Adaptation and validation of the Health Anxiety Inventory (short version) for medical settings

Abstract

The objectives of study 1 were to use expert opinion to identify the limitations of the Health Anxiety Inventory – Short Version (SHAI) for administration in medical populations and to develop an adapted version for medical populations. The objective of the second study was to evaluate the psychometric properties of the adapted measure. A three-round Delphi study method was used in study 1. Eight experts with experience of living with a physical health condition and four experts by profession working within physical health were invited to review the SHAI. Study 2 employed a cross-sectional mixed methods questionnaire design. Individuals with multiple sclerosis ( n =115), myalgic encephalomyelitis/chronic fatigue syndrome ( n =84) and chronic pain ( n =116) were invited to complete a battery of questionnaires via an online survey. The adapted version of the SHAI for Medical Populations (HAI-M) consisted of 12 items scoring from 0 to 3, reaching high consensus (75% agreement) for administration in medical populations. All groups rated the HAI-M as more acceptable than the SHAI and no significant differences were found on HAI-M scores between clinical groups. The HAI-M demonstrated high internal consistency (.875), good test–retest reliability (.812) and convergent validity (.801). Divergent validity was also acceptable (.515). This study provides preliminary evidence for a psychometrically sound health anxiety screening tool for use in medical populations.

Key learning aims
(1) To gain insights into the presentation of health anxiety in medical conditions.
(2) To consider the validity and reliability of using questionnaire measures developed using analogue sample norms, and how this may affect measurement when used in different context and settings.
(3) The process of systematically adapting, developing and testing standardised measures for use in special subgroups.

Full text

ORIGINAL RESEARCH
Adaptation and validation of the Health Anxiety
Inventory (short version) for medical settings
Jessica Colenutt and Jo Daniels
Department of Psychology, University of Bath, Bath, UK
Corresponding author: Jo Daniels; Email: j.daniels@bath.ac.uk
(Received 1 February 2024; revised 23 September 2024; accepted 6 November 2024)
Abstract
The objectives of study 1 were to use expert opinion to identify the limitations of the Health Anxiety
Inventory –Short Version (SHAI) for administration in medical populations and to develop an adapted
version for medical populations. The objective of the second study was to evaluate the psychometric
properties of the adapted measure. A three-round Delphi study method was used in study 1. Eight experts
with experience of living with a physical health condition and four experts by profession working within
physical health were invited to review the SHAI. Study 2 employed a cross-sectional mixed methods
questionnaire design. Individuals with multiple sclerosis (n=115), myalgic encephalomyelitis/chronic
fatigue syndrome (n=84) and chronic pain (n=116) were invited to complete a battery of questionnaires
via an online survey. The adapted version of the SHAI for Medical Populations (HAI-M) consisted of
12 items scoring from 0 to 3, reaching high consensus (75% agreement) for administration in medical
populations. All groups rated the HAI-M as more acceptable than the SHAI and no significant differences
were found on HAI-M scores between clinical groups. The HAI-M demonstrated high internal consistency
(.875), good test–retest reliability (.812) and convergent validity (.801). Divergent validity was also
acceptable (.515). This study provides preliminary evidence for a psychometrically sound health anxiety
screening tool for use in medical populations.
Key learning aims
(1) To gain insights into the presentation of health anxiety in medical conditions.
(2) To consider the validity and reliability of using questionnaire measures developed using analogue
sample norms, and how this may affect measurement when used in different context and settings.
(3) The process of systematically adapting, developing and testing standardised measures for use in
special subgroups.
Keywords: Delphi study; health anxiety; Health Anxiety Inventory; questionnaire development
Introduction
Health anxiety is characterised by a person’s pre-occupation with the belief that they have a
serious illness due to an enduring tendency to misinterpret ambiguous bodily sensations as
sinister symptoms (Abramowitz et al., 2007b). Health anxiety can be defined in people with
physical health conditions (PwPHC) as anxiety about a physical health condition or symptoms
that exceeds what might be seen in others with the same symptoms or condition (Janzen Claude
et al., 2014), causing clinical levels of distress or impairment in social, occupational or other
© The Author(s), 2025. Published by Cambridge University Press on behalf of British Association for Behavioural and Cognitive
Psychotherapies. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://
creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is
properly cited.
The Cognitive Behaviour Therapist (2025), vol. 18, e8, page 1 of 23
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important areas of functioning (Salkovskis et al., 2003). However, some argue elevated anxiety is a
realistic and proportionate response to the presence of severe or life-threatening physical
symptoms (Herschbach et al., 2010; Melchior et al., 2013; Mehnert et al., 2009).
Health anxiety can be conceptualised to occur along a continuum (Asmundson et al., 2010;
Taylor and Asmundson, 2004) ranging from the absence of anxiety about health, to severe levels
that present as clinical health anxiety (Asmundson and Fergus, 2019). At the lower end of the
scale, health related anxiety can also be protective and adaptive, helping to mobilise individuals to
take action to improve health or avoid potential health stressors (Ștefan et al., 2021); at the upper
end, behaviours originally designed to prevent or protect from illness or illness progression
become excessive, and thoughts of illness become a pre-occupation. These are core elements of the
cognitive behavioural model of health anxiety (Salkovskis et al., 2003), explaining the cycle of
persistent health anxiety and health concerns that are not assuaged by medical reassurance
(Hoffman et al., 2019). The multi-centre study of Tyrer et al.(2011) reported the incidence of
health anxiety in PwPHC to be higher than in the general population, ranging from 17.5% in
endocrinology clinics to almost 25% in neurology clinics. Health anxiety appears more common
when disease pathology is unknown, the nature of the condition is heterogeneous, or there is
uncertainty around illness progression. Examples of this include multiple sclerosis (25%; Kehler
and Hadjistavropoulos, 2009), myalgic encephalomyelitis/chronic fatigue syndrome (42.4%;
Daniels et al., 2020) and chronic pain (51.5%; Rode et al., 2006) which exceed the range of
incidence of Tyrer et al.(2011).
Current health anxiety screening questionnaires such as the Health Anxiety Inventory (HAI;
Salkovskis et al., 2002) were developed based on the criteria for ‘hypochondriasis’and use
outdated terminology (American Psychiatric Association, 2000) with some reports that
individuals with medical conditions find the language and inferences unacceptable (Daniels
et al., 2017; Fanous et al., 2020). Given the controversy of the term ‘hypochondriasis’and
movement towards the new diagnostic criteria of illness anxiety and somatic symptom disorder
(American Psychiatric Association, 2013) and more anxiety based conceptual models that are
empirically well supported and evidence based (Cooper et al., 2017), a review of the utility of
current measurements of health anxiety is warranted.
Symptom-specific outcome measures are commonly used to screen for health anxiety in
PwPHC to accommodate for ‘normal’responses to living with physical symptoms; for example,
the Falls Efficacy Scale in Parkinson’s disease (Yardley et al., 2005), the Hypoglycaemia Fear
Survey in diabetes (Gonder-Frederick et al., 2011) and the Fear of Cancer Recurrence Inventory in
cancer (Simard and Savard, 2009). Such measures are limited to exploring anxiety related to
existing specific physical symptoms and often lack normative data, making it difficult to
distinguish when anxiety may be conceptualised as excessive or disproportionate. The HAI and
Short Version HAI (SHAI; Salkovskis et al., 2002) and Health Anxiety Questionnaire (HAQ;
Lucock and Morley, 1996) were developed specifically to screen for health anxiety. The SHAI and
HAQ have not been directly compared; however, the SHAI has the strongest evidence base
consistently demonstrating high convergent validity, construct validity, internal consistency and
sensitivity to treatment across clinical and non-clinical samples (Abramowitz et al., 2007a; Alberts
et al., 2011; Salkovskis et al., 2002). When compared with the Illness Attitude Scale (IAS) and the
Whiteley Index (WI), the HAI performed similarly, but was preferred for use in clinical settings.
More recently, Alberts et al.(2013) conducted a systematic review and meta-analysis of the
psychometric properties of the SHAI, providing further evidence that the SHAI demonstrated
high internal consistency and strong validity across all core dimensions of measurement, in both
clinical, non-clinical and medical examples.
It also has a growing evidence base for reliable use in medical populations (Alberts et al.,2011;
Daniels et al., 2017; Donkin et al., 2006; Kehler and Hadjistavropoulos, 2009; Rode et al., 2006),
and therefore shows the most promise for assessing health anxiety in PwPHC.
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However, distinguishing a ‘normal’response from a ‘pathological’response associated with
distress remains controversial (Lebel et al., 2020). Some studies have proposed higher cut-off
scores are needed for PwPHC (Rode et al., 2006; Seivewright et al., 2004), as those with medical
complaints are likely to be more aware of aches/pain in their body all of the time (item 2) or feel
afraid of having a serious illness (item 5), resulting in elevated scores. Evidence shows that
individuals with physical health conditions report significantly higher levels of health anxiety
compared with those without such conditions (Daniels et al., 2020; Rode et al., 2006; Tyrer et al.,
2011), and so may be more likely to perceive somatic sensations and perceive them as ‘dangerous’
(i.e. signs of worsening/something new and problematic). This means that those with health
conditions may be more prone to achieving elevated cut-off scores.
Different response patterns are likely to be elicited across PwPHC depending on the symptoms
they experience, leading to differential item functioning and variable sample means across
conditions (LeBouthillier et al., 2015). This makes it difficult to calculate appropriate cut-off scores
generalisable to all medical populations. Kehler and Hadjistavropoulos (2009) found participants
with multiple sclerosis (MS) scored approximately one standard deviation above age-matched
controls when ‘other than Multiple Sclerosis (MS)’was added to items of the SHAI, suggesting a
higher incidence of health anxiety in MS as opposed to only elevated scores due to physical
symptoms. Unlike symptom-specific measures, adaptation of the SHAI by adding ‘other than’[the
symptoms of my physical health condition] does not allow for exploring anxiety in relation to
existing physical symptoms (in this case MS) and therefore excludes the possibility of screening
for clinical levels of health anxiety related to MS, compared with others that may be managing the
same symptoms. It is key that a health anxiety screening tool spans both illness anxiety and
somatic symptom disorder, reflecting the discussed movement towards updated diagnostic
classification systems, and more specifically, is able to capture health anxiety that is
notwithstanding a medical condition. Crucially, Kehler and Hadjistavropoulos (2009) found
over a third of those that scored within the clinical range for health anxiety did not score within
this range on a measure of generalised anxiety, emphasising that health anxiety is a distinct
phenomenon and should be screened for separately. This has also been found in myalgic
encephalomyelitis/chronic fatigue syndrome (ME/CFS) research (Daniels et al., 2017; Daniels
et al., 2020).
A recent study reported a number of barriers to clinicians using the SHAI with patients
experiencing ME/CFS (Fanous et al., 2020): firstly it unintentionally discredits concerns that
patients have about their physical health and questions the authenticity of symptoms; secondly
individuals felt judged as ‘hypochondriacs’; and thirdly the phrasing of the questionnaire was an
obstacle to engagement. Similar barriers were found by studies exploring patient experiences of
completing the SHAI in ME/CFS and chronic pain: patients reported items were inappropriate
and did not give their symptoms credence (Parker et al., 2023), items were associated with
negative connotations, perceived stigma and evoked strong emotions (Fanous et al., 2020), and
some patients felt completely delegitimised (Daniels et al., 2017). The ‘inappropriate’,
‘invalidating’and ‘outdated’language may be seen to locate the responsibility of distress
within the individual or to categorise a patient’s effort to manage their symptoms as ‘weak’
(Fanous et al., 2020); for example, participants commented ‘it’s saying that their illness might be in
their mind’(p. 6) and ‘it insinuates I am weak in character’(p. 10).
The reported incidence of health anxiety in PwPHC is high, suggesting a pressing clinical need
for a more appropriate health anxiety screening questionnaire, spanning both illness anxiety and
somatic symptom disorder to be developed, and for individuals to be offered psychological
intervention. Cognitive behavioural therapy for health anxiety (CBTHA) is effective in reducing
health anxiety in PwPHC and can be superior to existing treatment protocols (Cooper et al., 2017;
Daniels and Loades, 2017; Daniels et al., 2018; Tyrer et al., 2014; Tyrer et al., 2021). Measures of
symptom-specific or generalised anxiety are not sufficient to screen for the multi-faceted aspects
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of health anxiety or to identify individuals who may benefit from intervention, as these have been
established as separate, but related constructs (Daniels et al., 2017).
This study sought to adapt the SHAI for medical populations using a two-stage sequential
design: firstly, eliciting empirical data on the utility of the measure and developing an adapted
version of the SHAI for medical populations through an iterative process based on this empirical
data; secondly, evaluating the psychometric properties of the adapted measure in conditions
where a heightened incidence of health anxiety and clinical need has been identified: MS, ME/CFS
and chronic pain.
Study 1: Adaptation of the Health Anxiety Inventory –Short Version (SHAI)
The aim of study 1 was to adapt the 18-item SHAI in order to improve its acceptability for
administration in medical populations.
Method
Design
A multi-stage iterative Delphi approach was used in order to produce consensus agreement on the
barriers to completion of the SHAI. The Delphi method is used to produce statistically reliable and
valid responses (Fowles, 1978), and is appropriate when there is discrepancy within the literature
or incomplete knowledge of a topic (Trevelyan and Robinson, 2015), such as this area.
The Delphi study closely followed protocols described by Daniels (2017) and Hsu and Sandford
(2007). Three rounds were used to reach an evidence-based consensus for which adaptation of the
SHAI could be based upon. This number of rounds is considered optimal (Trevelyan and
Robinson, 2015) and was decided a priori. One of the 12 participants was unable to attend round 1
due to technological difficulties (91.6% response rate) and all participants participated in rounds 2
and 3 (100% response rate).
Different methods have been used to measure consensus, with 67% agreement often used to
indicate an evidence-based consensus on dichotomous scales (Heiko, 2012). Due to the lack of
unified agreement, a more stringent value of 75% agreement was set for indicating consensus on
dichotomous scales in this study. This value has been used in other studies using the Delphi
method (Navarrete-Dechent et al., 2020; Nieuwenhuys et al., 2016). The criterion set for
indicating consensus on Likert scales in Delphi studies has varied between 51 and 80% agreement
(Green, 1982; Loughlin and Moore, 1979; Putnam et al., 1995; Seagle and Iverson, 2002). A rate of
70% expert agreement rating 3 or higher and the median of 3.25 or higher on a 4-point Likert scale
was used in this study (Green, 1982).
Round 1
The first round consisted of two 1-hour online focus groups, facilitated on Microsoft Teams
software. Each focus group included a mixture of experts by profession and by experience. The
SHAI was sent to participants electronically a week prior to familiarise with the questionnaire.
Experts were interviewed with cognitive interviewing scripted verbal probes (Willis, 2005) as this
method pays explicit attention to the cognitive processes respondents use to answer questionnaire
items, with scripted probes particularly recommended for questionnaire development groups
(Collins, 2003; Willis, 2005). This allows for the patient experience of completing the SHAI to be
explored in a systematic and standardised way. The groups were audio recorded and data were
transcribed verbatim for thematic analysis.
Thematic analysis followed the protocol described by Braun and Clarke (2006). The researcher
(J.C.) took a critical realist position exploring the reported experiences, meanings and reality of
expert responses whilst acknowledging the broader social context of physical health conditions
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and health services. A deductive approach was used given the researchers’knowledge of relevant
theory. In order to account for bias towards pre-conceived themes, a researcher assistant (M.M.)
also completed a thematic analysis to ensure qualitative trustworthiness (Elliot et al., 1999). Any
differences were discussed before themes were agreed upon.
Round 2
A Qualtrics online survey was sent to the participants inviting them to comment if the themes
accurately reflected their experience of the focus group discussions. Taking into account the
themes identified, the experts were then invited to suggest adaptations, edits and deletions to the
wording of each SHAI item, or to indicate no modifications were needed if the item was deemed
appropriate.
The survey responses from round 2 were synthesised through quantitative content analysis,
following Rourke and Anderson’s(2004) guidelines for scoring and interpreting coding schemes.
Final codes were decided upon by majority consensus (75% of experts endorsing the code); this
criterion for consensus was chosen based on endorsing or not endorsing the code being
dichotomous. The researcher then used code frequencies to formulate item modifications through
an iterative process in consultation with an expert working in the field and a person with lived
experience.
Round 3
A version of the SHAI with the item modifications made in round 2 was sent to the experts via a
second Qualtrics online survey and participants were invited to indicate their level of agreement
with the adaptations made with regard to: how appropriate the modified item is for administration
in medical populations (1 =not appropriate to 4 =completely appropriate), and to what extent the
adaptations made have overcome the barriers to administration discussed in the focus groups
(1 =barriers have not been overcome to 4 =barriers have been overcome). This scale was chosen
following recommendations by Green (1982) allowing for two set criteria to be used to indicate
consensus on this 4-point Likert scale. A 4-point Likert scale is often chosen when measuring
consensus as it excludes the possibility of experts providing neutral responses. Experts were
invited to suggest further adaptations, edits and deletions for items where they lacked agreement.
They were also asked to indicate whether the adapted measure was appropriate for administration
in medical populations on a dichotomous scale (yes/no), with 75% agreement indicating
consensus.
Items that failed to meet Green’s(1982) two criteria for indicating consensus for both rating
scales were deleted as the adaptations made were deemed insufficient to overcome the barriers to
administration identified in the focus groups. Expert feedback was then used to modify the
remaining items in consultation with an expert working in the field and a person with lived
experience. These items were used to produce the Health Anxiety Inventory for Medical
Populations (HAI-M) for pilot testing in study 2.
Participants
Twelve participants were recruited through the University of Bath People with Personal
Experience Committee and through specialist health services. Research suggests 8–15 experts is a
sufficient expert panel number and experts are required to have specific knowledge and experience
of the issue being investigated (Keeney et al., 2011; Rowe and Wright, 2001; Skulmoski et al.,
2007). This selection of both experts by profession and experience allows for a wider range of
perspectives to be explored (Powell, 2003).
All medical groups were represented by at least one ‘expert by experience’who was living with
one of the physical health conditions included within the scope of the study, and one expert by
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profession working within the respective physical health settings. Eight participants were experts
by experience, defined as having a self-reported diagnosed physical health condition and having
experienced symptoms for over 6 months. The final four participants were experts by profession,
defined as professionals working clinically with PwPHC and health anxiety and who have
demonstrated their expertise in peer reviewed publications in the clinical field studied in this
research. The experts by experience worked across ME/CFS, chronic pain and neurorehabilitation
services. Expert demographics can be seen in Table 1.
Results
Round 1
Thematic analysis led to identification of three over-arching themes: (1) overlaps between the
symptoms of a physical health condition and health anxiety, (2) psychological adjustment to living
with a physical health condition and (3) inappropriate wording. High overlap between the themes
identified by the two researchers indicated high qualitative trustworthiness. Table 2displays the
subthemes, codes and example quotations associated with these themes.
Round 2
On consultation, the participants who attended the focus groups indicated the themes accurately
reflected their experience of the discussions (100% agreement). Consensus indicated by over 75%
agreement suggested good face validity of the themes identified. Table 3displays the codes and
code frequencies derived from quantitative content analysis of survey responses and the item
modifications made. The adapted questionnaire following round 2 can be seen in Appendix 1 of
the Supplementary material.
Round 3
Table 4displays the percentage of ratings above three and median ratings for each rating scale to
compare against Green’s two critera (1982) for indicating consensus. Eleven (73.33%) of the
15 items met both criterion on ratings of how appropriate the modified items are for
administration in medical populations and four items (26.67%) met neither criterion. The same
eleven items (73.33%) met both criterion for overcoming barriers to administration and the same
four (26.67%) met neither criterion.
Three of the four items were removed for failing to meet consensus on the two criteria for both
rating scales, as expert feedback suggested no further modifications would be sufficient to
Table 1. Expert panel demographics
Experts by profession (n=4) Experts by experience (n=8)
nPercentage nPercentage
Gender
Male 3 75 1 12.5
Female 1 25 7 87.5
Age (years)
20–29 ——3 37.5
30–39 ——225
40–49 2 50 1 12.5
50–59 2 50 1 12.5
60–69 ——1 12.5
Ethnicity
White British 4 100 8 100
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Table 2. Themes, subthemes, codes and example quotations from focus group discussions
Over-arching theme Subthemes Codes Example quote
(1) Overlaps between the
symptoms of a physical
health condition and
health anxiety
The items do not
account for already
having a physical
health condition
Questions lack understanding of living with a physical
health condition, behaviours are presumed to be
negative responses, questions need to take into account
existing health condition
‘You’re going to be worried as it is a very real possibility so
taking into consideration having an existing condition
I think would probably be useful’(P2)
Noticing aches and
pains is an important
part of monitoring
and managing a
physical health
condition
Benefits of being aware of bodily sensations, limited
understanding of the positive consequences of worry/
anxiety, symptom monitoring is helpful, lack of
understanding that aches and pains are more present
‘I do have aches and pains every day so I do notice them
and that’s not necessarily a problem. Like I’m not
noticing them more than I need to ::: I have to be
aware of some changes as there can be quite dangerous
implications with my medicine so it’s a good thing that
I notice certain symptoms’(P7)
Items are not able to
distinguish
heightened
awareness because
of a physical health
condition and safety
behaviours
maintaining health
anxiety
Lack of accommodation for healthy responses/behaviours,
pathologising ‘normal’worry/anxiety related to
managing a physical health condition, some anxiety is
beneficial
‘It seems to me that you’ve got a choice there between
expressing delusion or expressing anxiety and nothing in
the middle that’s normal ::: the structure of them is
actually just that, each item you have an array of
anxious, an array of levels of anxiety or something that
doesn’t really capture any sense of positive, adaptive,
balanced thinking in the face of somatic symptoms’(P4)
(2) Psychological
adjustment to living
with a physical health
condition
Heightened awareness
reflects an
appropriate response
to a physical health
condition
Experiences of anxiety, responses are contingent on
physical symptoms, anxiety is related to familiarity with
physical symptoms, it is unhelpful to resist thoughts of
illness when adjusting to having a physical health
condition
‘I think there’s a healthy amount of anxiety to have to help
you to be in tune to your body ::: if I was to completely
bury my head in the sand which wouldn’t be possible for
me, but to effectively try and ignore my pain and to push
it away I would end up in a worse state at the end of
that day than if I paced myself and really thought about
it. So I think a little bit of anxiety might be useful and
beneficial’(P8)
The questionnaire is
the inappropriate
during diagnostic
process
Stage of diagnosis can impact responses, time since
diagnosis can impact responses
‘The framing isn’t appropriate. The measure needs to take
into account that people are being investigated for or
living with serious illnesses, or both’(P4)
(3) Inappropriate wording The wording used is
insensitive to the
experience of those
living with physical
health conditions
Inappropriate use of words, insensitive use of words,
unsuitability of word used, questions phrased to imply
illness does not exist, questions encourage anxious
thinking
‘As a service we are concerned that this questionnaire
created a barrier before the assessment even started off’
(P3)
The measure is
confusing to
complete if you have
a diagnosed physical
health condition
Ambiguity around words illness/health, confusion around
wording, the need to clarify if other than existing
condition, use of words is vague
‘It talks about if I have a serious illness and I thought to
myself how do I go about that because I have got a
serious illness so I was a bit puzzled’(P9)
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Table 3. Codes, frequencies and item modifications made following content analysis
Code
Code
frequency Example survey response Item modification
It is unclear if the item is asking about
health concerns in relation to a pre-
existing health condition or an additional
health condition
19 ‘It’s confusing to make sense of what the question is asking.
I wonder whether it could specify whether the questions
are with regards to a diagnosed health condition/illness
as this is likely to influence how a person answers the
questionnaire, or whether there could be a space to
indicate any conditions they may have’
(1) The introduction was modified to clarify questions
are with regard to any health worries, including those
about a diagnosed physical health condition
(2) Space provided to list physical health conditions
The item does not account for already
having a physical health condition
12 ‘This item should be redrafted as many people with a pre-
existing health condition will be more alert to aches and
pains generally as this is part of their day to day
monitoring of their health condition. The insertion of the
words “with a health condition/illness”at the end of
statements A–C would help identify whether someone
with a condition may worry more or less about their
health than someone else their age also with the
condition’
(3) Items 3, 5, 6, 10, 11 and 13 were modified to reflect
already having a physical health condition as well as
screening for fears of an undiagnosed condition (to
span illness anxiety and somatic symptom
disorder)
The item does not account for ‘normal’
responses to living with a physical health
condition
16 ‘This is a tricky one because worrying about my health is
not necessarily a bad thing. The challenge here is
defining what “worry”is and at what point it becomes
problematic. I think the item needs to capture whether
responses are part of adjusting to living with a condition
or whether they have a negative impact’
(4) Item 2 was added to screen for distress related to
worry
(5) Items 4 and 7 were modified to acknowledge
‘normal’behavioural responses to worry
The item is insensitive to the experience of
living with a physical health condition
21 I think that the phrase “serious illness”is potentially
problematic to use in medical settings where patients
may have existing impairments.
Firstly, what is meant by “serious illness”? I think that it
is a very subjective phrase; it could mean very different
things to different patients, and could be a source of
confusion. Secondly, I think that it could be perceived as
offensive to patients who identify as having a “serious
illness”
(6) One item (SHAI item 12) was removed
(7) Items 6, 9, 10, 11, 12 and 13 were modified to
remove wording identified as ‘insensitive’or
‘invalidating’
The item is inappropriate for medical
populations
12 ‘I am not sure about the helpfulness of questions 15–18. For
example, question 16 –this seems a bit insensitive
because lots of serious illnesses cannot be cured, but that
does not mean people cannot live a fulfilling life
alongside a serious illness’
(8) The negative consequences scale (items 15–18) were
removed as consensus indicated these items were
inappropriate
There is a need to assess anxiety directly
relating to existing physical health
conditions
9‘Perhaps one (or more) questions about fear about the
future of the condition/fear of the physical effects. Or if it
is a progressive illness which may affect independence/
dependence on others, possibly also fear of coping
financially/being supported financially’
New item to replace negative consequence scale and
reflect ‘perceived awfulness’
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overcome the barriers identified. One item was further modified taking into account expert
feedback. All other items were deemed appropriate for administration in medical populations. Nine of
the 12 experts answered ‘yes’(75% agreement) when asked if the adapted measure was appropriate for
administration in medical populations, again indicating consensus on a dichotomous scale. Experts
who answered ‘no’commented the adapted measure has not overcome the issue of being ‘too lengthy’,
‘unnecessarily complicated’and could model ‘more positive self-statements as opposed to endorsing
the absence of negative ones’; however, these were individual reactions and therefore further
modification of the measure was not deemed necessary.
The final study one pilot version of the Health Anxiety Inventory for Medical Populations
(HAI-M) consisted of 12 items scoring from 0 to 3 (see Appendix 2 in the Supplementary
material).
Study 2: Validation of the Health Anxiety Inventory for Medical Populations (HAI-M)
The aim of study 2 was to evaluate the psychometric properties of the HAI-M in samples with MS,
ME/CFS and chronic pain.
Method
Design
An online cross-sectional mixed methods questionnaire design was used plus a follow-up one
week later.
Participants and procedure
Snowball sampling was used to recruit participants to test the validity and reliability of the HAI-M
between 10 September 2020 and 4 February 2021, inviting adults with a medically confirmed
Table 4. Ratings of appropriateness for administration in medical populations and overcoming barriers to administration
Appropriateness for administration in
medical populations (1 =not appropriate,
4=completed appropriate)
Overcoming the barriers to administration in
medical populations (1 =barriers have
not been overcome, 4 =barriers have
been overcome)
Item Mean (SD) Median
Ratings
above 3 (%) Mean (SD) Median
Ratings
above 3 (%)
1 4.33 (0.82) 4 78 4.11 (0.74) 4 78
2 4.22 (1.23) 4 89 4.11 (1.2) 4 89
3 4 (1.15) 4 78 4.11 (1.29) 4 78
4ᴿ3.78 (1.4) 3* 67* 3.78 (1.4) 3* 67*
5 4.22 (1.03) 4 78 4.33 (0.67) 4 89
6ᴬ3.67 (1. 15) 3* 67* 3.78 (0.79) 3* 56*
7ᴿ3.78 (1.31) 3* 56* 3.67 (1.15) 3* 56*
8 4.44 (0.68) 4 89 4.11 (0.87) 4 78
9 4.11 (1.1) 4 78 3.78 (1.31) 4 78
10 4.44 (0.68) 4 89 4 (1.25) 4 78
11 4.11 (0.99) 4 78 4.56 (0.68) 4 89
12 4.11 (1. 1) 4 78 4.44 (0.83) 4 78
13 4.44 (0.83) 4 78 4.33 (0.67) 4 89
14 4.22 (1.03) 4 78 4.22 (0.79) 4 78
15ᴿ3.22 (1.69) 3* 56* 3.33 (1.56) 3* 56*
*Below criterion;
ᴿitem deleted following round 3;
ᴬitem adapted following round 3.
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diagnosis of MS, ME/CFS or chronic pain to take part. Individuals meeting the inclusion criteria
participated through clicking on a weblink distributed by social media. Participants provided
informed consent and demographic information before completing a battery of online
standardised self-report questionnaires. Participants were then invited to complete the HAI-M
via a second weblink one week later. Debriefing information was provided to all participants. In
addition to standard questionnaire completion, participants were asked to rate their experience of
completing the SHAI and HAI-M on two Likert scales: how acceptable the wording is (1 =not
acceptable at all to 5 =completely acceptable) and how relevant the wording is (1 =not relevant at
all to 5 =completely relevant) for understanding health concerns.
Of 315 survey responses, 198 completed the whole battery of questionnaires (62.86%) and 78
completed the follow-up questionnaire one week later (24.76%). Chi squared analyses indicated
no significant differences between demographics of people that did and did not complete the
whole battery of questionnaires. Rates of fully completed survey responses were 72.7% in MS,
76.2% in ME/CFS and 45.7% in chronic pain. Imputation of missing data using person means has
been demonstrated to be superior to analysing only complete data in previous studies involving
the SHAI and HADS (Bell et al., 2016; Fergus and Valentiner, 2011), therefore this method of
imputation was used for missing data on two SHAI (1%) and five HADS questionnaires (2%).
The final sample who completed the HAI-M at time 1 included 115 participants with MS
(mean age =45.98, SD =12.86), 84 with ME/CFS (mean age =39.01years, SD =13.84) and 116
with chronic pain (mean age =47.77, SD =15.38). Table 5displays descriptive statistics for the
clinical populations and group differences.
Instruments
The Hospital Anxiety and Depression Scale (HADS) is a standardised 14-item questionnaire for
use in medical populations, made up of two subscales assessing generalised anxiety and
depression (Snaith and Zigmund, 1986). Items are rated from 0 to 3, with a score of 8
indicating clinical symptomology with good sensitivity and specificity on either subscale
(Bjelland et al., 2002). Internal consistency for the anxiety (α=.93) and depression (α=.90)
subscales is commendable, as is test–retest reliability, concurrent validity and discriminant
validity (Bjelland et al., 2002;Hermann,1997). In the study sample, the HADS Cronbach’s
alpha coefficient was high (α=.882) for the total scale, and for anxiety (α=.813) and
depression (α=.848), respectively.
The SHAI is a standardised 18-item questionnaire used to screen for health anxiety. Items are
scored from 0 to 3 and a cut-off score of 18 is used to indicate clinical caseness, with good
internal consistency (α=.88), criterion validity and sensitivity to treatment reported
(Salkovskis et al., 2002). The SHAI demonstrated excellent internal consistency in this sample,
with a Cronbach’s alpha coefficient of .906. The items also have good convergent and
divergent validity in non-clinical and health samples (Alberts et al., 2011; Abramowitz et al.,
2007a). Most studies exploring the factor structure of the 18-item SHAI provide support for a
2-factor model, illness likelihood and negative consequences. Often, the negative
consequences scale is removed and a score of 20 is used as a conservative cut-off in
medical populations (Seivewright et al., 2008;Tyreret al., 2021). A 2-factor model is also
reported for the 14-item version; however, there is a lack of consensus around how the two
factors are labelled. Some studies describe labels of illness likelihood and body vigilance, and
others suggest thought intrusion and fear of illness (LeBouthillier et al., 2015).
The Health Anxiety Inventory for Medical Populations (HAI-M) 12-item measure from study
1 was tested for its validity and reliability in study 2. The use of the HAI-M in this sample
indicated good internal consistency for the total sample (α=.875) and for the respective clinical
populations: MS (α=.855), ME/CFS (α=.877) and chronic pain (α=.887).
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Table 5. Descriptive statistics for individual clinical populations
Clinical population
Group differences
MS (n=115) ME/CFS (n=84) Chronic pain (n=116)
nPercentage nPercentage nPercentage
Gender χ2=13.020, p=<0.05*
Male 15 13 4 4.8 26 22.4
Female 100 87 79 94 88 75.9
Other 0 0 0 0 1 0.9
Prefer not to say 0 0 1 1.2 1 0.9
Ethnicity χ2=.864, p=.207
White British 106 92.2 82 97.6 110 94.8
White Irish 2 1.7 1 1.2 0 0
Black Carib- bean 2 1.7 0 0 0 0
Black African 1 0.9 0 0 2 1.7
American 0 0 1 1.2 0 0
European 0 0 0 0 2 1.7
Asian 4 3.5 0 0 2 1.7
Time since diagnosis (years) χ2=8.864, p=.545
Less than 1 10 8.7 8 9.5 7 6
1–2 8 6.9 8 9.5 6 5.2
2–5 18 15.7 21 25 30 25.9
5–10 25 21.7 18 21.4 30 25.9
More than 10 54 47 29 34.5 43 37
Health rating χ2=15.943, p=<0.05*
Excellent 4 3.5 0 0 0 0
Very good 14 12.2 4 4.8 7 6
Good 32 27.8 28.7 19 22.6 14 12.1 12.1
Fair 33 16.5 22 26.2 14 22.4
Poor 19 11.3 26 31 26 47.4
Missing data 13 13 15.5 55
Pain severity rating χ2=42.181, p=<0.01*
Mild 36 31.3 19 22.6 47.6 5 4.3
Moderate 42 36.5 40 13.1 30 25.9
Severe 15 13 11 16.7 26 22.4
Missing data 22 19.1 14 55 47.4
Fatigue severity rating χ2=28.108, p =.107
Mild 17 14.8 2 2.4 5 4.3
Moderate 34 29.6 36.5 35 41.7 39.3 27 23.3
Severe 42 19.1 33 16.7 29 25
Missing data 22 14 55 47.4
Co-morbid health conditions χ2=44.293, p=<0.01*
Diabetes 1 0.87 1 1.19 3 2.59
Tinnitus 1 —1 1.19 1 0.86
Hypertension 3 0.87 3 3.57 9 7.76
Arthritis 2 2.61 2 2.38 8 6.9
Fibromyalgia 2 —10 11.9 12 10.34
Asthma 1 1.74 10 11.9 4 3.45
Osteoporosis 1 1.74 1 1.19 3 2.59
Crohn’s disease 4 0.87 1 1.19 1 2.59
Epilepsy 4 0.87 1 1.19 2 0.86
Sleep apnoea 3.48 3 3.57 5 4.31
Migraine 3.48 4 4.76 8 6.9
Total 19 16.52 37 44.05 56 48.28
HADS Anxiety Scale χ2=5.459, p=.243
Borderline 24 24.3 21 26.6 15 24.6
Caseness 32 31.3 32 40.5 28 45.9
HADS Depression Scale α2=4.058, p=.398
Borderline 22 21.4 17 21.5 11 18
Caseness 23 22.3 26 32.9 21 34.4
*Significant difference between groups.
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Planned analysis
Statistical analysis was performed using SPSS version 26, with a criterion of p=<.05 set for
significance throughout. Effect sizes were chosen based on those given in the original SHAI
validation study (Salkovskis et al., 2002). The total sample size required was 96 participants in
order to complete correlational analyses and therefore a minimum of 48 participants per
population was required. This was determined through power analysis using G*Power (effect
size =.7, power =.95, αerror probability =.05). The total sample size required was 107
participants in order to compare group differences and therefore a minimum of 54 participants
per population was required. Again, this was determined through power analysis using G*Power
(effect size =.5, power =.95, αerror probability =.05). This study followed recommendations by
Kline (1994) who suggests an absolute minimum of 100 participants per population for factor
analyses is required.
Preliminary analyses
Preliminary analyses using Shapiro–Wilk tests revealed the HAI-M time 1, HAI-M time 2 and
SHAI data were positively skewed (p=<.05), therefore statistical analysis used non-parametric
tests where appropriate. Levene’s tests confirmed homogeneity of variance for all data. Spearman’s
correlations identified no collinearity between the main study variables (r=<.90; see Table 6),
and linear relationships were observed between all variables on scatterplots. Interquartile ranges
were explored using box plots allowing for identification of outliers; however, they were not
removed or transformed as this did not change the outcome of the result profile.
Group differences
Descriptive statistics were used to summarise demographics and scores on the standardised
questionnaires. Given F-statistics are robust against violations of normality (Glass et al., 1972;
Wilcox, 2012), mixed ANOVAs (participant ratings: HAI-M time 1 and SHAI; Clinical
population: MS, ME/CFS and chronic pain) were used to compare participant’s ratings of
acceptability and relevance. One-way analyses of variance (ANOVA) were then used to test
whether there were significant differences between (1) HAI-M time 1 scores and then (2) SHAI
scores for populations with MS, ME/CFS and chronic pain. Post-hoc Hochberg’s GT2 was also
used to account for unequal cell sizes when exploring group differences.
Reliability and validity
Cronbach’s alpha was used to evaluate internal consistency with a value of above .7 being deemed
acceptable. Items were excluded if the measure was found to be more reliable following their
removal. Spearman’s correlations were then used to explore test–retest reliability between scores
on the HAI-M at time 1 and time 2, to explore convergent validity between scores on the HAI-M
time 1 and SHAI, and to test the relationship between scores on the HADS anxiety scale and
HAI-M at time 1 given the skewed data set. The SHAI was used as a comparison for convergent
validity despite identified limitations in medical populations due to the measure’s demonstrated
ability to differentiate health anxiety from other anxiety disorders with high specificity and
sensitivity (Hedman et al., 2015), and in order to ensure the adaptations of the measure do not
diminish the original measure’s demonstrated construct validity (Salkovskis et al., 2002). The
HADS was used to explore divergent validity given its demonstrated sensitivity and specificity for
differentiating generalised anxiety from other anxiety disorders (Bjelland et al., 2002).
12 Jessica Colenutt and Jo Daniels
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Factor analyses
Exploratory factor analyses were used as Salkovskis et al.(2002) did not report eigenvalues, factor
loadings or percentage of variance explained making it difficult to compare against using
confirmatory factor analysis. Principal components analysis and an extraction method of
‘Eigenvalues above 1 retained’was used to determine the factors extracted. Rotation using the
oblique method (direct oblimin) was used due to Salkovskis et al.(2002) reporting a moderate
correlation between the factors of the SHAI, from which the HAI-M is adapted. Parallel analysis
and factor interpretability was also used to determine the number of factors retained within each
clinical population.
Results
Acceptability and relevance
Mean acceptability and relevance ratings are displayed in Table 7. No significant interaction
effects between the clinical populations and participant ratings were found when exploring
acceptability; however, a significant main effect of participant ratings was found (F
1,197
=9.555,
p=<.01). Post-hoc analyses found that participants rated the HAI-M (mean =4.164,SE=.67) as
significantly more acceptable for assessing their health concerns compared with the SHAI
(mean =3.965,SE=.73). A significant main effect of clinical population was also found (F
2,197
=4.228,p=.016) and post-hoc analyses demonstrated that participants with MS (mean =4.295,
SE =.095) rated the questionnaires as significantly more acceptable than participants with ME/
CFS (mean =3.898,SE=.108). There were no significant differences between acceptability ratings
for chronic pain (mean =4.000,SE=.118) and the two other clinical populations.
A significant main effect of clinical population was found when exploring relevance (F
2,197
=4.701,p=.01); however, there was no significant main effect of participant ratings or
interaction effect between the clinical populations and participant ratings. Post-hoc analyses found
that participants with MS (mean =4.084,SE=.101) rated the questionnaires as significantly more
relevant for assessing their health concerns compared with ME/CFS (mean =3.641,SE=.115).
There was no significant difference between relevance ratings for chronic pain (mean =3.745,
SE =.126) and the two other clinical populations.
Group differences
No significant differences were found between the clinical populations for HAI-M scores at time 1
(F
2,240
=2.977,p=.053), confirmed by post-hoc Hochberg’s GT2 analyses (p=.117; see Table 8).
Similarly, no significant differences were found between the clinical populations for SHAI scores
(F
2,197
=2.228,p=.110), also confirmed by post-hoc Hochberg’s GT2 analyses (p=.138).
Table 6. Spearman’s correlations between main study variables
Variable 12 3 4 5
HAI-M time 1 —.738* .801* .615* .460*
HAI-M time 2 —.810* .645* .548*
SHAI —.579* .434*
HADS-A —.546*
HADS-D —
*p=<0.01.
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Reliability
The HAI-M alpha coefficient indicated high internal consistency for the total sample
(α=.875) and clinical populations: MS (α=.855), ME/CFS (α=.877) and chronic pain
(α=.887). All items were retained. Test–retest reliability was good for the total sample
(r=.812,p=<.01) and for clinical populations with ME/CFS (r=.907,p=<.01) and
chronic pain (r=.843,p=<.01). Test–retest reliability was acceptable (>.7) for participants
with MS (r=.736,p=<.01).
Convergent validity
Strong (>.7) positive correlations were found between the HAI-M and SHAI for the total
sample (r=.801, p=<.01) and for participants with MS (r=.796, p=<.01), ME/CFS
(r=.775, p=<.01) and chronic pain (r=.852, p=<.01), supporting the convergent validity
of the HAI-M.
Divergent validity
Moderate (<.6) positive correlations were found between scores on the HADS anxiety scale and
the HAI-M at time 1 for the total sample (r=.515, p=<.01) and for the clinical populations: MS
(r=.472,p=<.01), ME/CFS (r=.539, p=<.01) and chronic pain (r=.557, p=<.01).
Moderate correlations support the HAI-M’s ability to differentiate health anxiety from generalised
anxiety.
Factor analysis
Initial inspection of the correlation matrices confirmed the presence of many correlation
coefficients above .3. The Kaiser–Meyer–Olkin value suggested good sampling adequacy
(KMO =.841 to .851) and Bartlett’s test of sphericity indicated the correlations between items
were sufficient to carry out a factor analysis for all clinical populations (p=<.01). Principal
components analyses with direct oblimin rotation were conducted on the 12-item HAI-M to
explore if items may load differently across the clinical populations: MS (n=103), ME/CFS
(n=79) and chronic pain (n=61). All analyses revealed three factors with eigenvalues exceeding
1, and scree plot inflexion and parallel analysis justified these being retained. The three factors
explained 57.98% of the variance in MS, 62.13% in ME/CFS and 65.17% in chronic pain. The
correlations between the three factors can be found in the Supplementary material.
Table 7. Acceptability and relevance ratings
HAI-M SHAI
Main effectMean Median SE Mean Median SE
Acceptability rating p=<0.01*
Total sample 4.19 4 .060 3.99 4 .073
MS 4.373 5 .102 4.217 5 .111
ME/CFS 4.063 4 .116 3.734 4 .126
Chronic pain 4.057 4 .128 3.943 4 .139
Relevance rating p=.136
Total sample 3.90 4 .065 3.81 4 .073
MS 4.120 4 .110 4.048 4 .112
ME/CFS 3.656 4 .125 3.625 4 .128
Chronic pain 3.830 4 .137 3.660 4 .141
SE, standard error;
*statistically significant difference.
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Table 8. Descriptive statistics for the clinical populations and total sample
MS ME/CFS Chronic pain Total sample
nMean (SD) 95% CI nMean (SD) 95% CI nMean (SD) 95% CI nMean (SD) 95% CI
HAI-M time 1 103 16.86 (5.72) 15.75–17.98 79 18.77 (6.23) 17.38–20.17 61 18.93 (7.11) 17.11–20.76 243 18 (6.31) 17.21–18.8
HAI-M time 2 33 11.79 (6.31) 9.55–14.03 25 14.2 (7.46) 11.12–17.28 20 14.4 (5.61) 11.77–17.03 78 13.23 (6.58) 11.71–14.71
SHAI 83 12.36 (6.53) 10.94–13.79 64 14.73 (6.89) 13.01–16.46 53 13.6 (7.04) 11.66–15.54 200 13.45 (8.83) 12.5–14.4
HADS-A 103 8.54 (4.39) 7.69–9.4 79 9.48 (4.38) 8.5–10.46 61 10.1 (4.91) 8.84–11.35 243 9.24 (4.55) 8.66–9.81
HADS-D 103 6.97 (4.37) 6.12–7.83 79 8.49 (4.35) 7.42–9.47 61 7.79 (5.09) 6.48–9.09 243 7.67 (4.58) 7.09–8.25
CI, confidence interval; SD, standard deviation.
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Eigenvalues, percentage of variance explained and factor loadings following rotation for the
clinical populations can be found in the Supplementary material. Substantial factor loadings were
set at greater than .4 as this value has been used in previous studies exploring the factor structure
of the SHAI (Abramowitz et al., 2007a; Alberts et al., 2011). Factor interpretability suggested items
assessing pre-occupation with thoughts about health load onto factor 1 (items 1, 2, 4, 6, 7 and 12).
Items assessing vigilance to bodily sensations loaded onto factor 2 (items 3, 9 and 11) and items
assessing perceived illness likelihood loaded onto factor 3 (items 5, 8 and 10). Items 5 and 6 loaded
onto two items in one clinical population, and so the highest factor loading was taken into account
when interpreting factors.
The 3-factor model described differs from studies that report a 2-factor model for the 14- and
18-item SHAI (Alberts et al., 2011; Salkovskis et al., 2002; Wheaton et al., 2010). These studies
report either illness likelihood and negative consequences as the two factor labels or thought
intrusion and fear of illness. Other studies have reported a 3-factor model labelled body vigilance,
illness likelihood and illness severity (Abramowitz et al., 2007a; Olatunji, 2009), although the
items load differently onto these factors across studies.
Although the same number of factors were retained across the clinical populations, there
appear to be differences in how the items load onto these factors; for example, items appear to load
most heavily onto factor 1 in MS and chronic pain and less heavily in the sample with ME/CFS. In
contrast, items appear to load most heavily onto factor 3 in ME/CFS. This is consistent with
previous studies that report differences in item scores across factors, for example Alberts et al.
(2011) found participants with MS scored higher on items relating to thought intrusion and fear of
illness than participants without MS.
The three-factor model here also demonstrates construct and content validity as evidenced by
tests of convergent validity and associated strong significant relationship (>.8) between the
HAI-M and the SHAI measure, tests of divergent validity with the HADS and the mapping of the
three factors onto the theoretical construct of the HA model (i.e. pre-occupation, hypervigilance
and perceived illness likelihood) for which the HAI has been designed to measure against.
Discussion
Study 1 describes the systematic development of the HAI-M, a health anxiety screening tool for
use in medical populations which was produced with a high level of agreement. The limitations of
administering the SHAI in medical populations, identified in the first round of the Delphi study,
are similar to those identified in a previous study by Fanous et al.(2020): the wording of the SHAI
is perceived to be inappropriate, lacking in sensitivity and questions the authenticity of symptoms
associated with physical health conditions. The experts in study 1 recommended changes to
phrases such as ‘serious illness’,‘resist’,‘lastingly relieved’and ‘hypochondriac’which were also
identified to be problematic phrases in the studies of Fanous et al.(2020) and Daniels et al.(2017).
Changes made in relation to phrasing were considered acceptable as reflected in the final round of
the Delphi study, with psychometric properties reflecting internal consistency of the measure.
The adapted screening tool was rated to be more acceptable compared with the SHAI,
suggesting this study met its primary aim of developing a more acceptable measure for use in
medical populations whilst also remaining both reliable and valid. Correspondence from
participants raised further concerns around the wording of the SHAI during study 2, with
participants stating the SHAI wording is ‘problematic’and ‘dismissive’. Further comments stated
‘to assume that having images of being ill is a negative thing is really ableist’, that it is ‘concerning
to see common physical symptoms being asked about to ascertain mental health difficulties’and
that the wording suggests a conceived assumption that these conditions are not ‘serious illnesses’.
These words and phrases were adapted or removed in the development of the HAI-M and
therefore such concerns have been addressed. This is reflected in the statistically significant
16 Jessica Colenutt and Jo Daniels
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difference in ratings of acceptability between the HAI-M and SHAI, although an even greater
difference may have been expected given this qualitative information and a rating scale allowing
for a greater spread of data may be useful in future research.
In contrast to studies that report highly variable scores across medical populations using the
SHAI (Alberts et al., 2013; Lebel et al., 2020), the findings of study 2 suggest HAI-M items are not
differentially endorsed across three physical health conditions where this was previously
documented as an issue.
These findings suggest that a pooled cut-off for the HAI-M could be used to indicate ‘clinical’
levels of health anxiety in MS, ME/CFS and chronic pain, signifying a threshold at which
psychological distress associated with the medical problem is likely to be affecting social and
occupational functioning beyond what might be usually be expected in the circumstances.
However, while clinical cut-offs in clinical settings can be used to signal that a fuller
assessment of health concerns may be warranted, it is imperative that the HAI-M, and indeed
all screening tools, are interpreted with caution and in clinical context of the presenting
illness, taking into account relevant factors such as stage and prognosis of illness, and personal
and familial history of the same or similar illnesses. The HAI-M has been adapted to aid the
recognition of the added burden of health anxiety in those with medical problems, aiming to
understand but not pathologise normal human responses to the personal catastrophe that
illnesscanoftenrepresent.
This study differed from previous studies in sampling the general population using an online
questionnaire, as opposed to sampling clinical populations attending medical settings, and it
should be acknowledged that health anxiety is known to be elevated during the process of medical
consultations and investigations, which may explain this difference in findings (Hadjistavropoulos
et al., 1998). Additionally, the global pandemic has had a significant impact on the incidence of
health anxiety (Rettie and Daniels, 2021), with emerging evidence that health anxiety may be a
particular difficulty for PwPHC who have been advised to shield, which could have led to
diminishing group differences across conditions (Chaplin, 2021; Sloan et al., 2020). That being so,
further validation is needed to be sure a pooled cut-off is generalisable across physical health
conditions.
The HAI-M was found to be psychometrically acceptable, and this study reports comparable
statistics to those reported in the SHAI validation study of Salkovskis et al.(2002). The good
convergent validity of the HAI-M conveys that the measure is able to screen for the multi-
faceted phenomenology of health anxiety drawing on cognitive behavioural theory. Good
divergent validity also suggests the HAI-M and HADS are measuring distinct but related
entities. This is consistent with previous research documenting a moderate correlation (.57)
between the SHAI and HADS anxiety scale (Tang et al., 2007a; Tang et al., 2007b). Weaker
correlations (.29–.42) tend to be observed between the SHAI and other measures of
generalised anxiety (Abramowitz et al., 2007a; Olatunji et al., 2007), which may be attributable
to the HADS being developed for medical populations and therefore capturing some aspects of
anxiety related to health.
There is disagreement within the literature of the factor structure of the SHAI (Alberts et al.,
2013). Study 2 suggests a 3-factor model for the HAI-M which bears good face and content
validity when comparing with the main tenets of the health anxiety model (pre-occupation,
symptom hypervigilance, perceived illness likelihood). The item modifications and factor
interpretation take into account changes in diagnostic terminology and are likely to explain some
disparities with previous literature. The factor of vigilance to sensations acknowledges PwPHC are
understandably more likely to be aware or to notice physical symptoms, and items were adapted
with the aim to distinguish ‘normal’reactions from pathological responses associated with
dysfunctional behaviours and distress as discussed by Lebel et al.(2020). The identified factors
share characteristics with those reported in previous studies exploring the SHAI factor structure;
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for example, factors labelled illness likelihood, thought intrusion, fear of illness and body vigilance
(Alberts et al., 2013; Abramowitz et al., 2007a; Salkovskis et al., 2002). The factors were also inter-
correlated as found in previous studies (Abramowitz et al., 2007a; Fergus and Valentiner, 2011). In
accordance with the findings of Alberts et al.(2011), items do appear to load differently onto these
factors across clinical populations, which may reflect heterogeneous expressions of health anxiety
in different physical health conditions. This is an unsurprising finding given the literature
documenting disease-specific cognitions and different physical symptoms of varying degrees and
pervasiveness, and therefore conveys an important rationale for cognitive behavioural
intervention to be informed by item responses on the HAI-M.
The Collaborative Outcomes Resource Network (2007) states that a score of one standard
deviation above the mean can be used to identify clinical significance. Given no significant
differences were found between HAI-M scores across clinical populations, one standard
deviation above the total sample mean in this study would be ≥24. It is currently unclear
whether a pooled cut-off would be clinically meaningful for use across physical health
conditions. Further work is needed to establish this. The use of ‘range’categories as seen in the
original SHAI, HADS and other questionnaires might prove more useful than a definitive
clinical threshold.
Limitations and future research
The two-stage adaptation of the SHAI was based largely on the opinion of white British
participants, therefore the relevance and acceptability of the HAI-M for other cultures is unclear.
Study 2 is also limited in generalisability due to the under-representation of males which may be
important due to differences in behavioural responses between men and women, for example
engaging in more reassurance-seeking and worry (MacSwain et al., 2009). Results should
additionally be considered in light of an additional limitation of study 2; participants were
recruited through social media, which meant that confirmation of their medical diagnoses was not
achieved. The study also took place during the COVID-19 pandemic where studies have reported
a higher incidence of health anxiety in the general population particularly for those who may be
vulnerable due to health conditions (Rettie and Daniels, 2021; Jungmann and Witthöft, 2020). It is
unclear if the scores on the HAI-M could be influenced by this context, and future research may
seek to clarify this and to validate this measure in other physical health conditions. We also
acknowledge that data pertaining to the acceptability of the HAI measure were limited to those
with ME/CFS; however, the issues reported by those with ME/CFS are unlikely to be specific to
this clinical group. When given the opportunity to refine, adapt and edit the HAI for medical
conditions, there was consensus agreement across all clinical groups and professionals that for
purposes of acceptability, the measure should be adapted, where there was an option to retain in
original form. This is also in context of existing criticisms regarding the suitability of the HAI for
this group (Daniels et al., 2017; Daniels et al., 2020; Parker et al., 2023). Finally, there is a need for
further research to provide further validation data and to evaluate the sensitivity and specificity of
a cut-off score when compared with practitioner report using the Structured Clinical Interview for
the DSM (First, 2014).
Conclusion
This study provides preliminary evidence for a more acceptable and a psychometrically robust
health anxiety screening tool for use in medical populations. The HAI-M appears to consist of
more appropriate items for assessing health anxiety in medical populations when compared
with the SHAI, and to demonstrate good reliability and validity. It therefore provides a
18 Jessica Colenutt and Jo Daniels
https://doi.org/10.1017/S1754470X2400045X Published online by Cambridge University Press

solution to the current unmet clinical need and may seek to improve access to therapy for
those with PwPHC.
Key practice points
(1) Rates of health anxiety are reported to be high in medical conditions. Practitioners should consider assessing for
health anxiety in order to gain a more thorough understanding of presenting difficulties and psychological
distress.
(2) When using standardised measures such as the HAI, do consider where questions may be artificially inflated by
virtue of the nature of having a medical problem.
(3) The health anxiety model by Salkovskis and colleagues can be used as a transdiagnostic model for health anxiety,
and can easily be used to work with health anxiety occurring alongside medical problems. See ‘Further reading’
list below for more details.
(4) DO take account of how illness is considered within a person’s culture and familial systems; this is an important
part of a holistic assessment. See ‘Further reading’list for key papers that examine anxiety cross-cultural and
cross cultural models of CBT.
Further reading
Fanous, M., Ryninks, K., & Daniels, J. (2020). What are the barriers to the SHAI being completed within a CFS/ME service?
the Cognitive Behaviour Therapist,13, e52. https://doi.org/10.1017/S1754470X20000525
Hofmann, S. G., & Hinton, D. E. (2014). Cross-cultural aspects of anxiety disorders. Current Psychiatry Reports,16, 450.
https://doi.org/10.1007/s11920-014-0450-3
Naeem, F. (2019). Cultural adaptations of CBT: a summary and discussion of the Special Issue on Cultural Adaptation of CBT.
the Cognitive Behaviour Therapist,12, e40. https://doi.org/10.1017/S1754470X19000278
Salkovskis, P. M., Warwick, H. M., & Deale, A. C. (2003). Cognitive-behavioral treatment for severe and persistent health
anxiety (hypochondriasis). Brief Treatment and Crisis Intervention,3, 353–367. https://doi.org/10.1093/brief-treatment/
mhg026
Tyrer, P., Cooper, S., Crawford, M., Dupont, S., Green, J., Murphy, D., Salkovskis, P., Smith, G., Wang, D., Bhogal, S.,
Keeling, M., Loebenberg, G., Seivewright, R., Walker, G., Cooper, F., Evered, R., Kings, S., Kramo, K., McNulty, A.,
Nagar, J., ::: & Tyrer, H. (2011). Prevalence of health anxiety problems in medical clinics. Journal of Psychosomatic
Research,71, 392–394. https://doi.org/10.1016/j.jpsychores.2011.07.004
Supplementary material. The supplementary material for this article can be found at https://doi.org/10.1017/
S1754470X2400045X
Data availability statement. The data that support the findings of this study are available from the corresponding author,
J.C., upon reasonable request. The data are not publicly available due to them containing information that could compromise
research participant privacy and consent.
Acknowledgements. The study authors would like to extend thanks to all who contributed to this project including
participants. The authors would also like to thank Mary-Jane Marffy for help with data analysis and Rita De Nicola and Chloe
Lee for help in preparing the manuscript.
Author contributions. Jessica Colenutt: Data curation (lead), Formal analysis (lead), Investigation (lead), Methodology
(lead), Resources (lead), Software (lead), Writing - original draft (lead), Writing - review & editing (lead); Jo Daniels:
Conceptualization (lead), Supervision (lead), Writing - review & editing (supporting).
Financial support. This research received no specific grant from any funding agency, commercial or not-for-profit sectors.
Competing interests. The authors declare none.
Ethical standards. Ethical approval was given by the Health Research Authority (reference: 20/YH/0085) and University of
Bath Psychology Research Ethics Committee (reference: 20-036). Any necessary informed consent to participate and for the
results to be published has been obtained. The authors have abided by the Ethical Principles of Psychologists and Code of
Conduct as set out by the BABCP and BPS.
The Cognitive Behaviour Therapist 19
https://doi.org/10.1017/S1754470X2400045X Published online by Cambridge University Press

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Cite this article: Colenutt J and Daniels J (2025). Adaptation and validation of the Health Anxiety Inventory (short version)
for medical settings. The Cognitive Behaviour Therapist.https://doi.org/10.1017/S1754470X2400045X
The Cognitive Behaviour Therapist 23
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