Content uploaded by Andrea Poli
Author content
All content in this area was uploaded by Andrea Poli on Dec 16, 2017
Content may be subject to copyright.
The role of metacognitive beliefs in health anxiety
Gabriele Melli
a,b,
⁎, Claudia Carraresi
b
,AndreaPoli
b
, Robin Bailey
c
a
University of Pisa, Italy
b
Institute of Behavioral and Cognitive Psychology and Psychotherapy of Florence (IPSICO), Italy
c
University of Central Lancashire, UK
abstractarticle info
Article history:
Received 6 July 2015
Received in revised form 26 September 2015
Accepted 1 October 2015
Available online xxxx
Keywords:
Health anxiety
Metacognition
Dysfunctional beliefs
Anxiety sensitivity
Research has supported the specific role that anxiety sensitivity and health-related dysfunctional cognitions may
have in the development and maintenance of health anxiety symptoms. Recent evidence suggests that
metacognitive beliefs may also be instrumental in the symptomatology of health anxiety. The aim of the present
study was to explore the association between metacognitive beliefs and health anxiety symptoms and to test
whether these beliefs are significant predictors of health anxietyafter controlling for anxiety, depression,anxiety
sensitivity and dysfunctional beliefs. A series of dimensional self-report measures were administered to a large
Italian non-clinical sample (N= 342). At a bivariate level, metacognitive beliefs about uncontrollability and in-
terference of illness thoughts had a stronger association withhealth anxiety than anyof the dysfunctionalbeliefs.
Results from hierarchical multiple regression analysis indicated that metacognitive beliefs about uncontrollabil-
ity and interference of illness thoughts predicted health anxiety symptoms over-and-above depression, general
anxiety, anxiety sensitivity, andhealth-related dysfunctionalbeliefs.Moreover, results from moderation analysis
indicated that metacognitive beliefs about uncontrollability and interference of illness thoughts moderated the
relationship between anxiety sensitivity and health anxiety. Overall, this study supported the hypothesis that
metacognition may have an important role in health anxiety.
© 2015 Elsevier Ltd. All rights reserved.
1. Introduction
Health anxiety refers to a condition characterized by misinterpreta-
tion of bodily sensations or physical changes as symptoms of serious
disease. Those affected by health anxiety have an obsessional preoccu-
pation with the idea that they are currently (or will be) experiencing a
physical illness. Typically conceptualized as a dimensional rather than
a categorical construct, health anxiety is often thoughtto exist on a con-
tinuum ranging from low to high symptoms (e.g. Ferguson, 2009).
One of the most popular and well researched theoretical accounts of
health anxiety has been Cognitive Behavioural Therapy (CBT). CBT
models put dysfunctional cognitions at the core of the phenomenology
of health anxiety. In particular, Salkovskis and Warwick (2001) de-
scribed four health-related dysfunctional beliefs of particular impor-
tance to the understanding of health anxiety: (a) the perceived
likelihood of experiencing a health problem, (b) the awfulness of
experiencing a health problem, (c) the inability to cope with an experi-
enced health problem, and (d) the inadequacy of medical resources to
treat an experienced health problem. Hadjistavropoulos et al. (2012)
found that the relationship between these dysfunctional beliefs and
health anxiety was robust after controlling for the effects of depression
and non-specific anxiety symptoms. Moreover, the findings from Fergus
(2014) support the specificity of the correlation between health-related
dysfunctional beliefs and health anxiety, after a comparison with the
relevanceof the same core cognitions in obsessive–compulsive disorder
(OCD).
Studies about health anxiety and anxiety sensitivity have shown an
important association between these two constructs, in both clinical
and non-clinical samples (e.g., Abramowitz, Deacon, & Valentiner,
2007; Abramowitz, Olatunji, & Deacon, 2007) and this correlation was
found to be significant also, after controlling for negative affect and in-
tolerance of uncertainty (Norton, Sexton, Walker, & Norton, 2005;
Sexton, Norton, Walker, & Norton, 2003). In particular, compared to
the cognitive and social dimensions, the physical dimension of anxiety
sensitivity is the one that seems most strongly associated with health
anxiety (Olatunji, Wolitzly-Taylor, Elwood, & Connolly, 2009). Research
on the risk factors for health anxiety have demonstrated that anxiety
sensitivity has a mediating role in the relationship between childhood
learning experiences and the development of health anxiety among
young adults (Watt & Stewart, 2000). However, although it is strongly
predictive, research indicates that it does not completely explain all of
the variance in health anxiety symptoms (Abramowitz, Olatunji, &
Deacon, 2007; Deacon & Abramowitz, 2008; Watt & Stewart, 2000),
and failed to emerge as a significant prospective predictor of changes
in health anxiety (Olatunji et al., 2009).
Personality and Individual Differences 89 (2016) 80–85
⁎Corresponding author at: Via Mannelli, 139, 50132 Firenze, Italy.
E-mail address: g.melli@ipsico.it (G. Melli).
http://dx.doi.org/10.1016/j.paid.2015.10.006
0191-8869/© 2015 Elsevier Ltd. All rights reserved.
Contents lists available at ScienceDirect
Personality and Individual Differences
journal homepage: www.elsevier.com/locate/paid
Recent advances in health anxiety research have begun to see more
modern process orientated forms of CBT conceptualizing and treating
health anxiety, such as Mindfulness based CBT (McManus, Surawy,
Muse, Vazquez-Montes, & Williams, 2012) and Acceptance and Com-
mitment Therapy (Eilenberg, Fink, Jensen, Rief, & Frostholm, 2015).
However although these approaches have been applied clinically, they
do not provide a supported conceptual framework to account for the
maintenance of health anxiety. One therapeutic approach which has
begun to explore the underlying conceptualmechanisms of health anx-
iety is Metacognitive Therapy (MCT).
Based on Wells Self-Regulatory Executive Function (S-REF) model
(Wells & Matthews, 1994; Wells, 2009) metacognitive theory posits
that metacognition maybe more important in psychopathology than
cognition in the ordinary domain (Wells, 2009). Metacognitive Therapy
(MCT) which clinically targets these metacognitive beliefs, has been
successfully applied to a range of emotional disorders, in particular de-
pression (e.g., Yilmaz, Gencoz, & Wells, 2015) and anxiety. In a recent
randomized controlled trial, MCT was found to be more efficacious in
the treatment of generalized anxiety disorder than a form of CBT
which targets the cognitive variable “Intolerance of Uncertainty”(van
der Heiden, Muris, & van der Molen, 2012). Also a recent meta-
analysis suggested that MCT is effective in treating disorders of anxiety
and depression and is superior compared to waitlist control groups and
CBT (Normann, van Emmerik, & Morina, 2014). Given the successful ap-
plication of MCT to anxiety disorders, it's reasonable to hypothesize that
its application to health anxiety may help improve the treatment's effi-
cacy and to produce more long-term effects.
According to metacognitive theory specific beliefs about uncontrol-
lability and danger of thinking are considered central and predictive of
psychological disorders in general (e.g. Spada, Caselli, Nikčević,&
Wells, 2015) and health anxiety specifically (Bailey & Wells, 2013;
Bouman & Meijer, 1999; Kaur, Butow, & Thewes, 2011), and responsible
for guiding and controlling the Cognitive Attentional Syndrome (CAS).
In health anxiety the CAS consists of toxic thinking processes such as
worry and rumination about illness, attentional threat monitoring for
illness related information and maladaptive coping responses such as
body scanning and reassurance seeking (Bailey & Wells, 2015).
Evidence exists that both worry and rumination have been implicated
as being strongly associated with health anxiety (Fergus, 2013; Marcus,
Hughes, & Arnau, 2008). Equally evidence from experimental studies
have demonstrated that metacognition has been associated with atten-
tional bias to both health related stimuli (Kaur et al., 2011). In a
recent study Bailey and Wells (2013) demonstrated that metacognitive
beliefs —in particular negative beliefs about the uncontrollability and
danger of worry —accounted for additional variance over and above
other variables associated with health anxiety, such as somatosensory
amplification, illness cognition and neuroticism. In this study metacogni-
tion was assessed by the Metacognitions Questionnaire-30 (Wells &
Cartwright-Hatton, 2004), a psychometrically sound measure, but not
specifically designed to explore metacognitive beliefs related to health
anxiety. In addition, both anxiety sensitivity and dysfunctional beliefs
were not assessed. Further to this Bailey and Wells (2015) also identified
that metacognition —specifically negative beliefs about the uncontrolla-
bility and danger of worry —was not only strongly associated with health
anxiety but moderated and explained the relationship between cata-
strophic misinterpretation and health anxiety.
In sum, existing studies broadly support the role of anxiety sensitivity
and dysfunctional cognitive beliefs in the etiology and maintenance of
health anxiety, while the role of metacognitive beliefs appears to be sig-
nificant but it has not accrued the same level of evidence. Hence, the
aim of the present study was to explore the association between
metacognitive beliefs and health anxiety symptoms and to examine
whether metacognitive beliefs predict and account for additional vari-
ance when controlling for general distress, anxiety sensitivity, and dys-
functional beliefs. In particular, it was hypothesized that: (a) health
anxiety would show a significant positive correlation with metacognitive
beliefs and the relationship would be stronger than that with dysfunc-
tional beliefs; (b) metacognitive beliefs would explain additional variance
and emerge as a significant predictor of health anxiety when controlling
for anxiety, depression, anxiety sensitivity and dysfunctional beliefs; and
(c) metacognitive beliefs would moderate the relationship between anx-
iety sensitivity and health anxiety.
2. Methods
2.1. Participants
Study participants comprised 342 subjects living in Central Italy
urban and suburban areas who responded to advertisements requesting
healthy volunteers for psychological studies. Inclusion criteria included
being older than 18 and the individual's consent to participate in the
research.
The mean age of participants was 37.69 (SD = 12.20) years, with a
range of 18–80, and 61.4% were females. 59.1% of the participants had
a medium level of education (12–13 years, high school degree), 26.3%
had a high level (16 or more years, bachelor's degree or Ph.D.) and the
remaining 14.6% had a low level (eight or less years, primary or second-
ary school license). Most of the participants were employed (69.6%),
18.1% were undergraduate university students, and the remaining
12.3% were homemakers, unemployed, or retired. Participants were
most likely to be married or cohabiting (49.7%), 43.0% were single,
5.0% were divorced, and 2.3% were widows or widowers.
2.2. Measures
2.2.1. Health Anxiety Questionnaire
(HAQ; Lucock & Morley, 1996). This is 21-item self-report measure
that assesses the severity of health anxiety. It consists of four subscales,
which measure health worry and preoccupation, fear of illness and
death, reassurance-seeking behavior and interference with life. The
original version of the HAQ has shown good psychometric properties,
and its Italian version (Melli, Coradeschi, & Smurra, 2007) has shown
adequate internal consistency (αN.77 for all subscales), temporal sta-
bility (r= .89) and construct validity. Given that we were interested
in assessing the global severity of health anxiety, in the present study
only the total score was computed.
2.2.2. Beck Depression Inventory-II
(BDI-II; Beck, Steer, & Brown, 1996). This 21-item self-report instru-
ment is used to assess depressive symptoms over the previous two
weeks. Response choices are scored from 0 (‘absent’)to3(‘severe’).
The BDI-II has shown good psychometric properties, and the Italian ver-
sion of the BDI-II (Sica & Ghisi, 2007) has been shown to have adequate
internal consistency (αsin the range .80–.87), test-retest reliability
(r = .76), and construct validity.
2.2.3. Beck Anxiety Inventory
(BAI; Beck & Steer, 1990). This is a 21-item self-report inventory that
assesses the severity of state anxiety. Statement choices are scored from
0(‘not at all’)to3(‘severely’). The original version has shown good psy-
chometric properties, and its Italian version has shown good internal
consistency (αN.80), adequate test-retest reliability (rN.62), and
good construct validity (Sica & Ghisi, 2007).
2.2.4. Anxiety Sensitivity Index-3
(ASI-3; Taylor et al., 2007). This is an 18-item self-report inventory
that assesses the degree to which the individual fears the potential neg-
ative consequences of anxiety-related symptoms and sensations
(e.g., “It scares me when my heartbeats fast”). The ASI-3 consists of a
single higher-order factor and three lower order factors (physical con-
cerns, cognitive concerns, social concerns). Statement choices are
scored from 0 (‘very little’)to4(‘very much’). The ASI-3 has shown
81G. Melli et al. / Personality and Individual Differences 89 (2016) 80–85
good psychometric properties, and its Italian version has shown good
internal consistency (αN.77 for all subscales), adequate temporal sta-
bility (ICC N.75 for all scales), and good construct validity (Pozza &
Dèttore, 2015). In the present study only the total score was computed.
2.2.5. Health Cognition Questionnaire
(HCQ; Hadjistavropoulos et al., 2012). This is a 21-item self-report
measure that assesses Salkovskis and Warwick's (2001) four health-
related dysfunctional beliefs using a 5-point scale from 1 (‘strongly dis-
agree’)to5(‘strongly agree’). The four HCQ scales are as follows: Like-
lihood of Illness; Awfulness of Illness; Difficulty Coping; and Medical
Services Inadequacy. The original version of the HCQ has shown good
psychometric properties, and its Italian version (Melli, Gelli, &
Carraresi, 2015) has shown good internal consistency (αN.82 for all
subscales), adequate temporal stability (rN.65 for all scales), and
good construct validity.
2.2.6. Metacognition about Health Anxiety
(MCHA; Bouman & Meijer, 1999). This is a 27-item self-report mea-
sure that assesses several components of metacognition using a 4-point
scale from 1 (‘do not disagree’)to4(‘agree very much’). The five MCHA
scales are as follows: Uncontrollability and interference of illness
thoughts (12 items); Cognitive self-consciousness (4 items); Responsi-
bility (3 items); Negative consequences (5 items); and Positive beliefs
(3 items). The original version of the MCHA has shown adequate psy-
chometric properties, although internal consistency of all the subscales
(.62 bαb.74) but the first (.93) were only moderate probably due to
their moderate length. We used the revised Italian version of the
MCHA, that improved its psychometric properties and consisted of 18
items and four subscales (the same as the original version except for
the Responsibility) - as emerged by a series of exploratory and confir-
matory factor analyses; it has shown adequate internal consistency
(αN.76 for all subscales), temporal stability (rN.69 for all scales), and
construct validity (Melli et al., 2015).
2.3. Procedures
All participants volunteered to take part in the study after being
presented with a detailed description of the procedure and signing a
written informed consent form. The measures were administered in a
counterbalanced fashion to control for order and sequence effects, and
batteries took between 15 and 25 min to complete. No external incen-
tives were offered for participating in this study.
2.4. Statistical analysis
To test the hypotheses about the relationships between metacogni-
tion and health anxiety symptoms, Pearson zero-order correlations be-
tween the HAQ, the HCQ, and MCHA subscales were examined. A
hierarchical regression analysis was then conducted to test the robust-
ness of these associations and determine whether metacognitive beliefs
contribute to the prediction of health anxiety symptoms above and be-
yond depression, general anxiety, anxiety sensitivity and dysfunctional
beliefs. In the first step of the regression BDI-II and BAI scores were en-
tered in order to control for confounding variables. In the second step
ASI-3 totalscore was entered to examine whether anxiety sensitivity in-
creased the proportion of explained variance. In the third step the four
subscales of the HCQ were entered to explore their role in predicting
health anxiety symptoms. In the fourth step the four subscales of the
MCHA were entered to test the hypothesis that metacognitive beliefs
had a predictive role above and beyond all the other variables. Finally,
the PROCESS program (Hayes, 2013) was used to test for moderation ef-
fect of metacognitive beliefs in the relationship between anxiety sensi-
tivity and health anxiety. A significance test of the indirect effect was
performed through a bootstrapping procedure performed using 5000
re-samples and a 95% bias corrected and accelerated confidence interval
(BCa-CI) was calculated.
3. Results
3.1. Descriptive statistics
Mean scores, standard deviations, ranges, skewness, kurtosis and
Cronbach's alphas for each measure are presented in Table 1.Thesam-
ple mean scores on all measures fell within the normal range reported
in other Italian clinical samples (e.g., Melli et al., 2007; Sica & Ghisi,
2007). Internal consistency estimatesfor all measures were good or bet-
ter (Cronbach's alpha N.76). None of the indices of univariate skewness
and kurtosis were sufficiently high so as to preclude the planned analy-
ses (Tabachnick & Fidell, 2013).
3.2. Zero-order correlations
As shown in Table 1, there was a large correlation between HAQ and
ASI-3 (r= .62) and between the HAQ and the MCHA-Uncontrollability
and interference of illness thoughts (MCHA-U; r= .50), while only a
moderate correlation between the HAQ and the HCQ-Likelihood of Ill-
ness (HCQ-L; r= .36). In particular, the association between HAQ and
MCHA-U was significantly (Z=2.54;pb.01) greater than that between
HAQ and HCQ-L. All the correlations between HAQ and other subscales
of HCQ and MCHA, although significant, were low.
3.3. Hierarchical linear regression analysis
Variance inflation factor (VIF) was calculated for the predictors and
waswithinnormalrangesforallofthem(VIF=1.23–1.96), indicating
that multicollinearity was not a problem (Menard, 1995). Further ex-
amination of the data also indicated that the assumptions of linearity
and homoscedasticity were met.
Results of the regression analysis are presented in Table 2.Inthefirst
step of the regression analysis predicting HAQ, the BDI-II and BAI scores
explained a significant proportion of variance (R
2
= .23; pb.001). In the
second step, adding the ASI-3 total score, the variance explained signif-
icantly increased (R
2
change = .19; pb.001) and anxiety sensitivity
emerged as an important significant predictor (β= .52; pb.001). In
the third step, adding the HCQ subscale scores, the variance explained
significantly increased (R
2
change = .03; pb.001). In particular, in
this model only the HCQ-L subscale emerged as a significant predictor
(β=.15;pb.001) together with BAI and ASI-3. In the fourth step,
adding the MCHA subscale scores, the variance explained significantly
increased (R
2
change = .05; pb.001). In particular, the MCHA-U
emerged as the most important predictor (β= .25; pb.001) after the
ASI-3, while the HCQ-L showed a very low, although significant,
predicting role (β=.10;pb.05). None of the other MCHA factors
were found to significantly add to the prediction of HAQ scores. The
final model accounted for 50% of the variance and was statistically sig-
nificant (R
2
=.50;pb.001).
3.4. Moderation analysis
As MCHA-U subscale emerged as the only significant predictor of
health anxiety, a moderation model was then tested with the effect of
X (anxiety sensitivity-ASI-3) on Y (health anxiety-HAQ) by M
(metacognitive beliefs about uncontrollability-MCHA-U).
The moderator variable demonstrated a statistically significant in-
teraction effect indicating the presence of moderation (Table 3). Further
inspection of bvalues at the mean and ±1SD indicated that the rela-
tionship between anxiety sensitivity and health anxiety symptoms
strengthens significantly stronger with an increase in metacognitive be-
liefs about uncontrollability.
82 G. Melli et al. / Personality and Individual Differences 89 (2016) 80–85
4. Discussion
The current study examined the role of health anxious metacognitive
beliefs in health anxiety whilst controlling for depression and anxiety,
anxiety sensitivity and dysfunctional beliefs in a non-clinical sample.
As expected, all variables measured had a significant andpositive as-
sociation with health anxiety, which is in line with previous cross-sec-
tional data. Both anxiety sensitivity and metacognitive beliefs about
uncontrollability and interference of illness thoughts, emerged as hav-
ing the strongest association with health anxiety. In line with our prima-
ry hypothesis the metacognitive beliefs about uncontrollability and
interference of illness thoughts had a stronger association than any of
the dysfunctional beliefs, whose association with health anxiety ranged
from low to moderate. This tentatively suggests and is in line with pre-
vious findings that beliefs about thoughts themselves may play a more
important role than the content of thoughtsin health anxiety symptoms
(Bailey & Wells, 2013, 2015). Inconsistent with previous studies (Albert
& Hadjistavropoulos, 2014; Bouman & Meijer, 1999; Hadjistavropoulos
et al., 2012) all the correlations between HAQ and other subscales of
HCQ and MCHA, although significant, were low. In particular, in two
studies (Albert & Hadjistavropoulos, 2014; Hadjistavropoulos et al.,
2012) the HCQ-Difficulty Coping subscale was moderately associated
with health anxiety symptoms, in contrast with our results. This may
be due to the fact that a different measure of health anxiety, i.e. The
Short Health Anxiety Inventory (SHAI: Salkovskis, Rimes, Warwick, &
Clark, 2002) was used in these studies as opposed to the HAQ in ours.
A hierarchical regression analysis was then performed to test the ro-
bustnessof the above reported associations and whether the association
between health anxiety symptoms and metacognitive beliefs would re-
main significant after controlling for anxiety, depression, anxiety sensi-
tivity and health-related dysfunctional cognitive beliefs. As expected,
metacognitive beliefs about uncontrollability and interference of illness
thoughts were predictive of the health anxiety symptoms over-and-
above all other variables. The other subscales of the MCHA were not
found to be significant predictors of health anxiety symptoms. This
may be due to the shortness of these scales (in the Italian version,
each one composed only 3 items, in contrast to the MCHA-U which con-
sists of 9 items) and to the inclusion in the model of the ASI-3 that
accounted for a large proportion of the variance. However the emer-
gence of metacognitive beliefs about uncontrollability and interference
Table 1
Descriptive statistics and correlations among the study measures in the non-clinical sample (N=342).
Measure MSDRange S K α1234567 8 91011
1. HAQ 32.47 8.70 21–73 1.43 3.07 .92
2. BDI-II 8.03 7.20 0–41 1.43 3.06 .88 .40⁎
3. BAI 10.42 9.79 0–49 1.67 2.80 .92 .40⁎.46⁎
4. ASI-3 11.22 9.85 0–53 1.44 2.27 .91 .62⁎.45⁎.45⁎
5. HCQ-L 8.17 3.11 4–19 .36 −.34 .81 .36⁎.19⁎.18⁎.35⁎
6. HCQ-A 13.84 4.07 4–20 −.84 .16 .91 .28⁎.14⁎.07 .34⁎.25⁎
7. HCQ-D 16.28 4.74 6–30 .27 .10 .88 .25⁎.20⁎.06 .34⁎.22⁎.49⁎
8. HCQ-M 8.39 3.06 3–15 .20 −1.03 .87 .16⁎.18⁎.01 .22⁎.19⁎.27⁎.36⁎
9. MCHA-U 14.48 5.10 9–33 1.12 .93 .85 .50⁎.28⁎.28⁎.42⁎.31⁎.26⁎.28⁎.14⁎
10. MCHA-C 6.16 2.02 3–12 .17 −.41 .78 .21⁎.11 .12 .18⁎.10 .02 −.01 −.01 .37⁎
11. MCHA-N 4.80 2.20 3–12 1.38 1.41 .76 .20⁎.12 .02 .23⁎.12 .21⁎.13 .13 .45⁎.24⁎
12. MCHA-P 4.44 1.65 3–12 1.29 1.56 .77 .24⁎.12 .18⁎.21⁎.23⁎.07 −.02 −.01 .41⁎.27⁎.10
Note:M=Mean;SD =Standard deviation;S = Skewness; K = Kurtosis; α= Cronbach's alpha;HAQ = Health AnxietyQuestionnaire;BDI-II = Beck Depression Inventory-II; BAI= Beck
AnxietyInventory; ASI-3 = Anxiety Sensitivity Index-3; HCQ= Health Cognition Questionnaire; HCQ-L= HCQ- Likelihood of Illness; HCQ-A = HCQ-Awfulness of Illness; HCQ-C = HCQ-
Difficulty Coping; HCQ-M = HCQ-MedicalServices Inadequacy; MCHA = Metacognitionabout Health Anxiety; MCHA-U = MCHA-Uncontrollabilityand Interference;MCHA-C = MCHA-
Cognitive Self-consciousness; MCHA-N = MCHA-Negative Consequences; MCHA-P = MCHA-Positive Beliefs.
⁎pb.01
Table 2
Hierarchical regression analysis predicting health anxiety symptoms (HAQ) in the non-
clinical sample (N=342).
R
2
Beta tp
Step 1 .23 b.001
BDI-II .26 4.86 b.001
BAI .29 5.32 b.001
Step 2 .42 b.001
BDI-II .10 2.11 b.05
BAI .12 2.48 b.05
ASI-3 .52 10.58 b.001
Step 3 .45 b.001
BDI-II .10 1.95 n.s.
BAI .13 2.69 b.01
ASI-3 .44 8.28 b.001
HCQ-L .15 3.27 b.001
HCQ-A .06 1.33 n.s.
HCQ-D .03 .53 n.s.
HCQ-M −.02 −.41 n.s.
Step 4 .50 b.001
BDI-II .08 1.71 n.s.
BAI .10 2.02 b.05
ASI-3 .39 7.41 b.001
HCQ-L .10 2.39 b.05
HCQ-A .05 1.10 n.s.
HCQ-C −.01 −.10 n.s.
HCQ-M −.01 −.26 n.s.
MCHA-U .25 4.54 b.001
MCHA-C .03 .65 n.s.
MCHA-N −.04 −.83 n.s.
MCHA-P .01 .07 n.s.
Note. HAQ = Health Anxiety Questionnaire; BDI-II = Beck Depression Inventory-II; BAI =
Beck Anxiety Inventory; ASI-3 = Anxiety Sensitivity Index-3; HCQ = Health Cognition
Questionnaire; HCQ-L = HCQ-Likelihood of Illness; HCQ-A = HCQ-Awfulness of Illness;
HCQ-C = HCQ-Difficulty Coping; HCQ-M = HCQ-Medical Services Inadequacy; MCHA =
Metacognition about Health Anxiety; MCHA-U = MCHA-Uncontrollability and Interference;
MCHA-C = MCHA-Cognitive Self-consciousness; MCHA-N = MCHA-Negative Conse-
quences; MCHA-P = MCHA-Positive Beliefs.
Table 3
Moderation analysis in the non-clinical sample (N=342).
R
2
b(SE) tp95% CI
.47 b.001
Constant 22.657 (1.530) 14.808 b.001 19.648–25.667
MCHA-U .337 (.107) 3.145 .002 .126–.548
ASI-3 .248 (.099) 2.491 .013 .052–.443
Interaction .012 (.005) 2.110 .035 .001–.023
Effect of ASI-3 on HAQ at values of the moderator
MCHA-U Low .358 (.055) 6.455 b.001 .249–.466
MCHA-U Mean .417 (.040) 10.349 b.001 .338–.497
MCHA-U High .477 (.042) 11.293 b.001 .394–.560
Note. SE = Standard Error; CI = Confidence Interval; HAQ = Heal th Anxiety Questionnaire;
ASI-3 = Anxiety Sensitivity Index-3; MCHA-U = MCH A-Metacognitive Beliefs about Uncon-
trollability and Interference of Illness Thoughts;
83G. Melli et al. / Personality and Individual Differences 89 (2016) 80–85
of illness thoughts being the strongest independent predictor of health
anxiety is in line with previous health anxiety research (Bailey &
Wells, 2013; Bouman & Meijer, 1999; Kaur et al., 2011). Furthermore,
it's possible that these beliefs are closer to the pure MCT model, while
the other metacognitive variables measured by the MCHA instrument
are less similar to the variables of the “pure”MCT model. It would be in-
teresting to repeat this study using the metacognition questionnaire by
Cartwright-Hatton and Wells (1997), given that this instrument focus
on the original MCT model.
As well as metacognitive beliefs, anxiety sensitivity emerged as a
strong cross sectional predictor of health anxiety which is consistent
with other studies which have implicated its importance in health anx-
iety (Abramowitz, Olatunji, & Deacon, 2007; Abramowitz, Deacon, &
Valentiner, 2007). Some of the items on the ASI-3 that relate to the
physical aspect of symptoms are similar in content to catastrophic mis-
interpretation of bodily symptoms; for example, “When my stomach is
upset, I worry that I might be seriously ill”. Recent studies (Bailey &
Wells, 2015)haveidentified that such misinterpretation of symptoms
are only problematic when metacognitive beliefs about uncontrollabil-
ity are high. Consistent with this, the present findings indicate that anx-
iety sensitivity may only become particularly problematic in health
anxiety when beliefs about uncontrollability and interference of
thoughts are high, and its relationship with health anxiety is dependent
upon these beliefs.
The current findings are consistent with the metacognitive model
of health anxiety and may have some interesting clinical implica-
tions. Firstly, it could be the case that dysfunctional beliefs may not
be as strongly associated with health anxiety as previously concep-
tualized and therefore challenging these specific beliefs may not be
optimal. As is the case in this study, and if these findings can be rep-
licated, targeting metacognitive beliefs may prove to be a more ad-
vantageous approach to reducing health anxiety related symptoms.
Secondly, the results of the moderation analysis are in line with S-
REF theory and indicate that the relationship between anxiety sensi-
tivity and health anxiety is dependent upon metacognitive beliefs
about uncontrollability. If people's evaluation of physical symptoms
is only problematic when these specific metacognitive beliefs are
high, therapeutic interventions that target metacognitive beliefs
rather than appraisal of symptoms maybe more effective. Consis-
tently, pilot studies have shown that targeting metacognitive beliefs
has produced some positive results (Bailey & Wells, 2014).
Some limitations of the current study need to be pointed out. First,
the present study involved non-clinical subjects. As health anxiety ex-
ists on a continuum, associations between variables in non-clinical sam-
ples may be consistent with what exists in clinical populations.
However, it is recommended that future studies use clinical samples.
Second, participants were self-selected so the sample may not have
been representative of the general population, and the relatively low
sample size may limit the generalizability of our conclusions. Third,
since our study is cross-sectional, the particular temporal order of the
variables can't be defined; alternative orders have not been ruled out,
and it is possible that these relationships are bi-directional in nature.
Longitudinal and experimental data are generally preferred over
cross-sectional data and are essential for drawing conclusions about
changes occurring within the individual over time. Fourth, all data
were derived from self-report measures; relying exclusively on self-
report data tends to inflate associations among variables. Fifth, anxiety
sensitivity, cognitive, and metacognitive beliefs are only some of the
variables that likely contribute to health anxiety symptoms. Future re-
search would benefit from the inclusion of additional potential vulner-
abilities, such as ‘somatosensory amplification’.
Despite these limitations, our results provide further support for
metacognitive beliefs being considered important variables in the con-
ceptualization and treatment of health anxiety. The present study iden-
tifies factors that would benefit from further evaluation and could have
important implications for the treatment of health anxiety symptoms.
References
Abramowitz, J.S., Deacon, B.J., & Valentiner, D.P. (2007). The short health anxiety invento-
ry in an undergraduate sample: Implications for a cognitive behavioral model of hy-
pochondriasis. Cognitive Therapy and Research,31,871–883.
Abramowitz, J.S., Olatunji, B.O.,& Deacon, B.J. (2007). Health anxiety, hypochondriasisand
anxiety dis order. Behavior Therapy,38,86–94.
Albert, N.M., & Hadjistavropoulos, H.D. (2014). Parental illness, attachment dimensions,
and health beliefs: Testing the cognitive-behavioural and interpersonal models of
health anxiety. Anxiety, Stress & Coping: An International Journal,27,216–228.
Bailey, R., & Wells, A. (2013). Does metacognition make a unique contribution to health
anxiety when controlling for neuroticism, illness cognition, and somatosensory am-
plification? Journal of Cognitive Psychotherapy: An Intern ational Quarter ly,27,
327–337.
Bailey, R., & Wells, A. (2014). Metacognitive therapy in the treatment of hypochondriasis:
A systematic case series. Cognitive Therapy and Research,38,541–550.
Bailey, R., & Wells, A. (2015). Metacognitive beliefs moderate the relationship between
catastrophic misinterpretation and health anxiety. Journal of Anxiety Disorders,34,
8–14.
Beck, J.S., & Steer, R.A. (1990). Beck anxiety inventory manual. San Antonio, TX: Psycholog-
ical Corporation.
Beck, J.S.,Steer, R.A., & Brown, G.K. (1996). Manual for the Beck depression inventory-II. San
Antonio,TX: Psychological Corporation.
Bouman, T.K., & Meijer, K.J. (1999). A preliminary study of worry and metacognition in
hypochondriasis. Clinical Psychology and Psychotherapy,6,96–101.
Cartwright-Hatton,S.,&Wells,A.(1997).Beliefs about worry and intrusions: The
metacognitions questionnaire and its correlates. Journal of Anx iety Disorders,11,
279–296.
Deacon, B.J., & Abramowitz, J.S. (2008). Is hypochondriasis related to obsessive–compul-
sive disorder, panic disorder or both? An empirical evaluation. Journal of Cognitive
Psychotherapy,22,115–127.
Eilenberg, T., Fink, P.,Jensen, J.S., Rief,W., & Frostholm, L. (2015). Acceptance and commit-
ment group therapy (ACT-G) for health anxiety: A randomized co ntrolled trial.
Psychological Medicine,1-13.
Fergus, T.A. (2013). Repetitive thought and health anxiety: Tests of specificity. Journal of
Psychopathology and Behavioral Assessment,35,366–374.
Fergus, T.A. (2014). Health related dysfunctional beliefs and health anxiety: Further evi-
dence of cognitive specificity. Journal of Clinical Psychology,70,248–259.
Ferguson, E. (2009). A taxometric analysis of health anxiety. Psychological Medicine,39,
277–285.
Hadjistavropoulos, H.D., Janzen, J.A., Keheler, M.D., Leclerc, J., Sharpe, D., & Bourgault-
Fagnou, M.D. (2012). Core cognitions related to health anxiety in self reported med-
ical and non-medical samples. Journal of Behavior Medicine,35,167–178.
Hayes, A.F. (2013). An introduction to mediation, moderation, and conditional process anal-
ysis: A regression-based approach. New York, NY: Guilford Press.
Kaur, A., Butow, P.N., & Thewes, B. (2011). Do metacognitions predict attentional bias in
health anxiety? Cognitive Therapy and Research,35,575–580.
Lucock, M.P., & Morley, S. (1996). The Health Anxiety Questionnaire. British Journal of
Health Psychology,1,137–150.
Marcus, D.K ., Hughes, K .T., & Arnau, R.C. (2008). Health anxiety, rumination, and negative
affect: A mediational analysis. Journal of Psychosomatic Research,64,495–501.
McManus, F., Surawy, C., Muse, K., Vazquez-Montes, M., & Williams, J.M.G. (2012). Aran-
domized clinical trialof mindfulness-basedcognitive therapyversus unrestrictedser-
vices for health anxie ty (hypochondriasis). Journal of Consulting and C linical
Psychology,80(5), 817.
Melli, G., Coradeschi, D., & Smurra, R. (2007). The Italian version of Health Anxiety Ques-
tionnaire: reliability and factorial analysis. Psicoterapia Cognitiva e Comportamentale,
13,37–48.
Melli, G., Gelli, S., & Carraresi, C. (2015). The Italian versions of the Health Cognition Ques-
tionnaire (HCQ) and the Metacognition about Health Anxiety (MCHA): Psychometric
properties and factor structures. (submitted for publication).
Menard, S. (1995). Applied logistic regression analysis. Sage University paper series on
quantitative applications in the social sciences, 07–106. Thousands Oaks, CA: Sage.
Normann, N., van Emmerik, A.A.P., & Morina, N. (2014). The efficacy of metacognitive
therapy for anxiety and depression: A meta-analytic review. Depression and Anx iety,
31,402–411.
Norton, P.J., Sexton, K.A., Walker,J.R., & Norton, G.R. (2005). Hierarchic al model of vulner-
abilities to anxiety: Replication and extension with a clinical sample. Cognitive
Behaviour Therapy,34,50–63.
Olatunji, B.O., Wolitzly-Taylor, K.B., Elwood, L., Connolly, K., Gonzales, & B., Amstrong, T.
(2009). Anxiety sensitivity and health anxiety in a nonclinical sample: Specificity
and prospective relations with clinical stress. Cognitive Therapy and Research, 33,
416–424.
Pozza, D., & Dèttore, D. (2015). Psychometric evidence of the Italian Anxiety Sensitivity
Index-3 (ASI-3 ) in patients with anxiety disorders and psycho sis. Research in
Psychology and Behavioral Sciences,1,7–17.
Salkovskis, P.M., & Warwick, H.C. (2001). Making sense of hypochondriasis: A cognitive
model in health anxiety. In G.J.G. Asmundson, S. Taylor, & B.J. Cox (Eds.), Health
anxiety: Clinical and research perspectives on hypochondriasis and related disorders
(pp. 46–64). London, UK: John Wiley and Sons Ltd.
Salkovskis, P.M., Rimes, K.A., Warwick, H.M.C., & Clark, D.M. (2002). The Health Anxiety
Inventory: Development and validation of scales for the measurement of health anx-
iety and hypochondriasis. Psychological Medicine,32,843–853.
Sexton, K.A., Norton, P. J., Walker, J.R., & Norton, G.R. (2003). A hierar chical model of
generalized and specific vulnerabilities in anxiety. Cognitive Behaviour Therapy,32,
1–13.
84 G. Melli et al. / Personality and Individual Differences 89 (2016) 80–85
Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the
Beck Depression Inventory-II: Psychometric properties and discriminant power. In
M.A. Lange (Ed.), Leading-edge psychological tests and testing research (pp. 27–50).
Hauppauge, New York: NOVA Publishers.
Spada, M.M., Caselli, G., Nikčević, A.V., & Wells, A. (2015). Metacognition in addictive be-
haviors. Addictive Behaviors,44,9–15.
Tabachnick, B.G., & Fidell, L.S. (2013). Using multivariate statistics (6th ed.). Boston, MA:
Allyn and Bacon.
Taylor, S., Zvolensky, M., Cox, B., Deacon, B., Heimberg, R., Ledley, D.R., et al. (2007). Ro-
bust dimensions of anxietysensitivity: Development and initial validationof the Anx-
iety Sensitivity Index-3 (ASI-3). Psychological Assessment,19,176–188.
van der Heiden, C., Muris,P., & van der Molen, H.T. (2012). Randomized controlled trial on
the effectiveness of metacognitive therapy and intolerance-of-uncertainty therapy
for generalized anxi ety disorder. Behavi our Research and Ther apy,50,100–109.
Watt, M.C.,& Stewart, S.H. (2000). Anxiety sensitivity mediates the relationship between
childhood learning experiences and elevated hypochondriacal concerns in young
adulthood. Journal of Psychosomatic Research,49,107–118.
Wells, A. (2009). Metacognitive therapy for anxiety and depression. New York, NY: Guilford
Press.
Wells, A., & Cartwright-Hatton, S. (2004). A short form of the metacognitions question-
naire: Properties of the MCQ-30. Behaviour Research and Therapy,42,385–396.
Wells, A., & Matthews, G. (1994). Attention and emotion: A clinical perspective. Hove,
England: Lawrence Erlbaum Associates.
Yilmaz, A.E., Gencoz, T., & Wells, A. (2015). Unique contributions of metacognition and
cognition to depressive symptoms. The Journal of General Psychology,142,23–33.
85G. Melli et al. / Personality and Individual Differences 89 (2016) 80–85
