I Really Believe I Suffer From a Health Problem: Examining an Association Between Cognitive Fusion and Healthy Anxiety Thomas A. Fergus Baylor University

Objective:

This 2-part study provided the first known examination of an association between cogThis association was examined using 2 samples of nitive fusion and health anxiety. Method: community adults recruited through the Internet (Study 1: N = 252, mean [M] age = 31.2 years, 65.5% male; Study 2: N = 371, M age = 33.1 years, 56.9% male). Results: In Study 1, cognitive fusion shared a moderate association with health anxiety that was not attributable to negative affect. Along with replicating Study 1 findings using an alternative measure of health anxiety, the association between cognitive fusion and health anxiety was found to be independent of experiential avoidance and anxiety sensitivity in Study 2. Cognitive fusion was most relevant to the affective and cognitive dimensions of health anxiety. Conclusion: The present findings are consistent with the possibility that cognitive fusion contributes to health anxiety. Future multivariate experimental and longitudinal C 2015 Wiley Periodicals, Inc. J. Clin. Psychol. 71:920–934, studies are required to establish causality.  2015. Keywords: anxiety sensitivity; cognitive fusion; experiential avoidance; health anxiety

Researchers have defined health anxiety as a “preoccupation with a belief in or fear of having a serious illness” (Warwick & Salkovskis, 1990, p. 105). Within cognitive-behavioral models, individuals experience health anxiety when they misappraise bodily experiences (e.g., sensations, symptoms) as the sign of a medical concern (Salkovskis & Warwick, 2001). This facet of health anxiety has been commonly referred to as disease conviction (Taylor & Asmundson, 2004). A growing body of research indicates contextual cognitive-behavioral therapies may alleviate health anxiety (Hoffmann, Halsboe, Eilenberg, Jensen, & Frostholm, 2014; Lovas & Barsky, 2010; McManus, Surawy, Muse, Vazquez-Montes, & Williams, 2012; Williams, McManus, Muse, & Williams, 2011). Contextual cognitive-behavioral therapies include mindfulness-based therapies (e.g., Segal, Williams, & Teasdale, 2002) and acceptance and commitment therapy (ACT; e.g., Hayes, Strosahl, & Wilson, 2012). Contextual cognitive-behavioral therapies target the function of thoughts, sensations, and emotions rather than the content or frequency of these experiences (Hayes, Villatte, Levin, & Hildebrandt, 2011). Wheaton, Berman, and Abramowitz (2010) cast doubt on the potential usefulness of contextual cognitive-behavioral therapies in treating health anxiety via findings that experiential avoidance did not share unique variance with health anxiety after accounting for anxiety sensitivity, a variable central to more traditional cognitive-behavioral therapies for health anxiety (Abramowitz & Braddock, 2008). Experiential avoidance refers to the nonfunctional alteration of unwanted inner experiences because of an unwillingness to remain in contact with them and is an important target of intervention within ACT (Hayes et al., 2012). In the context of health anxiety, experiential avoidance purportedly leads individuals to alter unwanted health-related thoughts and bodily sensations (Wheaton et al., 2010). Although experiential avoidance may initially reduce the intensity of unwanted health-related thoughts or bodily sensations, researchers hold that experiential avoidance ultimately amplifies such experiences and leads to a repetitive, self-sustaining cycle (Hayes et al., 2012). Experiential avoidance is one, of six, variable that encompasses psychological inflexibility, the core construct of ACT (Hayes et al., 2012). Psychological inflexibility purportedly occurs when Please address correspondence to: Thomas A. Fergus, Department of Psychology & Neuroscience, Baylor University, Waco, TX 76798. E-mail: [email protected] JOURNAL OF CLINICAL PSYCHOLOGY, Vol. 71(9), 920–934 (2015) Published online in Wiley Online Library (wileyonlinelibrary.com/journal/jclp).

 C 2015 Wiley Periodicals, Inc. DOI: 10.1002/jclp.22194

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“language and cognition interact with direct contingencies to produce an inability to persist or change behavior in the service of long-term valued ends” (Hayes, Luoma, Bond, Masuda, & Lillis, 2006, p. 6). It also results in a constriction of meaningful life activities that reduces quality of life (Bond et al., 2011). A central goal of ACT is to promote psychological flexibility, which is marked by three response styles. Of particular relevance to the present research is an open response style, which occurs when individuals fully engage in experiences without becoming entangled with unwanted inner experiences (Hayes et al., 2012). An open response style is of particular relevance to the present research because it is the antithesis of experiential avoidance and cognitive fusion, respectively. Cognitive fusion occurs when individuals are influenced by the literal meaning of their thoughts instead of viewing them as transient internal states (Hayes et al., 2012). Cognitive fusion is a broader variable than other variables commonly studied in relation to anxiety, such as the “believability of thoughts.” More precisely, Gillanders et al. (2014) stated that cognitive fusion pertains to (a) the overregulation of behavior by thoughts, (b) reacting emotionally to thoughts, and (c) overevaluating thought content. Gillanders et al. further stated that cognitive fusion may have potential transdiagnostic importance and, consistent with that possibility, found that cognitive fusion positively correlated with anxiety and depression (rs ranging from .45 to .85). Despite the potential transdiagnostic importance of cognitive fusion, no known published study has yet examined whether it is associated with health anxiety. The transdiagnostic nature of cognitive fusion suggests that a tendency to take the literal meaning of illness-related thoughts might predict those susceptible to health anxiety. The lack of available data regarding an association between cognitive fusion and health anxiety could be one reason why treatment studies examining the efficacy of contextual cognitivebehavioral therapies in reducing health anxiety have not examined changes in cognitive fusion (Hoffmann et al., 2014; Lovas & Barsky, 2010; McManus et al., 2012; Williams et al., 2011). Thus, the present research investigates whether cognitive fusion and health anxiety are associated. If an association is established, such a finding would suggest a need for experimental and longitudinal research on the potential causal contribution of cognitive fusion with a view to targeting this factor in health anxiety treatment. This two-part study examined the association between cognitive fusion and health anxiety. Study 1 predicted that cognitive fusion would share a moderate to strong correlation with health anxiety, which would be consistent with the magnitude of cognitive fusion and symptom relations found in prior studies (rs ranging from .45 to .85; Gillanders et al., 2014). Whether the predicted correlation was attributable to negative affect, a correlate of both cognitive fusion and health anxiety (Gillanders et al., 2014; Longley, Watson, & Noyes, 2005), was examined in Study 1. Study 2 was an augmented-replication of Study 1 using a four-dimension measure of health anxiety as the criterion variable. Study 2 was also completed to extend Study 1 findings via examining whether cognitive fusion incrementally contributes to our understanding of health anxiety beyond experiential avoidance and anxiety sensitivity. Although it remains important to examine health anxiety among carefully diagnosed patients, a sample of community respondents who were not selected based upon their severity of health anxiety was used in the present research. This methodology was supported by prior research findings that health anxiety is best conceptualized as a dimensional construct (Ferguson, 2009; Longley et al., 2010). Thus, differences in health anxiety are considered quantitative rather than qualitative in nature. A methodological consideration following from the dimensionality of health anxiety is that researchers should assess health anxiety using the full range of available scores, rather than extreme groups, to maximize statistical power and minimize information loss.

Study 1 Method Participants The sample comprised 252 community adults recruited via the Internet. The sample was 65.5% male and had an average age of 31.2 years (standard deviation [SD] = 9.8). In regard to racial/ethnic identification, 76.5% of the sample self-identified as White, 10.4% as Asian, 5.6%

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as Black/African American, 5.2% as Hispanic/Latino, 2.0% as multiracial, and 0.3% as Native American. The majority of respondents received an associate degree or higher (62.5%) and were currently employed at least part-time (77.0%). The median household income was between $25,001 and 40,000, although there was variability in household income (ࣘ $10,000 = 9.2%; $10,001–25,000 = 17.1%; $25,001–40,000 = 27.1%; $40,001–55,000 = 14.3%; $55,001– 70,000 = 11.6%; $70,001–85,000 = 6.8%; $85,001–100,000 = 5.2%; $100,001–115,000 = 6.4%; > $115,000 = 2.3%).

Measures Cognitive Fusion Questionnaire (CFQ; Gillanders et al., 2014). The CFQ is a sevenitem measure that assesses cognitive fusion (e.g., “I tend to get very entangled in my thoughts”). Responses are provided using a 7-point scale (ranging from 1 to 7). Gillanders et al. (2014) found that the CFQ is unifactorial and evidenced good internal consistency (Cronbach’s αs ranging from .88 to .93). Gillanders et al. further found that the CFQ had adequate 1-month test-retest reliability (r = .80). The CFQ shares a strong correlation (rs ranging from .72 to .87) with a measure of psychological inflexibility and moderate-to-strong correlations (rs ranging from .45 to .85) with symptom measures (Gillanders et al., 2014). Short Health Anxiety Inventory (SHAI; Salkovskis, Rimes, Warwick, & Clark, 2002). The SHAI is an 18-item measure that assesses health anxiety independent of actual physical health status. Responses are provided using a 4-point scale (ranging from 0 to 3), with response choices varying based on the question. Following the recommendations of Alberts, Sharpe, Kehler, and Hadjistavropoulos (2011), a 14-item version of the SHAI that contains only those items that directly assess health anxiety was used. Although the 14-item SHAI comprises a two-factor solution (Alberts et al., 2011), no predictions were made as to the differential performance of the separate scales. Moreover, a total scale score is typically used in studies using the SHAI (Alberts, Hadjistavropoulos, Jones, & Sharpe, 2013). In Alberts et al.’s (2013) review of the SHAI, they identified that the 14-item SHAI evidenced good internal consistency across published studies (αs of .81 and .84). No known published study has yet examined the test-retest reliability of the 14-item SHAI, although prior studies found that the full-length SHAI evidenced adequate 12-week test-retest reliability (r = .81; Olatunji et al., 2009). The 14-item SHAI correlates strongly (rs of .61 and .80) with other indices of health anxiety (Fergus, 2013).

Positive and Negative Affect Schedule (PANAS; Watson, Clark, & Tellegen, 1988). The PANAS is a 20-item measure that asks respondents to indicate to what extent single-word descriptors (e.g., distressed, scared) capture how they have felt over a given time frame (in this case, “past week”) using a 5-point scale (ranging from 1 to 5). The PANAS Negative Affect (NA) scale–the focus of this study–is a widely-used measure. Watson et al. (1988) found that the negative affect items load on a single factor. Moreover, PANAS-NA has shown good internal consistency (α = .87), adequate 8-week test-retest reliability (r = .47), and moderate to strong correlations with other indices of negative affect (rs ranging from .51 to .74; Watson et al., 1988).

Procedure Participant recruitment took place using Amazon Mechanical Turk (MTurk), an online labor market where researchers can recruit general population adults to complete questionnaires in exchange for payment. Studies support the quality of data collected via MTurk (Buhrmester, Kwang, & Gosling, 2011; Paolacci & Chandler, 2014; Shapiro, Chandler, & Mueller, 2013) and MTurk samples tend to be more demographically diverse than American undergraduate samples (Buhrmester et al., 2011). Participation was restricted to MTurk workers with approval ratings above 95%, a method which has been shown to increase the likelihood that data is of high quality (Peer, Vosgerau, & Acquisti, 2014). The study was posted with other potential HITs (“Human Intelligence Tasks”) on MTurk using a single batch, which restricts MTurk respondents with the same worker identification numbers from taking the study twice.

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Table 1 Descriptive Statistics and Zero-Order Correlations Among Study 1 Variables Variable

Mean

SD

Skew

Kurtosis

1

2

3

1. Cognitive Fusion Questionnaire 2. Short Health Anxiety Inventory 3. PANAS-Negative Affect

24.19 11.42 16.75

9.56 6.71 6.72

0.13 1.09 1.08

−0.59 1.78 0.56

(.95) .50** .56**

(.90) .30**

(.91)

Note. N = 252. SD = standard deviation; PANAS = Positive and Negative Affect Schedule. Cronbach’s alpha values listed in parentheses along the diagonal. **p < .001 (two-tailed).

This study was approved by the local institutional review board. Recruitment was limited to MTurk users located within the United Stated and older than 18 years of age. All participants reported that they had not been diagnosed with a medical condition (physical but not necessarily psychological disorders) by a doctor. This strategy was used to ensure physical health minimally contributed to observed levels of health anxiety (following Abramowitz, Deacon, & Valentiner, 2007). Informed consent and questionnaires were completed using a secure online survey program. Participants could complete the study from any computer with internet access. Participants were paid $1 upon study completion, an amount which is consistent with precedence for paying MTurk workers in similar questionnaire studies (Buhrmester et al., 2011).

Study 1 Results Missing Data No participant omitted responses to all items of a study measure and there was a small amount of missing item responses across measures (maximum percentage of missing item responses on a study measure was 2.4% of possible item responses on that measure). Following the recommendations of Enders (2010), multiple imputation (MI) was used to impute values for missing data. Five data sets with imputed values were created, all of the reported analyses were performed five times, and the estimates were combined across data sets. Standard errors incorporated both within- and between-imputation variance (Enders, 2010).

Descriptive Statistics Descriptive statistics of the study variables are presented in Table 1. All of the measures demonstrated good internal consistency in this study (αs ࣙ .90). There are few established standards for interpreting skew and kurtosis statistics (Kline, 2011), with some standards recommending that these values generally range from −1 to 1 (Morgan, Griego, & Gloeckner, 2001). Skew and kurtosis statistics for SHAI scores fell slightly outside of this recommended range. Results using a square root transformation of SHAI scores were identical to the results obtained using nontransformed scores. For ease of interpretation, only the results using nontransformed scores are presented below.

Demographic Variables Associations between the demographic variables and health anxiety were examined. Neither age (r = −.11, p = .08) nor household income (r = −.04, p = .53) correlated with health anxiety. Moreover, there were no significant differences in health anxiety scores based upon gender, t(250) = −1.37, p = .18, race/ethnicity, t(250) = 1.55, p = .12, education, t(250) = 0.70, p = .48, or employment, t(250) = −0.19, p = .85. Nonetheless, the demographic variables were retained as covariates in the subsequent multivariate analyses.

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Table 2 Unique Associations Between Cognitive Fusion and Health Anxiety in Study 1 Short Health Anxiety Inventory Variable

R2

F

Step 1 Age Gender Race/ethnicity Education Employment Income PANAS-Negative Affect Step 2 Cognitive Fusion Questionnaire

.11**

4.93

β −.05 −.03 −.06 .04 .08 −.04 .03

.16**

52.94 .49**

Note. N = 252. PANAS = Positive and Negative Affect Schedule. Demographic variables: age (continuously coded); gender (0 = female, 1= male); race/ethnicity (0 = White, 1 = non-White); education (0 = less than associate degree, 1 = associate degree or higher); employment (0 = unemployed, 1 = employed); and income (ordered-category scale ranging from 1 = [< $10,000] to 9 [> $115,000]). **p < .001 (two-tailed).

Cognitive Fusion and Health Anxiety Descriptive statistics and zero-order correlations among the main study variables are presented in Table 1. Cognitive fusion correlated with health anxiety and the magnitude of this correlation was moderate in size. Multiple linear regression analyses were used next to examine whether cognitive fusion shared unique variance with health anxiety. In these regression analyses, demographic variables and negative affect were entered into Step 1 and cognitive fusion was entered into Step 2 of a regression model with health anxiety serving as the criterion variable. An examination of scatterplots indicated that the following regression assumptions were met for the regression model (see Cohen, Cohen, West, & Aiken, 2003): (a) the correct specification of the form of the relationship between the predictors and dependent variable, (b) the correct specification of the predictors, (c) homoscedasticity, (d) the independence of residuals, and (e) the normality of residuals for the models. The maximum variance inflation factor (VIF) among the predictors was 1.16 in the regression model, below conventional guidelines (< 10; Cohen et al., 2003) for indicating problems with multicollinearity. The condition number was 17.35, below conventional guidelines (< 30; Cohen et al., 2003) for indicating problems with multicollinearity. No values appeared overly influential (defined as > 1 Cook’s Di ; Cohen et al., 2003) on the overall regression estimates for the regression model (maximum Cook’s Di value was 0.16). Regression results from Step 2 of the model are presented in Table 2. As shown, cognitive fusion was uniquely associated with health anxiety.

Study 1 Summary As predicted, cognitive fusion shared a moderate correlation with health anxiety and this correlation was not attributable to demographic variables or negative affect. It is possible the association between cognitive fusion and health anxiety may be accounted for by at least two other variables, one of which is experiential avoidance. Experiential avoidance and cognitive fusion share conceptual ties (Hayes et al., 2012). Although experiential avoidance has been previously found to correlate with health anxiety, Wheaton et al. (2010) found that experiential avoidance no longer shared unique variance with health anxiety after accounting for anxiety sensitivity. Anxiety sensitivity represents the fear of arousal-related sensations (Reiss, 1987) and comprises cognitive (i.e., mental incapacitation), physical (i.e., physical calamity), and social (i.e., public embarrassment) concerns (Taylor et al., 2007).

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All of the facets of anxiety sensitivity have been previously found to correlate with health anxiety, although the physical concerns facet consistently has been found to share the most unique association with health anxiety in prior studies (Fergus & Barden, 2013; Olatunji et al., 2009; Stewart, Sherry, Watt, Grant, & Hadjistavropoulos, 2008). All three facets of anxiety sensitivity were examined in Study 2.1 Examining the independence of cognitive fusion from experiential avoidance and anxiety sensitivity is an important next step for understanding whether cognitive fusion incrementally contributes to our understanding of health anxiety. A second aim of Study 2 was to examine whether cognitive fusion was particularly relevant to certain dimensions of health anxiety. Health anxiety comprises four dimensions, including affective (worry about health), cognitive (disease conviction), perceptual (vigilance to physical sensations), and behavioral (avoidance behavior, typically reassurance seeking) dimensions, within cognitive-behavioral models (Longley et al., 2005). Because cognitive fusion pertains to interactions with cognitive events, it was predicted that cognitive fusion would uniquely relate to only the affective (i.e., worry) and cognitive dimensions of health anxiety.

Study 2 Method Participants and Procedure The sample comprised 371 community adults recruited via the Internet and the procedure was identical to that used in Study 1. The sample was 56.9% male and had an average age of 33.1 years (SD = 9.9). In regard to racial/ethnic identification, 78.4% of the sample selfidentified as White, 7.3% as Asian, 6.2% Hispanic/Latino, 5.9% as Black/African American, 1.3% as multiracial, 0.6% as Native American, and 0.3% as “other.” The majority of respondents received an associate degree or higher (62.6%) and was currently employed at least part time (78.7%). The median household income was between $40,001 and 55,000, although there was variability in household income (ࣘ $10,000 = 6.5%; $10,001–25,000 = 18.4%; $25,001–40,000 = 23.8%; $40,001–55,000 = 15.9%; $55,001–70,000 = 10.8%; $70,001–85,000 = 6.8%; $85,001– 100,000 = 7.8%; $100,001–115,000 = 2.4%; > $115,000 = 7.6%). Participants completed the CFQ (Gillanders et al., 2014) and the PANAS (Watson et al., 1988), as well as the following measures that were not included in Study 1.

Measures Brief Experiential Avoidance Questionnaire (BEAQ; G´amez et al., 2014). The BEAQ is a 15-item short form of the 62-item Multidimensional Experiential Avoidance Questionnaire (MEAQ) developed by G´amez et al. (2011). Items are rated using a 6-point scale (ranging from 1 to 6). The BEAQ is a one-factor measure that includes items tapping the following six facets of experiential avoidance: behavioral avoidance (e.g., “I’m quick to leave any situation that makes me feel uneasy”); distress aversion (e.g., “The key to a good life is never feeling any pain”); procrastination (e.g., “I won’t do something until I absolutely have to”); suppression (e.g., “When unpleasant memories come to me, I try to put them out of my mind”); repression/denial (e.g., “I feel disconnected from my emotions”); and distress endurance (e.g., “Fear or anxiety won’t stop me from doing something important”). G´amez et al. (2014) found that the BEAQ evidenced adequate internal consistency (αs ranging from .80 to .89). No known published study has yet examined the test-retest reliability of the BEAQ. G´amez et al. (2014) found that the BEAQ shares a strong correlation (rs ranging from .61 to .73) with a measure of psychological inflexibility and moderate to strong correlations (rs ranging from .40 to .51) with symptom measures.

1 The

cognitive and social facets of anxiety sensitivity were erroneously omitted from Study 2 analyses in the original submission of this manuscript. We thank an anonymous reviewer for encouraging that all three facets of anxiety sensitivity be included in Study 2.

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Anxiety Sensitivity Index-3 (ASI-3; Taylor et al., 2007). The ASI-3 is an 18-item measure that assesses anxiety sensitivity using a 5-point scale (ranging from 0 to 4). The ASI-3 scales tap physical (e.g., “It scares me when I become short of breath”), cognitive (e.g., “When I feel ‘spacey’ or spaced out I worry that I may be mentally ill”), and social (e.g., “It is important for me not to appear nervous”) concerns. Taylor et al. (2007) found that the ASI-3 has a threefactor solution and that the physical (αs ranging from .76 to .86), cognitive (αs ranging from .79 to .91), and social (αs ranging from .73 to .86) scales evidenced adequate internal consistency. No known published study has yet examined the test-retest reliability of the ASI-3. The physical (rs ranging from .92 to .99), cognitive (rs ranging from .83 to .99), and social (rs ranging from .92 to .99) scales of the ASI-3 share strong convergent correlations with an earlier measure of anxiety sensitivity assessing the corresponding facet of anxiety sensitivity (Taylor et al., 2007). Multidimensional Inventory of Hypochondriacal Traits (MIHT; Longley et al., 2005). The MIHT is a 31-item measure that assesses health anxiety using a 5-point scale (ranging from 1 to 5). The MIHT was specifically chosen as the measure of health anxiety in Study 2 because it assesses for four dimensions of health anxiety already introduced. These four dimensions are represented by affective (e.g., “I worry a lot about my health”), cognitive (e.g., “Others do not seem sympathetic to my health problems”), perceptual (e.g., “I am usually aware of how I feel physically”), and behavioral (e.g., “I turn to others for support when I do not feel well”) scales. Longley et al. (2005) found that the MIHT has a four-factor solution and that the affective (αs ranging from .80 to .82), cognitive (αs ranging from .87 to .89), perceptual (αs ranging from .81 to .86), and behavioral (αs ranging from .82 to .86) scales of the MIHT evidenced adequate internal consistency. Longley et al. (2005) further found that the MIHT scales evidenced adequate 8-week test-retest reliability (rs ranging from .75 to .78). The four MIHT scales significantly load on a higher-order health anxiety factor (Stewart et al., 2008) and the MIHT total scale shares strong correlations (rs of .61 and .63) with other indices of health anxiety (Fergus, 2013).

Study 2 Results Missing Data No participant omitted responses to all items of a study measure. There was once again a small amount of missing item responses across study measures (maximum percentage of missing item responses on a study measure was 2.2% of possible item responses on that measure). As with Study 1, MI was used to impute values for missing data (following Enders, 2010).

Descriptive Statistics Descriptive statistics of the study variables are presented in Table 3. All of the measures demonstrated adequate internal consistency in this study (αs ࣙ .80). Skew and kurtosis statistics for PANAS-NA scores fell slightly outside of recommended ranges (−1 to 1; Morgan et al., 2001). Results using a square root transformation of PANAS-NA scores were identical to the results obtained using non-transformed scores. For ease of interpretation, only the results using nontransformed scores are presented below.

Demographic Variables Age (r = −.11, p = .03) and household income (r = −.11, p = .03) correlated with health anxiety (MIHT total scale). There were no significant differences in health anxiety scores (MIHT total scale) based upon gender, t(369) = −1.62, p = .10, race/ethnicity, t(369) = −0.23, p = .82, or employment, t(369) = 0.20, p = .84. However, there was a significant difference in health anxiety scores (MIHT total scale) based upon education, t(369) = 2.18, p = .03. Participants with an associate degree or higher (M = 93.67, SD = 15.18) had significantly less health anxiety than

23.35 50.13 5.78 4.67 9.55 95.11 20.67 16.70 33.49 24.26 16.46

1. CFQ 2. BEAQ 3. ASI-3-Physical 4. ASI-3-Cognitive 5. ASI-3-Social 6. MIHT-Total 7. MIHT-Affective 8. MIHT-Cognitive 9. MIHT-Perceptual 10. MIHT-Behavioral 11. PANAS-NA

9.37 12.03 5.37 4.96 5.43 16.28 6.12 5.85 5.71 6.05 6.66

SD

0.17 −0.22 0.96 1.06 0.33 −0.33 −0.12 0.15 −0.62 −0.44 1.24

Skew

1 (.95) .51** .51** .63** .58** .48** .53** .51** .13* .14* .59**

Kurtosis −0.50 0.07 0.34 0.32 −0.53 0.58 −0.50 −0.48 1.28 −0.09 1.16 (.85) .43** .45** .53** .44** .46** .39** .15* .20** .32**

2

(.91) .63** .52** .47** .58** .43** .07 .19** .30**

3

(.89) .52** .44** .50** .52** .05 .14* .47**

4

(.82) .41** .45** .30** .21** .16* .40**

5

(.90) .79** .68** .57** .70** .26**

6

(.85) .49** .26** .38** .25**

7

(.89) .10* .28** .33**

8

(.83) .23** .05

9

(.82) .08

10

(.92)

11

Note. N = 371. SD = standard deviation; CFQ = Cognitive Fusion Questionnaire; BEAQ = Brief Experiential Avoidance Questionnaire; ASI = Anxiety Sensitivity Index; MIHT = Multidimensional Inventory of Hypochondriacal Traits; PANAS = Positive and Negative Affect Schedule. Cronbach’s alpha values listed in parentheses along the diagonal. **p < .001. *p < .05 (two-tailed).

Mean

Variable

Descriptive Statistics and Zero-Order Correlations Among Study 2 Variables

Table 3

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Table 4 Unique Associations Between Cognitive Fusion and Health Anxiety in Study 2 Multidimensional Inventory of Hypochondriacal Traits Total Variable

R2

F

Affective β

R2

F

Cognitive β

R2

F

Perceptual β

R2 F

β

Behavioral R2 F

β

Step 1 .32** 15.21 .42** 23.03 .34** 16.22 .07* 2.37 .07* 2.32 Age −.04 .04 −.03 .02 −.08 Gender .03 .04 −.03 −.02 .09 Race/ethnicity .02 .03 .06 .01 −.04 Education −.02 .05 −.05 −.07 .01 Employment .06 .02 .01 .07 .06 Income −.02 .01 .01 −.07 .01 PANAS-NA −.05 −.10 −.01 −.03 .01 BEAQ .17* .15* .13* .03 .14* ASI-3-Physical .23** .35** .14* .01 .11 ASI-3-Cognitive .10 .08 .29** −.10 −.01 .06 ASI-3-Social .05 .04 −.07 .22* .03** 17.32 .03** 17.08 .00 0.43 .00 0.52 Step 2 .02* 8.08 CFQ .20* .27** .28** .05 −.05 Note. N = 371. PANAS = Positive and Negative Affect Schedule; BEAQ = Brief Experiential Avoidance Questionnaire; ASI-3 = Anxiety Sensitivity Index-3; CFQ = Cognitive Fusion Questionnaire. Demographic variables: age (continuously coded); gender (0 = female, 1= male); race/ethnicity (0 = White, 1 = non-White); education (0 = less than associate degree, 1 = associate degree or higher); employment (0 = unemployed, 1 = employed); and income (ordered-category scale ranging from 1 = [< $10,000] to 9 [> $115,000]). **p < .001, *p < .05 (two-tailed).

participants with less than an associate degree (M = 97.48, SD = 17.85). As with Study 1, all of the demographic variables were retained as covariates in the subsequent multivariate analyses.

Cognitive Fusion and Health Anxiety Zero-order correlations among the study variables are presented in Table 3. Cognitive fusion shared a moderate correlation with health anxiety. As predicted, cognitive fusion clustered particularly strongly with the affective and cognitive dimensions of health anxiety at the zeroorder level. Multiple linear regression analyses were used to examine whether cognitive fusion shared unique variance with health anxiety. In these regression analyses, demographic variables, negative affect, experiential avoidance, and all three facets of anxiety sensitivity were entered into Step 1 and cognitive fusion was entered into Step 2 of a regression model with health anxiety serving as the criterion variable. An examination of scatterplots indicated that the regression assumptions examined in Study 1were again met for all regressions models. The maximum VIF among the predictors was 2.10, below conventional guidelines (< 10; Cohen et al., 2003) for indicating problems with multicollinearity. The condition number was 24.46, below conventional guidelines (< 30; Cohen et al., 2003) for indicating problems with multicollinearity. No values appeared overly influential (defined as > 1 Cook’s Di ; Cohen et al., 2003) on the overall regression estimates for the regression models (maximum Cook’s Di values were: MIHT-Total = 0.11; MIHT-Affective = 0.04; MIHT-Cognitive = 0.05; MIHT-Perceptual = 0.11; MIHT-Behavioral = 0.05). Regression results from Step 2 of each model are presented in Table 3. As shown, cognitive fusion was uniquely associated with the health anxiety total score, as well as the affective and cognitive dimension.

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Statistical Suppression The relationship between PANAS-NA and MIHT-Affective appeared suppressed, as evidenced by a change from the significant positive zero-order correlation between these variables to a trending significant (p = .06) negative association in the respective regression model. In an attempt to identify a possible suppressor variable, a series of partial correlations was conducted in which each of the other predictor variables from the regression model was controlled for, one at a time, while examining the correlation between PANAS-NA and MIHT-Affective. None of the predictor variables changed the zero-order correlation between PANAS-NA and MIHT-Affective from significant and positive to trending significant and negative in the partial correlation analysis. It bears repeating that the suppression effect was trending in its significance, the regression assumptions were met, there were no robust violation of multicollinearity, and no values appeared overly influential on the overall regression estimates for the regression model. As such, it appeared that a combination of variables in the regression model suppressed the relationship between PANAS-NA and MIHT-Affective rather than there being either a suppressor variable or problems with the modeling of the regression analysis.

Study 2 Summary Replicating Study 1 findings, cognitive fusion shared a moderate correlation with health anxiety in Study 2 using an alternative measure of health anxiety. Study 2 extended Study 1 findings in at least two ways. First, among the health anxiety dimensions, cognitive fusion appeared most relevant to the affective and cognitive dimensions. Finding cognitive fusion to cluster most strongly with these two health anxiety dimensions in Study 2 was expected, as both dimensions pertain largely to cognitively-based events. Second, cognitive fusion shared unique variance with health anxiety, particularly the affective and cognitive dimensions, even after accounting for the effects of negative affect, experiential avoidance, and anxiety sensitivity. As such, cognitive fusion incrementally contributes to our understanding of health anxiety. Study 2 findings should be interpreted with the caveat that there is no established threshold value on the condition number for indicating problems with multicollinearity. The condition number found in Study 2 was above a more conservative threshold of 20 sometimes used in studies (Cohen et al., 2003). It should further be noted that the amount of unique variance accounted for by cognitive fusion in Study 2 was smaller than found in Study 1, which is to be expected given that Study 2 accounted for the effects of additional covariates related to cognitive fusion and health anxiety.

Discussion The reported associations are consistent with the hypothesis that individuals who become fused with their thoughts might be more prone to experience health anxiety. This possibility is consistent with Hayes et al.’s (2012) psychological inflexibility model, in which cognitive fusion leads individuals to experience the content of thoughts as if it was immediately present. As an example, consider an individual experiencing fatigue and who has the thought that the fatigue is a sign of a health concern (e.g., multiple sclerosis). If this individual becomes fused with the thought I have multiple sclerosis, which would seemingly parallel the concept of disease conviction, then this individual would likely begin to respond to the thought in a manner consistent with the content of the thought being immediately present. Autonomic nervous arousal is a natural alarm response to perceived treat. Cognitive fusion may both enhance, and be enhanced by, such psychophysical states via systematic changes in threat perceptions. If so, then individuals who become fused with alarming medically-related thoughts may be at greater risk of developing health anxiety.2

2 We

thank an anonymous reviewer for clarifying the expression of this point.

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The results are consistent with theories that see cognitive fusion and experiential avoidance as distinct processes, as both variables were uniquely associated with health anxiety. Experiential avoidance relates to a broader array of unwanted inner experiences than does cognitive fusion, which fundamentally relates to cognitive events, and thus experiential avoidance would be expected to uniquely relate to more criterion variables than cognitive fusion. Indeed, experiential avoidance, but not cognitive fusion, was uniquely associated with the behavioral dimension of health anxiety. The behavioral dimension of health anxiety (i.e., reassurance seeking) may be viewed as a manifestation of experiential avoidance, as reassurance-seeking behavior is viewed as a short-term way to mitigate unwanted inner experiences related to health concerns (Abramowitz & Braddock, 2008; Asmundson & Taylor, 2004). Because experiential avoidance may amplify unwanted bodily experiences and lead to heightened vigilance (Wheaton et al., 2010), it was surprising that experiential avoidance did not share a unique association with the perceptual dimension of health anxiety. It should be noted that, relative to the other MIHT scales, the perceptual scale relatively weakly loads on a higher-order health anxiety construct (Stewart et al., 2008). Further, the items of the perceptual scale have questionable face validity (Olatunji, 2008). Limitations of the perceptual scale could thus be one tenable reason for the unexpected finding. Among the study variables, only the social concerns facet of anxiety sensitivity was uniquely associated with the perceptual dimension. This finding converges with Stewart et al.’s (2008) results that the social concerns facet of anxiety sensitivity was the facet of anxiety sensitivity that accounted for the most unique variance in the perceptual dimension. However, given the noted concerns surrounding the perceptual scale of the MIHT, study findings related to this scale should be interpreted with caution. The present results indicate that anxiety sensitivity incrementally contributes to our understanding of health anxiety beyond variables central to contextual cognitive-behavioral therapies. The breadth of the respective constructs could be one potential explanation for why cognitive fusion, experiential avoidance, and anxiety sensitivity share unique associations with health anxiety. For example, anxiety sensitivity might reflect a proclivity to experience greater physiological arousal (both in intensity and duration) when alarmed by any threat, whereas cognitive fusion and experiential avoidance relate to difficulties that extend beyond physiological arousal. It is important to note that the pattern of results does not mean that anxiety sensitivity is relevant only to health anxiety per se, as anxiety sensitivity has been found to have transdiagnostic importance (Naragon-Gainey, 2010). The present findings stand in contrast to Wheaton et al.’s (2010) findings that experiential avoidance did not share unique variance with health anxiety after accounting for anxiety sensitivity. Sampling differences could be one explanation for these divergent findings, as Wheaton et al. examined the relation between experiential avoidance and health anxiety using participants with extreme scores on a measure of health anxiety. Wheaton et al.’s use of an extreme-groups approach may suggest their findings are more generalizable to respondents with heightened health anxiety than are the present results. However, it bears repeating that the continuous nature of health anxiety highlights the methodological consideration that researchers should use the full range of available scores, as was done in the present research, when examining health anxiety (Ferguson, 2009; Longley et al., 2010). Another difference across studies is that Wheaton et al. used a measure of experiential avoidance that has been criticized as being overly saturated with general distress (G´amez et al., 2011, 2014). The use of a measure of experiential avoidance that appears to address this content overlap with general distress in the present research could be another explanation for the divergent findings. As noted, preliminary studies have evaluated the usefulness of contextual cognitive-behavioral therapies in reducing health anxiety (Hoffmann et al., 2014; Lovas & Barsky, 2010; McManus et al., 2012; Williams et al., 2011). One limitation of these prior studies is that none of them included cognitive fusion as an outcome variable. We thus do not currently know whether contextual cognitive-behavioral therapies for health anxiety in fact adequately target this process, although there are indirect signs that these interventions may promote cognitive defusion. For example, Lovas and Barsky (2010) found that the believability of hypochondriacal thoughts decreased during an 8-week session of mindfulness-based cognitive therapy. Because many of

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the central features of health anxiety can be conceptualized as a type of cognitive fusion as well as the unique association between cognitive fusion and health anxiety found in the present research, future treatment studies should consider assessing cognitive fusion as an outcome variable. In addition, although cognitive fusion may be targeted as part of a broader treatment package for health anxiety, it would be beneficial for future research to examine whether cognitive defusion specifically reduces health anxiety. Laboratory-based experimental research is one type of methodology that would allow researchers to examine that question via controlling and manipulating cognitive defusion at a level that would be difficult to achieve in treatment outcome studies (Levin, Hildebrandt, Lillis, & Hayes, 2012). Mennin, Ellard, Fresco, and Gross (2013) asserted that traditional cognitive-behavioral therapies and contextual cognitive-behavioral therapies share a number of commonalities, one of which is promoting cognitive distancing. In the context of contextual cognitive-behavioral therapies, cognitive distancing may be targeted using defusion techniques. Mennin et al. noted that traditional cognitive-behavioral therapies often achieve cognitive distancing by having individuals self-monitor distressing thoughts and note related emotional reactions to those thoughts in written form. According to Mennin et al., such cognitive distancing techniques promote cognitive change. Traditional cognitive-behavioral therapies also seek to promote cognitive change through the use of cognitive restructuring. For example, the believability of hypochondriacal thoughts is typically targeted, in part, using cognitive restructuring techniques within cognitive-behavioral therapies (Abramowitz & Braddock, 2008; Taylor & Asmundson, 2004). Although contextual cognitive-behavioral therapies do not explicitly use cognitive restructuring techniques per se, Mennin et al. opined that having individuals reconsider the usefulness of attempts to control unwanted inner experiences may lead to cognitive change within contextual cognitive-behavioral therapies.

Limitations Study limitations must be acknowledged. The sample was not selected based on the severity of health anxiety and there was no screening for mental health status. Although the use of a sample not selected based upon symptom severity is supported by the dimensionality of health anxiety, the mean health anxiety scores in Study 1 and the mean scores among some of the other variables in both studies were relatively low. Replication of the present results among respondents who consistently evidence high scores on the study measures will be important in ensuring the generality of the present findings. The generality of the present findings are further limited by use of a physically healthy sample of respondents, which was used to ensure physical health status minimally contributed to observed levels of health anxiety (following Abramowitz et al., 2007). It should be noted that health anxiety is experienced by individuals with physical health problems as well (Hadjistavropoulos et al., 2012). The quality of data obtained via remote collection efforts is an issue that has not yet been fully vetted in the psychopathology literature. It bears repeating that known methods shown to increase the quality of remotely collected data were used in the present research (e.g., using only high reputation MTurk workers; Peer et al., 2014) and accumulating body of research supports MTurk as a viable method for data collection (Buhrmester et al., 2011; Paolacci & Chandler, 2014; Shapiro et al., 2013). Whereas MTurk samples tend to be more diverse than standard internet samples or American undergraduate samples, MTurk samples should not be considered representative of the general population (Buhrmester et al., 2011; Paolacci & Chandler, 2014). The generality of the findings would thus be further supported by examining other groups of nonclinical respondents and having respondents complete the study measures in-person. An additional study limitation is the use of a cross-sectional, self-report study design. Future research using longitudinal and experimental study designs will help elucidate temporal relations among the study variables. One advantage of the present methodology was that it allowed for a simultaneous investigation of relations among cognitive fusion, health anxiety, and several relevant covariates, which was deemed to be an important analysis given the current state

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of the literature. However, for practical reasons, other covariates were not assessed, including alexithymia (Taylor & Asmundson, 2004), attachment insecurities (Sherry et al., 2014), rumination (Marcus, Hughes, & Arnau, 2008), and somatosensory amplification (Barsky, Wyshak, & Klerman, 1990). Recent life stressors were also not assessed, which is an additional covariate of potential interest. Further, cognitive fusion may be more likely to occur in relation to health anxiety because it impairs processing efficiency (Eysenck, Derakshan, Santos, & Calvo, 2007), which could be examined by controlling for the effects of attentional control. The small amount of unique variance accounted for by cognitive fusion in Study 2 highlights the possibility that its association with health anxiety could be accounted for by the above-noted covariates.

Conclusion Limitations notwithstanding, the present results indicate that cognitive fusion shares a unique association with health anxiety that is not accounted for by negative affect, experiential avoidance, or anxiety sensitivity. Cognitive fusion appears most relevant to the affective and cognitive dimensions of health anxiety. These results add to an accumulating body of research suggesting that cognitive fusion underlies multiple symptom types. Future research examining the association between cognitive fusion and health anxiety will help speak to the possibility that psychological interventions for health anxiety should directly target cognitive fusion.

References Abramowitz, J. S., & Braddock, A. E. (2008). Psychological treatments of health anxiety & hypochondriasis: A biopsychosocial approach. Cambridge, MA: Hogrefe & Huber. Abramowitz, J. S., Deacon, B. J., & Valentiner, D. P. (2007). The Short Health Anxiety Inventory: Psychometric properties and construct validity in a non-clinical sample. Cognitive Therapy and Research, 31, 871–883. Alberts, N. M., Hadjistavropoulos, H. D., Jones, S. L., & Sharpe, D. (2013). The Short Health Anxiety Inventory: A systematic review and meta-analysis. Journal of Anxiety Disorders, 27, 68–78, Alberts, N. M., Sharpe, D. S., Kehler, M. D., & Hadjistavropoulos, H. D. (2011). Health anxiety: Comparison of the latent structure in medical and non-medical samples. Journal of Anxiety Disorders, 25, 612–614. Barsky, A. J., Wyshak, G., & Klerman, G. L. (1990). The Somatosensory Amplification Scale and its relationship to hypochondriasis. Journal of Psychiatric Research, 24, 323–334. Bond, F. W., Hayes, S. C., Baer, R. A., Carpenter, K. M., Guenole, N., Orcutt, H. K., & Waltz, T. (2011). Preliminary psychometric properties of the Acceptance and Action Questionnaire-II: A revised measure of psychological inflexibility and experiential avoidance. Behavior Therapy, 42, 676–688. Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon’s Mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6, 3–5. Cohen, J., Cohen, P., West, S. G., & Aiken, L. A. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Hillsdale, NJ: Lawrence Erlbaum. Enders, C. K. (2010). Applied missing data analysis. New York: Guilford. Eysenck, M. W., Derakshan, N., Santso, R., & Calvo, M. G. (2007). Anxiety and cognitive performance: Attentional control theory. Emotion, 7, 336–353. Fergus, T. A. (2013). Repetitive thought and health anxiety: Tests of specificity. Journal of Psychopathology and Behavioral Assessment, 35, 366–374. Fergus, T. A., & Bardeen, J. R. (2013). Anxiety sensitivity and intolerance of uncertainty: Evidence of incremental specificity in relation to health anxiety. Personality and Individual Differences, 55, 640–644. Ferguson, E. (2009). A taxometric analysis of health anxiety. Psychological Medicine, 39, 277–285. G´amez, W., Chmielewski, M., Kotov, R., Ruggero, C., Suzuki, N., & Watson, D. (2014). The Brief Experiential Avoidance Questionnaire: Development and initial validation. Psychological Assessment, 26, 35–45.

Cognitive Fusion and Health Anxiety

933

G´amez, W., Chmielewski, M., Kotov, R., Ruggero, C., & Watson, D. (2011). Development of a measure of experiential avoidance: The Multidimensional Experiential Avoidance Questionnaire. Psychological Assessment, 23, 692–713. Gillanders, D. T., Bolderston, H., Bond, F. W., Dempster, M., Flaxman, P. E., Campbell, L., . . . Remington, B. (2014). The development and initial validation of the Cognitive Fusion Questionnaire. Behavior Therapy, 45, 83–101. Hadjistavropoulos, H. D., Janzen, J. A., Kehler, M. D., Leclerc, J., Sharpe, D., & Bourgault-Fagnou, M. D. (2012). Core cognitions related to health anxiety in self-reported medical and non-medical samples. Journal of Behavior Medicine, 35, 167–178. Hayes, S. C., Luoma, J. B., Bond, F. W., Masuda, A., & Lillis, J. (2006). Acceptance and commitment therapy: Model, process, and outcomes. Behaviour Research and Therapy, 44, 1–25. Hayes, S. C., Strosahl, K. D., & Wilson, K. G. (2012). Acceptance and commitment therapy: The process and practice of mindful change (2nd ed.). New York: Guilford. Hayes, S. C., Villatte, M., Levin, M., & Hildebrandt, M. (2011). Open, aware, and active: Contextual approaches as an emerging trend in the behavioral and cognitive therapies. Annual Review of Clinical Psychology, 7, 141–168. Hoffmann, D., Halsboe, L., Eilenberg, T., Jensen, J. S., & Frostholm, L. (2014). A pilot study of process of change in group-based acceptance and commitment therapy for health anxiety. Journal of Contexual Behavioral Science, 3, 189–195. Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York: Guilford. Levin, M. E., Hildebrandt, M. J., Lillis, J., & Hayes, S. C. (2012). The impact of treatment components suggested by the psychological flexibility model: A meta-analysis of laboratory-based component studies. Behavior Therapy, 43, 741–756. Longley, S. L., Broman-Fulks, J. J., Calamari, J. E., Noyes, R., Wade, M., & Orlando, C. M. (2010). A taxometric study of hypochondriasis symptoms. Behavior Therapy, 41, 505-514. Longley, S. L., Watson, D., & Noyes, R., Jr. (2005). Assessment of the hypochondriasis domain: The Multidimensional Inventory of Hypochondriacal Traits (MIHT). Psychological Assessment, 17, 3–14. Lovas, D. A., & Barsky, A. J. (2010). Mindfulness-based cognitive therapy for hypochondriasis, or severe health anxiety: A pilot study. Journal of Anxiety Disorders, 24, 931–935. Marcus, D. K., Hughes, K. T., & Arnau, R. C. (2008). Health anxiety, rumination, and negative affect: A mediational analysis. Journal of Psychosomatic Research, 64, 495–501. McManus, F., Surawy, C., Muse, K., Vazquez-Montes, M., & Williams, J. M. G. (2012). A randomized clinical trial of mindfulness-based cognitive therapy versus unrestricted services for health anxiety (hypochondriasis). Journal of Consulting and Clinical Psychology, 80, 817–828. Mennin, D. S., Ellard, K. K., Fresco, D. M., Gross, J. J. (2013). United we stand: Emphasizing commonalities across cognitive-behavioral therapies. Behavior Therapy, 44, 234–248. Morgan, G. A., Griego, O. V., & Gloeckner, G. (2001). SPSS for Windows: An introduction to use and interpretation in research. Mahwah, NJ: Lawrence Erlbaum. Naragon-Gainey, K. (2010). Meta-analysis of the relations of anxiety sensitivity to the depressive and anxiety disorders. Psychological Bulletin, 136, 128–150. Olatunji, B. O. (2008). New directions in research on health anxiety and hypochondriasis: A commentary on a timely special series. Journal of Cognitive Psychotherapy, 22, 183–190. Olatunji, B. O., Wolitzky-Taylor, K. B., Elwood, L., Connolly, K., Gonzales, B., & Armstrong, T. (2009). Anxiety sensitivity and health anxiety in a nonclinical sample: Specificity and prospective relations with clinical stress. Cognitive Therapy and Research, 33, 416–424. Paolacci, G., & Chandler, J. (2014). Inside the turk: Understanding Mechanical Turk as a participant pool. Current Directions in Psychological Science, 23, 184–188. Peer, E., Vosgerau, J., & Acquisti, A. (2014). Reputation as a sufficient condition for data quality on Amazon Mechanical Turk. Behavior Research Methods, 46, 1023–1031. Reiss, S. (1987). Theoretical perspectives on the fear of anxiety. Clinical Psychology Review, 7, 585–596. Salkovskis, P. M., Rimes, K. A., Warwick, H. M., & Clark, D. M. (2002). The Health Anxiety Inventory: Development and validation of scales for the measurement of health anxiety and hypochondriasis. Psychological Medicine, 32, 843–853. Salkovskis, P. M., & Warwick, H. C. (2001). Making sense of hypochondriasis: A cognitive model of health anxiety. In G. J. G. Asmundson, S. Taylor, & B. J. Cox (Eds.), Health anxiety: Clinical and

934

Journal of Clinical Psychology, September 2015

research perspectives on hypochondriasis and related disorders (pp. 46–64). London: John Wiley and Sons. Segal, Z. V., Williams, J. M. G., & Teasdale, J. D. (2002). Mindfulness-based cognitive therapy for depression: A new approach to preventing relapse. New York: Guilford Shapiro, D. N., Chandler, J., & Mueller, P. A. (2013). Using Mechanical Turk to study clinical populations. Clinical Psychological Science, 1, 213–220. Sherry, D. L., Sherry, S. B., Vincent, N. A., Stewart, S. H., Hadjistavropoulos, H. D., Doucette, S., & Hartling, N. (2014). Anxious attachment and emotional instability interact to predict health anxiety: An extension of the interpersonal model of health anxiety. Personality and Individual Differences, 56, 89–94. Stewart, S. H., Sherry, S. B., Watt, M. C., Grant, V. V., & Hadjistavropoulos, H. D. (2008). Psychometric evaluation of the Multidimensional Inventory of Hypochondriacal Traits: Factor structure and relationship to anxiety sensitivity. Journal of Cognitive Psychotherapy, 22, 97–114. Taylor, S., & Asmundson, G. J. G. (2004). Treating health anxiety: A cognitive-behavioral approach. New York: Guilford. Taylor, S., Zvolensky, M. J., Cox, B. J., Deacon, B., Heimberg, R. G., Ledley, D. R., . . . Cardenas, S. J. (2007). Robust dimensions of anxiety sensitivity: Development and initial validation of the Anxiety Sensitivity Index-3 (ASI-3). Psychological Assessment, 19, 176–188. Warwick, H. M. C., & Salkovskis, P. M. (1990). Hypochondriasis. Behaviour Research and Therapy, 28, 105–117. Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54, 1063–1070. Wheaton, M. G., Berman, N. C., & Abramowitz, J. S. (2010). The contribution of experiential avoidance and anxiety sensitivity in the prediction of health anxiety. Journal of Cognitive Psychotherapy, 24, 229–239. Williams, M. J., McManus, F., Muse, K., & Williams, J. M. G. (2011). Mindfulness-based cognitive therapy for severe health anxiety (hypochondriasis): An interpretative phenomenological analysis of patients’ experiences. British Journal of Clinical Psychology, 50, 379–397.