Norming of the Tampa Scale for Kinesiophobia across pain diagnoses and
various countries
Jeffrey Roelofs
a,
⇑
, Gerard van Breukelen
b
, Judith Sluiter
c
, Monique H.W. Frings-Dresen
c
,
Mariëlle Goossens
a
, Pascal Thibault
d
, Katja Boersma
e
, Johan W.S. Vlaeyen
a,f
a
Department of Clinical Psychological Science, Maastricht University, Maastricht, The Netherlands
b
Department of Methodology and Statistics, Maastricht University, Maastricht, The Netherlands
c
Coronel Institute of Occupational Health, Academic Medical Center Amsterdam, Amsterdam, The Netherlands
d
Department of Psychology, University Centre for Research on Pain and Disability, McGill University, Montreal, Canada
e
Department of Behavioral, Social and Legal Sciences, Örebro University, Örebro, Sweden
f
Department of Psychology, University of Leuven, Leuven, Belgium
Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
article info
Article history:
Received 17 March 2010
Received in revised form 20 December 2010
Accepted 14 January 2011
Keywords:
Chronic pain
Norms
Tampa Scale for Kinesiophobia
TSK
abstract
The present study aimed to develop norms for the Tampa Scale for Kinesiophobia (TSK), a frequently used
measure of fear of movement/(re)injury. Norms were assessed for the TSK total score as well as for scores
on the previously proposed TSK activity avoidance and TSK somatic focus scales. Data from Dutch,
Canadian, and Swedish pain samples were used (N = 3082). Norms were established using multiple
regression to obtain more valid and reliable norms than can be obtained by subgroup analyses based
on age or gender. In the Dutch samples (N = 2236), pain diagnosis was predictive of all TSK scales. More
specifically, chronic low back pain displayed the highest scores on the TSK scores followed by upper
extremity disorder, fibromyalgia, and osteoarthritis. Gender was predictive of TSK somatic focus scores
and age of TSK activity avoidance scores, with male patients having somewhat higher scores than female
patients and older patients having higher scores compared with younger patients. In the Canadian
(N = 510) and Swedish (N = 336) samples, gender was predictive of all TSK scales, with male patients hav-
ing somewhat higher scores than female patients. These norm data may assist the clinician and
researcher in the process of decision making and treatment evaluation.
Ó 2011 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.
1. Introduction
A plethora of research has shown that pain-related fear is asso-
ciated with the maintenance of (chronic) pain [2,16,34]. More spe-
cifically, pain-related fear is associated with impaired physical
performance [1,8,32] and increased self-reported disability [2,33],
and may predict future occupational disability [9,11,14,36]. A num-
ber of self-report measures of pain-related fear are available, such
as the Pain Anxiety Symptoms Scale [18] and the Fear-Avoidance
Beliefs Questionnaire [35], all tapping different forms or aspects
of pain-related fear. The Tampa Scale for Kinesiophobia (TSK) [19]
is a widely used instrument to assess fear of movement/(re)injury
and has been applied to various pain conditions such as chronic
low back pain [8,12,23], fibromyalgia [23], osteoarthritis [13],
traumatic neck pain [6,20], sports injury [15], sickle cell disease
[22], and burn pain [37].
Most factor-analytic studies of the TSK have favored a 2-factor
solution [5,10,12,23,38]. Recently, Roelofs et al. [24] found that a
2-factor model of the TSK comprising TSK activity avoidance
(TSK-AA; beliefs that activity may result in [re]injury or increased
pain) and TSK somatic focus (TSK-SF; beliefs in underlying and
serious medical problems) could be applied to (ie, was invariant
across) Dutch, Swedish, and Canadian samples and various pain
conditions. Although these findings illustrate the transdiagnostic
nature of the 2 TSK scales, there is a need for norm scores in both
clinical and research settings, so that the raw score of a single pain
patient can be compared with the scores in the reference popula-
tion. Norm scores can assist in providing quantitative labels for
the degree to which a raw TSK score is to be considered as average,
elevated, or extreme and might be useful for diagnostic purposes,
clinical decision making, or the evaluation of treatment effects.
A traditional approach to deriving norm scores is to compare an
individual’s raw score to a reference group that matches on back-
ground variables such as age and gender. Test norms are thus
0304-3959/$36.00 Ó 2011 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.
doi:10.1016/j.pain.2011.01.028
⇑
Corresponding author. Address: Department of Clinical Psychological Science,
Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands. Tel.: +31
433881607; fax: +31 433884155.
E-mail address: j.roelofs@maastrichtuniversity.nl (J. Roelofs).
www.elsevier.com/locate/pain
PAIN
Ò
152 (2011) 1090–1095
generally obtained by splitting the reference group into subgroups
based on these background variables [21]. However, it is unknown
what background variables are relevant for deriving norm scores,
and splitting a sample into subgroups leads to a loss of information
and consequently unstable and unreliable norms. Multiple regres-
sion as a technique for norming questionnaire data overcomes
these 2 limitations of traditional norming as this approach allows
for an examination of which background variables are predictive
of the scores in the full sample. This way, reliable and valid norm
scores can be obtained [30]. This study sought to develop norm
scores of the TSK (total score) and 2 TSK subscales (ie, TSK-AA
and TSK-SF) in Dutch, Canadian, and Swedish samples. Based on
previous research that has shown that TSK scores correlate with
gender [28,32,33] and studies showing that TSK scores seem to de-
pend on pain diagnosis [3,4,13,23,26,27], these variables were in-
cluded as norming variables. In a more exploratory way, age,
country, and language of TSK (only for Canada) were also included
as norming variables.
2. Methods
2.1. Participants and procedure
In line with Roelofs et al. [24], data from Dutch patients with
upper extremity disorders (N = 1109) [26], chronic low back pain
(N = 482, 225 from the original study and another 257 referred
patients) [23], fibromyalgia (N = 391) [23], and osteoarthritis
(N = 254) [13] were included as well as data from Canadian patients
with musculoskeletal pain who completed the English version of
the TSK (N = 335) [27] or the French version of the TSK (N = 175,
unpublished data), and data from Swedish patients with musculo-
skeletal pain (N = 336) [3,4]. Thus, a total number of 3082 patients
with various chronic musculoskeletal pain conditions were in-
cluded. Table 1 presents an overview of age and gender character-
istics of all pain samples included in this study. A more detailed
description of the samples can be found in the original articles.
2.2. TSK
The TSK [19,32] was used to assess fear of movement and (re)-
injury. The TSK has 17 items, with items 4, 8, 12, and 16 being re-
versely scored items. Each item is scored on a 4-point Likert-type
scale. Scoring possibilities range from strongly disagree (score = 1)
to strongly agree (score = 4). Total TSK scores range between 17
and 68. Previous research has supported a 2-factor model of the
TSK comprising 11 items [24,38]. More specifically, the TSK-AA
(sample item: ‘‘I’m afraid that I might injure myself if I exercise’’)
scale comprises 6 items with scores ranging between 6 and 24,
and the TSK-SF (sample item: ‘‘My body is telling me I have
something dangerously wrong’’) consists of 5 items with scores
ranging from 5 to 20. The remaining 6 items of the total TSK were
not included in the TSK-AA or the TSK-SF scales. Table 2 presents
descriptive information of the TSK scales for all samples.
2.3. Statistical analyses
The Statistical Package for the Social Sciences (version 15.0,
SPSS Inc., Chicago, Illinois, USA) was used to carry out the regres-
sion analyses to determine a parsimonious model for obtaining
TSK norms (see van Breukelen et al. [30] for a detailed description).
Based on earlier work [24,38], 11 of 17 TSK items were selected for
norming the subscales, whereas all 17 items were used for nor-
ming the total score. More specifically, norm scores were devel-
oped for the TSK-AA scale (6 items, range 6 to 24), the TSK-SF
scale (5 items, range 5 to 20), and TSK total scale (TSK-Total; 17
items, range 17 to 68). For norming the TSK scales, scores on the
TSK were the dependent variables in the regression analyses, and
gender, age, pain diagnosis, country, and language (only for
Canada) were the predictor variables. Dummy coding was used
for the categorical predictors gender (0 = male, 1 = female) and
pain diagnosis. For pain diagnosis, low back pain served as the ref-
erence category. Due to collinearity of country with diagnosis in
our sample (ie, the Dutch sample did not include musculoskeletal
pain, whereas the Canadian and Swedish samples consisted of this
diagnosis group only), norms were determined for the Dutch sam-
ple separately. Country was therefore only examined for Canada
and Sweden (0 = Canada, 1 = Sweden). By using dummy coding, a
regression weight is included in the model to represent the mean
scale difference between the reference category and each other
category, adjusted for all other predictors in the model. Linear
and quadratic terms were included for the quantitative predictor
Table 1
Descriptive information about the various samples.
Country and sample Mean age SD % Female
Dutch
Low back pain (N = 482) 51.9 14.1 61
Fibromyalgia (N = 391) 47.4 10.1 94
Osteoarthritis (N = 254) 51.7 5.0 59
Upper extremity disorders (N = 1109) 40.7 8.7 67
Canadian
Musculoskeletal pain (N = 335)
(English sample) 41.8 8.6 44
Musculoskeletal pain (N = 175)
(French sample) 41.4 11.3 53
Swedish
Musculoskeletal pain (N = 336) 46.2 9.4 53
Table 2
Descriptive statistics and internal consistency ratings of the TSK-11 and its subscales
in various pain populations.
Pain population Mean SD
Upper extremity disorders (N = 1109)
TSK-Total 37.8 7.6
TSK-AA 14.3 3.6
TSK-SF 11.3 3.2
Chronic low back pain (N = 482)
TSK-Total 43.2 8.4
TSK-AA 16.1 4.3
TSK-SF 12.1 3.6
Fibromyalgia (N = 391)
TSK-Total 36.6 8.4
TSK-AA 14.0 3.8
TSK-SF 10.4 3.3
Osteoarthritis (N = 254)
TSK-Total 24.5 6.0
TSK-AA 13.9 3.7
TSK-SF 10.6 3.2
Musculoskeletal pain English version (N = 335)
TSK-Total 42.0 8.2
TSK-AA 15.5 3.5
TSK-SF 13.0 3.5
Musculoskeletal pain French version (N = 175)
TSK-Total 44.2 8.7
TSK-AA 16.2 3.9
TSK-SF 13.9 3.5
Musculoskeletal pain Swedish version (N = 336)
TSK-Total 39.4 6.8
TSK-AA 12.5 3.7
TSK-SF 11.5 3.9
TSK-AA = Tampa Scale for Kinesiophobia activity avoidance; TSK-SF = Tampa Scale
for Kinesiophobia somatic focus.
J. Roelofs et al. /PAIN
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152 (2011) 1090–1095
1091
age to examine linear and curvilinear effects of age [30]. The age
variable was centered (by subtracting the mean age from each
individual value) before computing the quadratic term to prevent
near-collinearity with the quadratic age term.
The regression models were reduced in a stepwise fashion by
eliminating the least significant predictor (P > .05). For the final
models, residuals were plotted and analyzed to check the assump-
tions of normality and homogeneity of residual variance across the
entire range of predicted scale scores and the absence of outliers.
With the final model, a raw scale score of an individual can be con-
verted into a standardized z-score by computing the predicted
score Y (by means of filling in the regression analysis), computing
the residual error (subtracting predicted Y from observed Y), and
finally, dividing the residual error by the SD(e), which is the square
root of the MS(residual). If the residuals are normally distributed
with the same variance, then z is normally distributed and the
standard normal distribution can be used to interpret z-values
[30].
3. Results
3.1. Predictors of the TSK scores
To examine whether separate norms for the Canadian and
Swedish samples were needed, interactions between country
(0 = Canada, 1 = Sweden) and age, and between country and gender
were tested. These were not significant (P > .05), and so norms for
Canada and Sweden could be calculated from the joint data by
using a regression model with a main effect of country in addition
to age, gender, and language effects. Within the Dutch and Cana-
dian/Swedish samples, the interaction between age and gender
was not significant, and within the Canadian sample, the interac-
tions between language and age and between language and gender
were not significant. Thus, norm scores for the TSK scales did not
have to be calculated for subgroups based on age, gender, or lan-
guage separately, but could be obtained from the total sample
(Dutch, or Canadian/Swedish) with a regression model with main
effects of age, gender, country, language. This gives more reliable
norms due to larger sample size than in subgroup analyses.
Regression analysis revealed that, except for TSK-AA in the
Dutch samples, gender emerged as a significant predictor of all
TSK scales in the Dutch samples as well as the Canadian/Swedish
samples, with male patients having higher scores than female pa-
tients. Age emerged as a significant predictor for TSK-AA and TSK-
Total only in the Dutch samples, with older patients having higher
TSK scores. Further, pain diagnosis was a significant predictor of
TSK-AA, TSK-SF, and TSK-Total. Patients with chronic low back pain
had the highest TSK scores, followed by upper extremity disorders,
fibromyalgia, and osteoarthritis. Country was a significant predic-
tor of TSK scores, with higher TSK scores on all scales in Canada
compared with Sweden. Language was not a predictor of TSK
scores in the Canadian sample.
3.2. Model checks
Before continuing with computing z-scores, some model checks
are necessary. More specifically, the use of (standardized) residuals
requires a normal distribution with homogeneous variances. In all
models, normality was checked by means of skewness and kurtosis
(ie, values should be in the range of 1 to +1) as well as visual
inspection of histograms and by means of the Kolmogorov–Smir-
nov test. Residuals were normally distributed for TSK-AA, TSK-SF,
and TSK-Total. Further, actual percentiles (5, 10, 25, 50, 75, 90,
95) of the standardized residuals were compared with the corre-
sponding percentiles of the standard normal distribution, and
revealed no deviation larger than .10. The homogeneity of vari-
ances was tested by grouping patients into quartiles of the pre-
dicted scale score and applying Levene’s test to the residuals. The
homogeneity assumption was not violated (P > .05), and the resid-
ual standard deviation within each quartile did not deviate more
than 10% from the overall residual standard deviation of the scale.
Thus, the overall residual standard deviations were used to com-
pute z-scores.
3.3. Computing z-scores
The final models for the 3 TSK scales can be used to convert raw
scores of an individual into a standardized residual or z-score. For
the Dutch sample, results of the final models are presented in Table
3. As can be seen in Table 3, the linear and quadratic age terms were
replaced with dummy indicators to simplify computation of the
predicted score for a person and thereby to increase user-
friendliness of our regression models at the price of a small loss
of precision, by categorizing age into 5 groups (18 to 30 years, 31
to 40 years, 41 to 50 years, 51 to 60 years, and >60 years), with
the 18 to 30 years group as the reference group. These 5 age groups
comprised respectively 9.4%, 9.6%, 24.5%, 32.1%, and 24.4% of the to-
tal sample size. The reference categories for age and pain diagnosis
were chosen such as to simplify the regression output because TSK
scores were lowest in the reference age group and highest in the
reference diagnosis (low back pain). To illustrate how Table 3 can
be applied, consider a Dutch male patient with fibromyalgia with
a TSK-SF score of 14. Table 3 gives a predicted score of 12.50 (con-
stant) – .70 (gender = 0) – 1.40 (fibromyalgia = 1) – 1.53 (osteoar-
thritis = 0) – .77 (upper extremity disorders = 0) = 11.10. The
residual standard deviation is
p
10.68 = 3.27. Thus, the z-score is
equal to (14 11.10)/3.27 = .89, which means that this man’s score
is considered in the normal range (see below). In a similar vein,
Table 3
Final regression model for TSK-SF, TSK-AA, and TSK-Total in Dutch samples
(N = 2236).
Predictor B SE of B P (2-tailed)
TSK-SF (R
2
= .04, MS residual = 10.68)
Constant 12.50 .17 <.001
Gender .70 .15 <.001
Fibromyalgia 1.40 .23 <.001
Osteoarthritis 1.53 .25 <.001
Upper extremity disorders .77 .18 <.001
TSK-AA (R
2
= .05, MS residual = 14.23)
Constant 15.36 .33 <.001
Fibromyalgia 1.88 .26 <.001
Osteoarthritis 2.06 .31 <.001
Upper extremity disorders 1.38 .23 <.001
Age (31–40) .04 .31 .89
Age (41–50) .55 .31 .08
Age (51–60) .70 .32 .03
Age (61 or more) 1.62 .42 <.001
TSK-Total (R
2
= .10; MS residual = 62.01)
Constant 42.26 .72 <.001
Gender 1.02 .36 .004
Fibromyalgia 5.68 .57 <.001
Osteoarthritis 6.28 .65 <.001
Upper extremity disorders 4.30 .48 <.001
Age (31–40) .13 .66 .85
Age (41–50) .88 .65 .17
Age (51–60) 1.15 .65 .10
Age (61 or more) 3.54 .69 <.001
Low back pain is the reference group. Gender is coded as dummy (0 = male;
1 = female). Age 18 to 30 years is the reference category for age effects. TSK-SF
range: 5 to 20; TSK-AA range: 6 to 24; TSK-Total scores range: 17 to 68.
MS = Mean square; SE = standard error; TSK-AA = Tampa Scale for Kinesiophobia
activity avoidance; TSK-SF = Tampa Scale for Kinesiophobia somatic focus; TSK-
Total = Tampa Scale for Kinesiophobia total scores.
1092 J. Roelofs et al. / PAIN
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152 (2011) 1090–1095
results of the final regression models for the Canadian/Swedish
samples are presented in Table 4.
3.4. Interpreting z-scores
On the basis of the obtained z-score, the following interpreta-
tion can be given. Scores that lie in the interval between 1 and
+1 are considered normal scores. Scores above 1 are considered
elevated scores, whereas scores above 2 are indicative of high
scores. Scores below 1 are reduced scores, whereas scores below
2 are low scores. To ensure user-friendly norm procedures, tables
with norm scores are presented in Appendices 1–3. Because the
residuals from which the z-scores were computed had a normal
distribution and the actual percentiles agreed very well with per-
centiles according to the normal distribution, the z-scores of 2,
1, 0, 1, 2 correspond to the 2nd, 16th, 50th, 84th, and 98th per-
centiles, respectively.
4. Discussion
The present study sought to develop norms for the TSK total
score as well as the previously reported TSK-AA and TSK-SF scales
[12,21,23]. Multiple regression analysis was used to investigate
which background variables were important to take into account
for deriving norm data as well as to obtain reliable and stable
norms. Due to statistical constraints (collinearity of diagnosis with
country if all 3 countries were included in the analysis), norms
were determined for Dutch and for Canadian/Swedish samples
separately. Appendices 1–3 present norm data (as computed with
the regression models in Tables 3 and 4) in a user-friendly form.
These Appendices can be used to compare the raw TSK score of a
single patient with scores in the reference population by determin-
ing the z-interval in which the raw TSK score lies, and subse-
quently, how extreme a raw TSK score is.
In deriving norm data, relevant background variables that influ-
ence the norm data were identified. It was hypothesized that pain
diagnosis and gender would emerge as significant norming vari-
ables. Pain diagnosis significantly predicted TSK scores in the
Dutch samples, with patients with low back pain (reference group)
having higher scores on all TSK scales compared with patients with
fibromyalgia, osteoarthritis, and upper extremity disorders. Thus,
patients with low back pain seem to endorse beliefs that the occur-
rence of pain indicates underlying serious bodily damage (TSK-SF)
and anxious beliefs that activity may result in (re)injury or in-
creased pain (TSK-AA) to a greater extent than patients with an-
other pain diagnosis. Except for TSK-AA in the Dutch samples,
gender was found to be predictive of all TSK scales in all samples.
Male patients were found to have somewhat higher scores com-
pared with female patients, which is in line with previous studies
[28,32,33] showing that male patients endorse higher levels of
fear-avoidance beliefs than female patients. In accounting for this
gender difference, it should be noted that the effect of gender
was significant but quite modest. Nevertheless, the finding that
male patients have somewhat higher scores than female patients
contradicts research showing that female patients generally dis-
play higher levels of somatic and anxiety symptoms compared
with male patients. Future research should examine possible
explanations. Notwithstanding, the findings add to the idea that,
also in the light of gender, assessing different stimulus and re-
sponse dimensions of anxiety, and pain-related fear in particular,
is important [17,31]. Age was found to influence TSK-AA and
TSK-Total scores in the Dutch samples only, with older pain pa-
tients having higher scores. Studies that have examined the rela-
tion between age and TSK have produced mixed results [7,28].It
may be that (re)injury risks are higher for older adults and that
associated fears that are tapped specifically with the TSK are fueled
when catastrophic thinking occurs (see also Leeuw et al. [16]).
There is, however, some evidence to suggest that high pain catas-
trophizing is associated with younger age [25,29], which would be
associated with lower levels of fear in older pain patients. Clearly,
more research is needed to unravel the relation between age and
fear of movement/(re)injury.
Some differences between the results for the Dutch versus
Canadian/Swedish samples need discussion. In the Dutch sample,
gender did not predict TSK-AA, and age did not predict TSK-SF,
whereas both predicted TSK-Total scales. In the Canadian/Swedish
sample, age was not predictive and gender was predictive for each
scale. These differences between countries/samples cannot be
attributed to confounding by diagnosis, which was adjusted for
in the Dutch sample and constant in the other sample. Interaction
of diagnosis with age or gender could be an explanation because
diagnosis was collinear with country. However, an additional test
of such interaction in the Dutch sample (in which diagnosis varied)
did not show such interaction. Alternatively, these findings may be
explained by differences in the inclusion criteria for the various
studies, differences in the organization of the health care system
between countries, differences in the severity and comorbidity of
symptoms that patients present in the various pain clinics, and
the translational nature of the TSK questionnaire. Our data support
the notion that it is important to examine whether it is necessary
to provide norm data separately for subgroups based on back-
ground variables. A priori providing norms on the basis of sub-
groups (1) requires knowledge of which personal characteristics
are relevant for subgrouping (validity), and (2) may lead to a loss
of precision in estimating regression weights and residual variance.
With multiple regression, we can first determine which subgroups
have to be formed and then estimate effects and variance as reli-
ably as possible by preventing splitting of the sample into unnec-
essarily small groups. In the present study, splitting by country
was needed because of collinearity with diagnosis, but splitting
by age or gender within countries was not, because no age–gender
interaction was found.
How can these norms be applied? Because the interpretation of
raw TSK scores will differ for the various pain diagnoses, z-scores
computed from the residuals of the present regression of the TSK
scales can provide a more objective picture of the clinical relevance
of levels of pain-related fear in different groups of pain patients.
More specifically, the norms for the TSK scales (see Appendices)
can be used to assess fear of movement/(re)injury in individuals
Table 4
Final regression model for TSK-SF, TSK-AA, and TSK-Total in the Canadian (N = 510)
and Swedish (N = 336) samples.
Predictor B SE of B P (2-tailed)
TSK-SF (R
2
= .03, MS residual = 12.67)
Constant 13.74 .20 <.001
Country .72 .24 .004
Gender .96 .25 <.001
TSK-AA (R
2
= .24, MS residual = 14.33)
Constant 16.23 .21 <.001
Country 4.22 .27 <.001
Gender .95 .26 <.001
TSK-Total (R
2
= .12; MS residual = 75.85)
Constant 43.84 .49 <.001
Country 6.21 .62 <.001
Gender 2.19 .60 <.001
Gender is coded as dummy (0 = male; 1 = female) and country is coded as dummy
(0 = Canada; 1 = Sweden). TSK-SF range: 5 to 20; TSK-AA range: 6 to 24; TSK-Total
TSK scores range: 17 to 68.
MS = Mean square; SE = standard error; TSK-AA = Tampa Scale for Kinesiophobia
activity avoidance; TSK-SF = Tampa Scale for Kinesiophobia somatic focus; TSK-
Total = Tampa Scale for Kinesiophobia total scores.
J. Roelofs et al. / PAIN
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152 (2011) 1090–1095
1093
and compare a patient’s score to those of other patients with the
same background characteristics. Individuals with low or very
low z-scores on the TSK scales might experience few signs of
pain-related fear relative to patients with the same diagnosis,
age, and gender. However, it may also be the case that those with
extremely low scores on the TSK may in some way deny these
symptoms, so in clinical practice we advise interpretation of very
low scores in the light of scores on other measures available.
In sum, the results of the current study provide reliable norms
for various pain populations. Three limitations need to be ad-
dressed. First, for some models, the explained variance is rather
low and indicates that the background variables are perhaps of
minor importance. Other background variables such as education
or pain duration, which have shown to be related to a measure
of pain cognition [30], might also have been relevant, but this
information was not available. Second, generalization of the norms
beyond the diagnoses and countries included in the current study
is not warranted. Finally, the use of stepwise regression to reduce
the model entails some risk that replication in a new sample would
lead to a slightly different model. This is the price paid for model
reduction for the sake of user-friendliness of norming. The alterna-
tive would be to use the full model with all predictors and interac-
tions for norming at the expense of simplicity. But given this
stepwise model reduction, cross-validation of the current model
in a different sample would be useful. On the positive side, this
study used a large sample of pain patients with various diagnoses
for norming the TSK scales. The data add to previous studies that
have supported the reliability and validity of the TSK scales by pro-
viding norms that may assist the clinician and researcher in the
process of decision making and treatment evaluation.
Conflict of interest statement
The authors report that they have no conflict of interest regard-
ing this publication.
Acknowledgements
The authors express their gratitude to Steven J. Linton and
Michael J.L. Sullivan for their assistance. Participation of Jeffrey
Roelofs was supported by the EFIC-Grünenthal Grant. Participation
of Johan W.S. Vlaeyen was supported by the NWO Social Sciences
Research Council of the Netherlands, Grant No. 453-04-003 and
an Odysseus Grant funded by the Research Foundation – Flanders
(FWO-Vlaanderen, Belgium).
Appendix A. Supplementary data
Supplementary data associated with this article can be found, in
the online version, at doi:10.1016/j.pain.2011.01.028.
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