Cogn Ther Res (2013) 37:232–241 DOI 10.1007/s10608-012-9478-z
ORIGINAL ARTICLE
Interpretation of Ambiguity in Individuals with Obsessive-Compulsive Symptoms Jennie M. Kuckertz • Nader Amir Anastacia C. Tobin • Sadia Najmi
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Published online: 17 August 2012 Ó Springer Science+Business Media, LLC 2012
Abstract In two experiments we examined the psychometric properties of a new measure of interpretation bias in individuals with obsessive-compulsive symptoms (OCs). In Experiment 1, 38 individuals high in OC symptoms, 34 individuals high in anxiety and dysphoric symptoms, and 31 asymptomatic individuals completed the measure. Results revealed that the Word Sentence Association Test for OCD (WSAO) can differentiate those with OC symptoms from both a matched anxious/dysphoric group and a non-anxious/ non-dysphoric group. In a second experiment, we tested the predictive validity of the WSAO using a performance-based behavioral approach test of contamination fears, and found that the WSAO was a better predictor of avoidance than an established measure of OC washing symptoms (Obsessive Compulsive Inventory-Revised, washing subscale). Our results provide preliminary evidence for the reliability and validity of the WSAO as well as its usefulness in predicting response to behavioral challenge above and beyond OC symptoms, depression, and anxiety. Keywords Interpretation bias Obsessive-compulsive disorder Behavioral approach test Information processing
Introduction Obsessive-compulsive disorder (OCD) is characterized by intrusive thoughts and images that increase anxiety.
J. M. Kuckertz N. Amir (&) A. C. Tobin S. Najmi Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California, San Diego, 6386 Alvarado Ct., Suite 301, San Diego, CA 92120, USA e-mail: [email protected]
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Individuals with OCD perform rituals to prevent or alleviate this anxiety. These symptoms cause significant distress, marked interference, and are time consuming (American Psychiatric Association 2000). Cognitive models of OCD suggest that dysfunctional interpretations of thoughts (e.g., Clark and Purdon 1993; Rachman 1997, 1998; Salkovskis 1985, 1989) contribute to the origin and maintenance of this disorder. For example, according to Salkovskis (1985), intrusive thoughts with themes of responsibility, blame, or control become obsessions when they are interpreted as being important. Furthermore, the focus of cognitive therapy for OCD is on changing interpretations of thoughts, rather than stopping the thoughts themselves. Another model of OCD proposed by Rachman (1997) suggests that obsessions are caused by misinterpreting the importance of one’s thoughts. Thus, eliminating these misinterpretations should help decrease obsessions in OCD. Based on these theories, the Obsessive Compulsive Cognitions Working Group (OCCWG 1997) concluded that the primary cause of obsessions in OCD is flawed interpretations of the intrusive thoughts (OCCWG 2003). In response to current models of OCD, the OCCWG developed the Interpretation of Intrusions Inventory (III; OCCWG 2001). The III is a 31-item questionnaire that examines interpretations of intrusive thoughts, impulses, or images. Three subscales capture self-reported interpretations of general intrusions, including: (1) responsibility, (2) importance of thoughts, and (3) control of thoughts. This scale has good psychometric properties in participants with OCD (OCCWG 2001). Using a revised III with 19 items, Ferguson et al. (2006) found that scores on the III were predictive of OCD symptom severity and OCD subtypes in an undergraduate sample. The III requires an individual to first identify two unwanted thoughts that the individual considers intrusive or inappropriate and then to answer a
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series of questions focused on appraisals of those and similar intrusions (e.g., ‘‘Because I have this thought, it must be important’’). The participant rates the frequency of specific intrusions and the degree to which the individual believes in the intrusive thought. Given that participants respond to the III based on their own thoughts that they have identified as personally negative, the III does not assess an individual’s initial interpretation of ambiguous information as either threat-relevant or benign. While the III is an excellent tool in assessing constructs such as the importance, responsibility, and control of personally negative intrusions, there is no selfreport questionnaire to measure interpretation of ambiguous information relevant to OCD concerns. Despite the lack of such a questionnaire assessing OCDrelevant ambiguous scenarios, some research groups have examined interpretation of ambiguous information in other forms of anxiety. Specifically, participants are presented with ambiguous information and their interpretation is measured by testing the relationship between the presented information and negative information. For example, in a seminal study, Butler and Mathews (1983) examined interpretation in individuals with generalized anxiety disorder by presenting their participants with ten ambiguous scenarios (e.g., ‘‘you wake with a start in the middle of the night, thinking you heard a noise, but all is quiet’’). The researchers asked their participants to generate a response to an openended question related to the scenario, and then to rank three experimenter-provided explanations for the scenario as to how likely they would be to come to their mind. Results from this study revealed that anxious individuals were more likely to select negative interpretations as coming to mind when compared to non-anxious individuals. Researchers have adapted this methodology to test interpretation bias in individuals with other types of anxiety and related conditions (e.g., Amir et al. 1998; Buhlmann et al. 2002; Richards et al. 2001; Stopa and Clark 2000; Voncken et al. 2003). For example, Amir et al. (1998) compared participants with social phobia (SP), nonanxious controls (NACs), and participants with OCD on an interpretation questionnaire that examined biases for social and non-social ambiguous information. Individuals with SP tended to interpret ambiguous scenarios as negative when compared to both control groups. Moreover, the OCD group displayed a negative interpretation bias for social situations when compared to the NACs. Because the ambiguity related to social scenarios rather than OCD, the SP group demonstrated the largest interpretation bias when compared to the two control groups. Groups in this study did not differ in their interpretation of non-social ambiguous information. However, this methodology is limited in that it only allows the researcher to test bias for threat or benign interpretation, as though the two are exactly inversely related. For example, it may be the case that
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individuals with OCD are more likely to interpret a spot on the floor as feces, are less likely to interpret the spot as harmless, or both, relative to controls. In a study representing a notable advance in methodology, Buhlmann et al. (2002) administered a measure of ambiguous interpretation adapted from Butler and Mathews (1983) to participants with body dysmorphic disorder (BDD), OCD, and healthy controls. This adapted measure required participants to independently rate the likelihood of negative and benign interpretations of ambiguous scenarios. In contrast to the results of Amir et al. (1998), Buhlmann et al. found that both patient groups demonstrated a negative bias towards general negative ambiguous scenarios, relative to controls. Participants with OCD and controls did not differ on their endorsement of benign interpretations of general ambiguity. Therefore, current research is conflicting as to the extent that interpretation of ambiguity is involved in OCD. Another method of testing threat and benign interpretation biases independently is the Word Sentence Association Paradigm (WSAP; Beard and Amir 2009). The WSAP presents participants with ambiguous sentences as well as threat and benign related words. Participants are asked to rate the relationship between the words and the sentences. Research suggests that socially anxious individuals rate the ambiguous sentences as more related to threat words than benign words (Beard and Amir 2009). However, this methodology has not previously been used to examine interpretation bias in OCD. A number of studies have examined biased interpretation of ambiguous information for OCD-relevant ambiguity (Jelinek et al. 2009; Jhung et al. 2010; Olatunji et al. 2008), although they have not tested the role of benign versus threat interpretations independently. Olatunji et al. (2008) found that individuals with contamination-related obsessive-compulsive (OC) symptoms, compared to a control group, identified fewer reasons why a public bathroom might be safe, and more reasons why a public bathroom might be unsafe. Nevertheless, public bathrooms by definition may be inherently threat-relevant for individuals with contamination concerns, and thus not ambiguous. Jelinek et al. (2009) presented participants with homographic homophones (words with two meanings but the same spelling and sound; e.g., cancer) and asked their participants to write down words related to each homophone. They found that participants with OCD generated both more negative and more OC-related words than the control group, demonstrating a bias towards negative and OC-related interpretations. In another study, Jhung et al. (2010) presented individuals with OCD with unambiguous and ambiguous facial stimuli (morphed emotional faces) and asked their participants to identify the expressed emotion. Groups did not differ in their ratings of unambiguous faces; however,
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individuals with OCD were more likely than controls to judge the ambiguous faces as expressing disgust. While pivotal in expanding research of interpretation bias in OCD to ambiguous information, the authors in the above studies did not examine the predictive validity of their measures of biased interpretation. For example, behavioral approach tests (BATs) are a useful method of assessing real life predictive validity of self-report measures (Najmi et al. 2012). Moreover, in none of these studies did the authors include an anxious control group. Therefore, it is difficult to examine the specificity of this bias to OCD. In the present study, we developed a new measure of interpretation bias in individuals with OC symptoms. We predicted that individuals high in OC symptoms would differ from non-anxious/non-dysphoric controls in their interpretation bias for threat and benign relatedness ratings. To test the specificity of this bias to OCD we included an anxious/dysphoric control group. In a second experiment we tested the predictive validity of this measure when participants performed a BAT. Procedures for both experiments were approved by the San Diego State University Institutional Review Board.
Experiment 1 Method
presented in Table 1. Participants in the current study (N = 103) comprised three groups selected from a larger, unselected sample (N = 127) based on their scores on the Obsessive Compulsive Inventory-Revised (OCI-R; Foa et al. 2002): individuals with obsessive-compulsive symptoms (OC; n = 38), individuals matched to the OC group for anxious/dysphoric symptoms (MAD; n = 34), and a nonanxious/non-dysphoric control group (NAD; n = 31). Because we utilized data from an analogue sample in the current study, we selected an OC group with a mean OCI-R total score (M = 27.00, SD = 9.36) comparable to that of OCD patients (M = 26.3, SD = 12.8; Foa et al. 2002). We selected the MAD group to match the OC group in levels of anxiety and depression, but differing in OC scores. To this end, participants were included in the MAD group if they scored C8 on the Beck Depression Inventory-II (BDI-II; Beck et al. 1996), but B10 on the OCI-R. Additionally, this resulted in matched scores on the State-Trait Anxiety Inventory-Trait version (STAI-T; Spielberger et al. 1983) for both the OC and MAD groups. We also included a nonanxious/non-dysphoric (NAD) control group, which differed from both the OC and MAD groups on anxiety and depression symptoms. Participants in the NAD group were selected if they scored B7 on the BDI-II, which also resulted in STAIT scores that differed from the OC and MAD groups. Additionally, we selected participants for the NAD group to match the MAD group in OC symptoms, but to differ from the OC group (NAD group selected if OCI-R B11).
Participants Measures Participants were students recruited from San Diego State University who received course credit for their participation. The participants’ demographics for each experiment are
The Beck Depression Inventory-II (BDI-II; Beck et al. 1996) is a reliable and well-validated 21-item self-report
Table 1 Demographics and questionnaire data Experiment 1
Experiment 2
OC (n = 38)
NAD (n = 31)
MAD (n = 34)
N = 70
50a
48.4a
55.9a
44.3
Age
19.29 (0.98)a
19.61 (1.65)a
19.24 (1.42)a
18.74 (0.96)
Education
13.61 (0.95)a
13.94 (1.06)a
13.68 (1.55)a
13.30 (1.05)
BDI-II
17.00 (10.35)a
3.68 (2.39)b
13.94 (6.10)a
13.30 (1.05)
STAI-T
46.71 (10.77)a
31.68 (7.41)b
44.24 (10.34)a
39.97 (11.56)
OCI-R
27.00 (9.36)a
4.52 (3.38)b
5.82 (2.92)b
16.17 (11.07)
Threat Benign
2.61 (0.51)a 3.01 (0.53)a
1.89 (0.69)b 2.92 (0.85)a
1.90 (0.65)b 2.98 (0.77)a
2.98 (0.95) 4.02 (0.81)
Bias
0.40 (0.65)a
1.03 (0.85)b
1.07 (0.62)b
1.04 (0.88)
% Female
Means with different subscripts are significantly different OC obsessive-compulsive group, NAD non-anxious/non-dysphoric group, MAD matched anxious/dysphoric group, BDI-II Beck Depression Inventory-II, STAI-T State-Trait Anxiety Inventory-Trait version, OCI-R Obsessive Compulsive Inventory-Revised, Threat mean relatedness score of the OC-related threat word to the ambiguous sentence on the Word Sentence Association Test for OCD (WSAO), Benign mean relatedness score of the benign word to the ambiguous sentence on the WSAO; Bias = bias score of the WSAO (benign—threat)
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measure. Internal consistency was good (a = .81) for the total measure. We also analyzed internal consistency of the two subscales of the WSAO: benign words (a = .72) and threat words (a = .77). Procedure Participants completed a consent form, demographics questionnaire, the OCI-R, BDI-II, STAI-T, and WSAO. Results The three groups did not differ in age [F(2, 100) = 0.73, p = .482], years of education [F(2, 100) = 0.68, p = .508], or gender [v2(2) = 0.42, p = .812], as shown in Table 1. However, as expected, groups differed significantly in their scores on the OCI-R [F(2, 100) = 147.88, p \ .001], BDI-II [F(2, 100) = 30.13, p \ .001], and STAI-T [F(2, 100) = 22.59, p \ .001]. Follow up t tests revealed that the OC group endorsed significantly more OC symptoms than both the MAD [t(70) = 12.65, p \ .001] or the NAD group [t(67) = 12.71, p \ .001]. The OC and MAD groups did not differ on depression [t(70) = 1.51, p = .137) or anxiety [t(70) = 0.99, p = .324], but both groups differed significantly from the NAD group on these measures (ps \ .001). Next, we conducted a 3 (Group: OC, MAD, NAD) 9 2 (Word type: threat, benign) mixed model ANOVA with repeated measures on the second factor. Results revealed significant effects of Group [F(2, 100) = 5.53, p = .005] and Word type [F(1, 100) = 142.14, p \ .001], that were qualified by a Group 9 Word type interaction [F(2, 100) = 10.22, p \ .001] (see Fig. 1). To examine this interaction further, we compared groups on each word type. These analyses revealed that groups differed significantly in their rating of relatedness of threat words [F(2, 100) = 16.04, p \ .001] but not in 3.5
Mean Word Relatedness
measure of depressive and dysphoric symptoms. Items on the BDI-II are rated on a 0–3 point scale. The BDI-II has been shown to have good psychometric properties in college populations (Beck et al. 1996; Steer and Clark 1997), and internal consistency for the current study was good (a = .89). The Obsessive Compulsive Inventory-Revised (OCI-R; Foa et al. 2002) is an 18-item measure assessing general OC symptoms. The OCI-R comprises six subscales: obsessing, washing, checking, neutralizing, ordering, and hoarding. Each item is rated on a five-point Likert scale, with the total score ranging from 0 to 72 and each subscale ranging from 0 to 12. The scale has been shown to have good psychometric properties both in patient populations (Foa et al. 2002) as well as student populations (Hajcak et al. 2004). Internal consistency for the current study was acceptable (a = .74). The State-Trait Anxiety Inventory-Trait version (STAIT; Spielberger et al. 1983) is a measure of trait anxiety, i.e., participants’ typical level of anxiety regardless of their feelings at the time the measure is completed. The STAI-T is a 20-item questionnaire with items measured on a 4-point response scale ranging from 1 (Almost Never) to 4 (Almost Always). This measure has adequate psychometric properties in nonclinical undergraduate populations (Ramanaiah et al. 1983), and has demonstrated discriminant validity in differentiating psychiatric patients with and without an anxiety disorder (Kabacoff et al. 1997). Internal consistency was excellent (a = .90). The Word Sentence Association Test for OCD (WSAO) comprises 20 distinct ambiguous OC-related sentences across multiple domains of OC symptoms. Ten sentences are followed by an OC-related threat word and ten are followed by a benign word. For example, participants see the sentence ‘‘Part of the floor you are walking on is brown’’ and then circle how related the sentence is to the word ‘‘excrement’’ (threat word). Participants also see ambiguous sentences paired with benign words, such as ‘‘There were many appliances running when you left’’ and rate how related the sentence is the word ‘‘busy’’. Other examples from the WSAO include (1) ‘‘You notice a slight flaw in the otherwise smooth surface of the curtains’’ paired with the threat word ‘‘fix’’ and (2) ‘‘You lean against a wall in the gym’’ paired with the benign word ‘‘rest’’ (see Appendix for full measure). Participants are instructed to circle a number indicating how related the sentence and the word are to each other. Higher ratings of relatedness for threat words to the ambiguous sentences compared to ratings of relatedness of benign words to the ambiguous sentences reveals a threat interpretation bias on this measure. Conversely, higher ratings of relatedness for benign words to the ambiguous sentences compared to ratings of relatedness of threat words to the ambiguous sentences reveals a benign interpretation bias on this
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3 OC NAD MAD
2.5 2 1.5 1 0.5 0 Threat
Benign
Benign Bias
Fig. 1 Experiment 1 mean relatedness score for word type and bias score by group
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rating relatedness of benign words [F(2, 100) = 0.13, p = .875]. Follow up t tests revealed that the OC group rated threat words as significantly more related to the ambiguous sentences than did the NAD [t(67) = 4.97, p \ .001] and MAD [t(70) = 5.14, p \ .001] groups, but MAD and NAD groups did not differ in their ratings of the threat words (p [ .9). All groups rated benign words as significantly more related than threat words to the ambiguous sentences (ps \ .001). Because all participants rated the benign words as more related to the sentences than the threat words, we also calculated a benign interpretation bias score by subtracting mean threat relatedness ratings from mean benign relatedness ratings. We then compared the three selected groups on their benign bias scores. There was a significant effect of group [F(2, 100) = 10.22, p \ .001]. Follow up t tests revealed that the OC group had a significantly lower benign bias than both the NAD group [t(67) = 3.49, p = .001] and the MAD group [t(70) = 4.50, p \ .001], as shown in Table 1. The MAD and NAD group did not differ in this bias score [t(63) = 0.24, p = .813]. Discussion In this experiment we tested the role of interpretation in individuals high in OC symptoms. The internal consistency for the scores of the WSAO was good. Moreover, WSAO threat interpretations, as well as difference scores, demonstrated initial utility in measuring OC-related interpretation by differentiating individuals with OC symptoms and both matched anxious/dysphoric controls and nonanxious/non-dysphoric controls. Although the OC group rated the threat words as more related to the ambiguous sentences relative to controls, participants in all groups rated benign words as more related than threat words to the ambiguous sentences. This finding likely reflects the higher frequency and the greater familiarity of the neutral words (e.g., ‘‘paint’’) than threat words (e.g., ‘‘excrement’’) in all participants. To examine scores between groups in benign interpretation bias, we calculated a relative difference score between ratings of relatedness for benign versus threat words. This simplified difference score may offer clinical utility in assessing changes in interpretation bias. As hypothesized, the OC group demonstrated less of a benign bias (as well as greater endorsement of threat relatedness) than either the anxious/dysphoric or nonanxious/non-dysphoric controls. Because the OC group and MAD group were matched on depression and anxiety, it is unlikely that results were caused by these variables. Thus, both increased threat interpretations and reduced benign bias measured by WSAO appears to be specific to OC symptoms. In summary, our results suggest that WSAO is a reliable and sensitive measure of interpretation in individuals with
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OC symptoms. However, exclusive reliance on self-report of symptoms limits our enthusiasm for the current results. If WSAO is a useful measure of interpretation in individuals with OC symptoms, and if as cognitive models of OCD suggest, faulty interpretations play a causal role OCD (OCCWG 2003), then WSAO threat interpretations should predict participants’ responses to a realistic OC-related BAT (Najmi et al. 2012). Therefore, in the second experiment we tested the hypothesis that WSAO would predict number of steps completed in a BAT.
Experiment 2 Method Participants Participants were students recruited from San Diego State University who received course credit for their participation. Participants responded to an advertisement for individuals who ‘‘have concerns about germs, dirt, or contamination,’’ and comprised a different group of 70 individuals than those participants in Experiment 1. The average participant score was 3.17 (SD = 3.01) on the OCI-R washing subscale. Previous research has employed a cutoff score of 4 points on the OCI-R washing subscale to differentiate between individuals high and low in OC contamination-related symptoms (Najmi et al. 2010). However, in the current study we wanted to include a range of OCD symptoms. Moreover, participants had an average score of 16.17 points (SD = 11.07) on the OCI-R total measure, with 36% of participants (n = 25) scoring at or above the cutoff score of 21 points as recommended by Foa et al. (2002) for clinical differentiation of OCD from NACs. See Table 1 for detailed demographic and selfreport information. Measures Participants completed identical measures as those used in Experiment 1 (BDI-II, OCI-R, STAT-T, WSAO). Additionally, participants completed a BAT (Najmi et al. 2012; also see Cougle et al. 2007; Steketee et al. 1996). In brief, the BAT comprised three different behavioral tests that assessed avoidance of various OC contamination-related materials. The first BAT consisted of a pile of ‘‘dirty’’ underwear and clothing that participants were lead to believe ‘‘may have been touched with bodily fluids.’’ The second BAT consisted of a mixture of dirt, dead insects, and cat hair. The third BAT consisted of a toilet with the lid up that was made to look dirty with splotches of potting soil on the seat and inside of the bowl. Each BAT
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Table 2 Behavioral approach test (BAT) Dirty laundry
Dirt, dead insects, and cat hair mixture
Toilet seat
Step 1
Touch laundry with sheet of tissue
Touch mixture with a sheet of tissue
Touch toilet seat with a sheet of tissue
Step 2
Touch laundry with a finger
Touch mixture with a finger
Touch toilet seat with a finger
Step 3
Touch laundry with one hand
Touch mixture with one hand
Touch toilet seat with one hand
Step 4
Touch laundry with both hands
Touch mixture with both hands
Touch toilet seat with both hands
Step 5
Touch laundry, then touch arms and chest
Touch mixture, then touch arms and chest
Touch toilet seat, then touch arms and chest
Step 6
Touch laundry, then touch face
Touch mixture, then touch face
Touch toilet seat, then touch face
comprised six steps in a graduated hierarchy (see Table 2). If participants were able to complete the first step, they were asked to complete the next one in the hierarchy and if they refused to perform a step, that BAT was terminated. Instructions for each BAT were as follows: What I’m going to ask you to do now is a test of your ability to approach a feared situation for as long as you comfortably can. It is not a test of courage. You are free to refuse to engage in the task, so you can end the task at any point. If you do wish to stop the task, please let me know. This measure has good psychometric properties (Najmi et al. 2012). Because the purpose of Experiment 2 was to examine the predictive validity of the WSAO specifically for a contamination-related BAT, the washing subscale of the OCI-R was used in all main analyses. Internal consistency remained acceptable for the WSAO (a = .75), and was also acceptable for the OCI-R washing subscale (a = .78). Procedure Participants completed a consent form, demographics questionnaire, the OCI-R, BDI-II, STAI-T, and WSAO. Participants then completed the BAT and were debriefed. Results In order to replicate the results of Experiment 1, we first compared participants’ relatedness ratings for benign and threat words. As in Experiment 1, participants rated benign words as significantly more related to the ambiguous sentences than threat words [t(69) = 9.86, p \ .001]. To determine performance on the BAT, we calculated mean percentage of steps completed by dividing the number of steps completed by the total number of BAT steps. On average, participants completed 60.16% of BAT steps (SD = 33.52). The number of BAT steps completed was significantly negatively correlated with both the OCIR washing subscale (r = -.24, p = .048) and WSAO threat relatedness ratings (r = -.29, p = .014), but was
not correlated with WSAO benign relatedness ratings (r = -.01, ns). Additionally, the OCI-R washing subscale was significantly correlated with endorsement of threat relatedness (r = .27, p = .022), but was not significantly correlated with endorsement of benign relatedness (r = .22, p = .073). To determine whether the WSAO was more useful than symptom measures in predicting behavioral contaminationrelated OC avoidance, we conducted hierarchical regression analyses to predict BAT percentage of steps completed using the variables of interest. In model one, we used WSAO threat relatedness ratings and benign relatedness ratings as predictors. WSAO threat relatedness was a significant predictor of BAT percentage of steps completed (b = -.39, t = 2.91, p = .005) but benign relatedness was not (b = -19, t = 1.40, p = .165). Next, we examined the predictive utility of WSAO threat and benign relatedness ratings when accounting for endorsement of OC contamination-related symptoms. WSAO threat relatedness significantly predicted BAT percentage of steps completed (b = -.35, t = 2.58, p = .012), but benign relatedness (b = .21, t = 1.56, p = .124) and OCI-R washing subscale scores (b = -.19, t = 1.57, p = .121) did not. Finally, we controlled for anxiety and depression symptoms by adding STAI-T and BDI-II scores in a third model. WSAO threat relatedness was again the only significant predictor of BAT percentage of steps completed (b = -.33, t = 2.31, p = .024). WSAO benign relatedness (b = .23, ns), OCI-R washing subscale (b = -.19, ns), BDI-II (b = -.11, ns), and STAI-T (b = .01, ns) were not significant predictors of steps completed (see Table 3). Discussion In Experiment 2 we tested whether the WSAO was superior to endorsement of OC contamination symptoms in predicting behavior for a test of contamination-related avoidance. Consistent with previous research suggesting that this particular BAT is a valid measure of OC contamination-related avoidance (Najmi et al. 2012), in the current study endorsement of OC contamination symptoms
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Table 3 Summary of regression analyses for variables predicting BAT percentage of steps completed SE B
b
-13.71
4.71
7.69
5.48
-12.25 8.49
OCI-R washing Step 3c WSAO-threat
Variable
B
t
Sig. (p)
-.39
2.91
.005
.19
1.40
.165
4.75
-.35
2.58
.012
5.45
.21
1.56
.124
-2.08
1.33
-.19
1.57
.121
-11.53
4.99
-.33
2.31
.024
9.49
5.70
.23
1.67
.101
OCI-R washing
-2.10
1.34
-.19
1.57
.123
BDI-II
-0.39
0.68
-.11
0.57
.571
0.02
0.55
.01
0.04
.967
Step 1a WSAO-threat WSAO-benign Step 2b WSAO-threat WSAO-benign
WSAO-benign
STAI-T a
2
Step 1: R = .11, p = .019
b
Step 2: R2 = .14, p = .016; DR2 = .03, p = .12
c
Step 3: R2 = .14, p = .053; DR2 = .01, p = .78
was significantly negatively correlated with BAT performance, such that greater self-reported contamination concerns were associated with fewer steps completed. However, when accounting for OC-related threat interpretations, self-reported OC symptoms did not predict behavioral avoidance. This suggests that the degree to which individuals interpret ambiguous situations as threatening may better predict actual OC contaminationrelated behavior than does self-report of OC, anxiety, or depressive symptoms.
General Discussion Using a new measure (WSAO), we examined interpretations of ambiguous sentences in individuals with OC symptoms. As hypothesized, we found that individuals high in OC symptoms endorsed more threat-related interpretations, relative to both non-anxious/non-dysphoric and matched anxious/dysphoric controls. Groups, however, did not differ on endorsement of benign relatedness ratings. Although participants across both experiments, regardless of group, rated benign words as more related than threat words to the ambiguous sentences, analyses of a difference score (benign relatedness ratings minus threat relatedness ratings) suggested that non-OC individuals may be characterized by a bias towards benign information that is reduced in individuals with OC symptoms (see Fig. 1). Why would a measure of interpretation (e.g., WSAO threat relatedness) be a better predictor of performance on an actual behavioral test than symptom measures (e.g.,
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OCI-R)? One possibility is that self-report of symptoms may not be an accurate predictor of anxiety-related behavior. Indeed, some researchers have questioned the utility of self-report measures (MacLeod 1993). The WSAO, though a self-report measure, assesses cognitive variables that are implicated in the etiology of OCD. As such, it may have better predictive validity than symptom measures. As previously discussed, the OCCWG developed the III (OCCWG 2001) in order to study dysfunctional interpretations of intrusive thoughts that are theorized to be central to the etiology and maintenance of OCD (e.g., Clark and Purdon 1993; Rachman 1997, 1998; Salkovskis 1985, 1989). However, this measure requires an individual to identify thoughts that are ‘‘intrusive or inappropriate’’ (which, by definition, are threat-related for that individual), rather than to measure biased interpretation of ambiguous scenarios towards threat versus benign explanations. While the III assesses several important aspects of cognition in OCD, our results suggest that it is also useful to utilize measures that assess interpretation of ambiguous information, as these may depict some of the daily experiences of individuals with OCD and may predict actual OC-related behavior on a BAT. In an attempt to examine biased interpretation specifically of ambiguous information in OCD, Jelinek et al. (2009) presented participants with homographic homophones and found that individuals with OCD were more likely to generate negative and OC-related responses than the control group. In a similar attempt to measure perception bias towards disgust in OCD, Jhung et al. (2010) used a facial expression recognition task and found that OCD participants, relative to NACs, were more likely to perceive disgust in ambiguous facial expressions. Though these studies are seminal in terms of developing an understanding of biased interpretation of ambiguous information in OCD, neither Jelinek and colleagues nor Jhung et al. included an anxious control group. Therefore it is not clear whether their results can be attributed to general level of anxiety, rather than OCD symptoms in particular. To address this issue, we included a control group matched to the OC group in anxiety and depressive symptoms in our first experiment. Our results suggest that individuals high in OC symptoms, relative to controls, make more threat-related interpretations that is not likely explained by either anxiety or depressive symptoms in general. Given the central nature of biased interpretation in OCD, it is surprising that few attempts have been made to develop a reliable, valid measure of this bias in individuals with OC symptoms. The approach of investigating interpretation of ambiguous information has clinical utility. Rachman (1997) suggested that in order for OCD treatment to be effective, it must focus on weakening threatening misinterpretations of thoughts (thus strengthening a benign,
Cogn Ther Res (2013) 37:232–241
rather than threat bias). The goal of cognitive therapy in OCD is not to eliminate all intrusions, but rather, to change the way individuals interpret those thoughts or obsessions (e.g., interpreting a brown spot on the carpet as a coffee stain rather than as excrement). Therefore, clinicians can use the WSAO to help identify specific situations and content areas that are interpreted in a threat-relevant way by their patients. The WSAO might also serve as a baseline in clinical practice and research to assess interpretation bias towards OC concerns, and may help predict performance on a behavioral measure of OC symptoms. Our preliminary data from patients receiving treatment for OCD (N = 14) indicates a significant increase in WSAO benign bias from pre- to post-treatment. Future research should further examine differences in the WSAO before and after treatment for OCD to determine if this measure is sensitive to clinical change. Recent research suggests that OC-related maladaptive interpretations of intrusive thoughts can be successfully retrained towards benign interpretations (Clerkin and Teachman 2011). Similarly, the WSAO has the potential to be modified for use as a computerized interpretation training paradigm. For example, Beard and Amir (2008) trained individuals with social anxiety to endorse benign, rather than threatening, interpretations of ambiguous social scenarios. Training over a four week period resulted in reduction of social anxiety symptoms. Thus, that program may be adapted using the WSAO to train patients with OCD to interpret ambiguous scenarios as benign. Our study has several limitations. Both experiments are based on non-clinical samples and therefore may not generalize to individuals with a clinical diagnosis of OCD. In Experiment 1, we attempted to address this concern by selecting individuals for the OC group with OCI-R scores comparable to those of OCD patients (Foa et al. 2002). Nevertheless, these findings should be replicated in a sample of patients diagnosed with OCD. We also note that a high proportion of participants from the original, unselected sample in Experiment 1 fell within the clinical range of the OCI-R, which may not be representative of the population at large and highlights a potential disadvantage of using self-report measures to assess clinical characteristics
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in analogue samples. Additionally, it would have been useful to include other measures of interpretation bias in OCD (e.g., III) in order to compare the predictive validity of the WSAO to these measures. Finally, due to standardization concerns and the nature of the BAT used in Experiment 2, we focused specifically on contamination-related OC behavior, rather than a broader range of OC behavior. Although results from Experiment 1 demonstrate that individuals differ in interpretation when selected based on OC symptoms in general (OCI-R total score) rather than OC contamination symptoms in particular, we cannot assume that WSAO threat interpretation predicts behavior across multiple OC symptom domains. Therefore, future research should attempt to validate this measure across other OC subtypes. We also note that the overall regression model in Experiment 2, Step 3 was not significant (see Table 3), and that the R2 values and the strength of the correlation coefficients between measures were somewhat weak. Continued research may indicate whether these relationships are stronger in a clinical sample of individuals diagnosed with OCD. Despite these limitations, the WSAO offers a substantial contribution to the study of interpretation bias in OCD—an area that is theoretically rich but remains lacking in terms of assessment.
Appendix Word Sentence Association Test for OCD (WSAO) Please read each sentence below and the word next to it. Then circle a number indicating how related the sentence and the word are to each other for you personally. In making your decision please use the scale below to rate their degree of relatedness. For example: if the sentence is ‘‘You plan a fun activity for the weekend’’ and the word is ‘‘beach’’, and you think that going to the beach would be a fun activity so they are very much related you would circle a 7. However, If you think that going to the beach would not be a fun activity, you would circle a 1. In answering these questions, please be sure to use the entire scale (1 through 7).
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Cogn Ther Res (2013) 37:232–241 1 2 Not at all related
3 Somewhat related
4
7 Very Much related
1 You see a spot of dirt on your rug.
Pa in t
1 2 3 4 5 6 7
2 You smell something foul .
Toxic
1 2 3 4 5 6 7
3 There were many appliances running when you left.
Busy
1 2 3 4 5 6 7
4 You turned the water off before you left the house. You notice a slight flaw in the otherwise smooth surface of the 5 curtains.
Errands
1 2 3 4 5 6 7
Fix
1 2 3 4 5 6 7
6 You lean against the wall in the gym.
Rest
1 2 3 4 5 6 7
7 You g et dust in yo ur ey e s .
B l ink
1 2 3 4 5 6 7
8 You laid on your side and realize your hip and arm are numb.
Neurological
1 2 3 4 5 6 7
9 Y ou cannot find your favo rite sh irt .
B orr o wed
1 2 3 4 5 6 7
10 You feel a bump as you drive.
Hit and Run
1 2 3 4 5 6 7
11 You rehearse numbers in your head.
Continuously
1 2 3 4 5 6 7
12 After the meal you feel hot and sweaty.
Heart Problem
1 2 3 4 5 6 7
13 You think using the bathroom cleanser will make you sick.
Unreasonable
1 2 3 4 5 6 7
14 Part of the floor you are walking on is brown.
Excrement
1 2 3 4 5 6 7
15 There are many appliances running in your house.
Electricity
1 2 3 4 5 6 7
16 You shake hand with others.
Wash
1 2 3 4 5 6 7
17 You catch the baseball as your friend tosses it to you.
Coordination
1 2 3 4 5 6 7
18 While running errands you feel a hot flash.
Disabled
1 2 3 4 5 6 7
19 You don't have time to finish cleaning the bathroom.
Germs
1 2 3 4 5 6 7
20 You do not wi pe the toilet seat.
Clean
1 2 3 4 5 6 7
References American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders. (4th ed., text rev.). Washington, DC: Author. Amir, N., Foa, E. B., & Coles, M. E. (1998). Negative interpretation bias in social phobia. Behaviour Research and Therapy, 36, 945–957. doi:10.1016/S0005-7967(98)00060-6. Beard, C., & Amir, N. (2008). A multi-session interpretation modification program: Changes in interpretation and social anxiety symptoms. Behaviour Research and Therapy, 46, 1135– 1141. doi:10.1016/j.brat.2008.05.012. Beard, C., & Amir, N. (2009). Interpretation in social anxiety: When meaning precedes ambiguity. Cognitive Therapy and Research, 33, 406–415. doi:10.1007/s10608-009-9235-0. Beck, A. T., Steer, R. A., & Brown, G. K. (1996). Manual for the Beck Depression Inventory-II. San Antonio, TX: Psychological Corporation. Buhlmann, U., Wilhelm, S., McNally, R. J., Tuschen-Caffier, B., Baer, L., & Jenike, M. A. (2002). Interpretive biases for ambiguous information in body dysmorphic disorder. CNS
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Cogn Ther Res (2013) 37:232–241 Foa, E. B., Huppert, J. D., Leiberg, S., Langner, R., Kichic, R., Hajcak, G., et al. (2002). The Obsessive-Compulsive Inventory: Development and validation of a short version. Psychological Assessment, 14, 485–496. doi:10.1037/1040-3590.14.4.485. Hajcak, G., Huppert, J. D., Simons, R. F., & Foa, E. B. (2004). Psychometric properties of the OCI-R in a college sample. Behaviour Research and Therapy, 42, 115–123. doi:10.1016/j. brat.2003.08.002. Jelinek, L., Hottenrott, B., & Moritz, S. (2009). When cancer is associated with illness but no longer with animal or zodiac sign: Investigation of biased semantic networks in obsessive-compulsive disorder (OCD). Journal of Anxiety Disorders, 23, 1031–1036. doi:10.1016/j.janxdis.2009.07.003. Jhung, K., Namkoong, K., Kang, J. I., Ha, R. Y., An, S. K., Kim, C. H., et al. (2010). Perception bias of disgust in ambiguous facial expressions in obsessive-compulsive disorder. Psychiatry Research, 178, 126–131. doi:10.1016/j.psychres.2009.11.023. Kabacoff, R. I., Segal, D. L., Hersen, M., & Van Hasselt, V. B. (1997). Psychometric properties and diagnostic utility of the Beck Anxiety Inventory and the State-Trait Anxiety Inventory with older adult psychiatric outpatients. Journal of Anxiety Disorders, 11, 33–47. doi:10.1016/S0887-6185(96)00033-3. MacLeod, C. (1993). Cognition in clinical psychology: Measures, methods or models. Behaviour Change, 10, 169–195. Najmi, S., Kuckertz, J. M., & Amir, N. (2010). Automatic avoidance tendencies in individuals with contamination-related obsessivecompulsive concerns. Behaviour Research and Therapy, 48, 1058–1062. doi:10.1016/j.brat.2010.06.007. Najmi, S., Tobin, A. C., & Amir, N. (2012). Psychometric properties of a behavioral test of contamination-related obsessive-compulsive symptoms. Cognitive Therapy and Research, 36, 228–233. doi:10.1007/s10608-010-9332-0. Obsessive Compulsive Cognitions Working Group. (1997). Cognitive assessment of obsessive-compulsive disorder. Behaviour Research and Therapy, 35, 667–681. doi:10.1016/S00057967(97)00017-X. Obsessive Compulsive Cognitions Working Group. (2001). Development and initial validation of the obsessive beliefs questionnaire and the interpretation of intrusions inventory. Behaviour Research and Therapy, 39, 987–1006. doi:10.1016/S0005-7967(00)000 85-1. Obsessive Compulsive Cognitions Working Group. (2003). Psychometric validation of the obsessive beliefs questionnaire and the interpretation of intrusions inventory: Part I. Behaviour Research and Therapy, 41, 863–878. doi:10.1016/S0005-7967(02)00099-2.
241 Olatunji, B. O., Connolly, K., Lohr, J. M., & Elwood, L. S. (2008). Access to information about harm and safety in contaminationrelated obsessive-compulsive disorder. Journal of Cognitive Psychotherapy, 22, 57–67. doi:10.1891/0889.8391.22.1.57. Rachman, S. (1997). A cognitive theory of obsessions. Behaviour Research and Therapy, 35, 793–802. doi:10.1016/S0005-7967 (97)00040-5. Rachman, S. (1998). A cognitive theory of obsessions: Elaborations. Behaviour Research and Therapy, 36, 385–401. doi:10.1016/ S0005-7967(97)10041-9. Ramanaiah, N. V., Franzen, M., & Schill, T. (1983). A psychometric study of the State-Trait Anxiety Inventory. Journal of Personality Assessment, 47, 531–535. doi:10.1207/s15327752jpa4705_ 14. Richards, J. C., Austin, D. W., & Alvarenga, M. E. (2001). Interpretation of ambiguous interoceptive stimuli in panic disorder and nonclinical panic. Cognitive Therapy and Research, 25, 235–246. doi:10.1023/A:1010783427196. Salkovskis, P. M. (1985). Obsessional-compulsive problems: A cognitive-behavioural analysis. Behaviour Research and Therapy, 23, 571–583. doi:10.1016/0005-7967(85)90105-6. Salkovskis, P. M. (1989). Obsessions and compulsions. In J. Scott, J. M. G. Williams, & A. T. Beck (Eds.), Cognitive therapy in clinical practice: An illustrative casebook (pp. 50–77). Florence, KY: Taylor & Frances/Routledge. doi:10.4324/97802033593 65_chapter_three. Spielberger, C., Gorsuch, R., Lushene, R., Vagg, P. R., & Jacobs, G. A. (1983). Manual for the State-Trait Anxiety Inventory: STAI (Form Y). Palo Alto, CA: Consulting Psychologist Press. Steer, R. A., & Clark, D. A. (1997). Psychometric characteristics of the Beck Depression Inventory–II with college students. Measurement and Evaluation in Counseling and Development, 30, 128–136. Steketee, G., Chambless, D. L., Tran, G. Q., Worden, H., & Gillis, M. M. (1996). Behavioral avoidance test for obsessive compulsive disorder. Behaviour Research and Therapy, 34, 73–83. doi: 10.1016/0005-7967(95)00040-5. Stopa, L., & Clark, D. M. (2000). Social phobia and interpretation of social events. Behaviour Research and Therapy, 38, 273–283. doi:10.1016/S0005-7967(99)00043-1. Voncken, M. J., Bo¨gels, S. M., & de Vries, K. (2003). Interpretation and judgmental biases in social phobia. Behaviour Research and Therapy, 41, 1481–1488. doi:10.1016/S0005-7967(03)00143-8.
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ORIGINAL ARTICLE
Interpretation of Ambiguity in Individuals with Obsessive-Compulsive Symptoms Jennie M. Kuckertz • Nader Amir Anastacia C. Tobin • Sadia Najmi
•
Published online: 17 August 2012 Ó Springer Science+Business Media, LLC 2012
Abstract In two experiments we examined the psychometric properties of a new measure of interpretation bias in individuals with obsessive-compulsive symptoms (OCs). In Experiment 1, 38 individuals high in OC symptoms, 34 individuals high in anxiety and dysphoric symptoms, and 31 asymptomatic individuals completed the measure. Results revealed that the Word Sentence Association Test for OCD (WSAO) can differentiate those with OC symptoms from both a matched anxious/dysphoric group and a non-anxious/ non-dysphoric group. In a second experiment, we tested the predictive validity of the WSAO using a performance-based behavioral approach test of contamination fears, and found that the WSAO was a better predictor of avoidance than an established measure of OC washing symptoms (Obsessive Compulsive Inventory-Revised, washing subscale). Our results provide preliminary evidence for the reliability and validity of the WSAO as well as its usefulness in predicting response to behavioral challenge above and beyond OC symptoms, depression, and anxiety. Keywords Interpretation bias Obsessive-compulsive disorder Behavioral approach test Information processing
Introduction Obsessive-compulsive disorder (OCD) is characterized by intrusive thoughts and images that increase anxiety.
J. M. Kuckertz N. Amir (&) A. C. Tobin S. Najmi Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California, San Diego, 6386 Alvarado Ct., Suite 301, San Diego, CA 92120, USA e-mail: [email protected]
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Individuals with OCD perform rituals to prevent or alleviate this anxiety. These symptoms cause significant distress, marked interference, and are time consuming (American Psychiatric Association 2000). Cognitive models of OCD suggest that dysfunctional interpretations of thoughts (e.g., Clark and Purdon 1993; Rachman 1997, 1998; Salkovskis 1985, 1989) contribute to the origin and maintenance of this disorder. For example, according to Salkovskis (1985), intrusive thoughts with themes of responsibility, blame, or control become obsessions when they are interpreted as being important. Furthermore, the focus of cognitive therapy for OCD is on changing interpretations of thoughts, rather than stopping the thoughts themselves. Another model of OCD proposed by Rachman (1997) suggests that obsessions are caused by misinterpreting the importance of one’s thoughts. Thus, eliminating these misinterpretations should help decrease obsessions in OCD. Based on these theories, the Obsessive Compulsive Cognitions Working Group (OCCWG 1997) concluded that the primary cause of obsessions in OCD is flawed interpretations of the intrusive thoughts (OCCWG 2003). In response to current models of OCD, the OCCWG developed the Interpretation of Intrusions Inventory (III; OCCWG 2001). The III is a 31-item questionnaire that examines interpretations of intrusive thoughts, impulses, or images. Three subscales capture self-reported interpretations of general intrusions, including: (1) responsibility, (2) importance of thoughts, and (3) control of thoughts. This scale has good psychometric properties in participants with OCD (OCCWG 2001). Using a revised III with 19 items, Ferguson et al. (2006) found that scores on the III were predictive of OCD symptom severity and OCD subtypes in an undergraduate sample. The III requires an individual to first identify two unwanted thoughts that the individual considers intrusive or inappropriate and then to answer a
Cogn Ther Res (2013) 37:232–241
series of questions focused on appraisals of those and similar intrusions (e.g., ‘‘Because I have this thought, it must be important’’). The participant rates the frequency of specific intrusions and the degree to which the individual believes in the intrusive thought. Given that participants respond to the III based on their own thoughts that they have identified as personally negative, the III does not assess an individual’s initial interpretation of ambiguous information as either threat-relevant or benign. While the III is an excellent tool in assessing constructs such as the importance, responsibility, and control of personally negative intrusions, there is no selfreport questionnaire to measure interpretation of ambiguous information relevant to OCD concerns. Despite the lack of such a questionnaire assessing OCDrelevant ambiguous scenarios, some research groups have examined interpretation of ambiguous information in other forms of anxiety. Specifically, participants are presented with ambiguous information and their interpretation is measured by testing the relationship between the presented information and negative information. For example, in a seminal study, Butler and Mathews (1983) examined interpretation in individuals with generalized anxiety disorder by presenting their participants with ten ambiguous scenarios (e.g., ‘‘you wake with a start in the middle of the night, thinking you heard a noise, but all is quiet’’). The researchers asked their participants to generate a response to an openended question related to the scenario, and then to rank three experimenter-provided explanations for the scenario as to how likely they would be to come to their mind. Results from this study revealed that anxious individuals were more likely to select negative interpretations as coming to mind when compared to non-anxious individuals. Researchers have adapted this methodology to test interpretation bias in individuals with other types of anxiety and related conditions (e.g., Amir et al. 1998; Buhlmann et al. 2002; Richards et al. 2001; Stopa and Clark 2000; Voncken et al. 2003). For example, Amir et al. (1998) compared participants with social phobia (SP), nonanxious controls (NACs), and participants with OCD on an interpretation questionnaire that examined biases for social and non-social ambiguous information. Individuals with SP tended to interpret ambiguous scenarios as negative when compared to both control groups. Moreover, the OCD group displayed a negative interpretation bias for social situations when compared to the NACs. Because the ambiguity related to social scenarios rather than OCD, the SP group demonstrated the largest interpretation bias when compared to the two control groups. Groups in this study did not differ in their interpretation of non-social ambiguous information. However, this methodology is limited in that it only allows the researcher to test bias for threat or benign interpretation, as though the two are exactly inversely related. For example, it may be the case that
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individuals with OCD are more likely to interpret a spot on the floor as feces, are less likely to interpret the spot as harmless, or both, relative to controls. In a study representing a notable advance in methodology, Buhlmann et al. (2002) administered a measure of ambiguous interpretation adapted from Butler and Mathews (1983) to participants with body dysmorphic disorder (BDD), OCD, and healthy controls. This adapted measure required participants to independently rate the likelihood of negative and benign interpretations of ambiguous scenarios. In contrast to the results of Amir et al. (1998), Buhlmann et al. found that both patient groups demonstrated a negative bias towards general negative ambiguous scenarios, relative to controls. Participants with OCD and controls did not differ on their endorsement of benign interpretations of general ambiguity. Therefore, current research is conflicting as to the extent that interpretation of ambiguity is involved in OCD. Another method of testing threat and benign interpretation biases independently is the Word Sentence Association Paradigm (WSAP; Beard and Amir 2009). The WSAP presents participants with ambiguous sentences as well as threat and benign related words. Participants are asked to rate the relationship between the words and the sentences. Research suggests that socially anxious individuals rate the ambiguous sentences as more related to threat words than benign words (Beard and Amir 2009). However, this methodology has not previously been used to examine interpretation bias in OCD. A number of studies have examined biased interpretation of ambiguous information for OCD-relevant ambiguity (Jelinek et al. 2009; Jhung et al. 2010; Olatunji et al. 2008), although they have not tested the role of benign versus threat interpretations independently. Olatunji et al. (2008) found that individuals with contamination-related obsessive-compulsive (OC) symptoms, compared to a control group, identified fewer reasons why a public bathroom might be safe, and more reasons why a public bathroom might be unsafe. Nevertheless, public bathrooms by definition may be inherently threat-relevant for individuals with contamination concerns, and thus not ambiguous. Jelinek et al. (2009) presented participants with homographic homophones (words with two meanings but the same spelling and sound; e.g., cancer) and asked their participants to write down words related to each homophone. They found that participants with OCD generated both more negative and more OC-related words than the control group, demonstrating a bias towards negative and OC-related interpretations. In another study, Jhung et al. (2010) presented individuals with OCD with unambiguous and ambiguous facial stimuli (morphed emotional faces) and asked their participants to identify the expressed emotion. Groups did not differ in their ratings of unambiguous faces; however,
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individuals with OCD were more likely than controls to judge the ambiguous faces as expressing disgust. While pivotal in expanding research of interpretation bias in OCD to ambiguous information, the authors in the above studies did not examine the predictive validity of their measures of biased interpretation. For example, behavioral approach tests (BATs) are a useful method of assessing real life predictive validity of self-report measures (Najmi et al. 2012). Moreover, in none of these studies did the authors include an anxious control group. Therefore, it is difficult to examine the specificity of this bias to OCD. In the present study, we developed a new measure of interpretation bias in individuals with OC symptoms. We predicted that individuals high in OC symptoms would differ from non-anxious/non-dysphoric controls in their interpretation bias for threat and benign relatedness ratings. To test the specificity of this bias to OCD we included an anxious/dysphoric control group. In a second experiment we tested the predictive validity of this measure when participants performed a BAT. Procedures for both experiments were approved by the San Diego State University Institutional Review Board.
Experiment 1 Method
presented in Table 1. Participants in the current study (N = 103) comprised three groups selected from a larger, unselected sample (N = 127) based on their scores on the Obsessive Compulsive Inventory-Revised (OCI-R; Foa et al. 2002): individuals with obsessive-compulsive symptoms (OC; n = 38), individuals matched to the OC group for anxious/dysphoric symptoms (MAD; n = 34), and a nonanxious/non-dysphoric control group (NAD; n = 31). Because we utilized data from an analogue sample in the current study, we selected an OC group with a mean OCI-R total score (M = 27.00, SD = 9.36) comparable to that of OCD patients (M = 26.3, SD = 12.8; Foa et al. 2002). We selected the MAD group to match the OC group in levels of anxiety and depression, but differing in OC scores. To this end, participants were included in the MAD group if they scored C8 on the Beck Depression Inventory-II (BDI-II; Beck et al. 1996), but B10 on the OCI-R. Additionally, this resulted in matched scores on the State-Trait Anxiety Inventory-Trait version (STAI-T; Spielberger et al. 1983) for both the OC and MAD groups. We also included a nonanxious/non-dysphoric (NAD) control group, which differed from both the OC and MAD groups on anxiety and depression symptoms. Participants in the NAD group were selected if they scored B7 on the BDI-II, which also resulted in STAIT scores that differed from the OC and MAD groups. Additionally, we selected participants for the NAD group to match the MAD group in OC symptoms, but to differ from the OC group (NAD group selected if OCI-R B11).
Participants Measures Participants were students recruited from San Diego State University who received course credit for their participation. The participants’ demographics for each experiment are
The Beck Depression Inventory-II (BDI-II; Beck et al. 1996) is a reliable and well-validated 21-item self-report
Table 1 Demographics and questionnaire data Experiment 1
Experiment 2
OC (n = 38)
NAD (n = 31)
MAD (n = 34)
N = 70
50a
48.4a
55.9a
44.3
Age
19.29 (0.98)a
19.61 (1.65)a
19.24 (1.42)a
18.74 (0.96)
Education
13.61 (0.95)a
13.94 (1.06)a
13.68 (1.55)a
13.30 (1.05)
BDI-II
17.00 (10.35)a
3.68 (2.39)b
13.94 (6.10)a
13.30 (1.05)
STAI-T
46.71 (10.77)a
31.68 (7.41)b
44.24 (10.34)a
39.97 (11.56)
OCI-R
27.00 (9.36)a
4.52 (3.38)b
5.82 (2.92)b
16.17 (11.07)
Threat Benign
2.61 (0.51)a 3.01 (0.53)a
1.89 (0.69)b 2.92 (0.85)a
1.90 (0.65)b 2.98 (0.77)a
2.98 (0.95) 4.02 (0.81)
Bias
0.40 (0.65)a
1.03 (0.85)b
1.07 (0.62)b
1.04 (0.88)
% Female
Means with different subscripts are significantly different OC obsessive-compulsive group, NAD non-anxious/non-dysphoric group, MAD matched anxious/dysphoric group, BDI-II Beck Depression Inventory-II, STAI-T State-Trait Anxiety Inventory-Trait version, OCI-R Obsessive Compulsive Inventory-Revised, Threat mean relatedness score of the OC-related threat word to the ambiguous sentence on the Word Sentence Association Test for OCD (WSAO), Benign mean relatedness score of the benign word to the ambiguous sentence on the WSAO; Bias = bias score of the WSAO (benign—threat)
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measure. Internal consistency was good (a = .81) for the total measure. We also analyzed internal consistency of the two subscales of the WSAO: benign words (a = .72) and threat words (a = .77). Procedure Participants completed a consent form, demographics questionnaire, the OCI-R, BDI-II, STAI-T, and WSAO. Results The three groups did not differ in age [F(2, 100) = 0.73, p = .482], years of education [F(2, 100) = 0.68, p = .508], or gender [v2(2) = 0.42, p = .812], as shown in Table 1. However, as expected, groups differed significantly in their scores on the OCI-R [F(2, 100) = 147.88, p \ .001], BDI-II [F(2, 100) = 30.13, p \ .001], and STAI-T [F(2, 100) = 22.59, p \ .001]. Follow up t tests revealed that the OC group endorsed significantly more OC symptoms than both the MAD [t(70) = 12.65, p \ .001] or the NAD group [t(67) = 12.71, p \ .001]. The OC and MAD groups did not differ on depression [t(70) = 1.51, p = .137) or anxiety [t(70) = 0.99, p = .324], but both groups differed significantly from the NAD group on these measures (ps \ .001). Next, we conducted a 3 (Group: OC, MAD, NAD) 9 2 (Word type: threat, benign) mixed model ANOVA with repeated measures on the second factor. Results revealed significant effects of Group [F(2, 100) = 5.53, p = .005] and Word type [F(1, 100) = 142.14, p \ .001], that were qualified by a Group 9 Word type interaction [F(2, 100) = 10.22, p \ .001] (see Fig. 1). To examine this interaction further, we compared groups on each word type. These analyses revealed that groups differed significantly in their rating of relatedness of threat words [F(2, 100) = 16.04, p \ .001] but not in 3.5
Mean Word Relatedness
measure of depressive and dysphoric symptoms. Items on the BDI-II are rated on a 0–3 point scale. The BDI-II has been shown to have good psychometric properties in college populations (Beck et al. 1996; Steer and Clark 1997), and internal consistency for the current study was good (a = .89). The Obsessive Compulsive Inventory-Revised (OCI-R; Foa et al. 2002) is an 18-item measure assessing general OC symptoms. The OCI-R comprises six subscales: obsessing, washing, checking, neutralizing, ordering, and hoarding. Each item is rated on a five-point Likert scale, with the total score ranging from 0 to 72 and each subscale ranging from 0 to 12. The scale has been shown to have good psychometric properties both in patient populations (Foa et al. 2002) as well as student populations (Hajcak et al. 2004). Internal consistency for the current study was acceptable (a = .74). The State-Trait Anxiety Inventory-Trait version (STAIT; Spielberger et al. 1983) is a measure of trait anxiety, i.e., participants’ typical level of anxiety regardless of their feelings at the time the measure is completed. The STAI-T is a 20-item questionnaire with items measured on a 4-point response scale ranging from 1 (Almost Never) to 4 (Almost Always). This measure has adequate psychometric properties in nonclinical undergraduate populations (Ramanaiah et al. 1983), and has demonstrated discriminant validity in differentiating psychiatric patients with and without an anxiety disorder (Kabacoff et al. 1997). Internal consistency was excellent (a = .90). The Word Sentence Association Test for OCD (WSAO) comprises 20 distinct ambiguous OC-related sentences across multiple domains of OC symptoms. Ten sentences are followed by an OC-related threat word and ten are followed by a benign word. For example, participants see the sentence ‘‘Part of the floor you are walking on is brown’’ and then circle how related the sentence is to the word ‘‘excrement’’ (threat word). Participants also see ambiguous sentences paired with benign words, such as ‘‘There were many appliances running when you left’’ and rate how related the sentence is the word ‘‘busy’’. Other examples from the WSAO include (1) ‘‘You notice a slight flaw in the otherwise smooth surface of the curtains’’ paired with the threat word ‘‘fix’’ and (2) ‘‘You lean against a wall in the gym’’ paired with the benign word ‘‘rest’’ (see Appendix for full measure). Participants are instructed to circle a number indicating how related the sentence and the word are to each other. Higher ratings of relatedness for threat words to the ambiguous sentences compared to ratings of relatedness of benign words to the ambiguous sentences reveals a threat interpretation bias on this measure. Conversely, higher ratings of relatedness for benign words to the ambiguous sentences compared to ratings of relatedness of threat words to the ambiguous sentences reveals a benign interpretation bias on this
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3 OC NAD MAD
2.5 2 1.5 1 0.5 0 Threat
Benign
Benign Bias
Fig. 1 Experiment 1 mean relatedness score for word type and bias score by group
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rating relatedness of benign words [F(2, 100) = 0.13, p = .875]. Follow up t tests revealed that the OC group rated threat words as significantly more related to the ambiguous sentences than did the NAD [t(67) = 4.97, p \ .001] and MAD [t(70) = 5.14, p \ .001] groups, but MAD and NAD groups did not differ in their ratings of the threat words (p [ .9). All groups rated benign words as significantly more related than threat words to the ambiguous sentences (ps \ .001). Because all participants rated the benign words as more related to the sentences than the threat words, we also calculated a benign interpretation bias score by subtracting mean threat relatedness ratings from mean benign relatedness ratings. We then compared the three selected groups on their benign bias scores. There was a significant effect of group [F(2, 100) = 10.22, p \ .001]. Follow up t tests revealed that the OC group had a significantly lower benign bias than both the NAD group [t(67) = 3.49, p = .001] and the MAD group [t(70) = 4.50, p \ .001], as shown in Table 1. The MAD and NAD group did not differ in this bias score [t(63) = 0.24, p = .813]. Discussion In this experiment we tested the role of interpretation in individuals high in OC symptoms. The internal consistency for the scores of the WSAO was good. Moreover, WSAO threat interpretations, as well as difference scores, demonstrated initial utility in measuring OC-related interpretation by differentiating individuals with OC symptoms and both matched anxious/dysphoric controls and nonanxious/non-dysphoric controls. Although the OC group rated the threat words as more related to the ambiguous sentences relative to controls, participants in all groups rated benign words as more related than threat words to the ambiguous sentences. This finding likely reflects the higher frequency and the greater familiarity of the neutral words (e.g., ‘‘paint’’) than threat words (e.g., ‘‘excrement’’) in all participants. To examine scores between groups in benign interpretation bias, we calculated a relative difference score between ratings of relatedness for benign versus threat words. This simplified difference score may offer clinical utility in assessing changes in interpretation bias. As hypothesized, the OC group demonstrated less of a benign bias (as well as greater endorsement of threat relatedness) than either the anxious/dysphoric or nonanxious/non-dysphoric controls. Because the OC group and MAD group were matched on depression and anxiety, it is unlikely that results were caused by these variables. Thus, both increased threat interpretations and reduced benign bias measured by WSAO appears to be specific to OC symptoms. In summary, our results suggest that WSAO is a reliable and sensitive measure of interpretation in individuals with
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OC symptoms. However, exclusive reliance on self-report of symptoms limits our enthusiasm for the current results. If WSAO is a useful measure of interpretation in individuals with OC symptoms, and if as cognitive models of OCD suggest, faulty interpretations play a causal role OCD (OCCWG 2003), then WSAO threat interpretations should predict participants’ responses to a realistic OC-related BAT (Najmi et al. 2012). Therefore, in the second experiment we tested the hypothesis that WSAO would predict number of steps completed in a BAT.
Experiment 2 Method Participants Participants were students recruited from San Diego State University who received course credit for their participation. Participants responded to an advertisement for individuals who ‘‘have concerns about germs, dirt, or contamination,’’ and comprised a different group of 70 individuals than those participants in Experiment 1. The average participant score was 3.17 (SD = 3.01) on the OCI-R washing subscale. Previous research has employed a cutoff score of 4 points on the OCI-R washing subscale to differentiate between individuals high and low in OC contamination-related symptoms (Najmi et al. 2010). However, in the current study we wanted to include a range of OCD symptoms. Moreover, participants had an average score of 16.17 points (SD = 11.07) on the OCI-R total measure, with 36% of participants (n = 25) scoring at or above the cutoff score of 21 points as recommended by Foa et al. (2002) for clinical differentiation of OCD from NACs. See Table 1 for detailed demographic and selfreport information. Measures Participants completed identical measures as those used in Experiment 1 (BDI-II, OCI-R, STAT-T, WSAO). Additionally, participants completed a BAT (Najmi et al. 2012; also see Cougle et al. 2007; Steketee et al. 1996). In brief, the BAT comprised three different behavioral tests that assessed avoidance of various OC contamination-related materials. The first BAT consisted of a pile of ‘‘dirty’’ underwear and clothing that participants were lead to believe ‘‘may have been touched with bodily fluids.’’ The second BAT consisted of a mixture of dirt, dead insects, and cat hair. The third BAT consisted of a toilet with the lid up that was made to look dirty with splotches of potting soil on the seat and inside of the bowl. Each BAT
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Table 2 Behavioral approach test (BAT) Dirty laundry
Dirt, dead insects, and cat hair mixture
Toilet seat
Step 1
Touch laundry with sheet of tissue
Touch mixture with a sheet of tissue
Touch toilet seat with a sheet of tissue
Step 2
Touch laundry with a finger
Touch mixture with a finger
Touch toilet seat with a finger
Step 3
Touch laundry with one hand
Touch mixture with one hand
Touch toilet seat with one hand
Step 4
Touch laundry with both hands
Touch mixture with both hands
Touch toilet seat with both hands
Step 5
Touch laundry, then touch arms and chest
Touch mixture, then touch arms and chest
Touch toilet seat, then touch arms and chest
Step 6
Touch laundry, then touch face
Touch mixture, then touch face
Touch toilet seat, then touch face
comprised six steps in a graduated hierarchy (see Table 2). If participants were able to complete the first step, they were asked to complete the next one in the hierarchy and if they refused to perform a step, that BAT was terminated. Instructions for each BAT were as follows: What I’m going to ask you to do now is a test of your ability to approach a feared situation for as long as you comfortably can. It is not a test of courage. You are free to refuse to engage in the task, so you can end the task at any point. If you do wish to stop the task, please let me know. This measure has good psychometric properties (Najmi et al. 2012). Because the purpose of Experiment 2 was to examine the predictive validity of the WSAO specifically for a contamination-related BAT, the washing subscale of the OCI-R was used in all main analyses. Internal consistency remained acceptable for the WSAO (a = .75), and was also acceptable for the OCI-R washing subscale (a = .78). Procedure Participants completed a consent form, demographics questionnaire, the OCI-R, BDI-II, STAI-T, and WSAO. Participants then completed the BAT and were debriefed. Results In order to replicate the results of Experiment 1, we first compared participants’ relatedness ratings for benign and threat words. As in Experiment 1, participants rated benign words as significantly more related to the ambiguous sentences than threat words [t(69) = 9.86, p \ .001]. To determine performance on the BAT, we calculated mean percentage of steps completed by dividing the number of steps completed by the total number of BAT steps. On average, participants completed 60.16% of BAT steps (SD = 33.52). The number of BAT steps completed was significantly negatively correlated with both the OCIR washing subscale (r = -.24, p = .048) and WSAO threat relatedness ratings (r = -.29, p = .014), but was
not correlated with WSAO benign relatedness ratings (r = -.01, ns). Additionally, the OCI-R washing subscale was significantly correlated with endorsement of threat relatedness (r = .27, p = .022), but was not significantly correlated with endorsement of benign relatedness (r = .22, p = .073). To determine whether the WSAO was more useful than symptom measures in predicting behavioral contaminationrelated OC avoidance, we conducted hierarchical regression analyses to predict BAT percentage of steps completed using the variables of interest. In model one, we used WSAO threat relatedness ratings and benign relatedness ratings as predictors. WSAO threat relatedness was a significant predictor of BAT percentage of steps completed (b = -.39, t = 2.91, p = .005) but benign relatedness was not (b = -19, t = 1.40, p = .165). Next, we examined the predictive utility of WSAO threat and benign relatedness ratings when accounting for endorsement of OC contamination-related symptoms. WSAO threat relatedness significantly predicted BAT percentage of steps completed (b = -.35, t = 2.58, p = .012), but benign relatedness (b = .21, t = 1.56, p = .124) and OCI-R washing subscale scores (b = -.19, t = 1.57, p = .121) did not. Finally, we controlled for anxiety and depression symptoms by adding STAI-T and BDI-II scores in a third model. WSAO threat relatedness was again the only significant predictor of BAT percentage of steps completed (b = -.33, t = 2.31, p = .024). WSAO benign relatedness (b = .23, ns), OCI-R washing subscale (b = -.19, ns), BDI-II (b = -.11, ns), and STAI-T (b = .01, ns) were not significant predictors of steps completed (see Table 3). Discussion In Experiment 2 we tested whether the WSAO was superior to endorsement of OC contamination symptoms in predicting behavior for a test of contamination-related avoidance. Consistent with previous research suggesting that this particular BAT is a valid measure of OC contamination-related avoidance (Najmi et al. 2012), in the current study endorsement of OC contamination symptoms
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Table 3 Summary of regression analyses for variables predicting BAT percentage of steps completed SE B
b
-13.71
4.71
7.69
5.48
-12.25 8.49
OCI-R washing Step 3c WSAO-threat
Variable
B
t
Sig. (p)
-.39
2.91
.005
.19
1.40
.165
4.75
-.35
2.58
.012
5.45
.21
1.56
.124
-2.08
1.33
-.19
1.57
.121
-11.53
4.99
-.33
2.31
.024
9.49
5.70
.23
1.67
.101
OCI-R washing
-2.10
1.34
-.19
1.57
.123
BDI-II
-0.39
0.68
-.11
0.57
.571
0.02
0.55
.01
0.04
.967
Step 1a WSAO-threat WSAO-benign Step 2b WSAO-threat WSAO-benign
WSAO-benign
STAI-T a
2
Step 1: R = .11, p = .019
b
Step 2: R2 = .14, p = .016; DR2 = .03, p = .12
c
Step 3: R2 = .14, p = .053; DR2 = .01, p = .78
was significantly negatively correlated with BAT performance, such that greater self-reported contamination concerns were associated with fewer steps completed. However, when accounting for OC-related threat interpretations, self-reported OC symptoms did not predict behavioral avoidance. This suggests that the degree to which individuals interpret ambiguous situations as threatening may better predict actual OC contaminationrelated behavior than does self-report of OC, anxiety, or depressive symptoms.
General Discussion Using a new measure (WSAO), we examined interpretations of ambiguous sentences in individuals with OC symptoms. As hypothesized, we found that individuals high in OC symptoms endorsed more threat-related interpretations, relative to both non-anxious/non-dysphoric and matched anxious/dysphoric controls. Groups, however, did not differ on endorsement of benign relatedness ratings. Although participants across both experiments, regardless of group, rated benign words as more related than threat words to the ambiguous sentences, analyses of a difference score (benign relatedness ratings minus threat relatedness ratings) suggested that non-OC individuals may be characterized by a bias towards benign information that is reduced in individuals with OC symptoms (see Fig. 1). Why would a measure of interpretation (e.g., WSAO threat relatedness) be a better predictor of performance on an actual behavioral test than symptom measures (e.g.,
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OCI-R)? One possibility is that self-report of symptoms may not be an accurate predictor of anxiety-related behavior. Indeed, some researchers have questioned the utility of self-report measures (MacLeod 1993). The WSAO, though a self-report measure, assesses cognitive variables that are implicated in the etiology of OCD. As such, it may have better predictive validity than symptom measures. As previously discussed, the OCCWG developed the III (OCCWG 2001) in order to study dysfunctional interpretations of intrusive thoughts that are theorized to be central to the etiology and maintenance of OCD (e.g., Clark and Purdon 1993; Rachman 1997, 1998; Salkovskis 1985, 1989). However, this measure requires an individual to identify thoughts that are ‘‘intrusive or inappropriate’’ (which, by definition, are threat-related for that individual), rather than to measure biased interpretation of ambiguous scenarios towards threat versus benign explanations. While the III assesses several important aspects of cognition in OCD, our results suggest that it is also useful to utilize measures that assess interpretation of ambiguous information, as these may depict some of the daily experiences of individuals with OCD and may predict actual OC-related behavior on a BAT. In an attempt to examine biased interpretation specifically of ambiguous information in OCD, Jelinek et al. (2009) presented participants with homographic homophones and found that individuals with OCD were more likely to generate negative and OC-related responses than the control group. In a similar attempt to measure perception bias towards disgust in OCD, Jhung et al. (2010) used a facial expression recognition task and found that OCD participants, relative to NACs, were more likely to perceive disgust in ambiguous facial expressions. Though these studies are seminal in terms of developing an understanding of biased interpretation of ambiguous information in OCD, neither Jelinek and colleagues nor Jhung et al. included an anxious control group. Therefore it is not clear whether their results can be attributed to general level of anxiety, rather than OCD symptoms in particular. To address this issue, we included a control group matched to the OC group in anxiety and depressive symptoms in our first experiment. Our results suggest that individuals high in OC symptoms, relative to controls, make more threat-related interpretations that is not likely explained by either anxiety or depressive symptoms in general. Given the central nature of biased interpretation in OCD, it is surprising that few attempts have been made to develop a reliable, valid measure of this bias in individuals with OC symptoms. The approach of investigating interpretation of ambiguous information has clinical utility. Rachman (1997) suggested that in order for OCD treatment to be effective, it must focus on weakening threatening misinterpretations of thoughts (thus strengthening a benign,
Cogn Ther Res (2013) 37:232–241
rather than threat bias). The goal of cognitive therapy in OCD is not to eliminate all intrusions, but rather, to change the way individuals interpret those thoughts or obsessions (e.g., interpreting a brown spot on the carpet as a coffee stain rather than as excrement). Therefore, clinicians can use the WSAO to help identify specific situations and content areas that are interpreted in a threat-relevant way by their patients. The WSAO might also serve as a baseline in clinical practice and research to assess interpretation bias towards OC concerns, and may help predict performance on a behavioral measure of OC symptoms. Our preliminary data from patients receiving treatment for OCD (N = 14) indicates a significant increase in WSAO benign bias from pre- to post-treatment. Future research should further examine differences in the WSAO before and after treatment for OCD to determine if this measure is sensitive to clinical change. Recent research suggests that OC-related maladaptive interpretations of intrusive thoughts can be successfully retrained towards benign interpretations (Clerkin and Teachman 2011). Similarly, the WSAO has the potential to be modified for use as a computerized interpretation training paradigm. For example, Beard and Amir (2008) trained individuals with social anxiety to endorse benign, rather than threatening, interpretations of ambiguous social scenarios. Training over a four week period resulted in reduction of social anxiety symptoms. Thus, that program may be adapted using the WSAO to train patients with OCD to interpret ambiguous scenarios as benign. Our study has several limitations. Both experiments are based on non-clinical samples and therefore may not generalize to individuals with a clinical diagnosis of OCD. In Experiment 1, we attempted to address this concern by selecting individuals for the OC group with OCI-R scores comparable to those of OCD patients (Foa et al. 2002). Nevertheless, these findings should be replicated in a sample of patients diagnosed with OCD. We also note that a high proportion of participants from the original, unselected sample in Experiment 1 fell within the clinical range of the OCI-R, which may not be representative of the population at large and highlights a potential disadvantage of using self-report measures to assess clinical characteristics
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in analogue samples. Additionally, it would have been useful to include other measures of interpretation bias in OCD (e.g., III) in order to compare the predictive validity of the WSAO to these measures. Finally, due to standardization concerns and the nature of the BAT used in Experiment 2, we focused specifically on contamination-related OC behavior, rather than a broader range of OC behavior. Although results from Experiment 1 demonstrate that individuals differ in interpretation when selected based on OC symptoms in general (OCI-R total score) rather than OC contamination symptoms in particular, we cannot assume that WSAO threat interpretation predicts behavior across multiple OC symptom domains. Therefore, future research should attempt to validate this measure across other OC subtypes. We also note that the overall regression model in Experiment 2, Step 3 was not significant (see Table 3), and that the R2 values and the strength of the correlation coefficients between measures were somewhat weak. Continued research may indicate whether these relationships are stronger in a clinical sample of individuals diagnosed with OCD. Despite these limitations, the WSAO offers a substantial contribution to the study of interpretation bias in OCD—an area that is theoretically rich but remains lacking in terms of assessment.
Appendix Word Sentence Association Test for OCD (WSAO) Please read each sentence below and the word next to it. Then circle a number indicating how related the sentence and the word are to each other for you personally. In making your decision please use the scale below to rate their degree of relatedness. For example: if the sentence is ‘‘You plan a fun activity for the weekend’’ and the word is ‘‘beach’’, and you think that going to the beach would be a fun activity so they are very much related you would circle a 7. However, If you think that going to the beach would not be a fun activity, you would circle a 1. In answering these questions, please be sure to use the entire scale (1 through 7).
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Cogn Ther Res (2013) 37:232–241 1 2 Not at all related
3 Somewhat related
4
7 Very Much related
1 You see a spot of dirt on your rug.
Pa in t
1 2 3 4 5 6 7
2 You smell something foul .
Toxic
1 2 3 4 5 6 7
3 There were many appliances running when you left.
Busy
1 2 3 4 5 6 7
4 You turned the water off before you left the house. You notice a slight flaw in the otherwise smooth surface of the 5 curtains.
Errands
1 2 3 4 5 6 7
Fix
1 2 3 4 5 6 7
6 You lean against the wall in the gym.
Rest
1 2 3 4 5 6 7
7 You g et dust in yo ur ey e s .
B l ink
1 2 3 4 5 6 7
8 You laid on your side and realize your hip and arm are numb.
Neurological
1 2 3 4 5 6 7
9 Y ou cannot find your favo rite sh irt .
B orr o wed
1 2 3 4 5 6 7
10 You feel a bump as you drive.
Hit and Run
1 2 3 4 5 6 7
11 You rehearse numbers in your head.
Continuously
1 2 3 4 5 6 7
12 After the meal you feel hot and sweaty.
Heart Problem
1 2 3 4 5 6 7
13 You think using the bathroom cleanser will make you sick.
Unreasonable
1 2 3 4 5 6 7
14 Part of the floor you are walking on is brown.
Excrement
1 2 3 4 5 6 7
15 There are many appliances running in your house.
Electricity
1 2 3 4 5 6 7
16 You shake hand with others.
Wash
1 2 3 4 5 6 7
17 You catch the baseball as your friend tosses it to you.
Coordination
1 2 3 4 5 6 7
18 While running errands you feel a hot flash.
Disabled
1 2 3 4 5 6 7
19 You don't have time to finish cleaning the bathroom.
Germs
1 2 3 4 5 6 7
20 You do not wi pe the toilet seat.
Clean
1 2 3 4 5 6 7
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