carnell Professor, Temple University, Philadelphia, Former President of the American Psychological Association (APA)

Ryan M. Niemiec / Danny Wedding

Positive Psychology at the Movies

Using Films to Build Character Strengths and Well-Being 2nd edition 2014, xvi + 486 pp.

US $59.00 / € 41.95 ISBN 978-0-88937-443-0 Also available as eBook Positive psychology is regarded as one of the most important developments in the field of psychology over the past century. This inspiring book uses movies as a medium for learning about the latest research and concepts, such as mindfulness, resilience, meaning, positive relationships, achievement, well-being, as well as the 24 character strengths laid out by the VIA Institute of Character.

Films offer myriad examples of character strengths and other positive psychology concepts and are uniquely suited to learning about them and inspiring new ways of thinking. This book systematically discusses each of the 24 character strengths, balancing film discussion, related psychological research, and practical applications.

Each chapter outlines Key Concepts, Relevant Research, an Exemplar from a key movie, Overuse/Underuse, Key

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Enablers and Inhibitors, Practical Applications, International Cinema, and a Summary. Watching the films recommended in this book will help the reader to practice the skill of strengths-spotting in themselves and others, inspiring self-improvement.

Practical resources include a suggested syllabus for a complete positive psychology course based on movies, a list of suitable movies for children, adolescents, and families as well as a list of questions for classroom and therapy discussions.

Positive Psychology at the Movies is conceived for educators, students, practitioners, and researchers, but anyone who loves movies and wants to change their lives for the better will find it inspiring and relevant. Read this book to learn more about positive psychology – and watch these films to become a stronger person!

Hogrefe OpenMind Open Access Publishing?

It’s Your Choice!

Your Road to Open Access Authors of papers accepted for publication in any Hogrefe journal can now choose to have their paper published as an open access article as part of the Hogrefe OpenMind program. This means that anyone, anywhere in the world will – without charge – be able to read, search, link, send, and use the article for noncommercial purposes, in accordance with the internationally recognized Creative Commons licensing standards.

The Choice Is Yours 1. Open Access Publication: The final “version of record” of the article is published online with full open access. It is freely available online to anyone in electronic form. (It will also be published in the print version of the journal.)

2. Traditional Publishing Model: Your article is published in the traditional manner, available worldwide to journal subscribers online and in print and to anyone by “pay per view.”

Whichever you choose, your article will be peer-reviewed, professionally produced, and published both in print and in electronic versions of the journal. Every article will be given a DOI and registered with CrossRef.

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How Does Hogrefe’s Open Access Program Work?

After submission to the journal, your article will undergo exactly the same steps, no matter which publishing option you choose: peer-review, copy-editing, typesetting, data preparation, online reference linking, printing, hosting, and archiving. In the traditional publishing model, the publication process (including all the services that ensure the scientific and formal quality of your paper) is financed via subscriptions to the journal. Open access publication, by contrast, is financed by means of a one-time article fee (€ 2,500 or US $3,000) payable by you the author, or by your research institute or funding body. Once the article has been accepted for publication, it’s your choice – open access publication or the traditional model. We have an open mind!

Recommendations and clinical tools for assessing and treating heart disease

“Where other texts might be ponderous or obtuse, this one cuts straight to the chase.”

Cheryl B. Travis, in PsycCRITIQUES, Vol. 51 (6)

Judith A. Skala / Kenneth E. Freedland / Robert M. Carney

Heart Disease (Series: Advances in Psychotherapy – Evidence-Based Practice – Vol. 2) 2005, viii + 82 pp.

US $29.80 / € 24.95 ISBN 978-0-88937-313-6 Also available as eBook This volume provides readers with a succinct introduction to behavioral and psychosocial treatment of the two most prevalent cardiac conditions, coronary heart disease and congestive heart failure. It summarizes the latest research on the intricate relationships between these conditions and psychosocial factors such as stress, depres-

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sion, and anger, as well as behavioral factors such as physical inactivity and non-adherence to cardiac medication regimens. The book provides practical, evidence-based recommendations and clinical tools for assessing and treating these problems – an indispensable treatment manual for professionals who work with cardiac patients.

Helping the bereaved cope after the traumatic death of a loved one “A wonderful synthesis of information on traumatic losses.”

Holly Prigerson, Professor of Psychiatry, Harvard Medical School, Director, Center for Psychosocial Epidemiology and Outcomes Research, Boston, MA

Diego De Leo / Alberta Cimitan / Kari Dyregrov / Onja Grad / Karl Andriessen (Editors)

Bereavement After Traumatic Death Helping the Survivors

2014, xiv + 208 pp.

US $39.80 / € 27.95 ISBN 978-0-88937-455-3 Also available as eBook Unless forced by circumstances, people in modern societies go to great lengths to deny death, to the extent that even death of a loved one from natural causes tends to catch us unprepared and unable to cope with its consequences. Death as the result of a sudden, catastrophic event (traffic accident, suicide, a natural disaster, etc.) can have even more extreme effects, sometimes striking survivors so violently and painfully that it leaves an indelible mark.

This book speaks about the consequences of such traumatic deaths in

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a wonderfully simple and straightforward way. The authors describe, step by step, what happens to people after the sudden death of a family member or close friend, the difficulties they face in coping, and how professionals and volunteers can help. With their wide experience, both personally and as internationally renowned authorities, they have written a book for professionals and volunteers who deal with bereavement in language that is accessible to all, so it will also help those who have suffered a traumatic loss themselves to understand what to expect and how to get help.

Assessment methods in health psychology “This book is an excellent overview of measurement issues that are central to health psychology.”

David French, PhD, Professor of Health Psychology, University of Manchester, UK

Yael Benyamini / Marie Johnston / Evangelos C. Karademas (Editors)

Assessment in Health Psychology (Series: Psychological Assessment – Science and Practice – Vol. 2) 2016, vi + 346 pp.

US $69.00 / € 49.95 ISBN 978-0-88937-452-2 Also available as eBook

Assessment in Health Psychology presents and discusses the best and most appropriate assessment methods and instruments for all specific areas that are central for health psychologists. It also describes the conceptual and methodological bases for assessment in health psychology, as well as the most important current issues and recent progress in methods.

A unique feature of this book, which brings together leading authorities on health psychology assessment, is its emphasis on the bidirectional link between theory and practice.

Assessment in Health Psychology is addressed to masters and doctoral students in health psychology, to all

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those who teach health psychology, to researchers from other disciplines, including clinical psychology, health promotion, and public health, as well as to health policy makers and other healthcare practitioners.

This latest volume in the series Psychological Assessment – Science and Practice provides a thorough and authoritative record of the best available assessment tools and methods in health psychology, making it an invaluable resource both for students and academics as well as for practitioners in their daily work.