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TMT: Normative Data for Quebec French-speaking Adults

Trail Making Test A and B: Regression-Based Normative Data

for Quebec French-speaking Mid and Older-Aged Adults

Alexandre St-Hilaire1, Camille Parent1,2, Olivier Potvin1, Louis Bherer3,4,5,

Jean-François Gagnon6,7, Sven Joubert4,8, Sylvie Belleville4,8, Maximiliano A.

Wilson1,9, Lisa Koski10,11, Isabelle Rouleau6,12, Carol Hudon1,2 & Joël Macoir1,9

1Centre de recherche CERVO, Institut universitaire en santé mentale de Québec, Québec, QC,

Canada2École de psychologie, Université Laval, Québec, QC, Canada3Department de médecine, Université de Montréal, Montréal, QC, Canada4Centre de recherche, Institut universitaire de gériatrie de Montréal, Montréal, QC, Canada5Centre de recherche, Institut de Cardiologie de Montréal, Montréal, QC, Canada6Département de psychologie, Université du Québec à Montréal, Montréal, QC, Canada7Centre d’Études Avancées en Médecine du Sommeil, Hôpital du Sacré-Coeur de Montréal,

Montréal, QC, Canada8Département de psychologie, Université de Montréal, Montréal, QC, Canada9Département de réadaptation, Université Laval, Québec, QC, Canada10Département de neurologie, Université McGill, Montréal, QC, Canada11Neurorehabilitation Research Centre, Montréal, QC, Canada12Centre de recherche, Centre Hospitalier de l'Université de Montréal, Montréal, QC, Canada

Corresponding author: Joël Macoir; Département de réadaptation, Faculté de médecine,

Pavillon Ferdinand-Vandry, Université Laval; 1036, rue de la Médecine, Bureau 4453; Québec,

QC, Canada, G1V 0A6; +1 418-656-2131 poste 12190; email: [email protected]

ASH: [email protected] CP: [email protected];

OP: [email protected]; LB: [email protected]; JFG: gagnon.jean-

[email protected]; SJ: [email protected]; SB: [email protected]; MW:

[email protected]; LK: [email protected]; IR: [email protected];

CH: [email protected]; JM: [email protected]

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mailto:[email protected]





Trail Making Test A and B: Regression-Based Normative data for Quebec

French-speaking Mid and Older-Aged Adults

Abstract

Objective: The Trail Making Test (TMT) is mainly used to assess visual scanning/processing

speed (part A) and executive functions (part B). The test has proven sensitive at detecting

cognitive impairment during aging. However, previous studies have shown differences between

normative data from different countries and cultures, even when corrected for age and education.

Such inconsistencies between normative data may lead to serious diagnostic errors, thus, the

development of local norms is warranted. The purpose of this study was to provide regression-

based normative data for TMT-A and -B, tailored for a large sample of French-speaking adults

from Quebec (Canada). Method: The normative sample consisted of 792 participants aged 50 to

91 years. Based on multiple linear regression, equations to calculate Z-scores were provided for

TMT-A and -B, and for a contrast score which compared performance between TMT-A and -B.

Percentiles, stratified by age, are presented for the number of recorded errors. Results: Age was

a significant predictor for TMT-A performance, while age and education were independently

associated with performance on TMT-B. Gender did not have any effect on performance, in

either condition. Education was the only significant predictor of the contrast score between

TMT-B and TMT-A. Examiners should remain vigilant when two or more errors are recorded on

the TMT-B since this was uncommon in the normative sample. Conclusions: Our TMT

normative data improve the accurate detection of visual scanning/processing speed and executive

function deficits in Quebec (Canada) French-speaking adults.

Key words: Norms/normative studies; Executive Functions; Attention; Trail Making Test;

Elderly/Geriatrics/Aging.

Word count: 4541 (including in the text authors’ names)

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Introduction

The Trail Making Test (TMT) is widely used in clinical settings to assess visual

scanning/psychomotor processing speed (Part A), and executive functions (Part B; primarily

cognitive flexibility/shifting) (Lezak, Howieson, Bigler, & Tranel, 2012; Salthouse, 2011). TMT

test performance is evaluated based on the speed of task completion as well as the number of

errors recorded during task execution. Both indices are important because they monitor different

cognitive processes (psychomotor speed vs. working memory/executive functions, respectively)

(Mahurin et al., 2006). Errors are uncommon in healthy participants, thus serving as sensitive

indicators of cerebral dysfunction (Mahurin et al., 2006). Many neuroimaging studies indicate

that TMT-B performance (especially the number of errors) is specifically related to cerebral

activity in the dorsolateral prefrontal region, because of its role in cognitive flexibility

(Davidson, Gao, Mason, Winocur, & Anderson, 2008; Moll, de Oliveira-Souza, Moll, Bramati,

& Andreiuolo, 2002; Stuss et al., 2001; Yochim, Baldo, Nelson, & Delis, 2007; Zakzanis, Mraz,

& Graham, 2005). However, a recent study showed that TMT-B performance did not differ

significantly in a group of patients with frontal brain lesions, compared to those with non-frontal

lesions. This suggests that the test is sensitive to general brain dysfunction, but not capable of

detecting damage in any specific brain region (Chan et al., 2015).

The TMT has also proved useful for the assessment of mild to severe traumatic brain

injury (Azouvi et al., 2016; de Guise et al., 2016), psychiatric conditions (Cotrena, Branco,

Shansis, & Fonseca, 2016), and neurodegenerative diseases (Ashendorf et al., 2008; Rasmusson,

Zonderman, Kawas, & Resnick, 1998; Roca et al., 2013). Recent studies have shown that TMT

performance was one of the best predictors of conversion from mild cognitive impairment to

dementia (Eckerstrom et al., 2015), especially dementia with Lewy bodies (Grenier Marchand,

Montplaisir, Postuma, Rahayel, & Gagnon, 2017). In a recent meta-analysis on the relationship

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between cognition and functional status, the TMT-B was shown to be a strong predictor of

everyday functioning in individuals with mild cognitive impairment (McAlister, Schmitter-

Edgecombe, & Lamb, 2016). The test is also associated with fitness to drive in older adults

(Bennett, Chekaluk, & Batchelor, 2016; Dickerson, Meuel, Ridenour, & Cooper, 2014).

Although task speed and the number of errors on the TMT-B more often identify pathology than

the TMT-A scores taken alone, authors argue that an algorithm that considers both speed and

errors are more sensitive markers of impairment (Ashendorf et al., 2008). Some scores seem to

be more useful, for example, time to completion of TMT-B (Eckerstrom et al., 2015; Grenier

Marchand et al., 2017) and total errors (Ashendorf et al., 2008), in predicting neurodegenerative

diseases. Time to completion of TMT-B and contrast scores between TMT-A and TMT-B have

also been shown to differentiate patients who will develop Parkinsonism from those who will

first develop dementia (Grenier Marchand et al., 2017).

In order to draw valid conclusions from an examinee’s performance, it is necessary to

employ normative data that control for the influence of sociodemographic variables (Mitrushina,

Boone, & D'Elia, 1999; Soukup, Ingram, Grady, & Schiess, 1998). A comparison of TMT

normative data from several countries and cultures has shown that norms across countries are

different, even when age, sex, and education are comparable across the samples (Fernández &

Marcopulos, 2008). Normative data are also different between Canada and the United States,

which have a similar western educational system (Mitrushina et al., 1999; Soukup et al., 1998).

Therefore, it is possible that using normative data derived from a different country or culture will

lead to unreliable results. Local norms are also more precise at detecting cognitive impairment

than non-cultural-specific norms (Arsenault-Lapierre et al., 2011).

A previous study that calculated normative data using Canadian English-speaking

participants suffers from a number of limitations (Tombaugh (2004). Tombaugh (2004)’s sample

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included participants who may have cognitive impairment or depression. Inclusion criteria

required a score of 23 or higher on the Mini-Mental State Examination (MMSE) (Folstein,

Folstein, & McHugh, 1975). A more conservative score of 26 or higher on the MMSE, or the

Montreal Cognitive Assessment test (MoCA), would aid exclusion of patients with mild

cognitive impairment (Nasreddine et al., 2005). Tombaugh (2004) also included participants with

a score of 14 and lower on the Geriatric Depression Scale (GDS), which is higher than the

suggested cut-off score of 11 (Yesavage, 1988). Additionally, the younger group included in the

normative data was exclusively composed of university students. This may limit the applicability

to populations with less education. The lowest performance from Tombaugh’s sample

corresponded to the 10th percentile (Z = -1.28), which does not meet the typical threshold for

statistical significance (alpha level of .05; 5th percentile; Z = -1.65). Moreover, very few studies

have calculated normative data for the number of errors recorded on the TMT (Amieva et al.,

2009; Ashendorf et al., 2008; Hankee et al., 2013).

Given the limitations mentioned above, the objective of the present study was to calculate

regression-based normative data for the TMT-A and -B, based on a large sample of Quebec

(Canada) French-speaking Mid and Older-Aged Adults. Studies have shown that norms based on

regression equations are useful to compare actual individual scores to predicted scores reflecting

specific demographic characteristics (Crawford & Howell, 1998). By using results from the

entire research sample, regression equations provide more stable and valid norms for any

subgroup. Regression equations have been used in several Quebec-French normative studies

(Callahan et al., 2014; Escudier et al., 2016; Larouche et al., 2016; Lavoie et al., 2018; St-Hilaire

et al., 2018; St-Hilaire et al., 2016; Tremblay et al., 2016; Tremblay et al., 2015).

Methods

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Participants

Researchers from across the province of Quebec (Canada) were invited to share anonymized data

from healthy French-speaking volunteers who had completed TMT-A and -B as part of other

research studies. All participants were mother tongue French (about 78% of the Quebec

population are mother tongue French (Government of Quebec, 2016)). All participants had

completed both TMT-A and -B conditions. All studies were approved by local Research Ethics

Boards and consent for the secondary use of these data was obtained during the primary studies.

The seven research sites which took part in this study were affiliated with universities in

Montreal and Quebec City, where participants were recruited and tested. The same instructions

were given to all participants according to a standardized protocol (Bowie & Harvey, 2006).

All participants scored within the normal range on the MMSE (≥ 26; Folstein et al., 1975)

or the MoCA (≥ 26; Nasreddine et al., 2005). In addition, participants had no clinically

significant depressive symptoms based on scores from the GDS (Yesavage, 1988), or the Beck

Depression Inventory second edition (BDI-II) (Beck, Steer, & Brown, 1996). Cut-offs for

inclusion were ≤ 10 for the 30-item GDS, ≤ 4 for the 15-item GDS, and ≤ 10 for the BDI-II. All

individuals self-reported good mental and physical health (i.e., no history of neurological

disease, untreated current psychiatric illness, traumatic brain injury, or any untreated medical

condition that could interfere with cognitive performance).

The normative sample consisted of 792 community-dwelling participants (510 women

and 282 men), aged between 50 and 91 years (mean age = 67.8 years; SD = 7.1) and having

between 3 and 23 years of formal education (mean education level = 14.9 years; SD = 3.5). The

majority of participants had received a high-school diploma (≥12 years) (83.7%; n = 663).

Women were somewhat overrepresented in our sample, at 64.4% (vs. 50.3% in overall Quebec

population) (Government of Canada, 2015).

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Materials and procedure

TMT paper sheets from Reitan (1979) were used for this study. No interference task between

TMT-A and -B conditions was performed. No time limit was set for task completion.

TMT-A is a paper sheet that contains circles, with the numbers 1 to 25, that are

distributed spatially in a semi-random order. The examinee is asked to draw a line connecting the

numbered circles, as fast as possible, in an ascending order. A practice trial with the numbers 1

to 8 is performed beforehand to ensure the participant understands the instructions. TMT-B is a

paper sheet that contains circles, with the numbers 1 to 13 and letters A to L, that are distributed

spatially in a semi-random order. The participant is instructed to draw a line connecting the

numbers and letters, as fast as possible, respecting an ascending and alphabetical order, by

alternating between numbers and letters (e.g., 1-A-2-B-3-C, etc.). A practice trial containing

eight items is performed beforehand to ensure the participant understands the instructions.

We followed the administration procedures and interpretive guidelines developed by

Bowie and Harvey (2006). The tests were administered in the French language. For both

conditions, mistakes were recorded and immediately corrected by the examiner, who drew an

“X” on the wrong connection. The participant was then instructed to return to the last properly

connected circle and continue the task from that point. The stopwatch would continue to run

during this time. Errors for TMT-B were of two types (Mahurin et al., 2006): (1) sequencing or

tracking (i.e., skipping a number or a letter on TMT-A or -B); and (2) set-loss or perseverative

(i.e., connecting two numbers or letters without alternation on TMT-B (e.g.: 1-A-2-B-C-3…or 1-

A-2-B-3-4-D…)).

Statistical Analyses

To identify the sociodemographic characteristics influencing task performance, a multiple linear

regression analysis was performed for each dependent variable using age, education, and gender

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as predictors. The distribution of both TMT-A and -B time scores were skewed. For that reason,

a logarithmic transformation (log10 (time)) was applied for both conditions (Tabachnick & Fidell,

2013).

Age and education were entered in the analyses as continuous variables, while gender

was coded 0 for women and 1 for men. Interactions between predictors were tested, (continuous

variables were centered), but none were significant. Therefore, they were not retained in the final

models.

Some patients may exhibit disproportionate performance on the TMT-B (difficulty in

cognitive flexibility/shifting, perseveration) relative to TMT-A (visual scanning/psychomotor

processing speed). In order to highlight significant differences between the two conditions, a

contrast score was computed. This analysis was based on the same procedure described by Delis,

Kaplan, and Kramer (2001). First, the uncorrected raw scores for TMT-A and -B (time) were

converted to scaled scores that are normally distributed and have a mean of 10 and a standard

deviation of 3. Second, for each participant, TMT-A scaled scores were subtracted from TMT-B

scaled scores. Finally, the scaled score differences were converted to a new distribution of scaled

scores. A contrast Z-score corrected for sociodemographic variables was then calculated using

linear regression. A Z-score under -1.65 (5th percentile) highlights significantly more difficulties

on the TMT-B, compared to the TMT-A condition. Meanwhile a Z-score higher than 1.65

indicates lower performance on the TMT-A condition. Visual and statistical analyses were

conducted to verify the underlying assumptions of the regression models (distribution and

residual normality, homogeneity of variance, linearity, multicollinearity, and outliers) using

common criteria (Tabachnick & Fidell, 2013). Semi-partial Pearson correlations (sr) were used

to describe the relative importance of the predictors. Due to the skewed distribution of error

scores, in both conditions, Spearman correlations were computed to determine the

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sociodemographic variables that were associated with error scores. Percentiles were generated

and stratified for each condition according to the significant correlations. All statistical analyses

were performed using SPSS software (version 21.0), with the alpha level set at .05.

Results

Table 1 shows the demographic characteristics of the participants in the normative sample. Table

2 shows the regression coefficients and intercepts for each measure of the TMT. The TMT-A

model accounted for 9.4% of the variance while only age (p < .001) was a significant predictor,

F(3, 788) = 27.4, p < .001 (education: p = .080, sex: p = .918). Semi-partial correlations revealed

that age (sr = .288) was by far the best predictor of TMT-A time, when compared to education

(sr = -.059) and gender (sr = -.004).

The TMT-B model accounted for 14.5% of the variance, F(3, 788) = 44.5, p < .001, while

both age (p < .001) and education (p < .001) were significant (sex: p = .559). Semi-partial

correlations revealed that age (sr = .295) was the best predictor of TMT-B time, followed by

education (sr = -.193), and gender (sr = -.019).

Finally, the model for the contrast score, between the TMT-B and -A conditions,

accounted for 2.3% of the variance, F(3, 788) = 6.1 p < .001, while only education (p < .001)

was significant (age: p = .637, gender: p = .825). Semi-partial correlations revealed that

education (sr = .151) was a better predictor of the contrast score than age (sr = .017), or gender

(sr = .008).

Table 3 shows the Z-scores for TMT-A and -B based on the results from the regression

models. In order to facilitate the calculation of Z-scores based on the regression formulas, we

prepared a Microsoft Excel® spreadsheet containing automatic formulas. This file can be

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downloaded from the journal’s website (see Supplemental online material) or by writing to the

corresponding author.

The number of TMT-A and -B errors was available for 80.9% of the sample (n = 641).

No sociodemographic predictors were significantly correlated with the number of errors on the

TMT-A (age: rs = -.005, p = .906; education: rs = .009, p = .823; gender: rs = .028, p = .487). The

majority of participants did not commit any errors on the TMT-A (90.8%, n = 581). Only age

was correlated with the number of errors on the TMT-B (age: rs = .089, p = .025; education: rs =

-.074, p = .061; gender: rs = .002, p = .951). The majority of participants did not perform any

errors on the TMT-B (73.8%; n = 473). Only 45 participants (0.07%) made two or more errors

on the TMT-B. These participants were also slower on the TMT-B (p = .009) and performed

more errors on the TMT-A (p = .042) than the rest of the sample. However, these participants did

not differ from the whole sample with respect to age, education, gender, global cognition,

depression or TMT-A time. Percentiles for total error scores on TMT-A and -B are shown in

Table 4. Due to the low number of errors on the TMT-B, we did not attempt to distinguish

between the types of errors (i.e., sequencing or set-loss). Of note, the slowest participants also

made the greatest number of errors in both conditions; TMT-A (rs = .079, p = .045), and

especially TMT-B (rs = .347, p < .001).

Discussion

Previous studies have found that normative TMT data from different countries and cultures are

different (Fernández & Marcopulos, 2008). That is why the main objective of this study was to

establish normative data for the TMT-A and -B, based on a large sample of French-speaking

adults from Quebec, who were aged between 50 and 91 years. Linear multiple regressions were

performed for each condition using age, education, and gender as predictors. Results indicated

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that age and education were independently associated with performance on the TMT-B, while

only age was a significant predictor of TMT-A scores. Faster task speeds were apparent in

individuals with higher levels of education (TMT-B) and those who were younger (TMT-A and -

B), which is consistent with studies showing declines in processing speed during aging

(Salthouse, 1996). Correlations between sociodemographic variables and scores on the TMT-A

and -B showed that age was more highly associated with performance than was education. This

result echoes those of other normative studies conducted in North America (Ashendorf et al.,

2008; Ivnik, Malec, Smith, Tangalos, & Petersen, 1996; Tombaugh, 2004). Education may

account for more variance than age on TMT-B scores in normative data that use samples with

significantly older and less educated participants (Lucas et al., 2005). On the other hand,

Tombaugh (2004) found that very little variance was explained by education when only adults

aged 55 and older were included in the analysis (TMT-A (1.5%) and -B (4.4%)). Thus, it appears

that controlling for the effect of age is sufficient to interpret performance on the TMT-A and that

education explains slightly more of the performance on the TMT-B.

The present study found no effect of gender on TMT performance in either condition,

which has been shown in previous studies (Ashendorf et al., 2008; Lucas et al., 2005; Schneider

et al., 2015; Strauss, Sherman, & Spreen, 2006; Tombaugh, 2004). The only study that reported

an effect of gender was conducted in France by Amieva et al. (2009). However, despite being

statistically significant, we do not know whether this result is clinically significant.

Education was the only significant predictor of the contrast score between TMT-B and

TMT-A scaled scores. The contrast score allows us to determine whether cognitive

flexibility/shifting (TMT-B) is significantly deficient in comparison to psychomotor processing

speed (TMT-A). It is still possible to perform worse on the TMT-A, in comparison to TMT-B,

for many reasons (time required to become familiar with the task, lack of motivation, less effort

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due to the apparent ease of TMT-A, etc.). One should also note that a contrast score could be

considered ‘normal’ if both conditions were impaired. Thus, contrast scores must be interpreted

with caution. A Z-score below -1.65 (5th percentile) indicates significantly worse performance on

the TMT-B while a Z-score above 1.65 instead indicates significantly worse performance on the

TMT-A.

Few North American studies have proposed normative data for the number of TMT

errors. Yet, recorded errors are useful for distinguishing between psychiatric conditions

(Mahurin et al., 2006), the detection of malingering (Ruffolo, Guilmette, & Willis, 2000), and for

tracking progression from normal aging to mild cognitive impairment and dementia (Ashendorf

et al., 2008). Moreover, both TMT time and errors are useful because, while they are both

influenced by working memory and executive functions, error rates are not influenced by faster

psychomotor speed (Mahurin et al., 2006). On the contrary, we found that it is the slowest

participants who make the most mistakes. However, one cannot completely rule out the possible

influence of psychomotor speed on the error rate. Given that the time required for the participant

to correct an error is included in the TMT time to completion, it is indeed possible that for some

participants, faster psychomotor speed led to more errors and subsequently increased their time

on task. The examiners must be vigilant when one or more errors are performed on the TMT-A

because it is unusual (9.2%). Likewise, two or more errors on the TMT-B were very uncommon

(0.07%) and lead to a significant deficit in performance (≤ 5th percentile), irrespective of the

participant’s age, which is in line with findings from Stuss et al. (2001). Thus, it seems that the

number of errors does not increase dramatically during normal aging and is more an indicator of

impairment (Ashendorf et al., 2008). Consistent with the results of the current study, Ashendorf

et al. (2008) found no significant association between sociodemographic variables and the

number of errors on the TMT-A. In the present study, TMT-B error rates was only associated

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with age, while Ashendorf et al. (2008) found a significant association with both age and

education. Differences may arise from the fact that their sample was composed of more

individuals with lower education than in our sample.

Strengths and Limitations

Despite all labs administering the TMT according to a standardized protocol, it was not

possible to perform quality control of the data collection. Moreover, the current study used an

incidental sampling method; however, the normative data presented here were acquired from a

large sample of adults living in the two most populous areas in the Province of Quebec (Montreal

and Quebec City). Participant demographics covered a respectable range of ages and education

levels. However, greater score variability may be present in subsamples comprised of very old

adults (only 6.2% of the sample was aged 80-91 years) and individuals with low educational

levels (< 7 years), since there were relatively few cases in our sample. Results should be

interpreted with caution for these groups. In addition, the current normative data should not be

applied to individuals outside the stated age range since it would represent estimated scores.

Finally, the current sample contained more women than men. However, since gender did not

have a significant effect on task performance, the present results appear generalizable. While

some authors showed that ethnicity may have an impact on both conditions of TMT performance

(Schneider et al., 2015), this information was not available in our data. The majority of

individuals in Montreal and Quebec City are white Caucasians.

Standardized administration commonly allows 180 seconds to complete the TMT-A

(Strauss et al., 2006) and 300 seconds to complete the TMT-B (Heaton, Miller, Taylor, & Grant,

2004). There was no time-limit in our study because it was found that this practice may mask

performance variability, especially among cognitively impaired individuals who cannot complete

the task (Correia et al., 2015). However, none of the participants in our sample had a completion

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time of over 180 seconds for TMT-A (range: 15 to 102), and only three participants had a

completion time of over 300 seconds for TMT-B (range: 25 to 348).

An original aspect of this study was the regression-based approach used to calculate

normative data for completion times on the TMT-A and -B. First, this normative method has the

advantage of better estimating the expected performance of a participant given their personal

characteristics, instead of discrete norms created using arbitrary age groups. In the latter case, the

relative standing of an individual may change dramatically as they move from one age category

to another (Crawford & Howell, 1998). Second, by contrast to norms based on means or

percentiles of arbitrary sociodemographic subgroups, norms based on regression equations have

shown to reduce demographic biases associated with the use of raw data in neuropsychological

tests (e.g., Heaton, Avitable, Grant, and Matthews (1999), Van der Elst, Van Boxtel, Van

Breukelen, and Jolles (2006)). Third, in addition to identifying which variables are relevant to

the norming, the regression-based approach also provides more stable norms for any subgroup by

using data from the entire participant sample (Van Breukelen & Vlaeyen, 2005)

To illustrate the advantage of regression equations, let us imagine a 75-year-old-man with

12 years of education who completed TMT-A in 66 seconds and TMT-B in 178 seconds. First,

based on the regression equations from Table 3, the patient’s Z-scores would be -1.32 (9th

percentile) and -1.55 (6th percentile), for TMT-A and TMT-B respectively. These results appear

to be indicative of slight to moderate slowness in processing speed and/or difficulty with

cognitive flexibility. If the results of this participant were instead compared to the Canadian

normative data from Tombaugh (2004), his Z-scores would be -1.28 (10th percentile), for both

TMT conditions. These results would be considered weak but would remain normal. Now, let us

rather imagine that this man who completed TMT-A in 66 seconds and TMT-B in 178 seconds:

(a) is aged 75 and has 7 years of education or (b) is aged 55 and has 12 years of education. Based

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on the regression equations, the Z-scores for the two TMT conditions would be -1.24 and -1.25

(11th percentile) for the hypothetical case (a) and -2.24 and -2.47 (< 1st percentile) for the

hypothetical case (b), for TMT-A and TMT-B respectively. Results for case (a) indicate slight

slowness while it is an indicator of deficit for the youngest man (b). Using normative data from

Tombaugh (2004), case (a) obtains higher scores (15th percentile; Z = -1.00) while we know at

best that patient (b) has a score below the 10th percentile (the smallest percentile for these

normative data), without knowing exactly which one. We remind the reader that Tombaugh

(2004) sample included individuals with MMSE scores ≥ 24 (instead of ≥ 26) and individuals

with GDS-30 scores ≤ 13 (instead of ≤ 10). This increases the risk of including participants with

mild cognitive impairment (Nasreddine et al., 2005) or active depressive symptomatology in the

normative data (Yesavage, 1988) and possibly lengthening the task completion time. Therefore,

our study is more likely to only include participants who do not have any cognitive impairment

or depression. With regards to cultural differences (e.g., Quebec French vs European French), we

compared the first hypothetical patient’s Z-scores (i.e. -1.32 and -1.55 for TMT-A and TMT-B

respectively) to those obtained from Amieva et al. (2009) European French normative data. The

differences are more impressive. Indeed, using Amieva et al. (2009) normative data, this 75-

year-old-man would reach a normal performance for TMT-A (Z = -0.67; 25th percentile) and a

somewhat inaccurate score on TMT-B (Z between -1.65 and -1.28; between 5th and 10th

percentile). Finally, compared to the most widely used normative data in the United States,

Heaton et al. (2004) (ZTMTA = -1.60; 5th percentile and ZTMTB = -1.00; 16th percentile) and

Mitrushina et al. (1999) (ZTMTA and –B = -1.00; 16th percentile), our normative data were once again

more sensitive at detecting deficits in psychomotor processing speed or executive functions, at

least on TMT-B. As stated by Fernández and Marcopulos (2008), these various examples

underline the importance of using culturally derived normative data. However, further studies are

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needed to establish the sensibility and specificity of our normative data in detecting cognitive

impairment in clinical populations. Our norms should only be used for Quebec French-speaking

adults. With regards to the use of the current norms with French-speaking Canadians living

outside Quebec, this practice should be applied with caution since the data of the present study

only come from French Quebecers. In sum, data from the present study will strengthen accurate

detection of deficits in psychomotor processing speed and executive functions in Quebec French-

speaking adults and will invite clinicians to push further their investigation of potential executive

deficits with additional testing.

Acknowledgements

CH, SJ, and JFG were supported by a salary award from the FRQ-S. JFG holds a Canada Research Chair

in Cognitive Decline in Pathological Aging and a salary award from the CIHR.We are grateful to Scott

Nugent and Lynn Maynard for their help with the English revision of this article.

Disclosure Statement

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or

publication of this article.

Funding

This work was supported by the Réseau québécois de recherche sur le vieillissement, the Canadian

Institutes of Health Research (CIHR), the Alzheimer Society of Canada, the Natural Sciences and

Engineering Research Council of Canada, the Fonds de Recherche du Québec – Santé (FRQ-S), and the

Fonds de recherche du Québec – Société et culture (FRQSC).

16


Table 1. Demographics of participants in the normative sample (n = 792)

17

Characteristics N %

Age 50-59 80 10,1 60-64 177 22.3 65-69 234 29.5 70-74 170 21.5 75-79 82 10.4 80-91 49 6.2

Gender (men/women) 282/510 35.6/64.4

Education Elementary (3-7 years) 15 1.9 High-School (8-12 years) 195 24.6 College (13-14 years) 140 17.7 University undergraduate (15-17 years) 238 30.1 University graduate (18-19 years) 132 16.7 University postgraduate (20-23 years) 72 9.1


Table 2. Coefficients for multiple linear regression analyses for TMT measures

Variable B β t p

TMT-Aa

Age 0.006 0.292 8.49 <.001Education -0.002 -0.061 -1.75 .080Sex -0.001 -0.004 -0.10 .918

TMT-Bb

Age 0.007 0.299 8.95 <.001Education -0.009 -0.197 -5.87 <.001Sex -0.007 -0.019 -0.59 .559

Contrast scorec

Age 0.007 0.017 0.47 .637Education 0.131 0.153 4.28 <.001Sex 0.049 0.008 0.22 .825

a Prediction for the log10 TMT-A time. Intercept = 1.221; Square root of the mean square residual = 0.130.b Prediction for the log10 TMT-B time. Intercept = 1.592; Square root of the mean square residual = 0.152.c Intercept = -12.475; Square root of the mean square residual = 2.971.

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Table 3. Normative equations to calculate corrected Z-scores for age, education and gender for TMT-A and -B (n = 792)

Variable Corrected Z-score for age, education and gender

TMT-A Z score (log10(TMT-A time) - (1.221 + (0.006*Age) + (-0.002*Education) + (0.001*Gender))) / 0.1304 * -1

TMT-B Z score (log10(TMT-B time) - (1.592 + (0.007*Age) + (-0.009*Education) + (0.007*Gender))) / 0.1517 * -1

Contrast score

TMT-A SS 3*((TMT-A time - 38.359) / 12.836) + 10

TMT-B SS 3*((TMT-B time - 88.014369) / 39.157) + 10

Contrast SS (3*(((TMT-B SS - TMT-A SS) - -8.301E-8) / 2.729) + 10) * -1

Contrast Z score (Contrast scale score - (-12.475 + (0.007*Age) + (0.131*Education) + (-0.049*Gender))) / 2.9712

Notes: Age: participant’s age (continuous variable; between 50 and 91); Education: years of education (continuous variable; between 3 and 23). Gender: female=0, male=1. Equation denominators corresponded to residual standard deviations of each model. Multiplication by -1 makes it possible to obtain a negative Z-score for slower performance. SS: scale score.

19


Table 4. Percentiles for error scores (n = 641)

20

Measure nPercentiles for the number of errors

1st 2nd 5th 10th 15th 25th 50th 95th

TMT-A 641 2 1 1 0 0 0 0 0

TMT-B

Age 50-69 413 3 3 2 1 1 0 0 0

Age 70-91 228 4 3 2 1 1 1 0 0


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