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Component fears of claustrophobia associated with mock magnetic resonance imaging
Journal of Anxiety Disorders 21 (2007) 367–380
Component fears of claustrophobia associated with mock magnetic resonance imaging F. Dudley McGlynn *, Todd A. Smitherman, Jacinda C. Hammel, Alejandro A. Lazarte Auburn University, Alabama, United States Received 16 May 2006; received in revised form 2 June 2006; accepted 20 June 2006
Abstract A conceptualization of claustrophobia [Rachman, S., & Taylor, S. (1993). Analyses of claustrophobia. Journal of Anxiety Disorders, 7, 281–291] was evaluated in the context of magnetic resonance imaging. One hundred eleven students responded to questionnaires that quantified fear of suffocation, fear of restriction, and sensitivity to anxiety symptoms. Sixty-four of them were then exposed to a mock magnetic resonance imaging assessment; maximum subjective fear during the mock assessment was self-reported, behavioral reactions to the mock assessment were characterized, and heart rates before and during the assessment were recorded. Scores for fear of suffocation, fear of restriction, and anxiety sensitivity were used to predict subjective, behavioral, and cardiac fear. Subjective fear during the mock assessment was predicted by fears of suffocation and public anxiousness. Behavioral fear (escape/avoidance) was predicted by fears of restriction and suffocation, and sensitivity to symptoms related to suffocation. Cardiac fear was predicted by fear of public anxiousness. The criterion variance predicted was impressive, clearly sufficient to legitimize both the research preparation and the conceptualization of claustrophobia that was evaluated. # 2006 Elsevier Ltd. All rights reserved. Keywords: Claustrophobia; Magnetic resonance imaging; Anxiety disorders; Anxiety sensitivity
Magnetic resonance image (MRI) scanning is a widely used procedure for diagnosing pathologies in the body (see Friday & Kubal, 1990). The protocol for MRI scanning typically entails inserting the patient into a small, tunnel-like chamber after having provided instructions to remain motionless while inside. Many MRI scans are failures due to patients’ refusals and premature terminations (Katz, Wilson, & Frazer, 1994). * Correspondence to: Auburn University, 226 Thach Hall, Alabama, AL 36849, United States. Tel.: +1 334 844 4412. E-mail address: [email protected] (F.D. McGlynn). 0887-6185/$ – see front matter # 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.janxdis.2006.06.003
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Refusal and premature termination of an MRI scan can reflect motives such as avoidance of diagnostic information and avoidance of discomfort during the procedure (Flahery & Hoskinson, 1989; Katz et al., 1994). Some reasons for refusal and premature termination reported by patients are emblematic of claustrophobia, for example being confined or being alone (Thorp, Owens, Whitehouse, & Dewey, 1990). Estimates concerning the numbers of failed MRI scans that result from claustrophobia are as high as 6.5% (Nazemi & Dager, 2003). Moreover, MRI scanning sometimes produces claustrophobia (Dager & Steen, 1992; Fishbain et al., 1988; Kilborn & Labbe´, 1990). The fear-related problems associated with MRI scanning prompted McGlynn, Karg, and Lawyer (2003) to evaluate mock MRI assessment of college students as a context for studying MRI-related claustrophobia. Two hundred students without panic disorder or ongoing medical problems were chosen randomly from among 336 psychology students who self-selected in return for course credit. On arrival they completed several psychometric instruments related to anxiety and claustrophobia. Later they participated in a mock MRI assessment while various measures of arousal and fear were recorded. Seven of the 200 students failed the mock scan behaviorally, 7 completed the mock scan but reported criterional fearfulness retrospectively, and 17 others completed the mock scan but manifested excessive heart-rate change while doing so. A logistic regression showed that total scores on the Claustrophobia Questionnaire (below) served to predict a student’s membership among the 31 students who were fearful in one way or another versus the 169 who were not. On the basis of those results, the authors suggested that mock MRI assessment among college students provides a feasible and conceptually relevant procedure for research on the nature of MRI-related claustrophobia. Rachman (1990) argued that a unidimensional conceptualization of claustrophobia is not appropriate; rather, a conceptualization is needed that incorporates partially independent influences from fears related to restriction and fears related to suffocation. There are ample data that support Rachman’s view. For example, restriction and suffocation have emerged as somewhat independent themes in factor analyses of responses on fear questionnaires (e.g., Valentiner, Telch, Petruzzi, & Bolte, 1996), and fears of restriction and suffocation have responded independently to treatments that target one but not the other (Harris, Robinson, & Menzies, 1999). The empirical literature suggests further that a complete picture of claustrophobia will include a role for cognitive elaborations such as catastrophic misappraisals of bodily anxiety symptoms (Booth & Rachman, 1992; Craske, Mohlman, Yi, Glover, & Valeri, 1995; Craske & Sipsas, 1992; Curtis, Hill, & Lewis, ¨ st & Csatlos, 1990; Rachman, Levitt, & Lopatka, 1988) and exaggerated expectations of danger (O 2000; Valentiner et al.). Indeed, Rachman and Taylor (1993) argued that something akin to ‘‘anxiety sensitivity’’ (Reiss, Peterson, Gursky, & McNally, 1986) might need to be added to fears of restriction and suffocation in order to describe claustrophobia adequately. The purpose of the work reported here was to evaluate the Rachman and Taylor (1993) account of claustrophobia as it occurs in the mock MRI assessment of college students. Students were assessed psychometrically and exposed to a mock MRI scan while arousal and fear were measured. The psychometric scores were then used as predictor variables and fear-measurement values used as criterion variables in several data analytic procedures. Some of the psychometric instruments selected were revised/improved versions of the questionnaires used by McGlynn et al. (2003). Arousal and fear measures were selected so as to sample the three standard domains of fear responding (i.e., subjective experience, behavior, and physiology). Other changes from the methods used by McGlynn et al. were made so as to afford clear-cut opportunities for measures of fear of restriction, fear of suffocation, and cognitive elaboration of anxiety symptoms to account for variance in measures of fear.
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1. Method 1.1. Participants One hundred sixty undergraduate students (114 females) completed the first psychometric phase of the study in return for extra course credit. One hundred fifty-five students reported their age and ethnicity. The mean age was 20.55 years (S.D. = 2.17). One hundred thirty-three were Caucasian; 22 were African-American, Hispanic, or Asian. One hundred eleven students returned and completed a second psychometric phase of the study. Forty of these 111 participants were then excluded due to having a wide shoulder span (n = 12) or to reporting ongoing medication use, an ongoing medical condition, or a history of panic disorder (n = 28). Seventy-one students were invited to participate in the third and final phase of the work. Four of these were excluded due to a possible panic disorder; three were excluded due to missing data or computer difficulties. Thus, 64 participants were included in the final phase of the study. 1.2. Apparatus and materials 1.2.1. MRI simulator The mock MRI device is described by Wood and McGlynn (2000). It is a full-scale replica of a GE Signa .5 T MRI apparatus. An 18-ft long and 23-in. wide table is elevated 42 in. from the floor. A track within the table is remote-controlled and can be operated by the participant or by the experimenter. A motor moves the track up to 712 ft into and out of a 22-in. diameter tube that is enclosed by a square chamber. The only openings in the mock scanner are at the two ends of the tube. 1.2.2. Subjective fear measure Participants were asked to provide a numerical rating of their fear intensity, using a Subjective Units of Distress Scale (SUDs) on which 0 (absolutely calm) and 100 (worst imaginable) served as anchor points. 1.2.3. Behavioral avoidance test Behavioral avoidance tests were conducted as described in Section 1.3. A tape measure attached at the end of the remote-controlled track registered the distance it traveled into the mock scanner. Quantification of avoidance was afforded by measuring the distance traveled into the tube (in inches) and the time spent inside (in seconds). 1.2.4. Psychophysiological equipment Heart rate was recorded via a J & J Instruments Co. (Poulsbo, WA) modular system comprised of a #P-401 module and a #I-330 interface. An optical densitometer attached to the pad of an index finger recorded pulse–volume variations. Heart-rate data were gathered continuously and later averaged across 30-s intervals via Data-Track software for a personal computer. 1.2.5. Psychometric measures The following questionnaires were administered: the revised Claustrophobia Questionnaire (Radomsky, Rachman, Thordarson, McIsaac, & Teachman, 2001), the Anxiety Sensitivity Index-Revised (Taylor & Cox, 1998), and the Agoraphobic Cognitions Questionnaire
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(Chambless, Caputo, Bright, & Gallagher, 1984). Sections of the Anxiety Disorders Interview Schedule-Four (Brown, Di Nardo, & Barlow, 1994) were used also as described below. The original Claustrophobia Questionnaire or CLQ (Rachman & Taylor, 1993) is a 30-item Likert-scale instrument on which respondents rate from 0 to 4 their anxiety when in a variety of situations. It provides a total score for claustrophobic fear and subscale scores for fear of restriction (CLQ-RS) and fear of suffocation (CLQ-SS). In its revised form (Radomsky et al., 2001), 14 items comprise the suffocation subscale (R = 0–56) and 12 items make up the restriction subscale (R = 0–48). The Radomsky et al. modification of the original CLQ followed a principal component analysis in which one of the original items loaded on the wrong factor and three other items were too complex. Thus, four items were removed from the original instrument, leaving the 14 items in the suffocation subscale and 12 items in the restriction subscale. The revised scale and subscales have been found to have internal consistency coefficients (Cronbach’s a) that range from .85 to .96, and 2-week test–retest stability correlations that range from .77 to .89, among undergraduate and community respondents (Radomsky et al.). The Radomsky et al. paper is the only one that has reported psychometric data from the revised CLQ. The Anxiety Sensitivity Index-Revised or ASI-R (Taylor & Cox, 1998) is a 36-item Likerttype instrument on which participants rate from 0 to 4 their fear of 36 anxiety-related symptoms. It is based on the original Anxiety Sensitivity Index (Reiss et al., 1986), a questionnaire that measures anxiety sensitivity, or proneness toward catastrophic misappraisals of anxiety phenomena. The ASI-R provides a subscale score for each of four lower order factors that load on a single higher order factor: fear of respiratory symptoms or ASI-R-RE (R = 0–48), fear of publicly observable anxiety reactions or ASI-R-PO (R = 0–28), fear of cardiovascular symptoms or ASI-R-CV (R = 0–44), and fear of cognitive dyscontrol or ASI-R-CD (R = 0–24). Our scoring provided a score for each of these four subscales and summed them for a total score (R = 0–144). Data about the internal consistencies and test–retest stabilities of these four factor scores and the total score have not been reported. The Agoraphobic Cognitions Questionnaire or ACQ (Chambless et al., 1984) is also a Likertscale instrument that assesses cognitive misappraisals of anxiety phenomena. Respondents rate from 1 to 5 their fear of 15 various possible consequences of panic. The scale provides a total score, obtained by calculating the mean score of all 15 items (R = 1–5), as well as mean subscale scores for physical concerns or ACQ-PC (R = 1–5) and for concerns related to loss of control or ACQ-LC (R = 1–5). Among individuals diagnosed with agoraphobia, the ACQ total score has shown a 31day test–retest stability correlation of .86 and internal consistency on the order of .80 (Chambless et al.). Psychometric data on the two subscales were not reported in the original article. The Anxiety Disorders Interview Schedule-Four or ADIS-IV (Brown et al., 1994) is a semistructured interview that affords clinical conceptualization of the nature and severity of a person’s anxiety. Oftentimes, the ADIS-IV assists in determining whether the symptoms warrant a DSM-IV (American Psychiatric Association, 2000) diagnosis. The ADIS-IV is composed of several modules that focus on anxiety disorders. We used the modules for panic disorder and agoraphobia to identify and eliminate participants who met the criteria for those diagnoses. 1.3. Procedure Undergraduate psychology students were self-selected via classroom and bulletin-board announcements. They were first group-administered the CLQ, ASI-R, and ACQ by undergraduate research assistants over the course of 2 days. At that time, they reported any ongoing medical conditions, any history of panic disorder, and any current medication regimen that would preclude
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further participation. The students then returned 1 week later and completed the questionnaires again. (The psychometric instruments were administered twice before the mock MRI scan because data concerning their test–retest stabilities are in short supply and were needed to legitimize using the questionnaires here.) The mean time between the first and second administrations was 6.88 days (S.D. = .72). Three weeks following the above screening procedure, each participant who was not excluded by medical status, history, or medication use met with two clinical psychology graduate students (second and third authors) for a final assessment and mock MRI session. At this session, participants were administered the panic disorder and agoraphobia modules of the ADIS-IV by one of the graduate students who was blind to previous questionnaire responses. Then participants met with the second graduate student, who was blind to their ADIS-IV responses. The heart-rate sensor was attached and participants were informed that the purpose of this monitoring was to record physiological responses during the experiment. Participants were instructed to lie on a cot while heart rate was recorded for 612 min. Following the assessment ‘‘at rest,’’ participants were escorted into the room that housed the mock scanner. They were shown the scanner for the first time and it was identified as a replica of an actual MRI device. They were told that the device would not scan body tissue or make the loud noises associated with actual MRI scanning. Participants were shown how to operate the remote controller and operated the controller once themselves. Participants were then informed that they would be asked to insert themselves fully into the mock scanner and remain for ‘‘several minutes,’’ at which time they would be withdrawn. They were told also that they could use the controller to stop the track or withdraw themselves at any time. At this point they were instructed to lie on their back atop the MRI track for 3 min. While not analyzed, heart rate was recorded during this period. At the end of the 3-min period, participants were instructed to insert themselves into the scanner head-first and to remain until the experimenter informed them that time had expired. Heart rate was recorded throughout the period along with the distance (in inches) the participant traveled into the mock scanner and the length of time (in seconds) that the participant remained inside. After withdrawal from the device, each participant provided an oral SUDs rating of the highest fear experienced during exposure. 2. Results 2.1. Psychometric data As noted above, each of the questionnaires of interest was administered twice before the mock scan to allow for calculating Pearson correlations across a 1-week test–retest interval. Correlations for total scores and for various subscale scores were calculated (n = 111). Item-toitem correlations were calculated also to allow for characterizing the internal consistencies (Cronbach’s as) of the total and subscale scores on the questionnaires. Data from the first of the two administrations were used to calculate Cronbach’s as because the first administration garnered the most respondents (n = 160). The Pearson correlations for total scores ranged from .80 to .91; those for subscale scores ranged from .77 to .87. Cronbach’s a values for total scores ranged from .85 to .95; those for subscales ranged from .80 to .91. The correlations were sufficiently strong to legitimize use of the questionnaires here; all correlations were significant at the .001 level. Pearson correlations were calculated also for total scores and subscale scores across the three questionnaires (n = 160). The intercorrelation matrix is shown in Table 1.
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ASI-R-RE ASI-R-PO ASI-R-CV ASI-R-CD ASI-R-TOT ACQ-PC ACQ-LC ACQ-TOT CLQ-SS CLQ-RS CLQ-TOT
ASI-R-RE
ASI-R-PO
ASI-R-CV
ASI-R-CD
ASI-R-TOT
ACQ-PC
ACQ-LC
ACQ-TOT
CLQ-SS
CLQ-RS
CLQ-TOT
1.00
.48 1.00
.70 .54 1.00
.46 .50 .73 1.00
.86 .75 .89 .75 1.00
.59 .41 .65 .48 .66 1.00
.41 .46 .53 .61 .58 .52 1.00
.55 .50 .65 .64 .70 .82 .92 1.00
.53 .46 .55 .49 .62 .50 .52 .58 1.00
.43 .37 .42 .38 .49 .33 .34 .38 .59 1.00
.53 .45 .53 .48 .61 .45 .46 .52 .85 .93 1.00
Note. ASI-R-RE: Respiratory Symptoms subscale; ASI-R-PO: Publicly Observable Anxiety Reactions subscale; ASI-R-CV: Cardiovascular Symptoms subscale; ASI-R-CD: Cognitive Dyscontrol subscale; ASI-R-TOT: Anxiety Sensitivity Index-Revised total score; ACQ-PC: Physical Concerns subscale; ACQ-LC: Loss of Control subscale; ACQTOT: Agoraphobic Cognitions Questionnaire total score; CLQ-SS: Suffocation subscale; CLQ-RS: Restriction subscale; CLQ-TOT: Claustrophobia Questionnaire total score. All correlations significant at P < .001.
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Table 1 Correlations between the ASI-R, the ACQ, the CLQ, and their subscales
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Fear was measured within each of the subjective, behavioral, and physiological domains. Initially, participants were classified within each domain as fearful versus non-fearful using the decision rules described by McGlynn et al. (2003). The participant did or did not retrospectively report a maximum SUDs rating of 70 or higher on exiting the mock scanner (subjective fear). The participant did or did not refuse to enter the scanner or exit the scanner prematurely (behavioral fear). During the mock scan the participant did or did not show at least one 30-s interval of excessive heart-rate change from ‘‘at rest’’ values as defined in the earlier paper (cardiac fear). Twenty-five of the 64 participants (39.06%) showed some variety of three-channel fear according to these decision rules. Table 2 shows the mean total scores and subscale scores of each of the three questionnaires obtained from participants who were and were not classified as showing subjective, behavioral, and cardiac fear. Tabular asterisks identify instances in which questionnaire scores for fearful versus nonfearful participants differed significantly according to t-tests for independent samples. The sizes of the groups that were compared via t-tests were quite different. Therefore, the questionnaire scores for participants classified as showing and not showing subjective, behavioral, and cardiac fear were compared also with Mann–Whitney U tests. The significant results from the Mann–Whitney tests were the same as those from t-tests. 2.2. Predicting fear The data summarized in Tables 1 and 2 serve to describe the data set and to inform subsequent analyses. Throughout the analyses SUDs ratings are used as a continuous variable, while behavioral and cardiac data are used to form dichotomous classification of participants. The use of SUDs as a continuous variable was dictated by the data-analytic approach. The reduction of behavioral and cardiac data to dichotomous categories was prompted by the distributions of the data and by interest in maintaining continuity with the report of McGlynn et al. (2003). 2.2.1. Ratings of maximum SUDs The six subscale scores from the second administration of the ASI-R and CLQ were used as predictor variables and retrospective SUDs as the criterion variable in a stepwise linear regression procedure. (Scores from the ACQ were omitted in the service of simplicity and because they were intercorrelated with scores from the ASI-R.) Scores from the CLQ-SS (fear of suffocation) accounted for the largest amount of variance, R2 = 34.3%, F(1, 62) = 32.33, P < .001. The stepwise procedure added-in scores from the ASI-R-PO to yield R2 = 40.4%, F(2, 61) = 20.70, P < .001. According to the analysis, retrospective reports of the maximum fear experienced while inside the mock scanner were influenced by a combination of fear of suffocation and fear of public anxiousness. Fear of suffocation was the overriding determinant of subjective fear in the mock MRI procedure. The CLQ-RS (fear of restriction) was not a significant predictor when entered into the multiple regression model with the CLQ-SS and ASI-R-PO. Given the importance of restriction fear in traditional accounts of claustrophobia, we undertook further examination of that result. Fig. 1 shows one of several path models that were fit to the correlations among the three predictor variables and the criterion variable of retrospective SUDs. It provides the best representation of how fear of suffocation (CLQ-SS), fear of restriction (CLQ-RS), and fear of public anxiousness
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SUDs 70 (n = 12) ASI-R-RE ASI-R-PO ASI-R-CV ASI-R-CD ASI-R-TOT ACQ-PC ACQ-LC ACQ-TOT CLQ-SS CLQ-RS CLQ-TOT
19.42 14.50 7.75 5.17 46.83 1.68 2.49 2.08 17.75 21.92 39.67
(14.09) * (6.36)* (9.76) (5.83) (31.61) * (0.62)* (0.99)* (0.74)* (10.06)* (12.26) (21.75) *
SUDs < 70 (n = 52)
Behavioral fear (n = 6)
9.06 8.85 3.50 1.96 23.37 1.27 1.79 1.53 9.98 15.98 25.96
18.83 9.67 8.00 5.00 41.50 1.60 2.10 1.85 19.17 27.50 46.67
(6.31) (5.98) (6.18) (3.42) (17.37) (0.31) (0.66) (0.45) (7.30) (9.03) (15.33)
(12.64) * (5.32) (11.06) (4.98) (24.51) (0.55) (0.37) (0.37) (6.37)* (7.71)* (14.01) *
Behavioral completion (n = 58)
Cardiac fear (n = 9)
No cardiac fear (n = 54)
10.19 9.93 3.91 2.31 26.34 1.32 1.90 1.61 10.64 16.02 26.66
14.67 15.00 4.33 1.33 35.33 1.44 2.13 1.79 15.56 20.44 36.00
10.11 9.17 4.02 2.57 25.87 1.32 1.88 1.60 10.48 16.20 26.69
(8.43) (6.54) (6.58) (3.99) (21.96) (0.39) (0.81) (0.57) (8.18) (9.50) (16.68)
(10.46) (5.68)* (6.93) (2.96) (23.34) (0.43) (1.01) (0.67) (9.21) (12.24) (21.15)
(8.68) (6.18) (6.96) (4.07) (21.94) (0.41) (0.74) (0.53) (7.92) (9.21) (16.08)
Note. ASI-R-RE: Respiratory Symptoms subscale; ASI-R-PO: Publicly Observable Anxiety Reactions subscale; ASI-R-CV: Cardiovascular Symptoms subscale; ASI-R-CD: Cognitive Dyscontrol subscale; ASI-R-TOT: Anxiety Sensitivity Index-Revised total score; ACQ-PC: Physical Concerns subscale; ACQ-LC: Loss of Control subscale; ACQTOT: Agoraphobic Cognitions Questionnaire total score; CLQ-SS: Suffocation subscale; CLQ-RS: Restriction subscale; CLQ-TOT: Claustrophobia Questionnaire total score. Total participants for cardiac fear is 63, rather than 64, due to 1 participant who refused to enter the device. Score significantly higher (*P < .05) than non-fearful group based on independent-samples t-test.
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Table 2 Mean predictor variable scores across criterion fear categories
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Fig. 1. Path model beginning with fear of suffocation (CLQ-SS). Note. CLQ-SS: fear of suffocation; CLQ-RS: fear of restriction; ASI-R-PO: fear of publicly observable anxiety reactions; SUDs: retrospective SUDs ratings. e1, e2, and e3 denote error terms.
(ASI-R-PO) influenced one another and retrospective SUDs within the data set. The model accounted for 41% of the variance in retrospective SUDs, x2(1) = .06, P = .80. Only the path between fear of restriction and retrospective SUDs was non-significant, Z = .60, P = .55. When that path was removed, the fit of the model was not affected, x2(2) = .42, P = .81. The model is consistent with the idea that fear of suffocation influenced retrospective SUDs both directly and indirectly via influencing fear of public anxiousness. Fear of suffocation also influenced fear of restriction, but fear of restriction did not affect retrospective SUDs directly. Similar path models were generated in which fear of restriction was allowed to influence fear of public anxiousness and vice-versa. The path coefficients between fear of restriction and fear of public anxiousness were non-significant in both directions. Hence those models are not reproduced here. The model above was guided by the reasonable notion that fear of suffocation leads to fear of restriction (and to fear of public anxiousness). Path modeling could be guided by the notion that fear of restriction leads to fear of suffocation, but that notion is not reasonable. Path modeling of the data here can, however, be guided by the plausible notion that fear of public anxiousness might augment fear of restriction and/or fear of suffocation. Hence we generated a path model of the retrospective SUDs data in which fear of public anxiousness was used as the first variable, while fears of restriction and suffocation were used as mediators, and fear of suffocation was allowed to influence fear of restriction (see Fig. 2). This model is just identified and accounted for 41% of the variance in retrospective SUDs. Only the two paths in which fear of restriction was a mediator variable were non-significant. When those paths were removed, the model became the same as in Fig. 1 when the path from fear of restriction to retrospective SUDs is dropped. The model is consistent with the idea that fear of public anxiousness influenced retrospective SUDs both directly and indirectly by influencing fear of suffocation.
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Fig. 2. Path model beginning with fear of publicly observable anxiety reactions (ASI-R-PO). Note. ASI-R-PO: fear of publicly observable anxiety reactions; CLQ-RS: fear of restriction; CLQ-SS: fear of suffocation; SUDs: retrospective SUDs ratings. e1, e2, and e3 denote error terms.
2.2.2. Behavioral and cardiac data The ASI-R-RE (fear of respiratory symptoms) and both subscales of the CLQ were used as predictor variables, and behavioral fear-category assignment as the criterion variable, in three separate logistic regression procedures. The best single predictor of behavioral fear was the CLQRS, x2(1) = 7.45, P < .01. It classified 89.1% of participants correctly, and the Hosmer– Lemeshow goodness-of-fit test did not find a significant difference between the observed and expected participant classifications, x2(8) = 4.84, P = .78. Each one-point increase on the CLQRS score increased by 13.5% the participant’s odds ratio of being classified as behaviorally fearful. The second best single predictor of behavioral fear was the CLQ-SS, x2(1) = 4.91, P < .05. The CLQ-SS performed in much the same way as did the CLQ-RS; a one-point increase in the CLQ-SS score increased by 10.9% the participant’s odds ratio of being classified as behaviorally fearful. However, when the CLQ-RS and the CLQ-SS were entered together in a logistic regression model, neither reached statistical significance. Such a result is not unusual because the two predictors were highly intercorrelated (see Table 1). More informative is the change in x2 that occurs when each predictor is added to the other. When the CLQ-SS is added to the CLQ-RS, the x2(1) change = .005, P = .94. On contrary, when the CLQ-RS is added to the CLQ-SS, the x2(1) change = 2.55, P = .11. Although these changes are non-significant, their relative sizes suggest that fear of restriction is a more important determinant of behavioral fear than is fear of suffocation. Scores on the ASI-R-PO provided the only significant prediction of assignment to fear versus non-fear categories when logistic regressions were performed based on cardiac classification, x2(1) = 6.17, P < .05. The ASI-R-PO classified 84.1% of the participants correctly. When using the Hosmer–Lemeshow goodness-of-fit test with the ASI-R-PO, there was no significant difference between the observed and predicted classifications, x2(8) = 3.81, P = .87. Each onepoint increase in the ASI-R-PO score increased by 14.9% the participant’s odds ratio of being classified as fearful according to cardiac data.
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3. Discussion McGlynn et al. (2003) were interested in establishing the viability of mock MRI assessment among college students as a means of studying claustrophobia as it occurs in the clinical MRI setting. As described earlier, they measured subjective fear, behavioral fear, and cardiac responsivity in connection with a mock MRI assessment among 200 self-selected college students. Before the mock assessment, the students provided ratings of self-efficacy for completing the mock assessment and responded to questionnaires about claustrophobia, panic, anxiety, and anxiety sensitivity. Thirty-one of the 200 students displayed criterional fearfulness based on subjective, behavioral, or cardiac measures. The best prediction of an individual student’s assignment to that group of 31 was afforded by the self-efficacy ratings. However, total scores on the original Claustrophobia Questionnaire (Rachman & Taylor, 1993) also predicted assignment to that group of 31 students. The latter result prompted the conclusion that mock MRI assessment of college students seems to provide a viable preparation for research on MRI-related claustrophobia in the clinic. In the work reported here, the basic viability of the mock MRI preparation was provisionally assumed; attention was directed to using an improved protocol to describe claustrophobia in the mock MRI arrangement. Pre-insertion self-efficacy ratings were omitted in order to eliminate the potential for experimental-demand contamination that such ratings introduce into results from obtrusive assessment. Improved versions of the several questionnaires were used, and choices of questionnaires were guided by the Rachman and Taylor (1993) view of the nature of claustrophobia. The short-term stabilities and internal consistencies of the questionnaires were more than adequate for the present purpose. The questionnaires predicted large proportions of criterion variance relative to other work of this genre. In short, the conditions for describing the events of mock MRI assessment in terms of claustrophobia were met. Subjective fear during the mock scan was predicted most strongly by fear of suffocation. That finding was expected; a nexus between claustrophobia and fear of suffocation has been recognized for some time (Rachman, Levitt, & Lopatka, 1987, 1988). Indeed, our research preparation would have been seriously compromised had fear of suffocation not predicted subjective fear during the mock scan. Subjective fear during the mock scan was also predicted by fears of public anxiousness. Taken together, the predictors of subjective fear during the mock scan suggest that participants feared possible suffocation and/or possibly behaving in a way that would attract negative social evaluation. The path models provided earlier show two ways to think about the relations among the most influential variables. Subjective fear during the mock scan was not predicted by fear of restriction beyond the prediction afforded by fear of suffocation and fear of public anxiousness. That result is inconsistent with the view (Rachman & Taylor, 1993) that fears of suffocation and restriction often participate jointly in claustrophobia. Differing methodologies might account for the inconsistency. The view that fears of suffocation and restriction both contribute to claustrophobia rests partially on results from factor analyses of questionnaire responses (e.g., Rachman & Taylor; Valentiner et al., 1996), whereas the absence of influence from fear of restriction in the present work resulted from pathmodeling the relations between questionnaire responses and laboratory self-report data. However, methodological differences do not explain the inconsistency completely. Rachman and Taylor, Radomsky et al. (2001), and others have provided behavior-test data that support a concept of claustrophobia that involves fears of both suffocation and restriction. The absence of significant effects from fear of restriction in the path models of subjective fear during the mock scan might simply mean that claustrophobic experience does not reflect fear of
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restriction when it occurs in the MRI setting. There are arguments against that explanation but they are not strong. Nazemi and Dager (2003) and Harris et al. (1999) reported studies in which fear of restriction was present in the MRI setting and was attenuated by successful scanning even while fear of suffocation was not. However, neither investigation used demonstrably claustrophobic participants, and both found scan-contingent attenuation of fear of restriction mainly among relatively non-fearful participants. The failure of fear of restriction to influence subjective fear during the mock scan here remains to be explained. Behavioral fear (escape/avoidance) associated with the mock scan was predicted strongly by fear of restriction even though subjective fear during the scan was not. The same relation between behavioral fear during a mock scan and fear of restriction was reported by McGlynn et al. (2003) using the original CLQ. Similar results were reported also by Valentiner, Telch, Ilia, and Hehmsoth (1993), who found that expected danger and expected anxiety predicted behavioral performance but not subjective fear during a claustrophobia test vis-a`-vis a small, dark tunnel. Behavioral fear associated with the mock scan was predicted also by fear of suffocation. Futhermore, the behaviorally fearful participants produced higher scores for sensitivity to respiratory symptoms (ASI-R-RE, Table 2) than did participants who completed the procedure. If our logistic approach to analysis of behavior data is accepted, then behavioral fear associated with the mock scan was influenced most strongly by fear of restriction. However, behavioral fear associated with the mock scan was influenced also by fear of suffocation and by heightened sensitivity to symptoms related to suffocation. Cardiac reactivity during the mock MRI assessment was predicted by scores for fear of public anxiousness, suggesting that some participants’ heart rates were elevated due to concerns about behaving fearfully and being negatively evaluated. Valentiner et al. (1996) reported a thematically similar result; cardiac responsivity during a claustrophobia test was predicted with a measure of self-efficacy regarding the test. However, McGlynn et al. (2003) and Valentiner et al. (1993) failed to predict cardiac responsivity related to claustrophobia with similar predictor variables. An understanding of cardiac responsivity during claustrophobic challenge awaits research on boundary conditions. Discordance between self-report, behavioral, and physiological measures of fear is the norm (Rachman & Hodgson, 1974). Hence, finding that measures from the different domains are influenced by different variables is to be expected. Nonetheless there were some mild surprises in the data. It was surprising that fear of restriction did not affect subjective fear during the mock assessment when the effects of fear of suffocation (and fear of public anxiousness) were taken into account. It was mildly surprising that the ASI-R-PO was the only ASI-R variable that predicted subjective fear when incorporated in the multivariate model. By and large, however, the results offer non-trivial support for the Rachman and Taylor (1993) view of claustrophobia when claustrophobia is instantiated as a three-channel (Lang, 1968) construct. Fears of suffocation and restriction, sensitivity to symptoms of suffocation, and possibly, a narrow variant of social phobia, influenced the college students’ fear in one way or another. While the basic viability of the research preparation was provisionally assumed, it was nonetheless true that the research preparation was being tested alongside the Rachman and Taylor (1993) conceptualization of claustrophobia. In that connection, the proportion of variance in fear ratings explained by the multiple regression model was impressive, as were the sizes of the oddsratio increments associated with the two logistic regressions. Along with the results reported by McGlynn et al. (2003), the present results serve to establish the mock MRI assessment of college students as a valuable approach to studying fear behavior related to the construct of claustrophobia.
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Many directions for research using mock MRI as a basic preparation can be envisioned. In our view, the most interesting research would use the mock MRI preparation to evaluate and compare different theoretical accounts of claustrophobia. In general, the current procedure would be retained, while competing theoretical constructions of claustrophobia would be embodied in different psychometric predictor variables. For example, one expectancy model (Reiss & McNally, 1985) posits that fear is governed by an expectation of danger coupled with an expectation of anxiety augmented by anxiety sensitivity (see also Reiss, 1991). Questionnaires that quantify these factors could be used as predictor variables in the research preparation used here, as was done by Valentiner et al. (1993) vis-a`-vis a small, dark chamber. References American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text revision). Washington, DC: Author. Booth, R., & Rachman, S. (1992). The reduction of claustrophobia: I. Behaviour Research and Therapy, 30, 207–221. Brown, T. A., Di Nardo, P. A., & Barlow, D. H. (1994). Anxiety Disorders Interview Schedule for DSM-IV (ADIS-IV). Albany, NY: Graywind Publications. Chambless, D. L., Caputo, G. C., Bright, P., & Gallagher, R. (1984). Assessment of fear in agoraphobics: the Body Sensations Questionnaire and the Agoraphobic Cognitions Questionnaire. Journal of Consulting and Clinical Psychology, 52, 1090–1097. Craske, M. G., Mohlman, J., Yi, J., Glover, D., & Valeri, S. (1995). Treatment of claustrophobia and snake/spider phobias: fear of arousal and fear of context. Behaviour Research and Therapy, 33, 197–203. Craske, M. G., & Sipsas, A. (1992). Animal phobias versus claustrophobias: exteroceptive versus interoceptive cues. Behaviour Research and Therapy, 30, 569–581. Curtis, G. C., Hill, E. M., & Lewis, J. A. (1990). Heterogeneity of DSM-III-R simple phobia and the simple phobia/ agoraphobia boundary: evidence from the ECA study (Report to the DSM-IV Anxiety Disorders Workgroup). Ann Arbor: University of Michigan. Dager, S., & Steen, R. G. (1992). Applications of magnetic resonance spectroscopy to the investigation of neuropsychiatric disorders. Neuropsychopharmacology, 6, 249–266. Fishbain, D. A., Goldberg, M., Labbe´, E., Zacher, D., Steele-Rosomoff, R., & Rosomoff, H. (1988). Long-term claustrophobia following magnetic resonance imaging. American Journal of Psychiatry, 145, 1038–1039. Flahery, J. A., & Hoskinson, K. (1989). Emotional distress during magnetic resonance imaging. New England Journal of Medicine, 320, 467–468. Friday, P. J., & Kubal, W. S. (1990). Magnetic resonance imaging: improved patient tolerance utilizing medical hypnosis. American Journal of Clinical Hypnosis, 33, 80–84. Harris, L. M., Robinson, J., & Menzies, R. G. (1999). Evidence for fear of restriction and fear of suffocation as components of claustrophobia. Behaviour Research and Therapy, 37, 155–159. Katz, R. C., Wilson, L., & Frazer, N. (1994). Anxiety and its determinants in patients undergoing magnetic resonance imaging. Journal of Behavior Therapy and Experimental Psychiatry, 25, 131–134. Kilborn, L. C., & Labbe´, E. E. (1990). Magnetic resonance imaging scanning procedures: development of phobic response during scan and at 1-month follow-up. Journal of Behavioral Medicine, 13, 391–401. Lang, J. (1968). Fear reduction and fear behavior: problems in treating a construct. In: J. M. Shlien (Ed.), Research in psychotherapy (pp. 90–102). Washington, DC: American Psychological Association. McGlynn, F. D., Karg, R., & Lawyer, S. R. (2003). Fear responses to mock magnetic resonance imaging among college students: toward a prototype experiment. Journal of Anxiety Disorders, 17, 335–347. Nazemi, H., & Dager, S. R. (2003). Coping strategies of panic and control subjects undergoing lactate infusion during magnetic resonance imaging confinement. Comprehensive Psychiatry, 44, 190–197. ¨ st, L. G., & Csatlos, P. (2000). Probability ratings in claustrophobic patients and normal controls. Behaviour Research O and Therapy, 38, 1107–1116. Rachman, S. (1990). Fear and courage (2nd ed.). New York: Freeman. Rachman, S., & Hodgson, R. S. (1974). Synchrony and desynchrony in fear and avoidance. Behaviour Research and Therapy, 12, 311–318. Rachman, S., Levitt, K., & Lopatka, C. (1987). Panic: the links between cognitions and bodily symptoms—I. Behaviour Research and Therapy, 25, 411–423.
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Rachman, S., Levitt, K., & Lopatka, C. (1988). Experimental analyses of panic—III. Claustrophobic subjects. Behaviour Research and Therapy, 26, 33–40. Rachman, S., & Taylor, S. (1993). Analyses of claustrophobia. Journal of Anxiety Disorders, 7, 281–291. Radomsky, A. S., Rachman, S., Thordarson, D. S., McIsaac, H. K., & Teachman, B. A. (2001). The Claustrophobia Questionnaire. The Journal of Anxiety Disorders, 15, 287–297. Reiss, S. (1991). Expectancy theory of fear, anxiety, and panic. Clinical Psychology Review, 11, 141–153. Reiss, S., & McNally, R. J. (1985). Expectancy model of fear. In: S. Reiss, & R. R. Bootzin (Eds.), Theoretical issues in behavior therapy (pp. 107–122). New York: Academic Press. Reiss, S., Peterson, R. A., Gursky, D. M., & McNally, R. (1986). Anxiety sensitivity, anxiety frequency, and the prediction of fearfulness. Behaviour Research and Therapy, 24, 1–8. Taylor, S., & Cox, B. J. (1998). An expanded Anxiety Sensitivity Index: evidence for a hierarchic structure in a clinical sample. Journal of Anxiety Disorders, 12, 463–483. Thorp, D., Owens, R. G., Whitehouse, G., & Dewey, M. E. (1990). Subjective experiences of magnetic resonance imaging. Clinical Radiology, 41, 276–278. Valentiner, D. P., Telch, M. J., Ilai, D., & Hehmsoth, M. M. (1993). Claustrophobic fear behavior: a test of the expectancy model of fear. Behaviour Research and Therapy, 31, 395–402. Valentiner, D. P., Telch, M. J., Petruzzi, D. C., & Bolte, M. C. (1996). Cognitive mechanisms in claustrophobia: an examination of Reiss and McNally’s expectancy model and Bandura’s self-efficacy theory. Cognitive Therapy and Research, 20, 593–612. Wood, B. S., & McGlynn, F. D. (2000). Research on posttreatment return of claustrophobic fear, arousal, and avoidance using mock diagnostic imaging. Behavior Modification, 24, 379–394.
Component fears of claustrophobia associated with mock magnetic resonance imaging F. Dudley McGlynn *, Todd A. Smitherman, Jacinda C. Hammel, Alejandro A. Lazarte Auburn University, Alabama, United States Received 16 May 2006; received in revised form 2 June 2006; accepted 20 June 2006
Abstract A conceptualization of claustrophobia [Rachman, S., & Taylor, S. (1993). Analyses of claustrophobia. Journal of Anxiety Disorders, 7, 281–291] was evaluated in the context of magnetic resonance imaging. One hundred eleven students responded to questionnaires that quantified fear of suffocation, fear of restriction, and sensitivity to anxiety symptoms. Sixty-four of them were then exposed to a mock magnetic resonance imaging assessment; maximum subjective fear during the mock assessment was self-reported, behavioral reactions to the mock assessment were characterized, and heart rates before and during the assessment were recorded. Scores for fear of suffocation, fear of restriction, and anxiety sensitivity were used to predict subjective, behavioral, and cardiac fear. Subjective fear during the mock assessment was predicted by fears of suffocation and public anxiousness. Behavioral fear (escape/avoidance) was predicted by fears of restriction and suffocation, and sensitivity to symptoms related to suffocation. Cardiac fear was predicted by fear of public anxiousness. The criterion variance predicted was impressive, clearly sufficient to legitimize both the research preparation and the conceptualization of claustrophobia that was evaluated. # 2006 Elsevier Ltd. All rights reserved. Keywords: Claustrophobia; Magnetic resonance imaging; Anxiety disorders; Anxiety sensitivity
Magnetic resonance image (MRI) scanning is a widely used procedure for diagnosing pathologies in the body (see Friday & Kubal, 1990). The protocol for MRI scanning typically entails inserting the patient into a small, tunnel-like chamber after having provided instructions to remain motionless while inside. Many MRI scans are failures due to patients’ refusals and premature terminations (Katz, Wilson, & Frazer, 1994). * Correspondence to: Auburn University, 226 Thach Hall, Alabama, AL 36849, United States. Tel.: +1 334 844 4412. E-mail address: [email protected] (F.D. McGlynn). 0887-6185/$ – see front matter # 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.janxdis.2006.06.003
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Refusal and premature termination of an MRI scan can reflect motives such as avoidance of diagnostic information and avoidance of discomfort during the procedure (Flahery & Hoskinson, 1989; Katz et al., 1994). Some reasons for refusal and premature termination reported by patients are emblematic of claustrophobia, for example being confined or being alone (Thorp, Owens, Whitehouse, & Dewey, 1990). Estimates concerning the numbers of failed MRI scans that result from claustrophobia are as high as 6.5% (Nazemi & Dager, 2003). Moreover, MRI scanning sometimes produces claustrophobia (Dager & Steen, 1992; Fishbain et al., 1988; Kilborn & Labbe´, 1990). The fear-related problems associated with MRI scanning prompted McGlynn, Karg, and Lawyer (2003) to evaluate mock MRI assessment of college students as a context for studying MRI-related claustrophobia. Two hundred students without panic disorder or ongoing medical problems were chosen randomly from among 336 psychology students who self-selected in return for course credit. On arrival they completed several psychometric instruments related to anxiety and claustrophobia. Later they participated in a mock MRI assessment while various measures of arousal and fear were recorded. Seven of the 200 students failed the mock scan behaviorally, 7 completed the mock scan but reported criterional fearfulness retrospectively, and 17 others completed the mock scan but manifested excessive heart-rate change while doing so. A logistic regression showed that total scores on the Claustrophobia Questionnaire (below) served to predict a student’s membership among the 31 students who were fearful in one way or another versus the 169 who were not. On the basis of those results, the authors suggested that mock MRI assessment among college students provides a feasible and conceptually relevant procedure for research on the nature of MRI-related claustrophobia. Rachman (1990) argued that a unidimensional conceptualization of claustrophobia is not appropriate; rather, a conceptualization is needed that incorporates partially independent influences from fears related to restriction and fears related to suffocation. There are ample data that support Rachman’s view. For example, restriction and suffocation have emerged as somewhat independent themes in factor analyses of responses on fear questionnaires (e.g., Valentiner, Telch, Petruzzi, & Bolte, 1996), and fears of restriction and suffocation have responded independently to treatments that target one but not the other (Harris, Robinson, & Menzies, 1999). The empirical literature suggests further that a complete picture of claustrophobia will include a role for cognitive elaborations such as catastrophic misappraisals of bodily anxiety symptoms (Booth & Rachman, 1992; Craske, Mohlman, Yi, Glover, & Valeri, 1995; Craske & Sipsas, 1992; Curtis, Hill, & Lewis, ¨ st & Csatlos, 1990; Rachman, Levitt, & Lopatka, 1988) and exaggerated expectations of danger (O 2000; Valentiner et al.). Indeed, Rachman and Taylor (1993) argued that something akin to ‘‘anxiety sensitivity’’ (Reiss, Peterson, Gursky, & McNally, 1986) might need to be added to fears of restriction and suffocation in order to describe claustrophobia adequately. The purpose of the work reported here was to evaluate the Rachman and Taylor (1993) account of claustrophobia as it occurs in the mock MRI assessment of college students. Students were assessed psychometrically and exposed to a mock MRI scan while arousal and fear were measured. The psychometric scores were then used as predictor variables and fear-measurement values used as criterion variables in several data analytic procedures. Some of the psychometric instruments selected were revised/improved versions of the questionnaires used by McGlynn et al. (2003). Arousal and fear measures were selected so as to sample the three standard domains of fear responding (i.e., subjective experience, behavior, and physiology). Other changes from the methods used by McGlynn et al. were made so as to afford clear-cut opportunities for measures of fear of restriction, fear of suffocation, and cognitive elaboration of anxiety symptoms to account for variance in measures of fear.
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1. Method 1.1. Participants One hundred sixty undergraduate students (114 females) completed the first psychometric phase of the study in return for extra course credit. One hundred fifty-five students reported their age and ethnicity. The mean age was 20.55 years (S.D. = 2.17). One hundred thirty-three were Caucasian; 22 were African-American, Hispanic, or Asian. One hundred eleven students returned and completed a second psychometric phase of the study. Forty of these 111 participants were then excluded due to having a wide shoulder span (n = 12) or to reporting ongoing medication use, an ongoing medical condition, or a history of panic disorder (n = 28). Seventy-one students were invited to participate in the third and final phase of the work. Four of these were excluded due to a possible panic disorder; three were excluded due to missing data or computer difficulties. Thus, 64 participants were included in the final phase of the study. 1.2. Apparatus and materials 1.2.1. MRI simulator The mock MRI device is described by Wood and McGlynn (2000). It is a full-scale replica of a GE Signa .5 T MRI apparatus. An 18-ft long and 23-in. wide table is elevated 42 in. from the floor. A track within the table is remote-controlled and can be operated by the participant or by the experimenter. A motor moves the track up to 712 ft into and out of a 22-in. diameter tube that is enclosed by a square chamber. The only openings in the mock scanner are at the two ends of the tube. 1.2.2. Subjective fear measure Participants were asked to provide a numerical rating of their fear intensity, using a Subjective Units of Distress Scale (SUDs) on which 0 (absolutely calm) and 100 (worst imaginable) served as anchor points. 1.2.3. Behavioral avoidance test Behavioral avoidance tests were conducted as described in Section 1.3. A tape measure attached at the end of the remote-controlled track registered the distance it traveled into the mock scanner. Quantification of avoidance was afforded by measuring the distance traveled into the tube (in inches) and the time spent inside (in seconds). 1.2.4. Psychophysiological equipment Heart rate was recorded via a J & J Instruments Co. (Poulsbo, WA) modular system comprised of a #P-401 module and a #I-330 interface. An optical densitometer attached to the pad of an index finger recorded pulse–volume variations. Heart-rate data were gathered continuously and later averaged across 30-s intervals via Data-Track software for a personal computer. 1.2.5. Psychometric measures The following questionnaires were administered: the revised Claustrophobia Questionnaire (Radomsky, Rachman, Thordarson, McIsaac, & Teachman, 2001), the Anxiety Sensitivity Index-Revised (Taylor & Cox, 1998), and the Agoraphobic Cognitions Questionnaire
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(Chambless, Caputo, Bright, & Gallagher, 1984). Sections of the Anxiety Disorders Interview Schedule-Four (Brown, Di Nardo, & Barlow, 1994) were used also as described below. The original Claustrophobia Questionnaire or CLQ (Rachman & Taylor, 1993) is a 30-item Likert-scale instrument on which respondents rate from 0 to 4 their anxiety when in a variety of situations. It provides a total score for claustrophobic fear and subscale scores for fear of restriction (CLQ-RS) and fear of suffocation (CLQ-SS). In its revised form (Radomsky et al., 2001), 14 items comprise the suffocation subscale (R = 0–56) and 12 items make up the restriction subscale (R = 0–48). The Radomsky et al. modification of the original CLQ followed a principal component analysis in which one of the original items loaded on the wrong factor and three other items were too complex. Thus, four items were removed from the original instrument, leaving the 14 items in the suffocation subscale and 12 items in the restriction subscale. The revised scale and subscales have been found to have internal consistency coefficients (Cronbach’s a) that range from .85 to .96, and 2-week test–retest stability correlations that range from .77 to .89, among undergraduate and community respondents (Radomsky et al.). The Radomsky et al. paper is the only one that has reported psychometric data from the revised CLQ. The Anxiety Sensitivity Index-Revised or ASI-R (Taylor & Cox, 1998) is a 36-item Likerttype instrument on which participants rate from 0 to 4 their fear of 36 anxiety-related symptoms. It is based on the original Anxiety Sensitivity Index (Reiss et al., 1986), a questionnaire that measures anxiety sensitivity, or proneness toward catastrophic misappraisals of anxiety phenomena. The ASI-R provides a subscale score for each of four lower order factors that load on a single higher order factor: fear of respiratory symptoms or ASI-R-RE (R = 0–48), fear of publicly observable anxiety reactions or ASI-R-PO (R = 0–28), fear of cardiovascular symptoms or ASI-R-CV (R = 0–44), and fear of cognitive dyscontrol or ASI-R-CD (R = 0–24). Our scoring provided a score for each of these four subscales and summed them for a total score (R = 0–144). Data about the internal consistencies and test–retest stabilities of these four factor scores and the total score have not been reported. The Agoraphobic Cognitions Questionnaire or ACQ (Chambless et al., 1984) is also a Likertscale instrument that assesses cognitive misappraisals of anxiety phenomena. Respondents rate from 1 to 5 their fear of 15 various possible consequences of panic. The scale provides a total score, obtained by calculating the mean score of all 15 items (R = 1–5), as well as mean subscale scores for physical concerns or ACQ-PC (R = 1–5) and for concerns related to loss of control or ACQ-LC (R = 1–5). Among individuals diagnosed with agoraphobia, the ACQ total score has shown a 31day test–retest stability correlation of .86 and internal consistency on the order of .80 (Chambless et al.). Psychometric data on the two subscales were not reported in the original article. The Anxiety Disorders Interview Schedule-Four or ADIS-IV (Brown et al., 1994) is a semistructured interview that affords clinical conceptualization of the nature and severity of a person’s anxiety. Oftentimes, the ADIS-IV assists in determining whether the symptoms warrant a DSM-IV (American Psychiatric Association, 2000) diagnosis. The ADIS-IV is composed of several modules that focus on anxiety disorders. We used the modules for panic disorder and agoraphobia to identify and eliminate participants who met the criteria for those diagnoses. 1.3. Procedure Undergraduate psychology students were self-selected via classroom and bulletin-board announcements. They were first group-administered the CLQ, ASI-R, and ACQ by undergraduate research assistants over the course of 2 days. At that time, they reported any ongoing medical conditions, any history of panic disorder, and any current medication regimen that would preclude
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further participation. The students then returned 1 week later and completed the questionnaires again. (The psychometric instruments were administered twice before the mock MRI scan because data concerning their test–retest stabilities are in short supply and were needed to legitimize using the questionnaires here.) The mean time between the first and second administrations was 6.88 days (S.D. = .72). Three weeks following the above screening procedure, each participant who was not excluded by medical status, history, or medication use met with two clinical psychology graduate students (second and third authors) for a final assessment and mock MRI session. At this session, participants were administered the panic disorder and agoraphobia modules of the ADIS-IV by one of the graduate students who was blind to previous questionnaire responses. Then participants met with the second graduate student, who was blind to their ADIS-IV responses. The heart-rate sensor was attached and participants were informed that the purpose of this monitoring was to record physiological responses during the experiment. Participants were instructed to lie on a cot while heart rate was recorded for 612 min. Following the assessment ‘‘at rest,’’ participants were escorted into the room that housed the mock scanner. They were shown the scanner for the first time and it was identified as a replica of an actual MRI device. They were told that the device would not scan body tissue or make the loud noises associated with actual MRI scanning. Participants were shown how to operate the remote controller and operated the controller once themselves. Participants were then informed that they would be asked to insert themselves fully into the mock scanner and remain for ‘‘several minutes,’’ at which time they would be withdrawn. They were told also that they could use the controller to stop the track or withdraw themselves at any time. At this point they were instructed to lie on their back atop the MRI track for 3 min. While not analyzed, heart rate was recorded during this period. At the end of the 3-min period, participants were instructed to insert themselves into the scanner head-first and to remain until the experimenter informed them that time had expired. Heart rate was recorded throughout the period along with the distance (in inches) the participant traveled into the mock scanner and the length of time (in seconds) that the participant remained inside. After withdrawal from the device, each participant provided an oral SUDs rating of the highest fear experienced during exposure. 2. Results 2.1. Psychometric data As noted above, each of the questionnaires of interest was administered twice before the mock scan to allow for calculating Pearson correlations across a 1-week test–retest interval. Correlations for total scores and for various subscale scores were calculated (n = 111). Item-toitem correlations were calculated also to allow for characterizing the internal consistencies (Cronbach’s as) of the total and subscale scores on the questionnaires. Data from the first of the two administrations were used to calculate Cronbach’s as because the first administration garnered the most respondents (n = 160). The Pearson correlations for total scores ranged from .80 to .91; those for subscale scores ranged from .77 to .87. Cronbach’s a values for total scores ranged from .85 to .95; those for subscales ranged from .80 to .91. The correlations were sufficiently strong to legitimize use of the questionnaires here; all correlations were significant at the .001 level. Pearson correlations were calculated also for total scores and subscale scores across the three questionnaires (n = 160). The intercorrelation matrix is shown in Table 1.
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ASI-R-RE ASI-R-PO ASI-R-CV ASI-R-CD ASI-R-TOT ACQ-PC ACQ-LC ACQ-TOT CLQ-SS CLQ-RS CLQ-TOT
ASI-R-RE
ASI-R-PO
ASI-R-CV
ASI-R-CD
ASI-R-TOT
ACQ-PC
ACQ-LC
ACQ-TOT
CLQ-SS
CLQ-RS
CLQ-TOT
1.00
.48 1.00
.70 .54 1.00
.46 .50 .73 1.00
.86 .75 .89 .75 1.00
.59 .41 .65 .48 .66 1.00
.41 .46 .53 .61 .58 .52 1.00
.55 .50 .65 .64 .70 .82 .92 1.00
.53 .46 .55 .49 .62 .50 .52 .58 1.00
.43 .37 .42 .38 .49 .33 .34 .38 .59 1.00
.53 .45 .53 .48 .61 .45 .46 .52 .85 .93 1.00
Note. ASI-R-RE: Respiratory Symptoms subscale; ASI-R-PO: Publicly Observable Anxiety Reactions subscale; ASI-R-CV: Cardiovascular Symptoms subscale; ASI-R-CD: Cognitive Dyscontrol subscale; ASI-R-TOT: Anxiety Sensitivity Index-Revised total score; ACQ-PC: Physical Concerns subscale; ACQ-LC: Loss of Control subscale; ACQTOT: Agoraphobic Cognitions Questionnaire total score; CLQ-SS: Suffocation subscale; CLQ-RS: Restriction subscale; CLQ-TOT: Claustrophobia Questionnaire total score. All correlations significant at P < .001.
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Table 1 Correlations between the ASI-R, the ACQ, the CLQ, and their subscales
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Fear was measured within each of the subjective, behavioral, and physiological domains. Initially, participants were classified within each domain as fearful versus non-fearful using the decision rules described by McGlynn et al. (2003). The participant did or did not retrospectively report a maximum SUDs rating of 70 or higher on exiting the mock scanner (subjective fear). The participant did or did not refuse to enter the scanner or exit the scanner prematurely (behavioral fear). During the mock scan the participant did or did not show at least one 30-s interval of excessive heart-rate change from ‘‘at rest’’ values as defined in the earlier paper (cardiac fear). Twenty-five of the 64 participants (39.06%) showed some variety of three-channel fear according to these decision rules. Table 2 shows the mean total scores and subscale scores of each of the three questionnaires obtained from participants who were and were not classified as showing subjective, behavioral, and cardiac fear. Tabular asterisks identify instances in which questionnaire scores for fearful versus nonfearful participants differed significantly according to t-tests for independent samples. The sizes of the groups that were compared via t-tests were quite different. Therefore, the questionnaire scores for participants classified as showing and not showing subjective, behavioral, and cardiac fear were compared also with Mann–Whitney U tests. The significant results from the Mann–Whitney tests were the same as those from t-tests. 2.2. Predicting fear The data summarized in Tables 1 and 2 serve to describe the data set and to inform subsequent analyses. Throughout the analyses SUDs ratings are used as a continuous variable, while behavioral and cardiac data are used to form dichotomous classification of participants. The use of SUDs as a continuous variable was dictated by the data-analytic approach. The reduction of behavioral and cardiac data to dichotomous categories was prompted by the distributions of the data and by interest in maintaining continuity with the report of McGlynn et al. (2003). 2.2.1. Ratings of maximum SUDs The six subscale scores from the second administration of the ASI-R and CLQ were used as predictor variables and retrospective SUDs as the criterion variable in a stepwise linear regression procedure. (Scores from the ACQ were omitted in the service of simplicity and because they were intercorrelated with scores from the ASI-R.) Scores from the CLQ-SS (fear of suffocation) accounted for the largest amount of variance, R2 = 34.3%, F(1, 62) = 32.33, P < .001. The stepwise procedure added-in scores from the ASI-R-PO to yield R2 = 40.4%, F(2, 61) = 20.70, P < .001. According to the analysis, retrospective reports of the maximum fear experienced while inside the mock scanner were influenced by a combination of fear of suffocation and fear of public anxiousness. Fear of suffocation was the overriding determinant of subjective fear in the mock MRI procedure. The CLQ-RS (fear of restriction) was not a significant predictor when entered into the multiple regression model with the CLQ-SS and ASI-R-PO. Given the importance of restriction fear in traditional accounts of claustrophobia, we undertook further examination of that result. Fig. 1 shows one of several path models that were fit to the correlations among the three predictor variables and the criterion variable of retrospective SUDs. It provides the best representation of how fear of suffocation (CLQ-SS), fear of restriction (CLQ-RS), and fear of public anxiousness
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SUDs 70 (n = 12) ASI-R-RE ASI-R-PO ASI-R-CV ASI-R-CD ASI-R-TOT ACQ-PC ACQ-LC ACQ-TOT CLQ-SS CLQ-RS CLQ-TOT
19.42 14.50 7.75 5.17 46.83 1.68 2.49 2.08 17.75 21.92 39.67
(14.09) * (6.36)* (9.76) (5.83) (31.61) * (0.62)* (0.99)* (0.74)* (10.06)* (12.26) (21.75) *
SUDs < 70 (n = 52)
Behavioral fear (n = 6)
9.06 8.85 3.50 1.96 23.37 1.27 1.79 1.53 9.98 15.98 25.96
18.83 9.67 8.00 5.00 41.50 1.60 2.10 1.85 19.17 27.50 46.67
(6.31) (5.98) (6.18) (3.42) (17.37) (0.31) (0.66) (0.45) (7.30) (9.03) (15.33)
(12.64) * (5.32) (11.06) (4.98) (24.51) (0.55) (0.37) (0.37) (6.37)* (7.71)* (14.01) *
Behavioral completion (n = 58)
Cardiac fear (n = 9)
No cardiac fear (n = 54)
10.19 9.93 3.91 2.31 26.34 1.32 1.90 1.61 10.64 16.02 26.66
14.67 15.00 4.33 1.33 35.33 1.44 2.13 1.79 15.56 20.44 36.00
10.11 9.17 4.02 2.57 25.87 1.32 1.88 1.60 10.48 16.20 26.69
(8.43) (6.54) (6.58) (3.99) (21.96) (0.39) (0.81) (0.57) (8.18) (9.50) (16.68)
(10.46) (5.68)* (6.93) (2.96) (23.34) (0.43) (1.01) (0.67) (9.21) (12.24) (21.15)
(8.68) (6.18) (6.96) (4.07) (21.94) (0.41) (0.74) (0.53) (7.92) (9.21) (16.08)
Note. ASI-R-RE: Respiratory Symptoms subscale; ASI-R-PO: Publicly Observable Anxiety Reactions subscale; ASI-R-CV: Cardiovascular Symptoms subscale; ASI-R-CD: Cognitive Dyscontrol subscale; ASI-R-TOT: Anxiety Sensitivity Index-Revised total score; ACQ-PC: Physical Concerns subscale; ACQ-LC: Loss of Control subscale; ACQTOT: Agoraphobic Cognitions Questionnaire total score; CLQ-SS: Suffocation subscale; CLQ-RS: Restriction subscale; CLQ-TOT: Claustrophobia Questionnaire total score. Total participants for cardiac fear is 63, rather than 64, due to 1 participant who refused to enter the device. Score significantly higher (*P < .05) than non-fearful group based on independent-samples t-test.
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Table 2 Mean predictor variable scores across criterion fear categories
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Fig. 1. Path model beginning with fear of suffocation (CLQ-SS). Note. CLQ-SS: fear of suffocation; CLQ-RS: fear of restriction; ASI-R-PO: fear of publicly observable anxiety reactions; SUDs: retrospective SUDs ratings. e1, e2, and e3 denote error terms.
(ASI-R-PO) influenced one another and retrospective SUDs within the data set. The model accounted for 41% of the variance in retrospective SUDs, x2(1) = .06, P = .80. Only the path between fear of restriction and retrospective SUDs was non-significant, Z = .60, P = .55. When that path was removed, the fit of the model was not affected, x2(2) = .42, P = .81. The model is consistent with the idea that fear of suffocation influenced retrospective SUDs both directly and indirectly via influencing fear of public anxiousness. Fear of suffocation also influenced fear of restriction, but fear of restriction did not affect retrospective SUDs directly. Similar path models were generated in which fear of restriction was allowed to influence fear of public anxiousness and vice-versa. The path coefficients between fear of restriction and fear of public anxiousness were non-significant in both directions. Hence those models are not reproduced here. The model above was guided by the reasonable notion that fear of suffocation leads to fear of restriction (and to fear of public anxiousness). Path modeling could be guided by the notion that fear of restriction leads to fear of suffocation, but that notion is not reasonable. Path modeling of the data here can, however, be guided by the plausible notion that fear of public anxiousness might augment fear of restriction and/or fear of suffocation. Hence we generated a path model of the retrospective SUDs data in which fear of public anxiousness was used as the first variable, while fears of restriction and suffocation were used as mediators, and fear of suffocation was allowed to influence fear of restriction (see Fig. 2). This model is just identified and accounted for 41% of the variance in retrospective SUDs. Only the two paths in which fear of restriction was a mediator variable were non-significant. When those paths were removed, the model became the same as in Fig. 1 when the path from fear of restriction to retrospective SUDs is dropped. The model is consistent with the idea that fear of public anxiousness influenced retrospective SUDs both directly and indirectly by influencing fear of suffocation.
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Fig. 2. Path model beginning with fear of publicly observable anxiety reactions (ASI-R-PO). Note. ASI-R-PO: fear of publicly observable anxiety reactions; CLQ-RS: fear of restriction; CLQ-SS: fear of suffocation; SUDs: retrospective SUDs ratings. e1, e2, and e3 denote error terms.
2.2.2. Behavioral and cardiac data The ASI-R-RE (fear of respiratory symptoms) and both subscales of the CLQ were used as predictor variables, and behavioral fear-category assignment as the criterion variable, in three separate logistic regression procedures. The best single predictor of behavioral fear was the CLQRS, x2(1) = 7.45, P < .01. It classified 89.1% of participants correctly, and the Hosmer– Lemeshow goodness-of-fit test did not find a significant difference between the observed and expected participant classifications, x2(8) = 4.84, P = .78. Each one-point increase on the CLQRS score increased by 13.5% the participant’s odds ratio of being classified as behaviorally fearful. The second best single predictor of behavioral fear was the CLQ-SS, x2(1) = 4.91, P < .05. The CLQ-SS performed in much the same way as did the CLQ-RS; a one-point increase in the CLQ-SS score increased by 10.9% the participant’s odds ratio of being classified as behaviorally fearful. However, when the CLQ-RS and the CLQ-SS were entered together in a logistic regression model, neither reached statistical significance. Such a result is not unusual because the two predictors were highly intercorrelated (see Table 1). More informative is the change in x2 that occurs when each predictor is added to the other. When the CLQ-SS is added to the CLQ-RS, the x2(1) change = .005, P = .94. On contrary, when the CLQ-RS is added to the CLQ-SS, the x2(1) change = 2.55, P = .11. Although these changes are non-significant, their relative sizes suggest that fear of restriction is a more important determinant of behavioral fear than is fear of suffocation. Scores on the ASI-R-PO provided the only significant prediction of assignment to fear versus non-fear categories when logistic regressions were performed based on cardiac classification, x2(1) = 6.17, P < .05. The ASI-R-PO classified 84.1% of the participants correctly. When using the Hosmer–Lemeshow goodness-of-fit test with the ASI-R-PO, there was no significant difference between the observed and predicted classifications, x2(8) = 3.81, P = .87. Each onepoint increase in the ASI-R-PO score increased by 14.9% the participant’s odds ratio of being classified as fearful according to cardiac data.
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3. Discussion McGlynn et al. (2003) were interested in establishing the viability of mock MRI assessment among college students as a means of studying claustrophobia as it occurs in the clinical MRI setting. As described earlier, they measured subjective fear, behavioral fear, and cardiac responsivity in connection with a mock MRI assessment among 200 self-selected college students. Before the mock assessment, the students provided ratings of self-efficacy for completing the mock assessment and responded to questionnaires about claustrophobia, panic, anxiety, and anxiety sensitivity. Thirty-one of the 200 students displayed criterional fearfulness based on subjective, behavioral, or cardiac measures. The best prediction of an individual student’s assignment to that group of 31 was afforded by the self-efficacy ratings. However, total scores on the original Claustrophobia Questionnaire (Rachman & Taylor, 1993) also predicted assignment to that group of 31 students. The latter result prompted the conclusion that mock MRI assessment of college students seems to provide a viable preparation for research on MRI-related claustrophobia in the clinic. In the work reported here, the basic viability of the mock MRI preparation was provisionally assumed; attention was directed to using an improved protocol to describe claustrophobia in the mock MRI arrangement. Pre-insertion self-efficacy ratings were omitted in order to eliminate the potential for experimental-demand contamination that such ratings introduce into results from obtrusive assessment. Improved versions of the several questionnaires were used, and choices of questionnaires were guided by the Rachman and Taylor (1993) view of the nature of claustrophobia. The short-term stabilities and internal consistencies of the questionnaires were more than adequate for the present purpose. The questionnaires predicted large proportions of criterion variance relative to other work of this genre. In short, the conditions for describing the events of mock MRI assessment in terms of claustrophobia were met. Subjective fear during the mock scan was predicted most strongly by fear of suffocation. That finding was expected; a nexus between claustrophobia and fear of suffocation has been recognized for some time (Rachman, Levitt, & Lopatka, 1987, 1988). Indeed, our research preparation would have been seriously compromised had fear of suffocation not predicted subjective fear during the mock scan. Subjective fear during the mock scan was also predicted by fears of public anxiousness. Taken together, the predictors of subjective fear during the mock scan suggest that participants feared possible suffocation and/or possibly behaving in a way that would attract negative social evaluation. The path models provided earlier show two ways to think about the relations among the most influential variables. Subjective fear during the mock scan was not predicted by fear of restriction beyond the prediction afforded by fear of suffocation and fear of public anxiousness. That result is inconsistent with the view (Rachman & Taylor, 1993) that fears of suffocation and restriction often participate jointly in claustrophobia. Differing methodologies might account for the inconsistency. The view that fears of suffocation and restriction both contribute to claustrophobia rests partially on results from factor analyses of questionnaire responses (e.g., Rachman & Taylor; Valentiner et al., 1996), whereas the absence of influence from fear of restriction in the present work resulted from pathmodeling the relations between questionnaire responses and laboratory self-report data. However, methodological differences do not explain the inconsistency completely. Rachman and Taylor, Radomsky et al. (2001), and others have provided behavior-test data that support a concept of claustrophobia that involves fears of both suffocation and restriction. The absence of significant effects from fear of restriction in the path models of subjective fear during the mock scan might simply mean that claustrophobic experience does not reflect fear of
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restriction when it occurs in the MRI setting. There are arguments against that explanation but they are not strong. Nazemi and Dager (2003) and Harris et al. (1999) reported studies in which fear of restriction was present in the MRI setting and was attenuated by successful scanning even while fear of suffocation was not. However, neither investigation used demonstrably claustrophobic participants, and both found scan-contingent attenuation of fear of restriction mainly among relatively non-fearful participants. The failure of fear of restriction to influence subjective fear during the mock scan here remains to be explained. Behavioral fear (escape/avoidance) associated with the mock scan was predicted strongly by fear of restriction even though subjective fear during the scan was not. The same relation between behavioral fear during a mock scan and fear of restriction was reported by McGlynn et al. (2003) using the original CLQ. Similar results were reported also by Valentiner, Telch, Ilia, and Hehmsoth (1993), who found that expected danger and expected anxiety predicted behavioral performance but not subjective fear during a claustrophobia test vis-a`-vis a small, dark tunnel. Behavioral fear associated with the mock scan was predicted also by fear of suffocation. Futhermore, the behaviorally fearful participants produced higher scores for sensitivity to respiratory symptoms (ASI-R-RE, Table 2) than did participants who completed the procedure. If our logistic approach to analysis of behavior data is accepted, then behavioral fear associated with the mock scan was influenced most strongly by fear of restriction. However, behavioral fear associated with the mock scan was influenced also by fear of suffocation and by heightened sensitivity to symptoms related to suffocation. Cardiac reactivity during the mock MRI assessment was predicted by scores for fear of public anxiousness, suggesting that some participants’ heart rates were elevated due to concerns about behaving fearfully and being negatively evaluated. Valentiner et al. (1996) reported a thematically similar result; cardiac responsivity during a claustrophobia test was predicted with a measure of self-efficacy regarding the test. However, McGlynn et al. (2003) and Valentiner et al. (1993) failed to predict cardiac responsivity related to claustrophobia with similar predictor variables. An understanding of cardiac responsivity during claustrophobic challenge awaits research on boundary conditions. Discordance between self-report, behavioral, and physiological measures of fear is the norm (Rachman & Hodgson, 1974). Hence, finding that measures from the different domains are influenced by different variables is to be expected. Nonetheless there were some mild surprises in the data. It was surprising that fear of restriction did not affect subjective fear during the mock assessment when the effects of fear of suffocation (and fear of public anxiousness) were taken into account. It was mildly surprising that the ASI-R-PO was the only ASI-R variable that predicted subjective fear when incorporated in the multivariate model. By and large, however, the results offer non-trivial support for the Rachman and Taylor (1993) view of claustrophobia when claustrophobia is instantiated as a three-channel (Lang, 1968) construct. Fears of suffocation and restriction, sensitivity to symptoms of suffocation, and possibly, a narrow variant of social phobia, influenced the college students’ fear in one way or another. While the basic viability of the research preparation was provisionally assumed, it was nonetheless true that the research preparation was being tested alongside the Rachman and Taylor (1993) conceptualization of claustrophobia. In that connection, the proportion of variance in fear ratings explained by the multiple regression model was impressive, as were the sizes of the oddsratio increments associated with the two logistic regressions. Along with the results reported by McGlynn et al. (2003), the present results serve to establish the mock MRI assessment of college students as a valuable approach to studying fear behavior related to the construct of claustrophobia.
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