How do elements of a reduced capacity to withstand uncertainty relate to the severity of health anxiety?

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How Do Elements of a Reduced Capacity to Withstand Uncertainty Relate to the Severity of Health Anxiety? a

a

a

Mathew G. Fetzner , Gordon J.G. Asmundson , Cori Carey , a

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Michel A. Thibodeau , Chad Brandt , Michael J. Zvolensky & R. a

Nicholas Carleton a

Department of Psychology, University of Regina, Regina, Saskatchewan, Canada b

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Department of Psychology, University of Houston, Houston, TX 77204-5022, USA Published online: 25 Jun 2014.

To cite this article: Mathew G. Fetzner, Gordon J.G. Asmundson, Cori Carey, Michel A. Thibodeau, Chad Brandt, Michael J. Zvolensky & R. Nicholas Carleton (2014) How Do Elements of a Reduced Capacity to Withstand Uncertainty Relate to the Severity of Health Anxiety?, Cognitive Behaviour Therapy, 43:3, 262-274, DOI: 10.1080/16506073.2014.929170 To link to this article: http://dx.doi.org/10.1080/16506073.2014.929170

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Cognitive Behaviour Therapy, 2014 Vol. 43, No. 3, 262–274, http://dx.doi.org/10.1080/16506073.2014.929170

How Do Elements of a Reduced Capacity to Withstand Uncertainty Relate to the Severity of Health Anxiety? Mathew G. Fetzner1, Gordon J.G. Asmundson1, Cori Carey1, Michel A. Thibodeau1, Chad Brandt2, Michael J. Zvolensky2 and R. Nicholas Carleton1 1

Department of Psychology, University of Regina, Regina, Saskatchewan, Canada; Department of Psychology, University of Houston, Houston, TX 77204-5022, USA


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Abstract. Intolerance of uncertainty (IU)—a multidimensional cognitive vulnerability factor—is associated with a variety of anxiety disorders and health anxiety (HA). To date, few studies have assessed whether IU dimensions (prospective and inhibitory IU) are differentially associated with HA and whether their contributions are independent of anxiety sensitivity (AS). This study addressed these issues using independent community (n ¼ 155; 81% women) and undergraduate (n ¼ 560; 86% women) samples. Results indicated that prospective IU, but not inhibitory IU, had significant positive associations with HA in community dwellers and undergraduate students. AS somatic and cognitive concerns were also significant predictors among both samples. In addition, severity of IU dimensions among individuals reporting elevated HA were compared against individuals diagnosed with generalized anxiety disorder, social anxiety disorder, panic disorder, and obsessive–compulsive disorder. Results indicated minimal differences between those with elevated HA and each of the anxiety disorder diagnoses. Findings lend support to the unique transdiagnostic nature of IU and support commonalities between HA and anxiety disorders. Key words: health anxiety; intolerance of uncertainty; anxiety sensitivity. Received 24 February 2014; Accepted 26 May 2014 Correspondence address: Mathew G. Fetzner, Department of Psychology, University of Regina, Regina, Saskatchewan, Canada. Email: [email protected]

Introduction Intolerance of uncertainty (IU) has recently received burgeoning empirical attention and been conceptualized in a number of different ways (e.g., Holaway, Heimberg, & Coles, 2006; Koerner & Dugas, 2008; McEvoy & Mahoney, 2011); more recently, Carleton (2012) reviewed research and conceptualizations of IU and explained IU as a characteristic resulting from negative beliefs about uncertainty and its implications. Originally thought to be specific to generalized anxiety disorder (Buhr & Dugas, 2006; Koerner & Dugas, 2008), increasing evidence indicates that IU appears across anxiety disorders (e.g., social anxiety disorder, Carleton, Collimore, & Asmundson, 2010; post-traumatic stress disorder, Fetzner, Horswill, Boelen, & Carleton, 2013; panic disorder, Carleton, Fetzner, Hackl, McEvoy, 2013; obsessive compulsive q 2014 Swedish Association for Behaviour Therapy

disorder, Tolin, Abramowitz, Brigidi, & Foa, 2003). Importantly, IU comprises two dimensions: cognitive perceptions of threat pertaining to future uncertainty (prospective IU; e.g., being overly concerned with organization in order to avoid disaster) and subjective appraisals of behavioral symptoms indicating apprehension due to uncertainty (inhibitory IU; e.g., being unable to effectively act or function in uncertain situations; Carleton, Norton, & Asmundson, 2007; McEvoy & Mahoney, 2011). Existing work has linked IU total score and subscale scores to a number of anxiety disorders (e.g., Fetzner et al., 2013). Like IU, anxiety sensitivity (AS)—the fear of anxiety-related sensations based on expectations of negative somatic, cognitive, or social consequences (Reiss & McNally, 1985)—has been associated with the development and maintenance of anxiety disorders


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(Reiss, 1991; Wheaton, Deacon, McGrath, Berman, & Abramowitz, 2012). AS and IU appear to be independent yet related constructs, such that sensitivity to the symptoms of anxiety (AS) and an inability to tolerate uncertainty regarding the implications of such symptoms (IU) becomes mutually maintaining (Carleton, Norton, & Asmundson, 2007). While IU is exacerbated by exposure to ambiguous stimuli lacking objective focus, AS involves catastrophic interpretations of identifiable anxiety-related stimuli (Carleton, Sharpe, & Asmundson, 2007; Fergus & Bardeen, 2013; Reiss, Peterson, Gursky, & McNally, 1986). AS (Deacon & Abramowitz, 2008) has been positively associated with elevated health anxiety (HA), while mounting evidence suggests IU is an important, yet distinct, component of HA (Boelen & Carleton, 2012; Fergus & Bardeen, 2013; Fergus & Valentiner, 2011; Kurita, Garon, Stanton, & Meyerowitz, 2013). For example, HA—a condition arising from misinterpretation of bodily sensations as being indicative of serious disease (Asmundson, Abramowitz, Richter, & Whedon, 2010)—is an inability to tolerate the inherent uncertainty associated with interpreting somatic sensations (Eifert, Zvolensky, & Lejuez, 2000; Taylor & Asmundson, 2004). Targeting AS (for review, see Taylor & Asmundson, 2004), and more recently IU (Hedman et al., 2013), have also been implicated as having positive impact in reductions in HA symptoms among clinical populations, supporting the need for theoretical clarification of the interrelationships between these constructs. Given the transdiagnostic nature of IU (for review, see Carlelton, 2012), observed associations between AS and HA (Deacon & Abramowitz, 2008; Lees, Mogg, & Bradley, 2005), and the shared features of clinically elevated HA and other anxiety disorders (Collimore, Asmundson, Taylor, & Abramowitz, 2009; Deacon & Abromowitz, 2008), the role that IU may play in elevated HA warrants further investigation. Beyond theoretical rationale, research is needed to address divergent findings in available literature. An earlier study (Sexton et al., 2003) found that IU was positively correlated with HA in undergraduate students. This finding has been extended in samples of psychiatric patients (Norton, Sexton, Walker, & Norton, 2005),

Intolerance of Uncertainty and Health Anxiety

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undergraduates, and bereaved adults (Boelen & Carleton, 2012), and among these studies has remained significant after controlling for AS. In addition, patients seeking treatment for HA have reported elevated IU compared with individuals with anxiety disorders (Deacon & Abramowitz, 2008), and IU treatments have demonstrated effectiveness for reducing HA (Langlois & Ladouceur, 2004). However, some studies have found no significant relationship between IU and HA when controlling for AS, anxiety, and body vigilance (Fergus & Valentiner, 2009), whereas others have found no relationship between IU and HA when controlling for negative problem orientation, cognitive avoidance, and faulty beliefs about the usefulness of worry (Langlois, Gosselin, Brunelle, Drouin, & Ladouceur, 2007). Due to divergent findings when assessing the HA – IU relationship while controlling for AS, focused a priori oriented research is needed in order to elucidate understanding. Furthermore, past studies often assess IU and AS as single factor constructs, rather than as multifactorial (i.e., prospective and inhibitory IU; AS somatic, cognitive, and social concerns), which may provide insight into the intricacies of the relationship. Lastly, examining patterns in the HA – IU relationship in multiple samples, and comparing assessments from different measures of HA, may further knowledge by providing a means of inter-rater reliability beyond that which could be established through the use of single samples and unitary measures of HA. This study explored the unique relationships between dimensions of IU (i.e., prospective IU and inhibitory IU) and HA after controlling for dimensions of AS. In addition, we compared IU dimensions between individuals with high levels of HA and those with primary diagnoses of anxiety disorders crosssectionally to (a) shed light on the transdiagnostic nature of IU, (b) provide further information on the independent contribution of IU above and beyond AS to HA, and (c) elucidate potential differences in IU presentations within clinical samples and those with high levels of HA. This study drew from two separate data collections (community and undergraduate samples), each of which examined inter-relationships between transdiagnostic cognitive vulnerabilities (i.e., IU and AS)

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and HA using different measures. Based on previous research (e.g., Boelen & Carleton, 2012; Carleton, Norton, & Asmundson, 2007), both IU dimensions were expected to positively associate with HA. Given the implicit relationship between HA and anxiety disorders, as well as evidence that IU is comparable across anxiety disorders, IU scores in a high HA sample were expected to be similar to those found among individuals with anxiety disorders.

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Participants Total 715 participants were gathered from two separate data collections of community dwelling adults (n ¼ 155) and undergraduate students (n ¼ 560). The community sample was mostly female (81.3%), Caucasian (76%), single/never married (54%), and reported a mean age of 31.8 years (SD ¼ 14.2). The undergraduate sample was mostly female (86.4%) and single (49%) or in a relationship without cohabitation (28%). The undergraduate sample was ethnically/ racially diverse, with 27% of the sample reporting being Caucasian, 27% Hispanic, 18% Black/African American, and 21% South Asian or East Asian. The mean age of the undergraduate sample was 22.9 (SD ¼ 6.1).

question, and did not allow multiple entries from the same IP address. In the undergraduate sample, 810 participants were presented with all measures, 736 completed the survey, and 119 were removed due to inappropriate responses to response apathy check questions, bringing the sample total to 560. The study targeting community members included a large number of measures; as such, steps were taken to ensure even distribution of participants to study measures and reduce careless answering resulting from writer fatigue. One-thousand seventy-five community participants were presented with measures assessing anxiety disorder risk factors (including AS and IU). Once complete, participants were given the option to complete additional measures assessing anxiety disorders symptoms. Threehundred fifty-seven participants agreed to continue and were divided randomly into three groups that were presented with different psychometric measures. One-hundred fiftyfive participants were directed to complete measures assessing HA and were retained in the community sample. The community and undergraduate samples were administered different, but nonetheless reliable (e.g., Abramowitz, Deacon, & Valentiner, 2007; Welch, Carleton, & Asmundson, 2009), measures of HA given the independent nature of each study; all other measures were identical.

Materials Procedures Community and undergraduate samples were gathered independently as ongoing internetbased (surveymonkey.com) cross-sectional investigations of anxiety. Data were combined to more thoroughly assess the relationships of interest in this study. Permission to conduct each study was obtained from the local University research ethics boards. Advertisements were delivered through social media (community) or University participant pool (undergraduate). Response apathy check questions (e.g., “My favorite singer is Marty Bumble”; “I’m usually quite sure what country I am from”) were imbedded within study measures to minimize careless responding (Meade & Craig, 2012); participants answering outside the obvious response were excluded. Settings for the online survey delivery system required an answer to each

Anxiety Sensitivity Index-3 (ASI-3; Taylor et al., 2007). The ASI-3 is a self-report measure designed to assess the tendency to fear anxiety symptoms based on the belief that they may have harmful consequences (e.g., “It scares me when my heart beats rapidly”). The 18 items are rated on a 5-point Likert scale ranging from 0 (agree very little) to 4 (agree very much). Factor analysis supports a three factor structure (i.e., somatic, cognitive, and social), which correspond to the three theorized dimensions of AS (i.e., fear of somatic sensations, fear of cognitive dyscontrol, and fear of socially observable signs of anxiety, respectively). The ASI-3 has good convergent, discriminant, and criterion validity (Taylor et al., 2007). Both the community and undergraduate samples completed the ASI-3 in this study. The internal consistencies for ASI-3 somatic (community ¼ .84; undergraduate ¼


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.88), cognitive (community ¼ .91; undergraduate ¼ .92), and social subscales (community ¼ .80; undergraduate ¼ .82) were acceptable for both samples in this study. Intolerance of Uncertainty Scale, Short Form (IUS-12; Carleton, Norton, & Asmundson, 2007). The IUS-12 is a short version of the original 27-item IUS (Freeston et al., 1994) that measures responses to uncertainty, ambiguous situations, and the future. The 12 items are rated on a 5-point Likert scale ranging from 1 (not at all characteristic of me) to 5 (entirely characteristic of me). The IUS-12 is considered comparable to the original scale as a measure of IU (Khawaja & Yu, 2010) and demonstrates two factors; seven items measure prospective-IU (e.g., “I can’t stand being taken by surprise”) and five items measure inhibitory-IU (“Uncertainty keeps me from living a full life”). The two independent factors of the IUS-12, constituting inhibitory and prospective IU, have demonstrated stability (Carleton, Norton, & Asmundson, 2007) and are commonly used independent measures in anxiety research (e.g., Carlton, Mulvogue, et al., 2012; Fetzner et al., 2013). Good internal consistency has been demonstrated by the total score and both subscale scores (Carleton, Norton, & Asmundson, 2007). The community and undergraduate samples completed the IUS-12. The internal consistencies for the IUS-12 prospective (community ¼ .90; undergraduate ¼ .88) and inhibitory subscales (community ¼ .89; undergraduate ¼ .89) were acceptable for each sample. Short Health Anxiety Inventory (SHAI; Salkovskis, Rimes, Warwick, & Clark, 2002). Only the undergraduate sample completed the SHAI. The SHAI is an 18-item self-report questionnaire designed to assess the severity of HA. Respondents are asked to select one of four statements that best characterizes their HA (e.g., “I spend most of my time worrying about my health”) in 18 different domains (e. g., time spent worrying, frequency of reassurance seeking). Statements were scored on a 4point likert scale ranging from 0 (Not at all) to 3 (Extremely), leading to a range of total scores from 0 to 54. The SHAI has demonstrated acceptable reliability and validity in previous studies (Alberts, Hadjistavropoulos, Jones, & Sharpe, 2013). Consistent with previous research (Alberts et al., 2013),


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individuals scoring 27 or above on the SHAI were classified as having high HA. The SHAI displayed good internal consistency in the undergraduate sample, with a Cronbach’s coefficient of .88. Whitely Index (WI; Pilowsky, 1967). Only the community sample completed the WI. Although the original WI included a “yes” “no” format, we used the 5-point Likert scale version of the WI. The scale comprised 14 items designed to measure HA. Participants answered each item (e.g., “Do you often worry about the possibility that you have a serious illness?”) on a 5-point Likert scale ranging from 1 (Not at all) to 5 (A great deal). As per the original dichotomous WI, item 9 (e.g., “Is it easy for you to forget about yourself, and think about all sorts of other things”) was reverse scored. Also in line with the original, the items were summed to provide a total score. This design created a WI total score range from 14 to 70, with greater scores indicating more severe HA. No cut-score has been established for the Likert scale version of the WI to indicate high HA; as such, we used a force dichotomy (scores of 1 or 2 were deemed as “no” and 3, 4, or 5 were deemed as “yes”) to categorize participant’s responses into a yes/no format consistent with the original scale so as to take advantage of the original cut-score (Pilowsky, 1967). Scores were then compared against a cut score of 8.99—which was developed for the original WI (Reif, Hessel, & Braehler, 2001)—to determine high probable HA. To avoid Type I error, we also used a K-means cluster analysis to separate the community sample into high and low HA. The resulting groups were consistent with the aforementioned cut-score obtained through the forced dichotomy procedure wherein participants falling above 8.99 on the WI were also placed in the high HA group. The WI displayed good internal consistency in the community sample, with a Cronbach’s coefficient of .92.

Analytic approach Power analyses were run independently with the undergraduate and community sample data in order to assess the suitability of the data for the planned analyses. Next, descriptive statistics (i.e., means, standard deviations, skewness, and kurtosis) were run to characterize the data, and Pearson correlations were run to


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assess for interrelationships between variables independently in the community and undergraduate samples. Bootstrapping (1000 samplings) was used on all variables (Byrne, 2001; Davison & Hinkley, 2006; Nevitt & Hancock, 2001) in subsequent analyses to maximize adherence to normality requirements. Hierarchal linear regressions were run on data from the community sample to assess the unique variance accounted for in WI scores by IU dimensions after accounting for AS dimensions. The first hierarchal regression analysis was run inputting both IUS-12 inhibitory and prospective scores in Step 1 followed by ASI-3 physical, cognitive, and social scores in Step 2. Evidence supports IU as an important, possibly necessary component of anxiety and AS (Carleton, 2012; Carleton et al., 2012; Carleton, Sharp, & Asmundson, 2007), suggesting primacy of placement in a regression model (Tabachnick & Fidell, 2013); however, this study was designed to determine whether IU dimensions would account for statistically significant and substantial variance beyond that accounted for by AS dimensions. As such, in the interest of thoroughness and paralleling prior research (Carleton et al., 2010), the hierarchal regression analyses were replicated reversing the order of entry described above. A second series of two hierarchal regressions were run using the data from the undergraduate sample. The analyses were identical to those run with the community sample data, except SHAI total scores were used as the dependent variable. Finally, data on IU levels reported by individuals high in HA in both the community and undergraduate samples (see above for descriptions of how high HA was determined in each sample) were combined to form a single group high in HA. Eighteen participants from the undergraduate sample and 26 from the community sample were retained in the high HA group, for a total of 44 participants (84% women). IU dimension scores from those high in HA were then compared against published data (Carleton et al., 2012) from a treatment-seeking group of individuals diagnosed with either social anxiety disorder (n ¼ 120), generalized anxiety disorder (n ¼ 63), obsessive compulsive disorder (n ¼ 60), and panic disorder (n ¼ 89)1 using a one-way ANOVA. The decision to combine


participants from separate samples was made on the basis that group comparisons were only conducted on measures that were common to each sample (i.e., ASI-3 and IUS-12) and the WI and SHAI were only used for grouping participants. While using participants who exceed cutscores on self-report symptom inventories does not match the more thorough assessment procedures inherent in clinical interviews, such a strategy has been used in a similar fashion in contemporary literature. Specifically, this strategy has been used to identify individuals with clinically elevated HA (e.g., Fergus & Valentiner, 2011) and has been shown to correspond with clinical interviews among persons seeking treatment for HA (e.g., Abramowitz, Olatunji, & Deacon, 2007). Such a strategy has also been used to identify persons for purposes of comparing IU levels to individuals with another mental disorder diagnosis (e.g., Carleton et al., 2012). As such, the strategy was implemented to clarify potential differences between HA and anxiety disorders.

Results Descriptive statistics and correlations Power analyses suggested that the number of participants from the community and undergraduate samples (hierarchal regression analyses, a ¼ .05; power ¼ .95; number of predictors ¼ 5), and each of the samples combined (one-way ANOVA, a ¼ .05; power ¼ .95; number of groups ¼ 5) was sufficient to conduct the proposed analyses (Erdfelder, Faul, & Buchner, 1996). Descriptive statistics and Pearson correlational analyses for each of the study variables specific to the community and undergraduate samples are summarized in Table 1. Means for ASI-3 and IUS-12 subscales differed substantially between the undergraduate and community samples. Differences may have been due to the population differences; notwithstanding, given that the relationships between IU dimensions, AS components, and HA were to be examined independently within the two samples, differences in independent variable scores were not expected to influence the results. Pearson correlation analyses of the community sample data revealed WI total scores to be significantly and positively

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Table 1. Descriptive statistics for both community and undergraduate samples


1.a. WI 1.b. SHAI 2.a. ASI cog 2.b. ASI cog 3.a. ASI soc 3.b. ASI soc 4.a. ASI som 4.b. ASI som 5.a. IUS-12 pro 5.b. IUS-12 pro 6.a. IUS-12 inh 6.b. IUS-12 inh

M

SD

S

K

1.a/b

2.a/b

3.a/b

4.a/b

5.a/b

30.23 12.13 16.14 3.74 19.44 6.61 15.34 4.79 22.31 17.07 14.92 8.86

11.84 6.96 7.19 5.25 5.93 5.23 6.12 5.21 7.07 6.47 5.89 4.41

.97 1.10 .11 1.55 2.25 .89 .27 1.17 2.08 .48 .03 1.16

.73 .50 2 1.15 1.63 2 .66 .27 2 .90 .66 2 .94 2 .48 2 1.08 .50

– – .45** .56** .18* .46** .54** .58** .39** .52** .33** .51**

– – .53** .71** .55** .72** .33** .46** .41** .66**

– – .48** .68** .39** .51** .43** .58**

– – .38** .54** .40** .55**

– – .77** .72**

Notes. a, data from the community sample; b, data from the undergraduate sample; M, mean; SD, standard deviation; S, skew; K, kurtosis; IUS-12, Intolerance of Uncertainty Scale short form; WI, Whitely index; SHAI, Short health anxiety inventory; ASI, Anxiety Sensitivity Index-3. Among the Community sample, standard error of skewness ¼ .20 and standard error of kurtosis ¼ .39. Among the University sample, standard error of skewness ¼ .10 and standard error of kurtosis ¼ .21.

correlated with both IUS-12 prospective and inhibitory scores and ASI-3 somatic, cognitive (all ps , .01), and social ( p ¼ .02) scores. Correlations between prospective and inhibitory subscales, within both community and undergraduate samples, were high; however, the strength of correlations was comparable to previous studies (e.g., .73, Carleton et al., 2010). Pearson correlation analyses of the undergraduate sample data revealed SHAI total scores to be significantly and positively correlated with the IUS-12 prospective and inhibitory subscale scores as well as the ASI-3 somatic, cognitive, and social (all ps , .01) subscale scores. No indices of univariate skewness and kurtosis in either sample were sufficiently out of range to preclude the planned analyses (i.e., had positive standardized skewness values that exceeded 2 or positive standardized kurtosis values that exceeded 7; Tabachnick & Fidell, 2013).

Hierarchal regression Results for the first hierarchal regressions using the community sample are presented in Table 2. Two problems were identified with independent variables from the models. First, ASI-3 social subscale exhibited a relatively small correlation with WI total scores yet accounted for substantial variance within the models. Second, the IUS-12 inhibitory subscale likewise demonstrated a relatively small correlation with WI total scores but accounted

for minimal variance within the models. Accordingly, given previous theories postulating that such conditions in hierarchal regression constitute the presence of suppressor variables (Pandey & Elliot, 2010), the ASI3 social subscale and the IUS-12 inhibitory subscale were considered as such and removed to provide a clearer picture of the interrelationships between the remaining variables and WI total scores. The second hierarchal regressions were run without the ASI-3 social subscale and the IUS-12 inhibitory subscale (Table 3). When the IUS-12 prospective subscale was inputted in Step 1, the model accounted for 14% of variance in WI total scores, and the IUS-12 prospective subscale ( p , .01, part r ¼ .65) accounted for statistically significant variance. The effect size for this step was small (ƒ2 ¼ .18). When the ASI-3 somatic subscale and the ASI-3 cognitive subscale were inputted in Step 1, the model accounted for 32% of variance in WI total scores, and the ASI-3 somatic subscale ( p , .01, part r ¼ .81) and the ASI-3 cognitive subscale ( p , .01, part r ¼ .36) both accounted for statistically significant variance. The effect size for this step was large (ƒ2 ¼ .56). For each analysis, when the ASI-3 subscales and the IUS-12 subscales were inputted in Step 2 the model accounted for 34% of variance in WI total scores, and the ASI-3 somatic subscale ( p , .01, part r ¼ .71), the ASI-3 cognitive subscale

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Table 2. Hierarchal regression analysis among community sample participants Coefficient statistics


IV Prospective-IU Inhibitory-IU Prospective-IU Inhibitory-IU ASI-Cog ASI-Som ASI-Soc ASI-Cog ASI-Som ASI-Soc ASI-Cog ASI-Som ASI-Soc Prospective-IU Inhibitory-IU

Correlations

Model step statistics

Model step

b

t

p

r

Part r

DR 2

DF

p

1

.33 .07 .27 2.06 .28 .43 2.25 .30 .48 2.21 .28 .43 2.25 .27 2.06

.2.80 .61 2.64 2 .55 3.40 5.30 2 3.14 3.53 5.87 2 2.56 3.40 5.30 2 3.14 2.64 2 .55

, .01 .56 , .01 .62 , .01 , .01 , .04 , .01 , .01 .02 , .01 , .01 , .01 .01 .60

.39 .33 .39 .33 .45 .54 .18 .45 .54 .18 .45 .54 .18 .39 .33

.05 .21 .17 2 .04 .22 .34 2 .20 .23 .38 2 .17 .22 .34 2 .20 .17 2 .04

.15

13.44

, .01

.25

20.18

, .01

.35

27.48

, .01

.04

5.25

, .01

2

1 2

Notes. IU, intolerance of uncertainty; ASI, Anxiety Sensitivity Index-3; Cog, cognitive subscale; Som, somatic subscale; Soc, social subscale. Dependent variable: Whitely Index.

( p , .01, part r ¼ .31), and the IUS-12 prospective subscale ( p ¼ .04, part r ¼ .30) accounted for statistically significant variance. The effect size for this step when ASI-3 subscales and IUS-12 prospective were entered in Step 2 were medium (ƒ2 ¼ .31) and small (ƒ2 ¼ .05), respectively. Results for hierarchal regressions using the undergraduate sample are presented in Table 4. When IUS-12 dimensions were inputted in Step 1, the model accounted for 30% of variance in SHAI total scores, and the IUS-12 prospective subscale ( p , .01, part r ¼ .52) and the inhibitory subscale ( p , .01,

part r ¼ .51) accounted for statistically significant variance. The effect size for this step was medium (ƒ2 ¼ .45). When the ASI-3 subscales were inputted in Step 1, the model accounted for 36% of variance in SHAI total scores, and the ASI-3 somatic subscale ( p , .01, r ¼ .14) and the ASI-3 cognitive subscale ( p , .01, r ¼ .26) both accounted for statistically significant variance. The effect size for this step was large (ƒ2 ¼ .56). For each analysis, when the ASI-3 subscales and the IUS-12 subscales were inputted in Step 2, the model accounted for 42% of variance in SHAI scores, and the ASI-3 somatic subscale

Table 3. Hierarchal regression analyses without suppressor variables among community sample participants

IV Prospective-IU Prospective-IU ASI-Cog ASI-Som ASI-Cog ASI-Som ASI-Cog ASI-Som Prospective-IU

Coefficient statistics

Correlations

Model step

b

t

P

r

Part r

DR 2

DF

p

1 2

.65 .30 .31 .71 .36 .81 .31 .71 .30

5.16 2.52 2.36 4.55 2.75 5.28 2.36 4.55 2.52

, .01 .01 .01 , .01 , .01 , .01 , .01 , .01 .01

.39 .60

.36 .17 .15 .30 .18 .35 .15 .30 .17

.15 .20

26.62 23.80

, .01 , .01

.36

36.63

01

.03

6.34

, .05

1 2

.57 .59


Notes. IU, intolerance of uncertainty; ASI, Anxiety Sensitivity Index-3; Cog, cognitive subscale; Som, somatic subscale; Soc, social subscale. Dependent variable: Whitely Index.

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Table 4. Hierarchal regression analyses among university sample participants Coefficient statistics


IV Prospective-IU Inhibitory-IU Prospective-IU Inhibitory-IU ASI-Cog ASI-Som ASI-Soc ASI-Cog ASI-Som ASI-Soc ASI-Cog ASI-Som ASI-Soc Prospective-IU Inhibitory-IU

Correlations


Model step

b

t

p

r

Part r

DR 2

DF

p

1

.32 .28 .27 .06 .17 .33 2 .06 .23 .40 .03 .17 .33 2 .06 .27 .06

6.31 5.48 5.54 1.00 3.07 6.62 21.20 4.18 7.63 .51 3.07 6.62 21.20 5.54 1.00

,.01 ,.01 ,.01 .37 .01 ,.01 .25 ,.01 ,.01 .630 ,.01 ,.01 .28 ,.01 .42

.52 .51 .52 .51 .53 .58 .46 .53 .58 .46 .53 .58 .46 .52 .51

.22 .19 .18 .03 .10 .21 2.04 .14 .26 .02 .10 .21 2.04 .18 .03

.31

122.36

, .01

.12

36.60

, .01

.36

103.76

, .01

.06

29.27

, .01

2

1 2

Notes. Prospective-IU, intolerance of uncertainty-12 prospective anxiety subscale; ASI-cog, Anxiety Sensitivity Index-3 cognitive subscale; ASI-som, Anxiety Sensitivity Index-3 somatic subscale. Dependent variable: Short Health Anxiety Inventory.

( p , .01, part r ¼ .58) and the ASI-3 cognitive subscale ( p , .01, part r ¼ .53), and the IUS12 prospective subscale ( p , .01, part r ¼ .52) all accounted for statistically significant variance. The effect size for this step when ASI-3 and IUS-12 subscales were entered in Step 2 were large (ƒ2 ¼ .54) and small (ƒ2 ¼ .10), respectively.

Group comparisons Table 5 summarizes the IUS-12 subscale scores for the high HA group and groups of individuals with an anxiety disorder diagnosis. Statistically significant between-group differences were identified for both the IUS-12 inhibitory subscale, F(4, 371) ¼ 5.08, p , .01, h 2 ¼ .04, and the IUS-12 prospective subscale scores F(4, 371) ¼ 3.64, p , .01, h 2 ¼ .05. Regarding differences between the high HA

group and specific anxiety disorders, post hoc comparisons for the IUS-12 prospective subscale scores using Tukey’s honest significant difference (HSD) indicated significant differences between panic disorder ( p , .01) and social anxiety disorder ( p . .05), but not obsessive compulsive disorder ( p . .05) or generalized anxiety disorder ( p ¼ .26). Results suggest that individuals with high HA had higher prospective IU scores than any group of individuals with an anxiety disorder diagnosis. Regarding the IUS-12 inhibitory subscale, post hoc comparisons using Tukey’s HSD indicated no statistically significant differences between individuals in the high HA group and those with an anxiety disorder diagnosis.

Table 5. IU scores among individuals with specific anxiety disorders and high health anxiety

Prospective-IU Inhibitory-IU

PD M(SD) n ¼ 89

GAD M(SD) n ¼ 63

SAD M(SD) n ¼ 120

OCD M(SD) n ¼ 60

HA M(SD) n ¼ 44

21.82 (7.49) 15.18 (5.55)

24.59 (6.84) 15.79 (5.19)

23.97 (6.48) 17.69 (4.75)

23.62 (6.45) 17.17 (5.28)

27.25 (5.57) 17.07 (5.43)

Notes. M, mean; SD, standard deviation; PD, panic disorder; GAD, generalized anxiety disorder; OCD, obsessive compulsive disorder; HA, health anxiety.

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Discussion The purpose of this study was two-fold. First, we sought to cross-sectionally assess the interrelationships between IU dimensions and HA while accounting for AS. Findings from both the undergraduate and community samples yielded similar results. Specifically, after controlling for AS, prospective IU demonstrated a significant relationship with HA and inhibitory IU demonstrated a weak relationship with HA. The reliability of the observed associations is strengthened by the consistency of findings obtained using two independent samples and two measures of HA. Second, we sought to compare dimensions of IU between individuals endorsing high HA and individuals with a primary diagnosis of one of several anxiety disorders. The current results suggested that the differences between those with high HA and each of the anxiety disorders were minimal and, where differences were observed (i.e., with panic disorder and social anxiety disorder), those with high HA reported higher prospective IU. Results from both samples suggest that prospective IU has a significant, albeit small, relationship with symptoms of HA, even after controlling for AS, a related variable that has been shown to be predictive of HA (e.g., Lees et al., 2005). The extent to which a relationship between prospective IU and HA exists may be explained by the way that prospective IU generates anxiety through the anticipation of uncertainty. Prospective anxiety denotes perceptions of threat due to future uncertainty (Carleton, Norton, & Asmundson, 2007; Carleton, Sharp, & Asmundson, 2007 McEvoy & Mahoney, 2011), which is inherent in symptoms of HA; indeed, those with high probable HA are highly concerned with the cause, authenticity, and prognosis of unwanted bodily sensations (see Olatunji, Deacon, & Abramowitz, 2009). Furthermore, much like prospective IU, the cognitive components of HA (i.e., health- and illness-related beliefs) involve fear of future unknown. For example, noticing a skin blemish may lead one to have fears related to the future potential of skin cancer, which are compounded by the intolerability of not knowing for certain whether one will die as a result. Alternatively, fears of future uncer-


tainty may precipitate increased preoccupation with bodily sensations and fear of disease. Elevated prospective IU may promote, rather than inhibit, anxiety-maintaining behaviors (e.g., reassurance seeking and checking) that are common for individuals with high levels of HA (Deacon, Lickel, & Abramowitz, 2008) and anxiety disorders (e.g., social anxiety disorder; Carleton et al., 2012). Differences between HA and other anxiety disorders may be explained by the fact that prospective IU could serve as a behavioral motivator to individuals high in HA in order to reduce anxiety brought about by seemingly threatening somatic sensations (e.g., making frequent appointments with a physician; Taylor & Asmundson, 2004), whereas in the case of generalized anxiety, for example, individuals may feel unable to act (e.g., reduced problem-solving capabilities; Behar & Borkovec, 2006) due to the uncertainty inherent in stressful situations. Accordingly, inhibitory IU may reasonably relate poorly, in comparison to prospective IU, to HA after controlling for AS. Indeed, it is more likely that an individual with high levels of HA, fearing the possibility that he or she may have a serious disease, would be motivated to make repeated appointments with a doctor to rule out this possibility rather than become inhibited from acting. The manifestation of IU in those with high levels of HA may not appear as behavioral inhibition but, instead, as a cognitive manifestation that activates behavioral attempts to minimize distress. As well, it may have been the case that operant behaviors implemented to reduce HA would relate more closely to HA symptoms than the subjective appraisals of behavioral manifestations of IU assessed in the IUS-12. It is also important to note that since participants were gathered from internet users who selfreported anxiety, our sample may have been made up of individuals more prone to seek reassurance on the internet, rather than those persons high in HA who engage in more avoidant coping behaviors. The current data suggest that there are minimal differences in IU between high HA and various anxiety disorders. There were no differences in inhibitory IU between the high HA group and those diagnosed with an anxiety disorder; however, prospective IU


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was significantly higher in those with high HA relative to patients with panic disorder and patients with social anxiety disorder. This is consistent with suggestions that HA involves greater fear of uncertainty regarding future events and an elevated cognitive element of fearing ambiguous situations (e.g., fear of death; Warwick & Salkovskis, 1990) when compared with anxiety disorders, which typically involve more immediate catastrophic consequences (e.g., fear of imminent death during a panic attack; Clark, 1978). Comparisons of panic and hypochondriacal concerns have been the topic of considerable empirical debate (Otto & Pollack, 1994; Taylor 1994; Taylor, 1995). The present findings support a distinction on the basis of prospective IU, yet there is an underlying similarity between HA and many anxiety disorders; as well, findings also speak to the transdiagnostic nature of IU given that the current findings reinforce the association between IU and HA. This study has several limitations that may provide direction for future research. First, the high HA group was established through a selfreport questionnaire as opposed to a diagnosis through clinical interview, and were not a treatmentseeking sample like the clinical comparison group. Future studies may consider investigation of IU in individuals with clinically significant forms of HA as defined in the recently released Diagnostic and Statistical Manual 5th edition (DSM-5; e.g., illness anxiety disorder, complex somatic symptom disorder) as determined through a combination of clinical interview, self-report measures, and behavioral or psychophysiological measures. Second, this study was crosssectional in nature and, as such, we cannot ascertain whether AS and IU precipitated or followed HA. Future studies may consider a longitudinal design to delineate the etiological mechanisms associated with AS and IU in those with high probable HA. Third, our cutoff scores for the high probable HA group were conservative; while this analytic strategy supported identifying persons likely to have problematic HA, it may have also unintentionally inflated the associated mean IU scores. Fourth, both of our samples were made up primarily of women, and the community sample was not as ethnically diverse as the undergraduate sample. Given previous research indicating that women tend


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to be more prone to reassurance seeking than men (MacSwain et al., 2009), and the intuitive relationship between IU and this method of coping with anxiety, future studies may benefit from a more balanced sex and ethnic distribution. Fifth, individuals in the high HA group had elevated HA by design, but whether this was indicative of pathology (e.g., complex somatic symptom disorder or another disorder) remains unknown. Finally, our study only examined IU beyond AS, which is one of many constructs relevant to HA (e.g., worrying; Langlois et al., 2007). Indeed, past research (Longley et al., 2010) has noted that AS is more of a predictor of panic symptoms than HA; thus, it is unclear if a portion of our findings can be contributed to overlapping anxiety symptoms in the current samples. Controlling for additional anxiety symptoms in future analyses may further elucidate the relationships identified in the current research. The current data support a relation between IU and HA, independent of AS. The analogous rates of IU when comparing persons with high HA to other anxiety disorders further supports suggestions to reclassify HA as an anxiety disorder (Collimore et al., 2009; Kroenke et al., 2007; Noyes et al., 2008). Future research comparing IU in the DSM-5 classification of anxiety disorders and somatic symptom and related disorders is warranted. Beyond issues of classification, the observed association between prospective IU and HA may have meaningful implications for the treatment of severe presentation of HA. Longitudinal research has demonstrated that reductions in IU can lead to reductions in anxiety symptoms among individuals with generalized anxiety disorder (Buhr & Dugas, 2009; Dugas & Ladouceur, 2000) and social anxiety (Mahoney & McEvoy, 2012). Given the commonalities in IU between HA and the anxiety disorders, treatment protocols designed to reduce IU (e.g., Dugas & Ladouceur, 2000) may provide benefit to individuals with a severe expression of HA. Indeed, components of the treatment for IU in GAD (e.g., exposure therapy; Dugas & Ladouceur, 2000) have been effective in reducing HA (Rosqvist, 2005) and warrant additional investigation.

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Conflict of interest The authors have no conflicts of interest to disclose.

Note


1. Participants in the clinical sample were gathered from an established research and treatment center wherein each participant was screened for the presence of Axis I disorders according to Diagnostic and Statistical Manual of Mental Disorders (4th ed., text revision; DSM-IV TR; American Psychiatric Association, 2000) standards. Diagnoses were made using the Structured Clinical Interview for DSM-IV (SCID-I; First, Spitzer, Gibbon, & Williams, 1996). Only participants with social anxiety disorder (n ¼ 120), generalized anxiety disorder (n ¼ 63), obsessive – compulsive disorder (n ¼ 60), and panic disorder (n ¼ 89) were retained for this study. Post-traumatic stress disorder was not included as it is not a disorder treated at the treatment center. The most common secondary diagnosis in the current sample was depression (29.5%). In addition to the clinicianadministered interview, participants were also asked to complete a paper-and-pencil measure assessing IU along with a bank of additional measures assessing risk factors for anxiety disorders.

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