The fear-avoidance model of chronic pain: Validation
and age analysis using structural equation modeling
Andrew J. Cooka,*, Peter A. Brawerb, Kevin E. Vowlesa,1
aDepartment of Anesthesiology, Division of Pain Management, University of Virginia Health System, Charlottesville, VA 22908, USA
bThe Miriam Hospital, Brown Medical School, Providence, RI, USA
Received 7 July 2005; received in revised form 21 November 2005; accepted 29 November 2005
The cognitive-behavioral, fear-avoidance (FA) model of chronic pain (Vlaeyen JWS, Kole-Snijders AMJ, Boeren RGB, van Eek
H. Fear of movement/(re)injury in chronic low back pain and its relation to behavioral performance. Pain 1995a;62:363–72) has
found broad empirical support, but its multivariate, predictive relationships have not been uniformly validated. Applicability of
the model across age groups of chronic pain patients has also not been tested. Goals of this study were to validate the predictive
relationships of the multivariate FA model using structural equation modeling and to evaluate the factor structure of the Tampa
Scale of Kinesiophobia (TSK), levels of pain-related fear, and fit of the FA model across three age groups: young (640), mid-
dle-aged (41–54), and older (P55) adults. A heterogeneous sample of 469 chronic pain patients provided ratings of catastrophizing,
pain-related fear, depression, perceived disability, and pain severity. Using a confirmatory approach, a 2-factor, 13-item structure of
the TSK provided the best fit and was invariant across age groups. Older participants were found to have lower TSK fear scores
than middle-aged participants for both factors (FA, Harm). A modified version of the Vlaeyen JWS, Kole-Snijders AMJ, Boeren
RGB, van Eek H (Fear of movement/(re)injury in chronic low back pain and its relation to behavioral performance. Pain
1995a;62:363–72.) FA model provided a close fit to the data (v2(29) = 42.0, p > 0.05, GFI = 0.98, AGFI = 0.97, CFI = 0.99,
RMSEA = 0.031 (90% CI 0.000–0.050), p close fit = 0.95). Multigroup analyses revealed significant differences in structural weights
for older vs. middle-aged participants. For older chronic pain patients, a stronger mediating role for pain-related fear was support-
ed. Results are consistent with a FA model of chronic pain, while indicating some important age group differences in this model and
in levels of pain-related fear. Longitudinal testing of the multivariate model is recommended.
? 2005 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.
Keywords: Chronic pain; Fear-avoidance; Pain-related fear; Structural equation modeling; Age differences
Pain-related fear is among the most salient predictors
of chronic pain and associated disability (Vlaeyen and
Linton, 2000; Keefe et al., 2004). The fear-avoidance
(FA) model of chronic pain was proposed by Lethem
et al. (1983) to explain why some musculoskeletal inju-
ries can lead to longstanding pain, depression, and
Vlaeyen et al. (1995a) elaborated the FA model to
suggest that fear of movement/(re)injury represents a
response to pain that is influenced by catastrophizing
(Fig. 1). This fear contributes to avoidance behaviors
and subsequent disuse, depression, and disability.
Research on this model has commonly employed the
Tampa Scale for Kinesiophobia (TSK) to assess fear
of movement/(re)injury (Kori et al., 1990). High levels
of fear have been consistently associated with impaired
0304-3959/$32.00 ? 2005 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.
*Corresponding author. Tel.: +1 434 243 6744; fax: +1 434 243
E-mail address: email@example.com (A.J. Cook).
1Present address: Pain Management Unit, Royal National Hospital
for Rheumatic Diseases and University of Bath, Bath BA1 1RL, UK.
Pain 121 (2006) 195–206
physical function and greater self-reported disability
(Vlaeyen et al., 1995a,b; Crombez et al., 1999). There
has been some debate regarding the factor structure of
the TSK (McNeil and Vowles, 2004).
Predictive relationships in the FA model have been
independently supported through studies of chronic
and acute pain patients, using correlational/regression
analyses (Vlaeyen et al., 1995a,b; Crombez et al., 1999;
Swinkels-Meewisse et al., 2003). The most consistent
finding is the strong predictive power of pain-related
fear for physical performance and perceived disability
(Vlaeyen et al., 1995a,b; Asmundson et al., 1997; Crom-
bez et al., 1999; Al-Obaidi et al., 2000; Verbunt et al.,
2003). Structural equation modeling (SEM) has been
applied to validate a modified FA model for prediction
of pain severity (Goubert et al., 2004a). However, the
full FA model, incorporating pain, disability, and
depression, has not been uniformly validated. SEM
offers significant benefits for model validation, including
a confirmatory approach, estimation and adjustment for
measurement error, and integration of both observed
and latent variables (Byrne, 2001; Schumacker and
Potential age differences in the FA model have
received little attention. In one study (Crombez et al.,
1999), age was significantly correlated (r = 0.40) with 1
of 3 scales of pain-related fear in a young chronic pain
sample. Two additional studies in this report found no
significant age – fear correlations. Another study found
a small correlation between age and TSK fear (Goubert
et al., 2004a). In acute low back pain patients under age
65, age did not correlate with two unique TSK factors
(Swinkels-Meewisse et al., 2003). General age group
comparisons for chronic pain have revealed multiple
similarities, as well as important differences for older
adults, including lower anxiety and a stronger relation-
ship between pain severity and depression (Middaugh
et al., 1988; Buckelew et al., 1990; Keefe and Williams,
1990; Sorkin et al., 1990; Corran et al., 1994; Cutler
et al., 1994; Benbow et al., 1995; Turk et al., 1995;
Gibson and Helme, 2000).
The goals of this study were to validate the multivar-
iate FA model of chronic pain using SEM and compare
the following across young, middle-age, and older
chronic pain patients: factor structure of the TSK (con-
firmatory approach), levels of pain-related fear, and
goodness-of-fit of the FA model.
Four hundred and eighty three chronic pain patients
evaluated at a multidisciplinary, university-based pain man-
agement clinic were eligible for inclusion. While there were
no exclusionary diagnoses, the vast majority of patients
evaluated in this clinic have musculoskeletal and neuropath-
ic diagnoses. All study measures were completed as part of
standard clinical evaluations. The study was approved by
the Human Investigation Committee of the university’s
Institutional Review Boards. Participants received no com-
pensation for their participation. Nine participants provided
incomplete data and were excluded from analyses. Five par-
ticipants were deemed multivariate outliers based on elevat-
ed Mahalanobis distances with a conservative probability
level of p < 0.001 (Tabachnick and Fidell, 2001). They were
deemed to be outside of the target population and were
excluded from analyses. The final sample included 469 par-
ticipants. For age analyses, groupings were: 40 and under
(n = 152), 41–54 (n = 198), and 55 and over (n = 119),
based on age distribution within the sample. Demographic
and pain characteristics are shown in Table 1, including
age group comparisons. There were expected age differences
for marital and employment status, and receipt of financial
benefits.Young participants had shorter average pain
Accurate estimation of measurement error in SEM requires
at least two measures for each latent variable. If only one mea-
sure is used, error variance must be specified based on external
data (Schumacker and Lomax, 2004). The following scales
comprised the measurement model for the SEM analyses, as
represented in Fig. 2:
Fig. 1. Cognitive-behavioral fear-avoidance model of chronic pain. Reprinted from Pain, Vol. 62, Vlaeyen et al., ‘‘Fear of movement/(re)injury in
chronic low back pain and its relation to behavioral performance’’, pp. 363–372, Copyright 1995, with permission from the International Association
for the Study of Pain and the author.
A.J. Cook et al. / Pain 121 (2006) 195–206
Demographic and pain characteristics by age groups
VariableYoung (n = 152)Middle-aged (n = 198)Older (n = 119)Total (n = 469)
Age – range
Gender – female
Race – Caucasian
Single, sep, divorced
High school graduate
Working full/part time
Receiving financial benefits
Pain duration – mean years4.4 (5.3)7.3 (8.9) 6.4 (8.8)6.2 (8.0)0.003
Ave pain intensity (0–10 NRS)6.3 (1.8)6.6 (1.8)6.2 (2.2)6.4 (1.9)n.s.
Other single sites
aChi-square and one-way ANOVA comparisons by age groups; n.s., not significant at p < 0.05.
Fig. 2. Structural equation model of fear-avoidance. Based on Vlaeyen et al. (1995a) fear-avoidance model as shown in Fig. 1. Numbers are mean
standardized structural and measurement weights from bootstrapped estimates. CSQ catas, Coping Strategies Questionnaire catastrophizing; TSK-
FA, Tampa Scale for Kinesiophobia fear-avoidance; TSK-Harm, Tampa Scale for Kinesiophobia Harm; CESD Total, Center for Epidemiological
Studies Depression total score; PANAS Neg, Positive and Negative Affect Scales negative affect score; PDI Total, Pain Disability Index total score;
WAS, Want-to-do Activities Scale; MPQ PRI, McGill Pain Questionnaire (short-form) Pain Rating Index; MPQ VAS, McGill Pain Questionnaire
(short-form) Visual Analogue Scale; MPQ PPI, McGill Pain Questionnaire (short-form) Present Pain Intensity.
A.J. Cook et al. / Pain 121 (2006) 195–206
2.2.1. Tampa Scale of Kinesiophobia (TSK; Kori et al., 1990)
The TSK is a self-report measure of fear of movement and
(re)injury. It consists of 17 items scored on a 4-point scale. Sev-
eral studies of the TSK in chronic pain samples have supported
a 13-item 2-factor structure, with omission of reverse-scored
items (Clark et al., 1996; Goubert et al., 2004a,b; Heuts
et al., 2004). Adequate psychometric properties have been doc-
umented (Vlaeyen et al., 1995b).
2.2.2. McGill Pain Questionnaire (short-form) (MPQ;
The short-form MPQ is a widely used measure of the sen-
sory, affective, and intensity dimensions of pain. It includes
15 pain descriptors that form the Pain Rating Index (PRI), a
10 cm visual analogue scale (VAS), and a 6-point present pain
intensity (PPI) scale. Good consistency, validity, treatment
sensitivity, and discriminant ability have been demonstrated
(Melzack, 1987; Melzack and Katz, 2001).
2.2.3. Pain Disability Index (PDI; Pollard, 1984)
The PDI is a brief measure of perceived pain-related dis-
ability for seven areas of daily functioning. It has been found
to be an internally consistent (a = 0.86) measure, with good
concurrent, criterion-related, and discriminative validity (Pol-
lard, 1984; Tait et al., 1990). The balance of research supports
a single underlying factor (Jacob and Kerns, 2001).
2.2.4. Coping Strategies Questionnaire Catastrophizing Scale
(CSQ-catastrophizing; Rosentiel and Keefe, 1983)
The CSQ is a widely used self-report measure of cognitive
and behavioral coping strategies in chronic pain. The 6-item
catastrophizing subscale has been used extensively as a mea-
sure of pain catastrophizing, with adequate psychometric
properties (Stewart et al., 2001; Sullivan et al., 2001; Turner
and Aaron, 2001).
2.2.5. Center for Epidemiological Studies – Depression scale
(CESD; Radloff, 1977)
The CESD is a 20-item self-report measure of depressive
sitivity and specificity (Radloff, 1977; Geisser et al., 1997).
2.2.6. Positive and Negative Affect Schedule (PANAS; Watson
et al., 1988)
The PANAS is a brief, self-report measure comprised of
two 10-item scales for positive and negative affect. The scales
have been shown to be internally consistent, valid, and largely
uncorrelated, with adequate stability (Watson et al., 1988).
2.2.7. Want-to-do Activities Scale (WAS)
The WAS is a single item self-rating of activity function
used in our clinical assessments. Patients are asked ‘‘what per-
cent of want-to-do activities do you get done in an average day
(0–100%)?’’ Responses to this question correlated r = ?0.37
with the PDI total disability score.
All questionnaires were administered in electronic format,
based on Microsoft Access 2000 (Microsoft Corporation,
USA) platforms. We have previously cross-validated the
SF-MPQ and PDI for chronic pain assessment in this format
(Cooket al.,2004). Data
1280 · 1024 pixel resolution, Accutouch resistance touchscreen
LCD monitors (Elo Touchsystems, USA) and/or a standard
two-button mouse, and with traditional keyboards, with setup
as previously described (Cook et al., 2004).
were entered via17-in.,
All participants completed the study measures in clinic,
immediately prior to initial clinical evaluations. Informed con-
sent was obtained for participation in a broader study of
chronic pain assessment, and participants were given a brief
information session to familiarize them with the relevant com-
puter components and response methods. A trained facilitator
was available to provide limited procedural assistance to par-
ticipants as needed, while providing for adequate privacy. Par-
ticipants were encouraged to take brief rest or stretching
breaks as needed.
2.5. Data analyses
Data were screened for integrity and assumptions of multi-
variate analyses. As previously noted, 5 participants were iden-
tified as multivariate outliers and were excluded from analyses.
The multivariate distribution was found to be nonnormal, with
a Mardia’s coefficientof multivariate
(p < 0.001). While SEM parameters with maximum likelihood
(ML) estimation are robust to nonnormality, fit indices and
standard errors can be biased (Bollen, 1989; West et al.,
1995). This can include overestimation of chi-square values,
underestimation of CFI, and underestimation of standard
errors (Byrne,2001). Bootstrapping
(n = 500 samples) (West et al., 1995) was employed to evaluate
potential bias. Bootstrapping provides repeated resampling of
the original sample (with replacement) to create a sampling
distribution that is not dependent on the parametric assump-
tion of normality. All SEM analyses were conducted with
AMOS v. 5.0 for Windows (Arbuckle, 2003). Single factor
analysis of variance (ANOVA) and multivariate analysis of
variance (MANOVA) using Wilks’ lambda criterion were
employed for mean comparisons by age group. Tukey’s hon-
estly significant difference (HSD) test was employed for post
hoc mean comparisons by age group.
3.1. Confirmatory factor analyses
Based on previous investigations of the factor struc-
ture of the TSK, four alternative models were evaluated
for goodness-of-fit (Vlaeyen et al., 1995b; Clark et al.,
1996; Goubert et al., 2004b; Heuts et al., 2004). The
chi-square statistic was evaluated as a measure of exact
model fit, but given its known limitations in relation to
sample size and evaluation of model approximations,
other indices of closeness of fit were employed. The root
mean square error of approximation (RMSEA) with
A.J. Cook et al. / Pain 121 (2006) 195–206
90% confidence interval and p value for test of close fit
(RMSEA < 0.05) were selected as primary indices, based
on widespread use, good interpretive guidelines, and
sensitivity to number of estimated parameters (Browne
and Cudeck, 1993; Byrne, 2001; Tomarken and Waller,
2005). Based on published guidelines, RMSEA values
less than 0.05 indicate close fit, less than 0.08 reasonable
fit, and less than 0.10 mediocre fit, with p values greater
than 0.50 indicating close fit (Byrne, 2001). Other fit
indices examined and their criteria levels were good-
ness-of-fit index (GFI; good fit > 0.90, close fit > 0.95),
adjusted goodness-of-fit index (AGFI; adjusted for
degrees of freedom, good fit > 0.80), and comparative
fit index (CFI; adequate fit > 0.90) (Bentler, 1990;
Byrne, 2001). The 4 models were: model 1: 1-factor
model including all 17 TSK items; model 2: 4-factor
12-item model proposed by Vlaeyen et al. (1995b); mod-
el 3: 1-factor 13-item model with omission of the inverse
scored items (numbers 4, 8, 12, and 16); model 4: 2-fac-
tor 13-item model proposed by Clark et al. (1996).
Table 2 summarizes the goodness-of-fit indices for
each of the four models. Consistent with published anal-
yses from other chronic pain populations (Goubert
et al., 2004b; Heuts et al., 2004), results suggested that
model 4 (2-factor model of Clark et al. (1996)) provided
the best fit in this sample. Goubert et al. (2004a,b)
have suggested Harm (TSK-Harm) and Fear-avoidance
(TSK-FA) as appropriate labels for the two factors.
The goodness-of-fit indices indicated mediocre and not
close fit of this model to the data. With bootstrapping
of maximum likelihood estimates for nonnormal data
(500 random samples), estimated biases for all standard-
ized regression weights in the model were less than 0.01.
As with the maximum likelihood chi-square test, the
Bollen–Stine modified chi-square test based on boot-
strapped estimates was rejected (p < 0.01). As with
maximum likelihood estimates, this chi-square test is
highly sensitive to sample size (Byrne, 2001). Underesti-
mation of standard errors was minimal to moderate.
The highest parameter bias (0.007) and error underesti-
mation (44%) were for item 10 on the TSK-FA factor.
This was the only item having a 90% confidence interval
for its parameter estimate that contained zero. Because
item 10 did not load well on either factor in model 4,
the model was re-evaluated with this item deleted. The
two models are nested and could be compared with a
likelihood ratio test. This comparison indicated an
improvedfit(Dv2(11) = 68.1,p < 0.01)whiletheRMSEA
value, in contrast, suggested a decline in overall fit
(RMSEA = 0.089,90%CI0.078–0.100).Sincetheoverall
improvement was marginal, model 4 was retained.
Model 3 (1 factor, 13 items) provided the next best fit.
Per Heuts et al. (2004), this model is nested within the
2-factor model of Clark et al., and the models can be
compared with a likelihood ratio test. The goodness-
of-fit of model 4 was significantly better than that of
model 3 (Dv2(1) = 68.1, p < 0.001). The fear-avoidance
(TSK-FA) and harm (TSK-Harm) scales of model 4
were significantly correlated (r = 0.66, p < 0.001). Inter-
nal consistencies of the two scales were moderate: TSK-
FA Cronbach’s alpha = 0.74, TSK-Harm alpha = 0.72.
Multigroup analysis, a set of procedures developed
for testing invariance of a SEM model across groups,
was employed for probability testing of the equivalence
of the 2-factor Clark et al. model across the three age
groups (Byrne, 2001; Schumacker and Lomax, 2004).
In this analysis, the validity of the factor structure is
tested simultaneously across the groups. Consistent with
the full sample analysis, an exact fit of the model
was rejected, but an adequate to close overall fit was
indicated across groups: v2(192) = 440.9 (p < 0.001),
GFI = 0.87, AGFI = 0.82,
0.053 (90% CI 0.046–0.059), p close fit = 0.24. In
sequential comparisons of models constraining measure-
ment weights (factor loadings), structural covariances,
and measurement residuals across the age groups,
chi-square differences for nested model comparisons
were nonsignificant (p > 0.08). Thus, invariance of the
fit of the 2-factor, 13-item model across the three age
groups was supported.
CFI = 0.86,RMSEA =
3.2. TSK score comparisons by age groups
MANOVA of the three age groups on the TSK fear-
avoidance (TSK-FA) and Harm (TSK-Harm) scales
revealed a significant main effect (F = 2.94, p < 0.05).
Step-down analysis revealed significant effects for both
TSK-FA and TSK-Harm (see Table 3). Post hoc analy-
ses indicated that older participants had significantly
lower TSK-FA and TSK-Harm scores than middle-aged
Goodness-of-fit indices for confirmatory factor analyses (CFA) of four alternative models of the Tampa Scale for Kinesiophobia (TSK)
dfGFIAGFICFIRMSEA (90% CI)
p close fit
Model 1 = 1-factor 17-item model, model 2 = 4-factor, 12-item model of Vlaeyen et al. (1995b), Model 3 = 1-factor 13-item model (without reverse
scored items), Model 4 = 2-factor, 13-item model of Clark et al. (1996).
A.J. Cook et al. / Pain 121 (2006) 195–206
participants. Because the TSK total score is frequently
employed in clinical and research practice, a total score
age group comparison was conducted with single-factor
ANOVA. A significant effect was found, with older par-
ticipants having lower TSK-Total scores than middle-
aged participants per post hoc comparisons (Table 3).
3.3. Evaluation of measurement model
correlations for the measurement scales are presented
in Table 4. Based on the cognitive-behavioral fear-
avoidance model of Vlaeyen et al. (1995a, Fig. 1) we
developed a structural equation model of latent
variables, with fear of (re)injury as a mediator between
pain catastrophizing and pain disability, depression,
and pain severity. Avoidance behavior, as proposed in
the Vlaeyen et al. model, was not included in our model.
SEM required selection of beginning and endpoints in
the cyclical model. We designated pain severity as the
endpoint for our model, consistent with contemporary
views regarding the multiple physical and psychosocial
standard deviations, and Pearson
influences on pain perception (Keefe et al., 2004). These
influences on pain perception would be expected to be
salient after protracted pain chronicity, as in our sample
(mean 6.2 years). As shown in Fig. 2, the latent con-
struct fear of (re)injury was specified by the two sub-
scales of the TSK: TSK-FA
Depression was estimated by the CESD total score
and the Negative Affect scale from the PANAS. Pain
disability was specified by the PDI and the WAS. The
Visual Analogue Scale (VAS), Present Pain Intensity
(PPI), and Pain Rating Index total (PRI) subscales from
the SF-MPQ were used to specify the latent construct of
pain severity. The latent construct of pain catastrophiz-
ing was specified solely by the CSQ catastrophizing
scale. The error variance for this measure was fixed at
a value of 0.43 based on a reliability coefficient of 0.80
and standard deviation of 1.47. Coefficient alpha for
CSQ Catastrophizing was 0.83 in our sample, and pub-
lished values have ranged from 0.78 to 0.84 (Rosentiel
and Keefe, 1983; Riley and Robinson, 1997; Robinson
et al., 1997; Stewart et al., 2001). Test–retest reliability
has been assessed at 0.77 (Stewart et al., 2001).
The first step of SEM analyses with latent variables is
evaluation of the measurement model. Confirmatory
factor analysis showed a very close fit of this model to
v2(26) = 31.5
AGFI = 0.97, CFI = 0.99, RMSEA = 0.021 (90% CI
0.000–0.044), p close fit = 0.984. The Bollen–Stine chi-
square test for acceptable fit for the model based on
bootstrapped estimates (500 samples) was accepted
(p = 0.27). All of the standardized path coefficients were
within acceptable range. This indicates that measure-
ment scales employed in the model can be considered
valid operationalizations of the latent constructs of fear
of (re)injury, catastrophizing, depression, pain disabili-
ty, and pain severity.
(p = 0.21), GFI = 0.99,
3.4. Evaluation of structural model
SEM testing of the Vlaeyen et al. (1995a)-based struc-
tural model shown in Fig. 2 revealed a marginal fit to the
Tampa Scale of Kinesiophobia (TSK) age group mean comparisons
Scales by age groupsMeanSD
TSK Total Score
aDiffering letters indicate significant difference between group
means. Post hoc analyses for MANOVA (TSK Fear-Avoidance and
TSK Harm) and ANOVA (TSK Total) using Tukey’s HSD test.
Means, standard deviations and correlations for measurement scales in the SEM models
1. CSQ catastrophizing
2. TSK fear avoidance
3. TSK harm
4. PDI perceived disability
5. WAS activity functioning
6. CESD depression
7. PANAS Negative Affect
8. SF-MPQ pain rating index
9. SF-MPQ VAS
10. SF-MPQ PPI
All correlations significant at p < 0.001 level; CSQ, Coping Strategies Questionnaire; TSK, Tampa Scale for Kinesiophobia; PDI, Pain Disability
Index; WAS, Want-to-do Activity Scale; CESD, Center for Epidemiological Studies Depression scale; PANAS, Positive and Negative Affect Scale;
SF-MPQ, Short-Form McGill Pain Questionnaire; VAS, Visual Analogue Scale; PPI, Present Pain Intensity.
A.J. Cook et al. / Pain 121 (2006) 195–206
data. The same fit indices considered for the confirmato-
ry factor analyses were employed for the structural
model testing: v2(31) = 148.1 (p < 0.001), GFI = 0.94,
AGFI = 0.89, CFI = 0.94, RMSEA = 0.089 (90% CI
0.076–0.105), P close fit < 0.001. Bootstrapping of
maximum likelihood estimates for nonnormal data
(500 random samples) revealed minimum bias of param-
eter estimates, with absolute value of estimated bias for
all standardized path coefficients being less than 0.01.
The Bollen–Stine test for acceptable fit was rejected
(p < 0.01). Mean standardized path coefficients from
the bootstrapped samples are shown in Fig. 2.
Interpretation of SEM results is strengthened by
comparisons of data fit for alternative, theory-based
models (Bollen, 1989; Tomarken and Waller, 2005).
We next evaluated a modified model in which the latent
variable catastrophizing was hypothesized to directly
influence the latent variables of disability and depres-
sion, in addition to the influence mediated by fear of
re(injury). This hypothesis was based on the substantial
literature supporting the strong predictive value of
catastrophizing for these variables (Keefe et al., 2004).
This modified SEM model is shown in Fig. 3. SEM anal-
ysis indicated a close fit of this model by all indices:
v2(29) = 42.00 (p = 0.06), GFI = 0.98, AGFI = 0.97,
CFI = 0.99, RMSEA = 0.031 (90% CI 0.000–0.050), p
close fit = 0.95. As the original model was nested within
this adapted model, a likelihood ratio comparison was
undertaken. The modified model (Fig. 3) was found to
be a significantly better fit to the data: Dv2(2) = 106.1,
p < 0.001. Bootstrapping of maximum likelihood esti-
mates for nonnormal data (500 random samples)
revealed minimum bias of parameter estimates, with
expected underestimation of some error variances.
Absolute value of estimated bias for all standardized
path coefficients was less than 0.01. Bootstrapped esti-
mates suggested a maximum 20% increase in standard
error estimates, with the majority of the error terms
being underestimated by 10% or less. The Bollen–Stine
chi-square test for acceptable fit based on bootstrapped
estimates (500 samples) was accepted (p = 0.10). Mean
standardized path coefficients from the bootstrapped
samples are shown in Fig. 3 and are consistent with a
good model fit.
3.5. Multigroup analyses of SEM by age groups
Potential age group differences in the adopted SEM
model (Fig. 3) were evaluated through multigroup anal-
ysis as described in the confirmatory factor analysis sec-
tion above. Simultaneous fitting of the model to the 3
age groups confirmed a close overall fit of the model:
v2(87) = 111.7 (p = 0.04), GFI = 0.95, AGFI = 0.91,
CFI = 0.99, RMSEA = 0.025 (90% CI 0.006–0.037), p
close fit = 1.00. A sequence of planned, nested compar-
isons of constrained models was initiated to test invari-
ance of model parameters across the three age groups, in
the following order: measurement weights, structural
weights (path loadings), structural covariances, structur-
al residuals, and measurement residuals. The critical
ratio of differences was nonsignificant when measure-
ment weights were constrained (Table 5), supporting
the invariance of these parameters across age groups.
However, with measurement weights constrained as
equal, the critical ratio for structural weights was
significant (p < 0.05) indicating age group differences
Fig. 3. Modified structural equation model of fear-avoidance. Addition of direct paths from catastrophizing to depression and disability. Numbers
are mean standardized structural and measurement weights from bootstrapped estimates. CSQ catas, Coping Strategies Questionnaire
catastrophizing; TSK-FA, Tampa Scale for Kinesiophobia fear-avoidance; TSK-Harm, Tampa Scale for Kinesiophobia Harm; CESD Total,
Center for Epidemiological Studies Depression total score; PANAS Neg, Positive and Negative Affect Scales negative affect score; PDI Total, Pain
Disability Index total score; WAS, Want-to-do Activities Scale; MPQ PRI, McGill Pain Questionnaire (short-form) Pain Rating Index; MPQ VAS,
McGill Pain Questionnaire (short-form) Visual Analogue Scale; MPQ PPI, McGill Pain Questionnaire (short-form) Present Pain Intensity.
A.J. Cook et al. / Pain 121 (2006) 195–206
(Table 5). With measurement weights, and structural
weights, covariances and residuals constrained equal,
measurement residuals were also found to vary between
groups (p < 0.01).
Subsequent multigroup analyses were conducted with
pairings of age groups to identify between-group differ-
ences. Measurement and structural weights did not differ
significantly for the following pairings: young vs. mid-
dle-aged, young vs. older. However, when the model
fit was compared for middle-aged and older partici-
pants, measurement weights were invariant but structur-
al weights differed (Dv2(7) = 16.5, p < 0.05). With
measurement weights and structural weights, covari-
ances and residuals constrained as equal, measurement
residuals differed between the older participants and
both middle-aged (p < 0.05) and younger (p < 0.001)
participants. Structural weights for older and middle-
aged participants are shown in Fig. 4. Fear of (re)injury
was found to play a stronger mediating role between
castastrophizing and the depression and disability vari-
ables for older participants. This difference was especial-
ly notable for the relationship between catastrophizing
and depression. For middle-aged participants, catastro-
phizing was a strong direct predictor of depression,
whereas this relationship was mediated by fear of (re)in-
jury to a much greater degree for older pain patients.
Depression and disability had less predictive strength
for pain severity among older participants, as compared
to their middle-aged counterparts.
Through cross-sectional analyses of a large, heteroge-
neous chronic pain sample via structural equation mod-
eling (SEM), we found a pattern of relationships
consistent with a cognitive-behavioral fear-avoidance
(FA) model of chronic pain. The 2-factor, 13-item struc-
ture of the Tampa Scale of Kinesiophobia (TSK) was
supported as the best fit across three adult age groups.
Older chronic pain patients (age 55 and older) were
found to have lower levels of pain-related fear than mid-
dle-aged patients, and structural weights for the adopted
SEM fear-avoidance model were found to differ signifi-
cantly between these two age groups.
Our findings are consistent with the multivariate FA
model and its application for chronic pain assessment
and treatment. Though much of prior research has
focused on low back and musculoskeletal pain (Keefe
et al., 2004), our findings suggest that the model could
apply to diverse chronic pain conditions. Both catastro-
phizing and fear of (re)injury are significantly associated
with important dimensions of chronic pain. Our results
are consistent with fear as a mediator between catastro-
phizing and perceived disability, depression, and pain,
particularly for older adults. Negative appraisals of inju-
ry and pain predict levels of fear, while both variables
predict increased pain and dysfunction ratings. This
model is consistent with the notion that fear is a more
important predictor of pain-related disability than pain
itself (Waddell et al., 1993), with reports of pain being
predicted by many factors. It is important to recognize,
however, that SEM does not establish the validity or
superiority of a model in relation to untested models
(Tomarken and Waller, 2005). Such models can be
equally or better fitted to the data and provide equally
plausible explanations. Causal relationships in the FA
Nested comparisons of sequentially constrained SEM models for
multigroup age analyses
Full unconstrained model111.7 87
Parameters constrained (sequentially):
Fig. 4. Structural model of fear-avoidance with age group comparisons (older vs. middle-aged). Comparison of standardized structural weights for
modified fear-avoidance model (Fig. 3) based on multigroup analysis. First numbers are for older participants (Page 55); bracketed numbers are for
middle-aged participants (ages 41–54). Structural models are significantly different (Dv2(7) = 16.5, p < 0.05).
A.J. Cook et al. / Pain 121 (2006) 195–206
The improvement of our SEM model by allowing
catastrophizing to directly predict disability and depres-
sion was not surprising, given the evidence for castatro-
phizing as a powerful predictor of depression, disability,
and pain (e.g., Sullivan and D’Eon, 1990; Turner et al.,
2002; Jones et al., 2003; Keefe et al., 2004). This elabo-
rated model may help to explain the complex relation-
ships between these variables, as past studies have
variably shown pain-related fear (Crombez et al., 1999;
Woby et al., 2004) or catastrophizing (van den Hout
et al., 2001; Denison et al., 2004) as superior predictors
Although various scoring schemes and factor struc-
tures have been suggested for the TSK in chronic pain,
our confirmatory analyses support the 2-factor method
of Clark et al. (1996) (subsequently named FA and
Harm), with reverse items omitted, for a heterogeneous
sample. This supports findings from different chronic
pain populations (Goubert et al., 2004b; Heuts et al.,
2004). We found the 2-factor structure to be invariant
across three age groups. However, fit of this model
was mediocre, suggesting need for further refinement
of the TSK for use in heterogenous clinical populations.
A recent study found problems with the TSK factor
structure in a fibromyalgia sample (Burwinkle et al.,
2005). Content overlap with the construct of catastro-
phizing was raised as a concern, though based on a
4-item factor scale. Pain-related fear and catastrophizing
are known to be significantly correlated, consistent with
prediction of the FA model (Fig. 1). Refinements in the
assessment of pain-related fear may benefit from further
content distinction of these important constructs.
Several authors have identified pain-related fear as
one of the most promising areas of research in persistent
pain (McCracken and Turk, 2002; Keefe et al., 2004).
The FA model has led to graded in vivo exposure inter-
ventions for chronic pain. These approaches have been
effective for reducing fear and associated behaviors (Vla-
eyen and Linton, 2000; Vlaeyen et al., 2001). Recent
research has suggested that brief education can reduce
fear and catastrophizing, while in vivo graded exposure
is required for changes in behaviors and long-term pain
(de Jong et al., 2005). Our findings support such treat-
ments that address both appraisals and reactions to
pain. Early identification of pain-related fear (Fritz
et al., 2001; Sieben et al., 2002; Boersma and Linton,
2005a) is likely to find increasing support in preventive
An important omission in the FA model is the role of
self-efficacy beliefs. These beliefs predict pain, physical
functioning, and disability in chronic pain patients,
and partially mediate the relationship between pain
intensity and disability (Arnstein et al., 1999; Asghari
and Nicholas, 2001). Recent studies have shown pain
self-efficacy to be equally or more important in predict-
ing disability than FA beliefs (Denison et al., 2004;
Woby et al., 2004). Self-efficacy may act through per-
ceived controllability, which produces attenuated activa-
tion in the anterior cingulate, insular, and secondary
somatosensory cortices, the three areas most consistent-
ly linked with pain processing (Salomons et al., 2004).
Future investigations should continue to evaluate these
important relationships and related interventions.
Our findings are in agreement with prior studies
showing both similarities and differences in the experi-
ence of chronic pain for older adults (Middaugh et al.,
1988; Buckelew et al., 1990; Keefe and Williams, 1990;
Sorkin et al., 1990; Corran et al., 1994; Cutler et al.,
1994; Benbow et al., 1995; Turk et al., 1995; Gibson
and Helme, 2000). We found a stronger mediating role
for fear between catastrophizing and the dependent vari-
ables’ depression and disability for older, as compared
to middle-aged, patients. Higher pain catastrophizing
has typically been associated with younger age (Santa-
virta et al., 2001; Turner et al., 2004), while the relation-
ship between age and fear of (re)injury ranges from nil
(Swinkels-Meewisse et al., 2003) to moderate (Crombez
et al., 1999). Age has been found to moderate the rela-
tionship between catastrophizing and pain (Santavirta
et al., 2001). It can be hypothesized that (re)injury risks
are higher for older adults and that associated fears are
fueled when catastrophic thinking occurs.
However, we found that older pain patients had low-
er fear relative to middle-aged patients, the most fre-
quently represented group in pain clinics. These
differences were present for both factors of the TSK,
and the total score. There is evidence from clinical and
community studies of more stoic beliefs and reactions
to pain among older adults (Cook and Chastain, 2001;
Yong et al., 2001), despite differences in nociception
and pain perception that could make older adults more
vulnerable to the negative impacts of pain (Gibson and
Farrell, 2004). We found a weaker association between
depression and pain severity among older adults, in con-
trast to some prior findings (Turk et al., 1995) while in
agreement with others (Santavirta et al., 2001). These
findings are consistent with reductions in the intensity
and frequency of negative emotions, declines in emo-
tional expressivity, and increases in emotional control
with increasing age (Gross et al., 1997). Very little is
known about how these differences interact with the
experience of chronic pain. Longitudinal research will
be needed to distinguish potential cohort and/or aging
factors in pain stoicism.
not include aseparate measure ofactivity avoidance,pre-
cluding confirmation of its role in the FA model. Though
self-reported activity avoidance overlaps with the TSK
FA scale,aseparate measureshouldbe included infuture
studies. Similarly a measure of pain vigilance (Vlaeyen
A.J. Cook et al. / Pain 121 (2006) 195–206
and Linton, 2000; Goubert et al., 2004a) could enhance
the analyses. A second potential limitation was the exclu-
sion of a measure of medical comorbidity. Though such
ity levels in older chronic pain patients (Farrell et al.,
1995), a recent study found no predictive value for phys-
comparisons by pain type could also prove valuable, as
some evidence suggests the FA model is less applicable
sistence could play a greater role (Vlaeyen and Morley,
2004). The heterogeneity of our sample could have
masked such potential differences.
Additionally, our age groupings were influenced by
our sampling distribution. These groupings differ from
those commonly used in age comparisons (<45, 45–64,
P65), though there is variability (e.g., Middaugh
et al., 1988) and the cutoff point for ‘‘old age’’ has been
arbitrarily based on social policy (Butler, 1987). Our
results could also be influenced by the unequal and rel-
atively small sizes of the age group samples. Finally, our
analyses were cross-sectional and retrospective. As the
fear-avoidance paradigm was developed to explain the
evolution of acute to chronic pain, prospective longitu-
dinal analyses of the multivariate model will be required.
Fear of pain has been found to predict low back pain
and disability at 6-month follow-up (Picavet et al.,
2002). Recent data also suggest that chronicity is a fac-
tor in the influence of fear on self-reported activities
(Boersma and Linton, 2005b). The inter-relations
among measures of disability, affect, fear/anxiety, and
pain intensity require further analyses, particularly those
that assess multidirectional relationships.
In conclusion, we have provided additional support
for the multivariate FA model of chronic pain, while
elaborating the complex inter-relationships, and adding
to the growing evidence-base for age differences in the
experience of chronic pain. The clinical practice of
chronic pain assessment and management will benefit
from further research in these areas.
The authors thank the staff of the University of Vir-
ginia Pain Management Center, especially Beth Hall,
David Roberts, and Lisa Van Winkle, and the patients,
for contributions to data collection.
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