Scandinavian Journal of Psychology, 2015, 56, 140–150

DOI: 10.1111/sjop.12170

Cognition and Neurosciences Neuropsychological test norms controlled for physical health: Does it matter? INGVAR BERGMAN1 and OVE ALMKVIST2,3 1

Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Karolinska Institutet, Karolinska University Hospital, Huddinge, Sweden 2 Department of Neurobiology, Care Sciences and Society, Division of Alzheimer Neurobiology Center, Karolinska Institutet, Karolinska University Hospital, Huddinge, Sweden 3 Department of Psychology, Stockholm University, Stockholm, Sweden

Bergman, I. & Almkvist, O. (2015). Neuropsychological test norms controlled for physical health: Does it matter? Scandinavian Journal of Psychology, 56, 140–150. The objective of the present study was to investigate the effects of physical health on neuropsychological test norms. Medical and neuropsychological data from 118 healthy volunteer controls, aged 26–91 years, were collected during five recruitment occasions. The examinations included a clinical investigation, brain neuroimaging, and a comprehensive neuropsychological test battery. Test-specific statistical regression-weights for age, education and gender were calculated to establish preliminary test norms. Hierarchical regression analyses demonstrated that control in addition for physical health moved best performance from age 60 to 65 for abstraction; replaced a plateau above age 70 for verbal fluency, with a continued rise in performance; eliminated significant negative influences of age on auditory learning, spatial reasoning and complex copying; reduced them on wordlist recall, psychomotor speed, visual scanning and mental shifting; and slightly reduced negative influences of low education on most verbal tests, several memory tests, and psychomotor speed, indicating rises in normative scores of up to 0.8 SD at age 80 and 0.4 SD at age 60. No differences were found at age 40. Although the sample size is not adequate to be used for normative data, the findings indicate that norms uncontrolled for health overestimate the negative influence of advanced age and low education, implying a risk of drawing false diagnostic conclusions. Key words: Cognitive aging, normal adults, normative data, physical health, regression-based norms. Ingvar Bergman, Trafikmedicinskt Centrum S31, Karolinska University Hospital, S-14186 Stockholm, Sweden. Tel: +46 8585 86416; fax: +46 8585 86490; e-mail: [email protected]

INTRODUCTION It is well known that performance on neuropsychological tests is influenced by demographic factors such as age, education, and gender (Lezak, Howieson & Loring, 2004). On the one hand, advanced age conveys positive influences due to enhanced knowledge and life experience; however, advanced age also conveys negative influences due to aging-related deterioration of the brain. This aging-related deterioration of the brain has been attributed to a number of causes such as exposure to free radicals (Droge, 2002), the presence of apolipoprotein E genotype e4 (Deary, Whiteman, Pattie et al., 2002), dopaminergic changes (Backman, Nyberg, Lindenberger, Li & Farde, 2006) and physical and mental inactivity (Davenport, Hogan, Eskes, Longman & Poulin, 2012; Jak, 2012). The role of other factors associated with Alzheimer’s disease in normal aging, such as cholinergic changes and the presence of senile plaques and neurofibrillary tangles (Terry & Buccafusco, 2003; Wilson, 2008), are under debate. Regarding the influence of education, it has been suggested that education is associated with greater control over processing and conceptualization ability (Le Carret, Lafont, Mayo & Fabrigoule, 2003). In addition, gender might influence the pattern of cognitive abilities due to both genetic and sociocultural factors (Geary, 1989). Aging-related deterioration of test performance among nondemented individuals is also associated with a decline in physical health due to common cardiovascular diseases, disorders of the nervous system, and endocrine, nutritional and metabolic diseases. © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd

However, there is no consensus regarding the size of the effect; some authors hold that the effect size is small (e.g., Aarts, van den Akker, Tan, Verhey, Metsemakers & van Boxtel, 2011; Christensen, Jorm, Henderson, Mackinnon, Korten & Scott, 1994a; Verhaegen, Borchelt & Smith, 2003; van Boxtel, Buntinx, Houx, Metsemakers, Knottnerus & Jolles, 1998; Zelinski, Crimmins, Reynolds & Seeman, 1998), while others assert that it has a great importance (e.g., Howieson, Holm, Kaye, Oken & Howieson, 1993; Perlmutter & Nyquist, 1990; Piguet, Grayson, Broe et al., 2002). More substantial effects of age-related diseases in normal aging have also been reported by our own research group (Bergman, Blomberg & Almkvist, 2007). The health-related impairment of cognitive abilities in normal aging appears to be associated with the decline of fluid rather than crystallized intelligence (Bergman & Almkvist, 2013; Christensen, Mackinnon, Jorm, Henderson, Scott & Korten, 1994b; Schaie, 2005). The present investigation emerged out of the need to have reliable information regarding the normal cognitive performance of healthy adults, to enable valid evaluations of patients with suspected cognitive impairment. However, there is a conceptual uncertainty regarding what should be considered as normal in studies of normal cognitive functioning. One position is to consider normal values as the level of performance of individuals who present a minimal risk of influence from disadvantageous health factors. In this position, great effort is taken to examine health status. This position leads to the exclusion of individuals to a large extent, which will be more marked in older samples because the prevalence of common diseases is clearly age-

Scand J Psychol 56 (2015)

related. Because a normal sample that is based on an optimal health criterion may be highly selective, it might not be very typical and most elderly people will be considered to be, more or less, aberrant from normal. A more common position is to regard normal as what is known of the level of performance from unselected population samples, including chronic medical problems and excluding only those individuals with specific diagnoses (e.g., Alzheimer’s disease) and treatments (e.g., psychoactive drugs) or conditions reported to interfere with cognitive function. The Mayo Clinic has advocated in favor of the latter position in their publication of neuropsychological norms for adults (Ivnik, Malec & Smith, 1992; Ivnik, Malec, Smith, Tangalos & Petersen, 1996; Ivnik, Malec, Tangalos, Peterson, Kokmen & Kurland, 1990). A shortcoming of this position is the risk of including individuals with undiscovered cognitive impairments associated with chronic medical problems, implying a risk for underestimating truly healthy functioning in old age. In the present study, we advocate a third position. That is, by starting out with individuals who have a clinically well examined health status, the health-related impact on neuropsychological test performance can be controlled for statistically. Another shortcoming of using conventional norms is the interpretation of data based on discrete age bands, a practice that may be problematic in periods of cognitive development (e.g., in childhood and adolescence) as well as in periods of cognitive decline (e.g., in late adulthood). Another common impediment to the valid interpretation of conventional norms is the lack of control for demographic factors other than age. Kalechstein, van Gorp and Rapport (1998) and more recently Smerbeck, Parrish, Yeh et al. (2012), have called attention to these weaknesses and advocated for a regression-based normative equation to adjust for the impact of moderator variables rather than stratifying samples. The objective of the present study was to consider the shortcomings of conventional norms and investigate the effects of physical health on neuropsychological test norms. Instead of providing traditional group-based test norms, we made estimations of test norms on an individual basis by providing test-specific regression weights for age, education, and gender. These test norms should be regarded as preliminary due to small sample size (see below).

METHODS The present study was based on the same sample as Bergman et al. (2007), who reported on the importance of impaired physical health and age in normal cognitive aging, and Bergman and Almkvist (2013), who reported on the capacity of physical health to mediate the effects of age on fluid intelligence; this study should be considered as supplementary to them.

Participants In total, 131 individuals were recruited to volunteer as healthy control subjects at the Geriatric Clinic, Karolinska University Hospital in Huddinge, Stockholm and the Geropsychiatric ward, St. G€orans Hospital in Stockholm. The volunteers had to meet the following normal criteria.

1. No active central nervous system diseases or psychiatric conditions. 2. No treatments (e.g., psychoactive drugs) or conditions that are reported to interfere with cognitive function. Chronic medical prob-

© 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd

Preliminary norms controlled for physical health 141 lems (e.g., diabetes, hypertension, cardiac problems) were not considered an exclusion criterion as long as they were reported to not interfere with cognitive function. 3. No prior history of disorders that could potentially affect cognition (e.g., evident or suspected stroke, significant head trauma, recent or current substance abuse). 4. No handicaps (e.g., dyslexia) that could interfere with normal cognitive function. These criteria eliminated 13 individuals from the study: seven individuals with a history of significant head trauma, five with premorbid dyslexic problems and one with a history of evident brain infarction. The remaining 118 volunteers (48 women and 70 men) had an age range from 26 to 91 years, with an average of 69.3 (12.8) years. There was a negative age distribution, though not to an extreme extent (skewness = –1.5; kurtosis = 2.1). Their level of formal education ranged from 6 to 19 years, with an average of 10.4 (3.2) years. The average in our study was significantly higher than Sweden’s national mean of approximately 8.5 years for the corresponding generations and age distribution (p < 0.001; Statistics Sweden, 1995). Education was negatively correlated with age (Pearson r = –0.178; p = 0.05). The volunteers were respondents to calls that occurred on five occasions between 1987 and 1996, which included the following: two clinical trial studies, one in 1993 (n = 9) and the other in 1996 (n = 35); one study on Alzheimer’s disease between 1992 and 1995 (n = 12); one study on optimal health between 1987 and 1988 (n = 23); and one study on automobile driving ability in 1994 (n = 39).

Health assessment The medical examination was based on a comprehensive objective assessment of physical health. It included the following elements: self-reports on medical history and present health status, coronary and pulmonary examinations, blood-pressure tests (n = 112), neurological examinations (n = 108), electrocardiograms (ECG; n = 73), and Mini-Mental State Examinations (MMSE; Folstein, Folstein & McHugh, 1975; n = 106). Laboratory analyses were carried out on blood and cerebrospinal fluid (n = 42). Brain neuroimaging was conducted using T1 and T2 weighted magnetic resonance imaging (MRI; n = 107) and perfusion scans (Single Photon Emission Tomography (SPECT); n = 42). A routine non-quantitative electroencephalogram (EEG; n = 103) conforming to clinical standards was also recorded. In addition, psychiatric health was assessed using a depression scale (Montgomery & Asberg, 1979; n = 97). Health remarks were defined as medical conditions and objective signs of health impairment that were associated with either central nervous system (CNS) impairment, systemic disease (affecting the entire body), or age-related sensory loss (e.g., loss of vision, hearing, and/or vibration sense). The health remarks were assessed according to a definition of clinical or subclinical severity conforming to customary clinical practices. More specifically, clinical health remarks were defined as manifestations of apparent symptoms and signs of diseases or abnormalities. Asymptomatic, mild or well-controlled systemic disease (e.g., well-controlled hypertension or well-controlled hypothyroidism and vitamin B12 deficiency), on the other hand, were considered as subclinical. For a detailed account of the clinical and subclinical health remarks, see Bergman and Almkvist (2013). The applied health calculation model is illustrated in Fig. 1. Clinical and subclinical health remarks were scored dichotomously as 0 = healthy or 1 = not healthy. Present health remarks regarding specific clinical or subclinical conditions (i.e., remarks that were given a score of 1) were totaled into health remark counts and sorted into domains (i.e., chapters) according to the International classification of diseases, tenth revision (ICD-10, 1993). These counts were totaled within each domain to constitute the health variables (i.e., each domain-specific health variable consisted of the sum of all clinical and subclinical health remarks within the domain). In addition, the health remarks reflecting age-related sensory loss (e.g., vision, hearing and/or vibration

142 I. Bergman and O. Almkvist

Scand J Psychol 56 (2015)

Health remark counts

Health variables

Clinical 1

ICD-10 chapter 1 (clinical + subclinical)

Clinical n Sorng

Subclinical 1

ICD-10 chapter n (clinical + subclinical) Sensory loss

Subclinical n

(subclinical)

Fig. 1. Illustration of the health calculation model.

sense) were totaled to compose the supplementing health variable. No clinical measurements of visual or auditory acuity were undertaken.

Cognitive assessment Neuropsychological tests assessed four cognitive domains: verbal function, spatial function, memory, and processing speed. Seven psychologists, four assistant psychologists and one speech therapist administered the tests. Verbal functions were examined using the Synonyms subtest1 from the Dureman S€alde test battery (DS; Dureman & S€alde, 1959), a test of knowledge (Information subtest) and a test of abstraction (Similarities subtest) from the Wechsler Adult Intelligence Scale-revised (WAIS-R; Bartfai, Nyman & Stegman, 1992; Wechsler, 1981), the Boston Naming Test (Kaplan, Goodglass & Weintraub, 1983), and the FAS Word Fluency test (Lezak et al., 2004). Spatial functions were examined using a test of reasoning based on visual stimuli (subtest Figure Classification2) from the DS battery, a test of construction (Block Design subtest) from the WAIS-R, a complex copying test (the Rey-Osterrieth Complex Figure test; RCFT; Lezak et al., 2004), and the Clock Reading and Clock Setting subtests from the Luria tests (Luria, 1966). See Bergman et al. (2007) for the scoring procedure of the Luria tests. Memory was examined using a test of verbal short-term and working memory (Digit Span subtest) from the WAIS-R, a 15-word auditory learning test (Rey Auditory Verbal Learning Test; RAVLT; Lezak et al., 2004), delayed retention of the 15 RAVLT words after 30 minutes, a test of free recall and recognition (d-prime) of a 12-word list from the Stockholm Gerontology Research Center3 (SGRC word list; Backman & Forsell, 1994), a spatial short-term memory test (Corsi Span; Milner, 1971), the RCFT immediate recall,4 and a face recognition test5 (Almqvist, Thoren, Saaf & Eriksson, 1986). See Bergman et al. (2007) for the administration- and scoring procedures of the Corsi Span. Processing speed was examined using a test of motor performance (Finger Tapping subtest; average number of taps of the right and left hand) from the computerized Swedish Performance Evaluation System (SPES; Iregren, Gamberale & Kjellberg, 1996), a test of psychomotor speed (Digit Symbol subtest) from the WAIS-R, tests of the speed of visual scanning and mental shifting (Trail Making Test A and B;6 Lezak et al., 2004), and a test of simple visual reaction time (Simple RT subtest) from the SPES. All tests, except for the RAVLT delayed retention, were administered to those participants who were recruited for the two clinical trials and the study on Alzheimer’s disease. However, only selected tests were administered to the group selected for optimal health, the group recruited for the study on automobile driving ability, and all participants. In addition, the records from single tests were missing in a few individual administrations. See Table 3 for the resulting sample size n and age range for each test, and Bergman et al. (2007) for further demographic details of the test samples. © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd

Statistical analyses Two series of hierarchical multiple regression analyses were computed for the neuropsychological tests; an uncontrolled first series (i.e., with only demographic variables entered into the analyses) and a second series controlled for health status (i.e., with health variables also entered into the analyses). In the first series, age was entered into the analyses followed by education and gender. In the second series, age was followed by health (as a step-wise block of the health variables), education and gender. Gender was excluded on tests that showed insignificant gender differences; renewed analyses excluding gender were conducted for those tests. To determine whether curvilinear interaction terms for age and education would improve the regression model, age squared followed by education squared was entered after the other variables into the two series of regression analyses. If any of these interaction terms added a significant change variance to the regression model in one of the two series of analyses, it was retained. However, due to some of the test samples being especially small (n = 55 or 56), a small statistical power was expected to prevent the change variance associated with the interaction terms from reaching significance in some cases. Therefore, visual appraisals based on scatter plot graphs were carried out to determine which regression model appeared to fit the data best. To examine the magnitude of health-related impact on the normative scores, examples were calculated from the resulting intercepts and regression weights for middle age (40 years), younger old age (60 years) and older old age (80 years) at both 6 and 15 years of formal education when uncontrolled and controlled for health status. These examples were chosen to be illustrative rather than reflective of typical patients.

RESULTS The medical examination The results of the medical examination were presented in Bergman and Almkvist (2013). For convenience, the results are also summarized here: the 118 individuals in the study had an average of 1.5 clinical and 2.2 subclinical health remarks associated with CNS or systemic medical conditions and symptoms and another 0.5 subclinical health remarks associated with agerelated sensory loss. The health remarks were sorted into nine health variables attributed to specific ICD-10 domains and one additional health variable attributed to impaired sensory function (See Table 1). For a detailed account of the resulting clinical and subclinical health remarks included in each health variable, see Bergman et al. (2007); for those included in sensory function, see Bergman and Almkvist (2013). The MMSE score range was between 25 and 30 with an average of 28.9 (1.15). The depression scale indicated that two individuals (2%) had a score greater than 5/60 (5.5 and 6), indicating that minimal or unlikely depressive symptoms did not interfere with social function. In other words, none of the included individuals had significant depressive symptoms according to their score or observations.

Neuropsychological assessment The results of the neuropsychological assessment were presented in Bergman and Almkvist (2013). For convenience, the results are presented again in Table 2. Eleven outliers (z ≤ 3) on single tests were identified. Eight of them had health remarks that likely explain the lowest scores on the Boston Naming, RCFT copy, Clock Reading and Clock Setting tests, and the slowest

Preliminary norms controlled for physical health 143

Scand J Psychol 56 (2015) Table 1. Assessed presence, range and mean of health remarks per health variable Health variable

n

%

Range

m (sd)

Diseases of the circulatory system (incl. CNS) Diseases of the nervous system Endocrine, nutritional and metabolic diseases Diseases of the genitourinary system Diseases of blood and the immune system Infectious diseases Diseases of the respiratory system Diseases of the digestive system Findings not classified elsewhere Impaired sensory function

89

75

0–7

1.72 ( 1.56)

50 37

42 31

0–5 0–4

0.64 ( 0.97) 0.41 ( 0.73)

24

20

0–1

0.20 ( 0.40)

16

14

0–1

0.14 ( 0.34)

15 14 12 6 43

13 12 10 5 36

0–2 0–1 0–2 0–1 0–3

0.14 0.12 0.11 0.05 0.48

( ( ( ( (

0.37) 0.33) 0.34) 0.22) 0.73)

Note: Findings not classified elsewhere correspond to the increased permeability of the Blood-Brain Barrier.

Table 2. Results from the neuropsychological assessment Cognitive test Verbal functions Synonyms Information FAS Word Fluency Boston Naming Similarities Spatial functions Figure Classification Block Design Rey-Osterrieth copy Clock Reading Clock Setting Memory Digit Span Rey AVLT learning Rey AVLT del. reten. SGRC free recall SGRC recognition (d’) Corsi Span Rey-Osterrieth recall Face Recognition Processing speed Digit Symbol Trail Making A time (s) errors Trail Making B time (s) errors

n

Range

m

sd

56 75 56 55 117

13–30 11–29 11–77 41–60 7–28

23.3 23.2 48.5 54.2 20.1

4.17 3.64 14.6 4.25 4.63

56 117 95 93 93

7.50–28.75 14–46 15–36 4.0–5.0 2.5–5.0

19.6 28.5 33.8 4.95 4.49

4.06 7.66 2.95 0.20 0.57

77 95 72 94 94 78 95 55

8–24 16–64 2–15 2–12 0.68–4.64 4.17–7.25 2.5–35.0 1.50–14.06

14.1 43.7 9.14 5.93 3.20 5.46 19.2 7.40

3.28 11.5 3.62 2.15 1.07 0.62 6.44 2.76

117 95 95 95 95

13–76 14–99 0–0 38–315 0–9

41.2 39.5 0 105 0.432

12.2 13.5 0 53.9 1.58

time scores for the Trail Making test A and B. See Bergman and Almkvist (2013) for further details. A comparison with previously published data is presented in Appendix A, showing that, for most tests, the mean scores at corresponding ages differed within 5% of previously published data. However, scores were lower, although not excessively, for the SGRC free recall, Corsi Span and Finger Tapping test. In addition, scores were notably higher for the Information, Block

© 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd

Design, FAS Word Fluency, RAVLT delayed retention, and DS Figure Classification tests. For tests with floor or ceiling effects (i.e., normal performance close to the floor or ceiling), the distribution of test scores was, as expected, strongly positive (i.e., a skewness of 4.1 for the TMT B errors), strongly negative (i.e., a skewness of –3.3 for the RCFT copy and –3.9 for the Clock Reading test), or clearly negative (i.e., a skewness of –1.1 for the Boston Naming Test). The distribution was also negative for 16 of the 21 remaining test scores, though only to a modest extent (skewness smaller than –1.0) for all scores except the Information, Trail Making A time, and Trail Making B time scores, which showed a skewness of –1.1, 1.3 and 2.0, respectively. Only the Digit Span, Corsi Span, Block Design, and Digit Symbol tests had positive distributions and all were to modest extent (skewness up to 0.7).

Intercepts and regression weights The resulting unstandardized intercepts and regression weights (B) with standard errors (SE) from both series of analyses (uncontrolled and controlled for health status) are presented in Table 3. These test-specific intercepts and regression weights for age, education and gender estimate the normative scores on an individual basis. For a given individual, the estimated normative score (Y) = Intercept + BAge 9 Age + BAge squared 9 Age2 + BEducation 9 Education + BWoman 9 Gender. Appendix B provides supplementary information on the resulting standardized regression weights (i.e., for the standard linear regression model only) and the specific health variables that are associated with each neuropsychological test when controlled for health status. Corresponding associations on the level of health remarks have been reported in Bergman et al. (2007).

The effects of age, education and gender when uncontrolled and controlled for health status Referring to Table 3, when uncontrolled for health status, age was associated with curvilinear relationships on most verbal tests. Performance increased with age up to 60 years of age (Synonyms, Information) or 70 years of age (FAS Word Fluency); at these points a plateau was reached with no further rise, or performance reached the highest level at 60 years of age (Similarities), after which a decline in performance was observed. For most spatial, memory and processing speed tests, old age was associated with a significant impairment in performance. Most of these relationships were linear; however, the RCFT copy and Clock Setting test were associated with curvilinear relationships implying an accelerated performance drop with age above 50 to 60 years. The simple RT subtest also showed a curvilinear relationship with age, with impaired reaction times up to 55 years of age, above which test scores unexpectedly improved with higher age. Education was associated with linear relationships and a significant advantage for all verbal tests, the Block design subtest, most memory tests, and the Digit Symbol subtest. No curvilinear relationships were observed with regard to education.

144 I. Bergman and O. Almkvist

Scand J Psychol 56 (2015)

Table 3. Size of the test samples (n), their age range, resulting intercepts and unstandardized demographic regression weights (B) with standard errors (SE) for each neuropsychological test when uncontrolled and controlled for health status Intercept

B

Age Range

Uncon.

56

26–81

3.46

3.46

WAIS-R Information

75

26–84

0.90

0.90

FAS Word Fluency

56

26–81

–15.4

–9.36

Boston Naming

55

26–81

48.6

48.6

117

26–91

Spatial functions DS Figure Classification

56

26–81

22.8

21.3

WAIS-R Block Design

117

26–91

40.1

40.1

Rey-Osterrieth copy

95

26–91

29.3

30.4

Luria Clock Reading

93

26–91

5.04

5.02

Luria Clock Setting

93

26–91

0.99

0.99

Memory WAIS-R Digit Span

77

26–84

9.53

9.22

RAVLT learning

95

26–91

55.8

49.3

RAVLT delayed retention

72

26–91

12.6

11.5

SGRC free recall

94

26–91

10.8

10.2

SGRC recognition (d’)

94

26–91

2.77

2.77

Corsi Span U&D

78

29–84

7.20

7.17

Rey-Osterrieth recall

95

26–91

Face Recogn. (hits 9 hit rate)

55

26–81

Processing speed Finger Tapping RL mean

73

26–84

67.7

67.7

WAIS-R Digit Symbol

117

26–91

68.7

67.2

Trail Making A time (s)

95

26–91

17.8

20.3

Cognitive function Verbal functions DS Synonyms

WAIS-R Similarities

n

0.18

30.1

6.11

Con.

–0.42

30.1

5.86

SE

Variable

Uncon.

Age Age squared Education Age Age squared Education Age Age squared Education Age Education Woman Age Age squared Education

0.414 –0.00294 0.541 0.492 –0.00348 0.515 1.28 –0.00844 1.70 –0.0152 0.717 –3.03 0.529 –0.00445 0.513

ns ns ** ** * *** ns ns ** ns *** ** * * ***

0.414 –0.00294 0.541 0.492 –0.00348 0.515 1.12 –0.00616 1.39 0.00571 0.648 –3.30 0.577 –0.00441 0.444

Age Education Age Education Age Age squared Education Age Education Age Age squared Education

–0.101 0.274 –0.270 0.686 0.233 –0.00264 0.119 –0.0024 0.00652 0.147 –0.00147 0.00131

** ns *** *** ns * ns ns ns ** ** ns

Age Education Age Education Woman Age Education Woman Age Education Age Education Age Education Age Education Woman Age Education

–0.018 0.531 –0.323 0.691 7.59 –0.102 0.280 2.42 –0.0811 0.051 –0.00559 0.0765 –0.0262 0.00119 –0.198 0.326 –3.18 –0.0389 0.327

Age Education Age Education Age Education

–0.253 0.358 –0.534 0.921 0.381 –0.367

© 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd

Con.

Uncon.

Con.

ns ns ** ** * *** ns ns * ns *** *** ** ** ***

0.274 0.002 0.163 0.194 0.002 0.121 0.973 0.009 0.579 0.037 0.16 0.972 0.219 0.002 0.124

0.274 0.002 0.163 0.194 0.002 0.121 0.932 0.008 0.567 0.037 0.159 0.955 0.194 0.002 0.110

–0.0497 0.234 –0.270 0.686 0.191 –0.00202 0.106 –0.00146 0.00552 0.147 –0.00147 0.00131

ns ns *** *** ns ns ns ns ns ** ** ns

0.037 0.167 0.047 0.184 0.146 0.001 0.087 0.002 0.007 0.056 0.000 0.034

0.042 0.162 0.047 0.184 0.139 0.001 0.083 0.002 0.007 0.056 0.000 0.034

ns *** *** * *** *** ** ** *** ns ns * *** ns *** ns * ns **

–0.00668 0.514 –0.124 0.575 5.74 –0.0739 0.271 2.08 –0.0589 0.0334 –0.00559 0.0765 –0.0248 0.0014 –0.198 0.326 –3.18 –0.0192 0.293

ns *** ns * ** * ** ** *** ns ns * *** ns *** ns * ns **

0.023 0.104 0.074 0.302 2.06 0.031 0.105 0.744 0.015 0.060 0.008 0.033 0.004 0.019 0.046 0.189 1.29 0.025 0.112

0.023 0.102 0.086 0.283 1.97 0.033 0.103 0.751 0.017 0.059 0.008 0.033 0.004 0.019 0.046 0.189 1.29 0.024 0.108

*** ns *** *** *** ns

–0.253 0.358 –0.441 0.802 0.277 –0.251

*** ns *** ** ** ns

0.055 0.262 0.069 0.272 0.099 0.400

0.055 0.262 0.069 0.258 0.098 0.380

Preliminary norms controlled for physical health 145

Scand J Psychol 56 (2015) Table 3 (continued)

Intercept Cognitive function

n

Age Range

Uncon.

Trail Making B time (s)

95

26–91

35.3

Trail Making B errors

95

26–91

–0.018

Simple RT (ms)

74

26–84

158

B Con. 45.8 0.065 154

Notes: Estimated normative test score (Y) = Intercept + BAge* Age + BAge where Gender is 0 for men and 1 for women. *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ns = p > 0.05.

SE

Variable

Uncon.

Age Education Age Education Age Age squared Education

1.51 –3.01 0.00871 –0.0128 4.95 –0.0453 –2.66

squared

Some significant gender differences did not remain after controlling for health status, implying that a selection bias exists.7 Other gender differences remained: women outperformed men in RAVLT learning and delayed retention, but scored significantly lower on the Boston Naming test and the RCFT recall test. Controlling for health status moved the top performance to 65 years of age for the Similarities subtest and replaced the plateau above age 70 for the FAS Word Fluency test, with a continued rise in scores with age. Controlling for health status also eliminated the significant associations with age for the RAVLT learning, Figure Classification test and RCFT copy, and weakened the association for two memory tests (the RAVLT delayed retention and the SGRC free recall) and three tests of processing speed (the Digit Symbol, Trail Making A and Trail Making B tests). The 95% confidence intervals overlapped between the uncontrolled and controlled weights related to age for all tests, implying that there were no significant differences. Table 3 also shows that controlling for health status slightly weakened the significant regressions related to education on most verbal tests (FAS Word Fluency, Boston Naming, Similarities), several memory tests (Digit Span, RAVLT learning and delayed retention, Face Recognition), and Digit Symbol.8 None of the differences between uncontrolled and controlled weights were significant.

The effects of health status on estimated normative scores Table 4 presents the resulting magnitude of the health-related effects, expressed as differences in estimated normative scores when uncontrolled and controlled for health status. The difference in score (DSD) is related to the test specific standard deviation (SD). The table shows that, at age 40, controlling for health status implied a score within an error margin of 0.2 SD compared with the uncontrolled score. However, at low education levels and age 60, controlling for health status implied increases of 0.4 SD for the Similarities subtest and the RAVLT learning. At low education levels and age 80, controlling for health status implied increases of 0.8 SD for the RAVLT learning, 0.7 SD for the Similarities subtest, 0.6 SD for the Figure Classification and RCFT copy tests, 0.5 SD for the SGRC free recall test and 0.4 SD for the FAS Word Fluency, Boston Naming, Face Recognition, Digit © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd

Con. *** ns ns ns * * ns

1.06 –2.52 0.000819 –0.00833 5.10 –0.0473 –2.91

** ns ns ns * ** *

Uncon.

Con.

0.388 1.57 0.013 0.051 2.16 0.019 1.39

0.380 1.47 0.012 0.048 2.06 0.018 1.33

* Age squared + BEducation* Education + BWoman* Gender,

Symbol, and Trail Making A and B tests. Corresponding rises in normative scores at high education levels were generally smaller.

DISCUSSION The principal findings of the present study were test-specific intercepts and regression weights for age, education and gender, which are used to make estimations of normative scores possible when uncontrolled and controlled for physical health. As previously reported for the present sample (Bergman & Almkvist, 2013), the effects of compromised physical health were mainly related to diseases of the circulatory and nervous systems that primarily affect fluid intelligence. In the present study, controlling for compromised physical health eliminated or reduced several significant negative influences of age in all cognitive domains (e.g., verbal, spatial, memory and processing speed). Controlling for health status also slightly reduced the negative influences of low education levels. Fictitious examples were calculated from the derived intercepts and regression weights to indicate the magnitude of health-related impact on the normative scores. We found that controlling for health clearly matters in older old age, reflecting rises of up to 0.8 SD of normative scores at age 80. As expected, controlling for health status was found to matter less in younger old age, reflecting rises of up to 0.4 SD of normative scores at age 60; controlling for health status did not appear to matter in middle age (at age 40) when no differences were found between unadjusted and adjusted scores. Three of the tests that showed rises in normative scores of 0.5 SD or more in older old age when controlled for health (e.g., the RAVLT, Similarities subtest, and RCFT copy) are well-known and widely used in clinical settings (although there are more recent versions of the Similarities subtest). These tests are sensitive to early signs of frontal, temporal and parietal dysfunction in dementia (Lezak et al., 2004). Therefore, using unadjusted conventional norms implies a risk for false negative errors due to an incorrect interpretation that no cognitive impairment exists beyond what can be expected at a particular age. For example, suppose that an 80-year-old man with 6 years of education and subjective memory impairment, who is otherwise healthy, attains a raw score of 14 on the Similarities subtest, 30 on the RCFT copy and 26 on the RAVLT learning. When uncontrolled for health status, these scores correspond to z-values within the normal- or average span

146 I. Bergman and O. Almkvist

Scand J Psychol 56 (2015)

Table 4. Increases in normative scores (DSD) when controlling for health status at ages 40, 60 and 80 years, and at 6 and 15 years of education DSD Cognitive test Verbal functions DS Synonyms WAIS-R Information FAS Word Fluency Boston Naming, men Boston Naming, women WAIS-R Similarities Spatial functions DS Figure Classification WAIS-R Block Design Rey-Osterrieth copy Luria Clock Reading Luria Clock Setting Memory WAIS-R Digit Span RAVLT learning, men RAVLT learning, women RAVLT delayed reten., men RAVLT delayed reten., women SGRC free recall SGRC recognition (d’) Corsi Span U&D Rey-Osterrieth recall, men Rey-Osterrieth recall, women Face Recognition (hits 9 hit rate) Processing speed Finger Tapping R&L mean WAIS-R Digit Symbol Trail Making A time (s) Trail Making B time (s) errors Simple RT (ms)

SD

A40 E6

A40 E15

A60 E6

A60 E15

A80 E6

A80 E15

4.17 3.64 14.6 3.49 4.74 4.63

0 0 0.1 0.1 0 0.2

0 0 –0.1 –0.1 –0.1 0.1

0 0 0.2 0.2 0.1 0.4

0 0 0 0.1 0 0.3

0 0 0.4 0.4 0.2 0.7

0 0 0.2 0.2 0.1 0.5

4.06 7.66 2.95 0.20 0.57

0.1 0 0.1 0.1 0

0 0 0 0 0

0.3 0 0.3 0.2 0

0.2 0 0.2 0.1 0

0.6 0 0.6 0.2 0

0.5 0 0.5 0.2 0

3.28 11.2 8.95 3.20 3.34 2.15 1.07 0.62 6.59 6.06 2.76

0 0.1 –0.1 0 –0.1 0.1 0 0 0 0 0.1

0 0 –0.2 0 –0.1 0 0 0 0 0 0

0.1 0.4 0.3 0.2 0.1 0.3 0 0.1 0 0 0.3

0 0.3 0.2 0.1 0 0.2 0 0.1 0 0 0.2

0.2 0.8 0.8 0.3 0.2 0.5 0 0.1 0 0 0.4

0.1 0.7 0.7 0.3 0.2 0.4 0 0.1 0 0 0.3

7.77 12.2 13.5 53.9 1.58 35.0

0 0.1 0.1 0.1 0.1 0.1

0 0 0 0 0.1 0.1

0 0.3 0.2 0.3 0.2 0.1

0 0.2 0.2 0.2 0.2 0.2

0 0.4 0.4 0.4 0.3 0.2

0 0.3 0.3 0.3 0.3 0.2

Note: A = Age, E = Education.

limits of 1 SD (i.e., between –0.6 and –0.7). However, when controlled for health, these same scores correspond to z-values that are clearly below these limits (i.e., between –1.2 and –1.5). If there are no common age-related health impairments, psychiatric conditions, or treatments that can explain the decline, an incipient neurodegenerative disorder may be present (e.g., Alzheimer’s disease). Norms that are controlled for health status have a greater sensitivity and make cognitive impairment in old age more evident, thereby prompting the clinical psychologist to investigate plausible explanations of impaired cognitive function, whereas conventional norms would not. Provided that physical health status is known and strictly considered, and the norms adjusted for physical health status are sensibly used, preferably in combination with norms uncontrolled for physical health status to avoid false positive and false negative errors, norms adjusted for health status may help the elucidation of correct diagnostic conclusions. The performance on the FAS Word Fluency test was found to rise with age until a plateau was reached at age 70. When controlled for health status, the plateau was replaced with a continued rise with age. The findings in previous studies on verbal fluency are confusing because advanced age has been shown to be associated with no decline, little decline or a significant decline (Lezak et al., 2004). The present findings appear to add © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd

to the confusion. However, our findings suggest the possibility that individuals with typical age-related health problems were included in some of the previous studies, therefore explaining some of the disparate findings. A health-related selection bias for gender implied that, for tests showing gender differences, controlling for health consistently raised the scores of male subjects in comparison with the scores of female subjects. These distinctions illustrate that normative values can be confused by uncontrolled sampling differences in demographic variables and health status. Adjusting for age, education and gender, as well as controlling for health status, in the present normative values can correct for biases due to sampling differences. The problem with sampling differences across normative data sets has been extensively studied (Kalechstein et al., 1998). The preliminary regression-based norms provided in this study are not diagnostic but comparative in nature. Although they may assist providers in clinical settings to discriminate individuals in two or more groups (e.g., demented from non-demented individuals), the primary purpose of these norms is as a reference to normality with which to compare the performance or relative standing of an individual. On the other hand, diagnostic norms usually take the form of cut scores and have been reported

Scand J Psychol 56 (2015)

to show the best discriminative validity when they are not controlled for demographic risk factors such as age and education (for dementia screening, see O’Connell, Tuokko, Graves & Kadlec, 2004; Sliwinski, Buschke, Stewart, Masur & Lipton, 1997). Future studies can further compare these methods in terms of the diagnostic sensitivity and specificity. Regression-based norms have been criticized by Fastenau (1998) on an empirical basis for overcorrecting demographic influences (e.g., education) when applied to his sample of 63 older adults. The regression-based norms that he addressed were published in 1991 by Heaton, Grant and Matthews and in 1993 by Heaton, Chelune, Talley, Kay and Curtiss. However, Heaton, Avitable, Grant and Matthews (1999) noted that, among the 29 “less educated” subjects in Fastenau’s sample, only one had an educational attainment of less than 12 years. They proposed that Fastenau’s findings resulted from a non-representative nature of his relatively small sample, rather than from statistical deficiencies of regression-based norms. Fastenau (1998) has also called attention to some basic conditions that must be met for regression-based norms, such as linearity of relationships (when using standard linear regression methods), homoskedasticity (i.e., homogeneity of variances), and a normal distribution of test scores. Linearity of relationships has been shown for the present study’s norms in most cases. However, for age, a curvilinear regression model was found to fit the data better for most verbal tests and two spatial tests. Therefore, the present study’s norms fulfill a more important proximity of relationships, if not a linearity of relationships for all tests. The degree of inter-individual variability in test performance is greater with increasing age (Christensen et al., 1994b). This violation of the homoscedasticity assumption weakens the analysis of a cross-sectional group. However, controlling for health also implies controlling for health-related violations of homoskedasticity in old age, which strengthens the cross-sectional analysis. Clearly negative distributions of test scores were found for the Information subtest scores and the Trail Making A and B scores. A relatively high mean score on the Information subtest and, in contrast to what has been previously reported (Heaton et al., 1991), no significant relationships with education for the Trail Making tests suggests that these departures from the normal distribution were most likely due to the under-representation of individuals with low education in the present sample. Except for those tests that were associated with ceiling-effects (the RCFT copy, the Clock reading and Naming tests), and TMT B errors that was associated with a floor-effect, all remaining tests showed a close to normal distribution of the test scores. In addition, comparatively high scores on the Block Design test, Word Fluency test, RAVLT delayed retention and the Figure Classification test likely relate to the under-representation of individuals with low education and a related lower presence of disease. Hence, the present sample’s bias, with relatively high scores for certain tests, calls attention to the risk of misidentifying individuals as being impaired, when they are of normal function but simply have a low education level. Such errors are known as false-positive errors (Hebben & Milberg, 2002; Kalechstein et al., 1998). Simple RT was surprisingly faster in older old age than in younger old age. A similar finding has been reported by Era, © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd

Preliminary norms controlled for physical health 147 Jokela and Heikkinen (1986), who noted that a possible explanation for this effect is selective mortality, which had earlier removed the most severe cases of cardiovascular diseases from their group. Such an explanation may apply also to the present test’s group, where 20 of the 74 individuals tested were selected for optimal health. Therefore, the present study’s findings regarding Simple RT should be utilized with particular caution. The present preliminary norms were derived from an exclusive population of ethnically and culturally Swedish adults that live in economically stable urban regions in Sweden; therefore, we cannot readily conclude that the findings of the present study will generalize to populations composed of other ethnic, cultural, social or economic backgrounds. Further restrictions for the generalization of this study’s findings are the use of the Swedish context-specific verbal tests (e.g., the Synonyms, Information and FAS Word Fluency tests), lack of a published language translation of the SGRC Word List, and utilization of the WAIS-R instead of the more recent WAIS-III or WAIS-IV. When keeping these restrictions in mind, the findings of this study can be utilized also in other countries. Of course, due consideration should be taken to their preliminary nature. Provided that due observance is paid to these limitations, the small sample size allows at the most tentative usage of the present findings in clinical applications.9 A larger sample size should be collected in order to apply the demographic characteristics for specific persons and statistical regression weights from the sample to calculate expected values. This individual-based continuous adjustment of moderators has the potential to minimize the misinterpretation of test data due to a biased selection of normative data. In conclusion, the present findings indicate that norms that are uncontrolled for health status overestimate the negative influences of advanced age and low education, implying a risk for drawing false diagnostic conclusions. It is possible that reference data on normal performance, as published by test authors or as reported from clinical research studies, are contaminated by the same errors. Those errors primarily indicate that healthy normal performance is underestimated in old age because normative values are based on cross-sectional samples from the population, which most likely include individuals with common age-related morbidity. The main results of the present study have been reported at the Ninth Nordic Meeting in Neuropsychology, August 19–22, 2007, in Gothenburg, Sweden. This work was supported in part by grants from the Alzheimer’s Foundation, the Foundation of Gamla Tj€anarinnor, Swedish Brain Power, the Swedish Medical Research Council, the Swedish Society of Medicine, Loo and Hans Osterman’s Foundation for Medical Research, the Claes Groschinsky Foundation, and Trafikmedicinskt Centrum as well as the Department of Psychology at Karolinska University Hospital. This study would not have been possible without the collaboration of a number of psychologists enrolled in the neuropsychological assessment (mentioned in alphabetical order): Kaarina Amberla, Mehrnaz Amintafreshni, Birgitta Ausen, Christina Fischler, Tarja Laaksonen, Maria Lindau, Catarina Lundberg, Beata Terzis, and  Ake Wallin. Additionally, the collaboration with physicians is gratefully acknowledged, and primarily includes Hans Basun, Mari Blomberg, Lena Bronge, Anita Garlind, Kurt Johansson, Lars Lannfelt, Matti Viitanen, and Lars-Olof Wahlund. In addition, we would like to thank the outpatient ward personnel for their essential assistance in taking care of all participants in the study over the years, in particularly, research nurse

148 I. Bergman and O. Almkvist Anna-Lena Wetterholm. Finally, we would like to thank all of the individuals who participated in this study for their time and effort.

NOTES 1 The DS Synonyms subtest consists of 30 items. The task is to choose, from five possible words in each item, the word that has the same meaning as the target word. 2 The DS Figure Classification subtest consists of 30 items. The task is to choose, from five geometrical figures in each item, those figures that have the same characteristics and to underline the one figure that differs from the others. To correct for guessing, the subtest was scored in accordance with the formula specified in the manual: Number of correct item responses – (Number of wrong item responses / 4). 3 The SGRC word list is a test where 12 words are presented aloud and simultaneously in a booklet at a rate of 5 s per word. 4 Immediate recall of the Rey-Osterrieth Complex Figure test was administered with no time delay in accordance with the most frequently used procedure among practicing neuropsychologists (Knight, Kaplan & Ireland, 2003); the test was not administered with a 3 min delay in accordance with the Osterrieth (1944) convention. 5 The Face Recognition test is a test of recognition of 16 unfamiliar elderly faces among 48. The scoring is calculated as follows: number of hits 9 hit rate. 6 The Trail Making A and B tests were administered according to an alternative procedure that required the examiner to note only the first error. For that reason, the standard Trail Making A and B test’s time scorings was supplemented by number of errors. As noted by Lezak et al. (2004), the reliability may benefit slightly from this procedure compared to the commonly used procedure described by Reitan (1958), which requires the examiner to note errors as they occur. 7 Women were significantly healthier than men on important health domains implying that, when uncontrolled for health, they scored significantly higher than men as artifacts on the Similarities, Digit Span and Trail Making A and B tests. Gender was excluded and renewed regression analyses were carried out for these tests. 8 Pearson’s correlation tests between education and the health variables showed that education tended to negatively correlate with diseases of the circulatory system (r = –0.17; p = 0.06) and genitourinary system (r = –0.17; p = 0.07). 9 An Excel spreadsheet for calculating expected values based on demographic characteristics is available by request from the first author.

REFERENCES Aarts, S., van den Akker, M., Tan, F. E., Verhey, F. R., Metsemakers, J. F. & van Boxtel, M. P. (2011). Influence of multimorbidity on cognition in a normal aging population: A 12-year follow-up in the Maastricht Aging Study. International Journal of Geriatric Psychiatry, 26, 1046–1053. Almqvist, O., Thoren, M., Saaf, M. & Eriksson, O. (1986). Effects of growth hormone substitution on mental performance in adults with growth hormone deficiency: A pilot study. Psychoneuroendocrinology, 11, 347–352. Backman, L. & Forsell, Y. (1994). Episodic memory functioning in a community-based sample of old adults with major depression: Utilization of cognitive support. Journal of Abnormal Psychology, 103, 361–370. Backman, L., Nyberg, L., Lindenberger, U., Li, S. C. & Farde, L. (2006). The correlative triad among aging, dopamine, and cognition: Current status and future prospects. Neuroscience and Biobehavioral Reviews, 30, 791–807. Bartfai, A., Nyman, H. & Stegman, B. (1992). Wechsler Adult Intelligence Scale – Revised: Manual (Swedish translation and adaption). Stockholm: Psykologif€orlaget. Bergman, I. & Almkvist, O. (2013). The effect of age on fluid intelligence is fully mediated by physical health. Archives of Gerontology and Geriatrics, 57, 100–109. © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd

Scand J Psychol 56 (2015) Bergman, I., Blomberg, M. & Almkvist, O. (2007). The importance of impaired physical health and age in normal cognitive aging. Scandinavian Journal of Psychology, 48, 115–125. Bulow, B., Hagmar, L., Orbaek, P., Osterberg, K. & Erfurth, E. M. (2002). High incidence of mental disorders, reduced mental wellbeing and cognitive function in hypopituitary women with GH deficiency treated for pituitary disease. Clinical Endocrinology (Oxford), 56, 183–193. Christensen, H., Jorm, A. F., Henderson, A. S., Mackinnon, A. J., Korten, A. E. & Scott, L. R. (1994a). The relationship between health and cognitive functioning in a sample of elderly people in the community. Age and Ageing, 23, 204–212. Christensen, H., Mackinnon, A., Jorm, A. F., Henderson, A. S., Scott, L. R. & Korten, A. E. (1994b). Age differences and interindividual variation in cognition in community-dwelling elderly. Psychology and Aging, 9, 381–390. Davenport, M. H., Hogan, D. B., Eskes, G. A., Longman, R. S. & Poulin, M. J. (2012). Cerebrovascular reserve: The link between fitness and cognitive function? Exercise and Sport Science Reviews, 40, 153–158. Deary, I. J., Whiteman, M. C., Pattie, A., Starr, J. M., Hayward, C., Wright, A. F., et al. (2002). Cognitive change and the APOE epsilon 4 allele. Nature, 418, 932. Droge, W. (2002). Free radicals in the physiological control of cell function. Physiological Reviews, 82, 47–95. Dureman, I. & S€alde, H. (1959). Psykometriska och experimentalpsykologiska metoder f€ or klinisk till€ ampning. [Psychometric and experimental methods for the clinical evaluation of mental functioning]. Stockholm: Almqvist & Wiksell. Era, P., Jokela, J. & Heikkinen, E. (1986). Reaction and movement times in men of different ages: A population study. Perceptual & Motor Skills, 63, 111–130. Fastenau, P. S. (1998). Validity of regression-based norms: An empirical test of the comprehensive norms with older adults. Journal of Clinical and Experimental Neuropsychology, 20, 906–916. Folstein, M. F., Folstein, S. E. & McHugh, P. R. (1975). “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–198. Geary, D. C. (1989). A model for representing gender differences in the pattern of cognitive abilities. American Psychologist, 44, 1155–1156. Heaton, R. K., Avitable, N., Grant, I. & Matthews, C. G. (1999). Further crossvalidation of regression-based neuropsychological norms with an update for the Boston Naming Test. Journal of Clinical and Experimental Neuropsychology, 21, 572–582. Heaton, R. K., Chelune, G. J., Talley, J. L., Kay, G. G. & Curtiss, G. (1993). Wisconsin Card Sorting Test manual: Revised and expanded. Odessa, FL: Psychological Assessment Resources. Heaton, R. K., Grant, I. & Matthews, C. G. (1991). Comprehensive norms for an expanded Halstead-Reitan Battery: Demographic corrections, research findings, and clinical applications. Odessa, FL: Psychological Assessment Resources. Hebben, N. & Milberg, W. (2002). Essentials of neuropsychological assesment. New York: John Wiley & Sons. Howieson, D. B., Holm, L. A., Kaye, J. A., Oken, B. S. & Howieson, J. (1993). Neurologic function in the optimally healthy oldest old. Neuropsychological evaluation. Neurology, 43, 1882–1886. ICD-10. (1993). International statistical classification of diseases and related health problems. Tenth revision. Geneva: World Health Organization. Iregren, A., Gamberale, F. & Kjellberg, A. (1996). SPES: A psychological test system to diagnose environmental hazards. Swedish Performance Evaluation System. Neurotoxicology and Teratology, 18, 485–491. Ivnik, R. J., Malec, J. F. & Smith, G. E. (1992). Mayo’s older Americans normative studies: WAIS-R norms for ages 56 to 97. The Clinical Neuropsychologist, 6, Supplement, 1–30. Ivnik, R. J., Malec, J. F., Smith, G. E., Tangalos, E. G. & Petersen, R. C. (1996). Neuropsychological tests’ norms above age 55: COWAT, BNT, MAE Token, WRAT-R Reading, AMNART, STROOP, TMT, and JLO. The Clinical Neuropsychologist, 10, 262–278.

Scand J Psychol 56 (2015) Ivnik, R. J., Malec, J. F., Tangalos, E. G., Peterson, R. C., Kokmen, E. & Kurland, L. T. (1990). The Auditory-Verbal Learning Test (AVLT): Norms for ages 55 years and older. Psychological Assessment, 2, 304–312. Jak, A. J. (2012). The impact of physical and mental activity on cognitive aging. Current Topics in Behavioral Neurosciences, 10, 273–291. Kalechstein, A. D., van Gorp, W. G. & Rapport, L. J. (1998). Variability in clinical classification of raw test scores across normative data sets. The Clinical Neuropsychologist, 12, 339–347. Kaplan, E. F., Goodglass, H. & Weintraub, S. (1983). The Boston Naming Test. Philadelphia, PA: Lea & Febiger. Kessels, R. P., van Zandvoort, M. J., Postma, A., Kappelle, L. J. & de Haan, E. H. (2000). The Corsi Block-Tapping Task: Standardization and normative data. Applied Neuropsychology, 7, 252–258. Knight, J. A., Kaplan, E. & Ireland, L. (2003). Survey findings of ReyOsterrieth Complex Figure usage. In J. A. Knight (Ed.), The handbook of Rey-Osterrieth Complex Figure usage: Clinical and research applications. Lutz, FL: Psychological Assessment Resources. Le Carret, N., Lafont, S., Mayo, W. & Fabrigoule, C. (2003). The effect of education on cognitive performances and its implication for the constitution of the cognitive reserve. Developmental Neuropsychology, 23, 317–337. Lezak, M. D., Howieson, D. B. & Loring, D. W. (2004). Neuropsychological assessment (4th edn). New York: Oxford University Press. Luria, A. R. (1966). Higher cortical functions in man. New York: Basic Books. Milner, B. (1971). Interhemispheric differences in the localization of psychological processes in man. British Medical Bulletin, 27, 272–277. Min, J. Y., Min, K. B., Paek, D., Sakong, J. & Cho, S. I. (2007). The association between neurobehavioral performance and lung function. Neurotoxicology, 28, 441–444. Mitrushina, M., Boone, K. B., Razani, J. & D’Elia, L. F. (2005). Handbook of normative data for neuropsychological assessment (2nd edn). New York: Oxford University Press. Montgomery, S. A. & Asberg, M. (1979). A new depression scale designed to be sensitive to change. British Journal of Psychiatry, 134, 382–389. Nystrom, S. (1983). Personality variations in population: intelligence. Scandinavian Journal of Social Medicine, 11, 97–106. O’Connell, M. E., Tuokko, H., Graves, R. E. & Kadlec, H. (2004). Correcting the 3MS for bias does not improve accuracy when screening for cognitive impairment or dementia. Journal of Clinical and Experimental Neuropsychology, 26, 970–980. Osterrieth, P. A. (1944). Le test de copie d’une figure complexe. Archives de Psychologie, 30, 206–356. Perlmutter, M. & Nyquist, L. (1990). Relationships between self-reported physical and mental health and intelligence performance across adulthood. Journal of Gerontology, 45, 145–155. Piguet, O., Grayson, D. A., Broe, G. A., Tate, R. L., Bennett, H. P., Lye, T. C., et al. (2002). Normal aging and executive functions in “old-old” community dwellers: Poor performance is not an inevitable outcome. International Psychogeriatrics, 14, 139–159. Reitan, R. M. (1958). Validity of the Trail Making Test as an indicator of organic brain damage. Perceptual and Motor Skills, 8, 271–276. Schaie, K. W. (2005). Development influences on adult intelligence: The Seattle longitudinal study. New York: Oxford University Press. Sliwinski, M., Buschke, H., Stewart, W. F., Masur, D. & Lipton, R. B. (1997). The effect of dementia risk factors on comparative and diagnostic selective reminding norms. Journal of the International Neuropsychological Society, 3, 317–326. Smerbeck, A. M. J., Parrish, E. A., Yeh, B., Weinstock-Guttman, M., Hoogs, D. & Serafin, L., et al. (2012). Regression-based norms improve the sensitivity of the National MS Society Consensus Neuropsychological Battery for Pediatric Multiple Sclerosis (NBPMS). The Clinical Neuropsychologist, 26, 985–1002. Spreen, O. & Strauss, E. (1998). A compendium of neuropsychological tests (2nd edn). New York: Oxford University Press.

© 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd

Preliminary norms controlled for physical health 149 Statistics Sweden (1995). Statistikdatabasen: Utbildning och forskning: alder och k€ on Befolkning 16–74  ar i riket efter utbildningsniv a,  Retrieved 22 January 2006 from http://www.ssd.scb.se/. Tallberg, I. M., Ivachova, E., Jones Tinghag, K. & Ostberg, P. (2008). Swedish norms for word fluency tests: FAS, animals and verbs. Scandinavian Journal of Psychology, 49, 479–485. Terry, A. V. Jr & Buccafusco, J. J. (2003). The cholinergic hypothesis of age and Alzheimer’s disease-related cognitive deficits: Recent challenges and their implications for novel drug development. Journal of Pharmacology and Experimental Therapeutics, 306, 821– 827. van Boxtel, M. P., Buntinx, F., Houx, P. J., Metsemakers, J. F., Knottnerus, A. & Jolles, J. (1998). The relation between morbidity and cognitive performance in a normal aging population. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 53, M147–154. Wechsler, D. (1981). Manual for the Wechsler Adult Intelligence Scale, revised. New York: Psychological Corporation. Verhaegen, P., Borchelt, M. & Smith, J. (2003). Relation between cardiovascular and metabolic disease and cognition in very old age: Cross-sectional and longitudinal findings from the Berlin aging study. Health Psychology, 22, 559–569. Wilson, R. S. (2008). Neurological Factors in Cognitive Aging. In S. M. Hofer & F. A. Duane (Eds.), Handbook of cognitive aging: Interdisciplinary perspectives. London: Sage. Zelinski, E. M., Crimmins, E., Reynolds, S. & Seeman, T. (1998). Do medical conditions affect cognition in older adults? Health Psychology, 17, 504–512. Received 10 February 2014, accepted 1 September 2014

APPENDIX A: COMPARISON OF THE RESULTS FROM THE NEUROPSYCHOLOGICAL ASSESSMENT WITH PREVIOUSLY PUBLISHED DATA The mean score of the neuropsychological assessment differed within  5% for the Similarities, Digit Span, and Digit Symbol tests compared to Bartfai et al. (1992) at 65–75 years of age; for the Synonyms subtest compared to Bulow, Hagmar, Orbaek, Osterberg & Erfurth (2002) at 64 years of age; for the Boston Naming test compared to Mitrushina, Boone, Razani and D’Elia (2005) at 60–64 years of age; for the RCFT copy and recall, the RAVLT learning, and the Trail Making A and B tests compared to Mitrushina et al. (2005) at 65–69 years of age; for the Simple RT compared to Spreen and Strauss (1998) at 60–69 years of age; and for the SGRC recognition test compared to Backman & Forsell (1994) at 83 years of age. On average, the mean scores for these tests only marginally exceeded those reported in the specified references (i.e., with 0.1%). Furthermore, mean scores were lower, but not excessively so, for the SGRC free recall test compared to Backman and Forsell (1994) at 83 years of age (5.9 vs. 6.1); for the Corsi Span test compared to Kessels, van Zandvoort, Postma, Kappelle and de Haan (2000) at 31 years of age (5.5 vs. 6.2); and for the Finger Tapping test compared to Min, Min, Paek, Sakong and Cho (2007) at 38 years of age (55 vs. 65). However, at corresponding ages, the mean scores were notably higher for the Information subtest (23,2 vs. 21 in Bartfai et al., 1992), the Block Design test (28.5 vs. 22–24 in Bartfai et al., 1992), the FAS Word Fluency test (48.5 vs. 42,3 in Tallberg, Ivachova, Jones Tinghag and Ostberg, 2008), and

150 I. Bergman and O. Almkvist

Scand J Psychol 56 (2015)

clearly higher scores were achieved for the DS Figure Classification test (19.6 vs. 16.5 in Nystrom and 13 in Dureman & S€alde). We found no reference data for the Luria Clock Test or the Face Recognition Test.

the RAVLT delayed retention (9.14 vs. 8.49 in Mitrushina et al., 2005). In addition, compared with population samples living in the county of Stockholm in 1970 (Nystrom, 1983) and in the neighborhoods of Uppsala in 1954 (Dureman & S€alde, 1959),

APPENDIX B Resulting standardized regression weights (b) for age, education, gender and health status, and the explained variance (R2), for each neuropsychological test Age Cognitive test

n

b

Verbal functions Synonyms Information FAS Word Fluency Boston Naming Similarities

56 75 56 55 117

0.36 0.36 0.42

Spatial functions Figure Classification Block Design Rey-Osterrieth copy

56 117 95 93 93

Clock Reading Clock Setting Memory Digit Span RAVLT learning RAVLT delayed retention SGRC free recall SGRC recognition Corsi Span Rey-Osterrieth recall Face Recogn. (hits 9 hit rate) Processing-speed Finger Tapping RL mean Digit Symbol Trail Making A time errors Trail Making B time errors Simple RT

73 117 95 95 95 95 74

Woman

b

p

** *** *** ns ns

0.36 0.50 0.31 0.47 0.34

** *** * *** ***

–0.45 –0.19

ns *** t

0.29

ns *** ns

ns ns ns

–0.33

ns **

ns ns

ns ns

–0.23 –0.37 –0.55 –0.41

–0.47 –0.48 0.26 0.25

ns ns

0.50 0.16

*** *

* *** ns *** *** ns

0.26

* ns * ns t *

*** *** ** ns ** ns

0.25 0.17 0.33

0.20

ns

ns ** ns ns ns ns ns

Note: The R2 coefficients are adjusted. *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, t = p > 0.05, ns = p > 0.1.

© 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd

b

Health status

p

77 95 72 94 94 78 95 55

Education

–0.20 –0.39

0.24 0.28

–0.24

0.13 –0.16 –0.16

p

ns * ns *** ns

ns ** ** ns ns ns * ns

ns t t ns t ns ns

Domain/Variable

b

Genitourinary system Genitourinary system Nervous system Circulatory system

–0.31 –0.24 –0.26 –0.25

Circulatory system

–0.33

Circulatory system Not elsewhere classified Nervous system

–0.29 –0.21 –0.22

Sensory function Circulatory system Sensory function Sensory function Circulatory system Not elsewhere classified Nervous system

–0.20 –0.24 –0.23 –0.21 –0.27 –0.26 –0.20

Respiratory system Genitourinary system

–0.29 –0.26

Circulatory system Nervous system

–0.24 0.31

Nervous system Not elsewhere classified Nervous system Nervous system

0.33 0.29 0.24 0.31

p

R2

ns ns * t ** *

0.25 0.31 0.28 0.34 0.31

* ns * * t ns

0.20 0.31 0.23

t * * t * ** * ns * * ns ** ** ns *** ** * **

0.04 0.09 0.28 0.44 0.39 0.29 0.09 0.34 0.20 0.25

0.24 0.49 0.25 0.00 0.29 0.11 0.07

Copyright of Scandinavian Journal of Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.