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Is the Latent Structure of Fear of Pain Continuous or Discontinuous Among Pain Patients? Taxometric Analysis of the Pain Anxiety Symptoms Scale
The Journal of Pain, Vol 8, No 5 (May), 2007: pp 387-395 Available online at www.sciencedirect.com
Is the Latent Structure of Fear of Pain Continuous or Discontinuous Among Pain Patients? Taxometric Analysis of the Pain Anxiety Symptoms Scale Gordon J.G. Asmundson,*,† Kelsey C. Collimore,*,† Amit Bernstein,‡ Michael J. Zvolensky,‡ and Heather D. Hadjistavropoulos† *Anxiety and Illness Behaviors Laboratory and † Department of Psychology, University of Regina, Regina, Saskatchewan, Canada. ‡ Department of Psychology, University of Vermont, Berlington, Vermont.
Abstract: Elevated fear of pain is believed to denote a potential mechanism through which pain is maintained over time; however, our knowledge about fear of pain, its measurement, and its conceptualization is far from complete. It has been assumed that the latent structure of fear of pain is multidimensional and continuous. Although there is factor analytic evidence that it is multidimensional, there have been no empiric efforts to establish whether fear of pain is continuous or discontinuous (ie, taxonic or dichotomous latent class variable) in nature. Using taxometric methods in a sample of 650 patients seeking treatment for musculoskeletal or headache pain, we evaluated the latent structure of fear of pain as indexed by the Pain Anxiety Symptoms Scale. Results from analyses of simulated Monte Carlo data, MAXEIG-HITMAX, and MAMBAC and L-mode external consistency tests indicated that the latent structure of fear of pain was nontaxonic, characterized by latent continuity. Results are discussed in relation to the conceptual understanding of fear of pain, implications for treatment, and future directions for research on issues pertinent to pain-related fear. Perspective: This article presents an analysis designed to establish whether fear of pain is continuous or discontinuous in clinical samples. The findings, indicating that fear of pain is continuous, are important for understanding the nature of fear of pain and to designing appropriately targeted interventions. © 2007 by the American Pain Society Key words: Pain, fear, taxon, continuous, Pain Anxiety Symptoms Scale.
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ear-avoidance models of chronic musculoskeletal pain and headache posit that fear of pain (FOP) is an individual difference characteristic that, like other fears,17,24 comprises multiple response modalities (ie, cognitive, behavioral, physiological).3,35,48 FOP represents an emotional state that can facilitate adaptive responses (eg, escape, withdrawal) to perceived threat (ie, pain, pain-related situations).5 When excessive, it can lead to Received June 15, 2006; Revised September 29, 2006; Accepted October 31, 2006. Supported in part by an operating grant from the Canadian Institutes of Health Research (CIHR). Dr. Asmundson was supported by a CIHR Investigator Award. K.C. Collimore was supported by a CIHR Canada Graduate Scholarship Master’s Award. Address reprint requests to Dr. Gordon J.G. Asmundson, Anxiety and Illness Behaviours Laboratory, University of Regina, Regina, Saskatchewan, S4S 0A2, Canada. E-mail: [email protected] 1526-5900/$32.00 © 2007 by the American Pain Society doi:10.1016/j.jpain.2006.10.007
catastrophic expectations regarding danger associated with pain sensations, pain-related activities (ie, work), injury, illness, and so forth, thereby motivating maladaptive responding (eg, avoidance).5 Thus, elevated FOP has been identified in fear-avoidance models as a potential mechanism through which pain and maladaptive painrelated behavior is maintained over time. Although fearavoidance models have led to advancements in understanding and treating pain conditions characterized by elevated FOP,5 our knowledge about FOP, its measurement, and its conceptualization is far from complete. The latent structure of FOP is assumed to be multidimensional and continuous (ie, occurring along a continuum ranging from low to high)5; however, these assumptions await unequivocal empiric confirmation. Factor analytic studies conducted across diverse populations (eg, patients with chronic musculoskeletal pain, community dwellers) and various FOP measures (see below) 387
388 have yielded evidence that it is indeed a multidimensional construct.10,25,29,31,37,38,49 In their attempts to better understand its impact on attentional processes and physical performance, some researchers have opted to dichotomize FOP by using cutoff rules (eg, those scoring high and low), thereby treating it as a categorical variable.1,11,13,20,21,52 Although our capacity to match the measurement model of FOP to its latent structure in a construct valid manner has a variety of research and clinical implications, there have been no empiric efforts to establish whether FOP is continuous or discontinuous. Taxometrics, a statistical approach related to classification of entities, can be used to test the latent structure of constructs and thereby determine whether they are best represented by continua or by naturally occurring dichotomous categories (ie, taxa). Taxometrics have been used to evaluate the latent structure of numerous psychopathology constructs.16 Anxiety sensitivity, a construct defined as fear of anxiety-related sensations based on the belief that they are personally harmful39 and known to be associated with but distinct from FOP,4 has been found to be taxonic in some6-8 but not all43 investigations of its latent structure. The latent structural status of FOP, as continuous or taxonic, has not been previously examined and remains unclear. Empirically testing the latent structure of FOP in terms of its latent continuous or discontinuous nature is important theoretically and clinically. Continuous and discontinuous variables indicate different underlying mechanisms and potentially require different treatment applications.44 The purpose of this study was to evaluate the latent structure of FOP through the use of taxometric methods in a large sample of patients seeking treatment for musculoskeletal or headache pain. There are currently several measures that denote various aspects of FOP, including those related to movement, painful activities, and nociception. The most popular are the Tampa Scale for Kinesiophobia,23 Fear-Avoidance Beliefs Questionnaire,49 Fear of Pain Questionnaire-III,31 and Pain Anxiety Symptoms Scale (PASS).29,30 We based the present analysis on FOP as measured by the PASS because it appears the least situationally-oriented and most closely associated with postulates of the fear-avoidance models of chronic pain.25,28
Methods Participants Participants included 650 patients with acute or chronic musculoskeletal or headache pain who completed a questionnaire package, including the PASS, during participation in 1 of several other studies.4,15,35,36 All patients were seeking treatment at the time of their participation, the studies in which they participated were approved by the research ethics boards of the University of Regina and Regina Qu’Appelle Health Region, and all participants provided informed consent. The patients with musculoskeletal pain were recruited from multidisciplinary tertiary care rehabilitation (n ⫽ 257) and physiotherapy (n ⫽ 224) treatment providers located
Taxometric Analysis of the Pain Anxiety Symptoms Scale
Characteristics of the 3 Pain Samples Combined for Taxometric Analyses
Table 1.
CHARACTERISTIC Women Age (years) Pain duration (months) Pain severity MPQ - VAS MPI - current pain intensity
TERTIARY CARE PHYSIOTHERAPY N ⫽ 257 N ⫽ 224
HEADACHE N ⫽ 169
41% 38.1 (11.5) 21 (39.9)
89% 41.3 (12.2) 6.5 (4.9)
56% 40.9 (15.2) 22.7 (53.2)
5.6 (2.0) 3.4 (1.2)
— 2.8 (1.5)
6.1 (2.7) —
Abbreviations: MPQ ⫺ VAS, McGill Pain Questionnaire ⫺ Visual Analog Scale34; MPI, Multidimensional Pain Inventory ⫺ Item 1 (0 ⫽ no pain to 6 ⫽ very intense pain).22 NOTE. Standard deviations shown in parentheses.
in the urban area of Regina, Canada. The patients with headache (n ⫽ 169) were recruited from a neurology clinic in the same region. Demographic information for each of these samples is provided in Table 1.22,34 The majority of the participants (58.9%) were female, their age ranged from 13 to 82 years (mean ⫽ 39.8 years, SD ⫽ 13.2 years), and their mean duration of pain was 18.1 months (SD ⫽ 41.1; range ⫽ 0 –348 months). The most commonly reported primary pain sites, coded using the International Association for the Study of Pain18 primary pain site coding system, included the head (27.5%), lower back (21.5%), back (12.3%), upper limbs (11.8%), and lower limbs (10.0%). Other reported areas included the neck (4.9%) and groin (2.0%). Only 1.8% of the sample reported primary pain located in multiple areas. Participants were primarily Caucasian, and all spoke English fluently.
Measures The PASS29,30 is a self-report instrument developed to measure FOP. It comprises 40 items distributed equally on 4 10-item subscales that measure factorially distinct dimensions of FOP,25,29,37 including cognitive anxiety/ interference related to pain (eg, “I find it hard to concentrate when I hurt”), fearful appraisals of pain (eg, “Pain sensations are terrifying”), escape and avoidance behavior in response to activities that are associated with pain (eg, “I try to avoid activities that cause pain”), and physiological symptoms arising from pain (eg, “Pain seems to cause my heart to pound or race”). Respondents are asked to indicate, on a 6-point scale anchored from 0 (never) to 5 (always), the extent to which they engage in the thoughts and activities represented by the items when they experience pain. Five items (ie, items 2, 8, 16, 31, and 40) are reverse scored. The PASS subscale scores can range between 0 and 50 and the total score can range between 0 and 200, with higher scores indicating greater FOP. The PASS has demonstrated good to excellent reliability and validity.2,27
Procedure Participants completed the PASS under generally similar conditions regardless of the site of recruitment. Spe-
ORIGINAL REPORT/Asmundson et al cifically, all participants received the PASS in an individual format as part of a larger questionnaire package that they were asked to complete. Those recruited from the tertiary care rehabilitation setting were permitted to complete the package while waiting for appointments, whereas those recruited from the physiotherapy and neurology clinics were allowed to take the package home and return it by mail. To address missing values, the mean score of the relevant PASS subscale was used to fill in the item value. Missing data were only addressed if less than 20% of the items on a given subscale were missing. The proportion of missing values was 1.0%.
Analytic Approach: Taxometric Analyses Indicator Selection Four theoretically and quantitatively based itemdomains of the 40-item PASS were identified and used to build item-parcel manifest indicators of FOP: (1) cognitive anxiety, (2) escape and avoidance, (3) fearful appraisals of pain, and (4) physiological anxiety. Each of the 4 indicators bore item compositions identical to a wellestablished factor structure of the PASS (ie, cognitive anxiety items 2, 6, 10, 14, 22, 26, 30, 34, 37, 40; escape and avoidance items 3, 7, 11, 15, 19, 23, 27, 31, 35, 39; fearful appraisals of pain items 1, 5, 8, 13, 16, 18, 21, 25, 29, 33; physiological anxiety items 4, 9, 12, 17, 20, 24, 28, 32, 36, 38).29,37 As noted above, these 4 indicators correspond closely to the multiple response modalities identified in the 3-system model of fear (ie, cognitive, physiological, and motivational/behavioral shifts that increase the chance of surviving a perceived threat)17,24 and are consistent with fear-avoidance models of chronic pain.3,35,48 Although there are various means of selecting manifest indicators for taxometric procedures, factor analytically derived indicators may produce optimal taxometric results.50 Factor analytically derived indicators limit artifactual nuisance correlations, provide meaningful sample indicators from all facets of a construct, and optimize internal consistency and distinctiveness of the indicators.43
MAXEIG-HITMAX MAXEIG, or maximum eigenvalue analyses,50 were conducted in line with recent recommended guidelines.42 MAXEIG derives the maximum eigenvalues of the covariance matrix of a set of output indicators at varying levels of an input indicator. In so doing, on a rotating basis, 3 indicators served as output indicators, whereas each remaining single indicator formed an input variable. Furthermore, MAXEIG divides the input variables with overlapping intervals (ie, windows with a conventional default value of 90% overlap). Overlapping windows enable investigators to detect very low base rate taxa. MAXEIG affords a powerful and unique internal consistency test. Consistency tests, which characterize the taxometric approach, are intended to rule out the detection of a pseudo-taxon by requiring convergence of data on a common conclusion and specific values that are un-
389 likely to result in the absence of a taxon. Similarly, consistency tests are important to guard against conclusions regarding pseudo-continuity.16 This inchworm test is achieved by repeatedly conducting the MAXEIG analysis while systematically increasing the number of overlapping windows, thereby systematically decreasing the size of the subsamples used to calculate maximum eigenvalues in each overlapping window. Plots of taxonic data yield an increasingly better defined unimodal peak, as the number of windows is increased and the size of each window is decreased. In the instance of continuous data, in contrast, the systematic increase of the number of overlapping windows will produce flat or cusped plots that do not systematically peak.50 The specific criteria used herein to conduct the covariance plot “nose-count” were based on the guidelines provided by Waller and Meehl.50 For example, only peaked MAXEIG plots were interpreted as evidence of latent taxonicity. Although in some instances right-end cusped MAXEIG plots also may represent latent taxonicity,43 such cusped plots may equally represent positively skewed, latently continuous data. Fifty internal replications were used to divide cases into overlapping windows by repeatedly resorting cases along the input indicator at random. Internal replications are particularly useful when MAXEIG input variables are divided by fixed intervals and placed arbitrarily between equal-scoring cases. Thus, use of these replications is intended to minimize sampling error and bolster the reliability of the results.40 At each replication, the maximum eigenvalue of the 2 output indicators within each window was calculated, and these calculations were averaged across all replications. In the present study, we conducted the inchworm test by generating 3 sets of MAXEIG plots (50 windows, 150 windows, 250 windows). These specific values were selected on an a priori basis to produce 4 sets of MAXEIG analyses that assign n ⫽ 25 cases per window in the final inchworm test, so that a sufficiently wide range of windows is used to detect a low base rate taxon, were it present, while guarding against sampling error. In addition, the nosecount and the coherency of the standard deviation of the base rate estimates served as additional internal consistency tests. Finally, after taxonicity was tested, the General Covariance Mixture Theorem50 was used to estimate the taxon base rate for each MAXEIG plot. If the MAXEIG analyses indicated a taxonic latent structure, then individual participants were assigned to the taxon and complement classes and latent parameters were subsequently estimated.
MAMBAC A nose-count of MAMBAC,33 or mean above minus below a cut, plot was used as an external consistency test of the MAXEIG procedure. External consistency tests are premised on the same notion as internal consistency tests and are specifically intended to rule out pseudo-taxa or pseudo-continua that result from artifact(s) of a taxometric procedure under particular methodological or
390
Taxometric Analysis of the Pain Anxiety Symptoms Scale 43,50
statistical conditions. MAMBAC d (ie, difference) plots are constructed by plotting the difference between the mean of each output indicator above and below each cut score evenly spaced along each input indicator. MAMBAC d plots were generated, 1 for each possible combination of indicators, until all combinations were exhausted. Like MAXEIG, the MAMBAC procedure was conducted with 50 internal replications and by using 30 evenly spaced cuts so as to ensure at least 20 cases per cut. The MAMBAC procedure was conservatively used as an external consistency test of the latent structure of FOP exclusively and was not used to derive parameter estimates.7
L-Mode L-mode, or latent mode analysis,50 was conducted as a second external consistency test to ensure maximal independent testing of the latent structure of the FOP. Lmode derives the underlying factor score distribution by means of conducting a factor analysis with all available indicators and plots the distribution of scores on the first unrotated factor. L-mode infers taxonic latent structure from a bimodal distribution of the observed factor scores and continuous latent structure from a unimodal distribution (ie, 1 factor). Finally, if the L-mode analysis indicated a taxonic latent structure, then individual participants were assigned to the taxon and complement classes, based on the values of the factor score distribution modes, and latent parameters were subsequently estimated.
Parameter-Matched Monte Carlo Simulations By following recently recommended guidelines for taxometric analyses,40,42 parameter-matched Monte Carlo simulated continuous and taxonic data were derived. Simulated continuous data were reproduced by bootstrap and thereby matched to the number of indicators, sample size, and, importantly, the observed indicator correlation matrix, and the distributions of all indicators including their skew, kurtosis, and number of levels. Simulated taxonic data were similarly matched to the parameters of the research data. Ten separate sets of
simulated continuous and taxonic data were derived to bolster the reliability and precision of the simulated data and the taxometric analyses conducted with these simulated data.40 Monte Carlo simulated data were derived for 2 primary purposes. First, for the purpose of a priori suitability testing,42 and, as conducted in previous investigations,7 (and Zvolensky MJ, Forsyth JP, Bernstein A, Leen-Feldner EW, A concurrent test of the anxiety sensitivity taxon: Its relation to bodily vigilance and perceptions of control over anxiety-related events in a sample of young adults. J Cogn Psychother [in press]) all proposed taxometric analyses were conducted on each of the 10 simulated continuous and each of the 10 simulated taxonic data, before taxometric analyses of the research data were interpreted. By examining the degree to which it is possible to distinguish the taxometric plots of the simulated continuous and taxonic data under the parametermatched conditions of the research data, the simulations demonstrate the capacity of the research data to afford meaningful taxometric analyses. Thus, only if each taxometric procedure could distinguish between the simulated continuous and taxonic data could the data be interpreted meaningfully. If the simulated data pass the suitability tests, then the simulated data permit a second primary function. Specifically, the simulations allow investigators to compare the shape of the research data plots with the plots of the simulated taxonic and continuous data. If simulated continuous and taxonic data are discernible, then a nontaxonic pattern of findings in the research data may be reliably interpreted as a marker of latent continuity or nontaxonicity, whereas a taxonic pattern of findings in the research data can be reliably interpreted as evidentiary of taxonicity that probably is neither artifactual nor pseudo-taxonic. Thus, in addition to contrasting the taxometric plots of the research data to predefined criteria,50 these parameter-matched simulations are useful because they provide an additional comparative idiographic benchmark for interpreting the data with respect to their unique parameters and thereby preclude pseudo-taxonic or pseudo-continuous conclusions.
Figure 1. MAXEIG-HITMAX plots of the manifest item-parcel indicators of the fear of pain: Parameter-matched research, simulated taxonic, and simulated continuous (dimensional) data. The research data do not demonstrate a peaked plot consistent with taxonic latent structure or the simulated taxonic data, and instead demonstrate a cusp consistent with dimensional structure and the simulated dimensional data.
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Figure 2. MAMBAC plots of the manifest item-parcel indicators of the fear of pain: Parameter-matched research, simulated taxonic, and simulated continuous (dimensional) data. The research data do not demonstrate a peaked plot consistent with taxonic latent structure or the simulated taxonic data, and instead demonstrate a cusp consistent with dimensional structure and the simulated dimensional data.
Results Suitability Testing Before the interpretation of taxometric analyses of the research data, the suitability of the data for MAXEIG, MAMBAC, and L-mode analyses was tested by using the parameter-matched, simulated continuous and taxonic data (Figs 1 and 2, simulated plots). First, the MAXEIG inchworm consistency test of the simulated continuous data failed to yield unimodally peaked plots as the number of overlapping windows was increased but instead yielded cusped plots, whereas the taxonic data yielded unimodally peaked plots resembling the head of an inchworm.50 Second, MAMBAC plots of the simulated data were not as conspicuously distinguishable as the MAXEIG plots of the simulated data; nevertheless, MAMBAC plots of the simulated continuous data yielded fully concave or parabolic plots, whereas the taxonic data demonstrated markedly more linear cusped plots consistent with a small base-rate taxon.33 Finally, of particular importance due to the somewhat ambiguous suitability of the research data for MAMBAC analysis, L-mode suitability was examined. Specifically, the L-mode analysis of the simulated continuous data yielded a unimodal factor score distribution, whereas the taxonic data yielded a bimodal factor score distribution. Because MAXEIG, MAMBAC, and L-mode analyses of the parametermatched simulated data each passed suitability tests, all
3 analyses of the research data are likely to produce systematically meaningful and reliable conclusions with respect to the latent structure of FOP.
MAXEIG Evidentiary of latent nontaxonicity of FOP, flat, cusped, or multipeaked plots did not unimodally peak even as the number of overlapping windows was increased (Fig 1).42,50 Thus, the MAXEIG analysis and the inchworm consistency test of the research data support the nontaxonic conjecture, as all 4 plots were characteristic of latent continuity.43,44 In addition, the MAXEIG plots of the research data were dissimilar to the matched simulated taxonic data, and, moreover, all were flat, cusped, or multipeaked and so were more similar to the parameter-matched simulated continuous data (Fig 1). Finally, parameter estimates are not meaningful because taxonicity was not observed; the proposed parameter estimates are, by definition, based on class assignment valid only when taxonicity is observed.
MAMBAC MAMBAC analyses of the 4 indicators produced a total of 12 d plots, 11 of which were concave and cusped (ie, not peaked), consistent with a nontaxonic structure (Fig 2).33,43 Furthermore, the MAMBAC plots of the research data were dissimilar to the matched simulated taxonic data and, moreover, most plots were concave and
Figure 3. L-mode factor score distribution plot of the manifest item-parcel indicators of the fear of pain: Parameter-matched research, simulated taxonic, and simulated continuous (dimensional) data. The research data do not demonstrate a second mode consistent with taxonic latent structure or the simulated taxonic data, and instead demonstrate a unimodal plot consistent with dimensional structure and the simulated dimensional data.
392 cusped and so were more similar to the parametermatched simulated continuous data (Fig 2). MAMBAC nose count results were therefore consistent with the MAXEIG results.
L-Mode L-mode analysis of the 4 indicators produced a single unimodal factor score distribution (ie, not bimodal), consistent with a nontaxonic (ie, continuous) structure (Fig 3).43,50 Furthermore, the L-mode plot of the research data was dissimilar to the parameter-matched simulated taxonic data and, moreover, similar to the parametermatched simulated continuous data (Fig 2). Because taxonicity was not observed, parameter estimates cannot be derived from the L-mode analysis; L-mode relies on the observation of a bimodal distribution to conduct class assignment and derive parameter estimates, neither of which was justified under the observed unimodal factor score distribution. Thus, convergence of evidence from MAXEIG, MAMBAC, and L-mode analyses of the research and Monte Carlo simulated data strongly supports the nontaxonic, or continuous, conjecture of FOP.
Discussion The FOP construct has a central role in contemporary fear-avoidance models of chronic pain.3,35,48 The latent structure of the construct has been assumed to be multidimensional and continuous.5 There is factor analytic evidence that FOP is indeed multifaceted25,31,37,38; however, despite several researchers using various methods to allocate participants to FOP categories,1,11,13,20,21,52 there have been no empiric tests to establish whether it is best conceptualized as a continuous or discontinuous (ie, categorical) construct. The present study addressed this issue through application of a taxometric analysis of a common measure of FOP, the PASS.29,30 Results from the MAXEIG-HITMAX internal consistency test, the MAMBAC and L-mode external consistency tests, and comparisons to simulated taxonic and continuous data all indicated that FOP is nontaxonic, that is, characterized by latent continuity. Specifically, the covariance plots generated by MAXEIG-HITMAX did not unimodally peak even as the number of overlapping windows was increased, d plots generated by MAMBAC procedures were almost all concave and cusped, and Lmode analysis of indicators produced a single unimodal factor score. These findings, indicative of latent continuity, are further bolstered by comparison of the research data with Monte Carlo simulated taxonic and continuous data showing dissimilarity and similarity, respectively. These results are unique in that they provide preliminary empiric evidence that FOP is continuous, not taxonic, in nature. The present findings hold important implications for understanding FOP. The first implication is that the nontaxonicity of FOP supports the assumption that FOP is a ubiquitous individual difference variable that varies between people only in degrees along a continuous latent dimension.5 That is, these data from a sample of patients
Taxometric Analysis of the Pain Anxiety Symptoms Scale seeking treatment for musculoskeletal or headache pain, suggest that FOP does not exist in qualitatively distinct pathological and normative forms (ie, it is not taxonic) but rather along a quantitative continuum ranging from very low to very high. These are the first such findings and thus warrant replication using independent clinical samples as well as other measures of FOP. Because the base rate of a hypothetical taxon may be disproportionately represented in clinical samples of patients with pain, such as the 1 used in the present investigation, it remains possible that FOP may be taxonic among nonclinical populations; thus, future research efforts are also needed to determine the latent structure of FOP in nonclinical samples. The second implication, somewhat related to the above, pertains more specifically to understanding the mechanisms associated with the experience of high, seemingly pathological levels of FOP. Indeed, it has been suggested that a clear understanding of the latent structure of a construct can aid in focusing research efforts directed toward discovery of the origin of such constructs.41 Given that there are multiple empirically supported conceptualizations of FOP, this seems particularly propitious. One view is that FOP is similar to specific phobias (eg, fear of spiders),23,48 wherein it is believed that the fear of pain sensations underlie avoidance and other negative pain-related behaviors. A somewhat different view is that FOP is a manifestation of a fundamental predilection to be generally fearful of anything that produces anxiety symptoms, including pain and its consequences; that is, FOP may be more appropriately conceptualized as a manifestation of anxiety sensitivity.3,14 A fundamental difference between these views is that the latter is rooted in the premise that anxiety sensitivity is a risk factor for experiencing high FOP and its behavioral consequences (eg, avoidance, physical inactivity, disability). The present findings do not unequivocally indicate which of these views is most accurate; however, combined with a growing body of evidence that the anxiety sensitivity construct is taxonic,7,8 the findings reported herein provide additional empiric evidence that FOP and anxiety sensitivity are distinct in nature.4,19 Indeed, insofar as FOP is a lower-ordered form of anxiety sensitivity, as suggested in the view that elevated anxiety sensitivity puts one at risk for high FOP, it remains possible that FOP may be fully continuous, whereas anxiety sensitivity is taxonic. The present findings also hold implications for treating pathological expressions of pain-related fear in patients with disabling pain. As mentioned previously, continuous and categorical variables imply different underlying mechanisms and perhaps require different approaches to facilitate recovery. These variable types also imply different expectations regarding patterns of change, with continuous variables changing gradually over time and taxa changing in a relatively all-or-none fashion.44 Partial change is more likely with a continuous variable because a continuum has multiple and intermediate levels. On the other hand, although it may be more difficult to initiate change in a taxonic variable, this change may be
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44
more complete once initiated. The success of in vivo exposure for treating patients with chronic pain with high FOP—showing variable but most often graduated patterns of decline in FOP over treatment sessions in those with chronic back pain (Fig 1 of Vlaeyen et al47 and Fig 2 of Boersma et al9) and complex regional pain syndrome type I (Figs 1 through 4 of de Jong et al12)—may be further augmented by including interoceptive exposure techniques proven successful in patients with anxiety disorder with elevated anxiety sensitivity.45,46 It remains to be determined if anxiety sensitivity is taxonic in patients with acute or chronic pain and, if so, whether FOP provides an index of latent continuity within these potential qualitatively latent taxon and nontaxon classes. Notwithstanding, consistent with the view FOP is a lower-ordered form of anxiety sensitivity, preliminary findings indicate that interoceptive exposure is effective in reducing FOP in people with high pretreatment anxiety sensitivity.51 A study incorporating both in vivo exposure for FOP and interoceptive exposure as well as close monitoring of change processes related to FOP and anxiety sensitivity holds promise for yielding important clinical and conceptual data. There are several limitations of the present study that warrant mention and may serve to guide future research efforts. First, the sample comprised patients seeking treatment for pain related to the musculoskeletal system, most often the back, or headache, and excluded people without significant pain experiences. This was a necessary exclusion to avoid the threat of pseudo-taxonicity secondary to sampling for bimodality. Further evaluation of the latent structure of FOP is warranted in samples of patients with other acute and chronic pain conditions, and, as noted above, in people drawn from nonclinical samples as well as samples representative of the general population. Second, only one measure of FOP—the PASS—was used in the current study. Although
the PASS corresponds closely with the 3-system model of fear17,24 and provides measurement of the FOP construct consistent with contemporary fear-avoidance models of chronic pain, future studies aimed at replication of the present findings might incorporate the PASS, or its psychometrically sound abbreviated version (ie, PASS20),10,26 along with other self-report (eg, Tampa Scale for Kinesiophobia,23 Fear-Avoidance Beliefs Questionnaire,49 Fear of Pain Questionnaire-III31) and behavioral (eg, laboratory-based fear-relevant challenge procedures) indicators of the FOP construct. Such a multimethod approach may serve to improve the accuracy of parameter estimates43 and aid in refining the assessment of the latent structure of FOP through application of bootstrapped taxometric procedures.32 Finally, given the relatively limited size of the present sample, we did not explore the extent to which the taxometric results vary as a function of sex, age, or culture. Given evidence that sex and emotionality may interact to moderate (and, perhaps, mediate) FOP, taxometric analyses of FOP within the context of sufficiently large samples of male and female subjects is warranted. We are currently in the process of evaluating whether the latent structure of FOP observed in the present study is similar in seniors and across various cultures. Overall, the present findings provide important preliminary evidence for latent continuity of the FOP construct as measured by the PASS. Future research efforts, including those outlined above, are needed to further advance understanding of FOP, its measurement, and its conceptualization. To the extent that it is possible to understand the mechanisms that make some people vulnerable to pathological expressions of FOP, we will be better positioned to develop and evaluate appropriately targeted interventions and, perhaps, even develop strategies to inoculate against the effects of these mechanisms.
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394 performance, and attentional processes in patients with fibromyalgia. Pain 104:121-130, 2003 12. de Jong, Vlaeyen JWS, Onghena P, Cuypers C, den Hollander M, Ruijgork J: Reduction of pain-related fear in complex regional pain syndrome type I: The application of graded exposure in vivo. Pain 116:264-275, 2005 13. Dehghani M, Sharpe L, Nicholas MK: Selective attention to pain-related information in chronic musculoskeletal pain patients. Pain 105:37-46, 2003 14. Greenberg J, Burns JW: Pain anxiety among chronic pain patients: Specific phobia or manifestation of anxiety sensitivity? Behav Res Ther 41:223-240, 2003 15. Hadjistavropoulos HD, Asmundson GJG, Kowalyk K: Measures of various anxiety constructs: Is there a difference in their ability to predict long-term functioning among pain patients? Eur J Pain 8:1-11, 2004 16. Haslam N, Kim HC: Categories and continua: A review of taxometric research. Genet Soc Gen Psychol Monogr 128: 271-320, 2002 17. Hugdahl K: The three-system model of fear and emotion: A critical examination. Behav Res Ther 19:75-85, 1981 18. International Association for the study of Pain: Classification of chronic pain. Pain S3:1-226, 1986 19. Keogh E, Asmundson GJG: Negative affectivity, catastrophizing, and anxiety sensitivity, in Asmundson GJG, Vlaeyen JWS, Crombez G (eds): Understanding and treating fear of pain. Oxford, Oxford University Press, 2004, pp 91115 20. Keogh E, Ellery D, Hunt C, Hannent I: Selective attentional bias for pain-related stimuli amongst pain fearful individuals. Pain 91:91-100, 2001
Taxometric Analysis of the Pain Anxiety Symptoms Scale 30. McCracken LM, Zayfert C, Gross RT: The pain anxiety symptoms scale (PASS): A multimodal measure of pain-specific anxiety symptoms. Behav Ther 16:183-184, 1993 31. McNeil DW, Rainwater AJ: Development of the Fear of Pain Questionnaire (III). J Behav Med 21:389-410, 1998 32. Meehl PE: Bootstrap taxometrics. Am Psychol 50:266275, 1995 33. Meehl PE, Yonce LJ: Taxometric analysis, I: Detecting taxonicity with two quantitative indicators using means above and below a sliding cut (MAMBAC procedure). Psychol Rep 74:1059-1274, 1994 34. Melzack R: The short-form McGill Pain Questionnaire. Pain 30:191-197, 1987 35. Norton PJ, Asmundson GJG: Amending the fear-avoidance models of chronic pain: What is the role of physiological arousal? Behav Ther 34:17-30, 2003 36. Norton PJ, Asmundson GJG: Anxiety sensitivity, fear, and avoidance behavior in headache pain. Pain 111:218-223, 2004 37. Osman A, Barrios FX, Osman JR, Schneekloth R, Troutman JA: The Pain Anxiety Symptoms Scale: Psychometric properties in a community sample. J Behav Med 17:511-522, 1994 38. Osman A, Breitenstein JL, Barrios FX, Gutierrez PM, Kopper BA: The Fear of Pain Questionnaire-III: Further reliability and validity with nonclinical samples. J Behav Med 25:155-173, 2002 39. Reiss S, McNally RJ: The expectancy model of fear, in Reiss S, Bootzin RR (eds): Theoretical issues in behavior therapy. New York, Academic Press, 1985, pp 107-121
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46. Taylor S, Rabian B, Fedoroff I: Anxiety sensitivity: Progress, prospects, and challenges, in Taylor S (ed): Anxiety sensitivity: theory, research, and treatment of the fear of anxiety. Mahwah, NJ, Erlbaum, 1999, pp 339-353
28. McCracken LM, Gross RT, Aikens J, Carnrike CLM Jr: The assessment of anxiety and fear in persons with chronic pain: A comparison of instruments. Behav Res Ther 34:927-933, 1996
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29. McCracken LM, Zayfert C, Gross RT: The pain anxiety symptoms scale: Development and validation of a scale to measure fear of pain. Pain 50:67-73, 1992
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ORIGINAL REPORT/Asmundson et al 49. Waddell G, Newton M, Henderson I, Somerville D, Main C: A Fear-Avoidance Beliefs Questionnaire (FABQ) and the role of fear-avoidance in chronic low back pain and disability. Pain 52:157-168, 1993 50. Waller NG, Meehl PE: Multivariate taxometric procedures. Thousand Oaks, CA, Sage, 1998 51. Watt MC, Stewart SH, Lefaivre M-J, Uman LS: A brief
395 cognitive-behavioral approach to reducing anxiety sensitivity decreases pain-related anxiety. Cogn Behav Ther 35:248-256, 2006 52. Williams J, Hadjistavropoulos T, Asmundson GJG: The effects of age and fear of pain on attentional and memory biases relating to pain and falls. Anxiety Stress Coping 18: 53-69, 2005
Is the Latent Structure of Fear of Pain Continuous or Discontinuous Among Pain Patients? Taxometric Analysis of the Pain Anxiety Symptoms Scale Gordon J.G. Asmundson,*,† Kelsey C. Collimore,*,† Amit Bernstein,‡ Michael J. Zvolensky,‡ and Heather D. Hadjistavropoulos† *Anxiety and Illness Behaviors Laboratory and † Department of Psychology, University of Regina, Regina, Saskatchewan, Canada. ‡ Department of Psychology, University of Vermont, Berlington, Vermont.
Abstract: Elevated fear of pain is believed to denote a potential mechanism through which pain is maintained over time; however, our knowledge about fear of pain, its measurement, and its conceptualization is far from complete. It has been assumed that the latent structure of fear of pain is multidimensional and continuous. Although there is factor analytic evidence that it is multidimensional, there have been no empiric efforts to establish whether fear of pain is continuous or discontinuous (ie, taxonic or dichotomous latent class variable) in nature. Using taxometric methods in a sample of 650 patients seeking treatment for musculoskeletal or headache pain, we evaluated the latent structure of fear of pain as indexed by the Pain Anxiety Symptoms Scale. Results from analyses of simulated Monte Carlo data, MAXEIG-HITMAX, and MAMBAC and L-mode external consistency tests indicated that the latent structure of fear of pain was nontaxonic, characterized by latent continuity. Results are discussed in relation to the conceptual understanding of fear of pain, implications for treatment, and future directions for research on issues pertinent to pain-related fear. Perspective: This article presents an analysis designed to establish whether fear of pain is continuous or discontinuous in clinical samples. The findings, indicating that fear of pain is continuous, are important for understanding the nature of fear of pain and to designing appropriately targeted interventions. © 2007 by the American Pain Society Key words: Pain, fear, taxon, continuous, Pain Anxiety Symptoms Scale.
F
ear-avoidance models of chronic musculoskeletal pain and headache posit that fear of pain (FOP) is an individual difference characteristic that, like other fears,17,24 comprises multiple response modalities (ie, cognitive, behavioral, physiological).3,35,48 FOP represents an emotional state that can facilitate adaptive responses (eg, escape, withdrawal) to perceived threat (ie, pain, pain-related situations).5 When excessive, it can lead to Received June 15, 2006; Revised September 29, 2006; Accepted October 31, 2006. Supported in part by an operating grant from the Canadian Institutes of Health Research (CIHR). Dr. Asmundson was supported by a CIHR Investigator Award. K.C. Collimore was supported by a CIHR Canada Graduate Scholarship Master’s Award. Address reprint requests to Dr. Gordon J.G. Asmundson, Anxiety and Illness Behaviours Laboratory, University of Regina, Regina, Saskatchewan, S4S 0A2, Canada. E-mail: [email protected] 1526-5900/$32.00 © 2007 by the American Pain Society doi:10.1016/j.jpain.2006.10.007
catastrophic expectations regarding danger associated with pain sensations, pain-related activities (ie, work), injury, illness, and so forth, thereby motivating maladaptive responding (eg, avoidance).5 Thus, elevated FOP has been identified in fear-avoidance models as a potential mechanism through which pain and maladaptive painrelated behavior is maintained over time. Although fearavoidance models have led to advancements in understanding and treating pain conditions characterized by elevated FOP,5 our knowledge about FOP, its measurement, and its conceptualization is far from complete. The latent structure of FOP is assumed to be multidimensional and continuous (ie, occurring along a continuum ranging from low to high)5; however, these assumptions await unequivocal empiric confirmation. Factor analytic studies conducted across diverse populations (eg, patients with chronic musculoskeletal pain, community dwellers) and various FOP measures (see below) 387
388 have yielded evidence that it is indeed a multidimensional construct.10,25,29,31,37,38,49 In their attempts to better understand its impact on attentional processes and physical performance, some researchers have opted to dichotomize FOP by using cutoff rules (eg, those scoring high and low), thereby treating it as a categorical variable.1,11,13,20,21,52 Although our capacity to match the measurement model of FOP to its latent structure in a construct valid manner has a variety of research and clinical implications, there have been no empiric efforts to establish whether FOP is continuous or discontinuous. Taxometrics, a statistical approach related to classification of entities, can be used to test the latent structure of constructs and thereby determine whether they are best represented by continua or by naturally occurring dichotomous categories (ie, taxa). Taxometrics have been used to evaluate the latent structure of numerous psychopathology constructs.16 Anxiety sensitivity, a construct defined as fear of anxiety-related sensations based on the belief that they are personally harmful39 and known to be associated with but distinct from FOP,4 has been found to be taxonic in some6-8 but not all43 investigations of its latent structure. The latent structural status of FOP, as continuous or taxonic, has not been previously examined and remains unclear. Empirically testing the latent structure of FOP in terms of its latent continuous or discontinuous nature is important theoretically and clinically. Continuous and discontinuous variables indicate different underlying mechanisms and potentially require different treatment applications.44 The purpose of this study was to evaluate the latent structure of FOP through the use of taxometric methods in a large sample of patients seeking treatment for musculoskeletal or headache pain. There are currently several measures that denote various aspects of FOP, including those related to movement, painful activities, and nociception. The most popular are the Tampa Scale for Kinesiophobia,23 Fear-Avoidance Beliefs Questionnaire,49 Fear of Pain Questionnaire-III,31 and Pain Anxiety Symptoms Scale (PASS).29,30 We based the present analysis on FOP as measured by the PASS because it appears the least situationally-oriented and most closely associated with postulates of the fear-avoidance models of chronic pain.25,28
Methods Participants Participants included 650 patients with acute or chronic musculoskeletal or headache pain who completed a questionnaire package, including the PASS, during participation in 1 of several other studies.4,15,35,36 All patients were seeking treatment at the time of their participation, the studies in which they participated were approved by the research ethics boards of the University of Regina and Regina Qu’Appelle Health Region, and all participants provided informed consent. The patients with musculoskeletal pain were recruited from multidisciplinary tertiary care rehabilitation (n ⫽ 257) and physiotherapy (n ⫽ 224) treatment providers located
Taxometric Analysis of the Pain Anxiety Symptoms Scale
Characteristics of the 3 Pain Samples Combined for Taxometric Analyses
Table 1.
CHARACTERISTIC Women Age (years) Pain duration (months) Pain severity MPQ - VAS MPI - current pain intensity
TERTIARY CARE PHYSIOTHERAPY N ⫽ 257 N ⫽ 224
HEADACHE N ⫽ 169
41% 38.1 (11.5) 21 (39.9)
89% 41.3 (12.2) 6.5 (4.9)
56% 40.9 (15.2) 22.7 (53.2)
5.6 (2.0) 3.4 (1.2)
— 2.8 (1.5)
6.1 (2.7) —
Abbreviations: MPQ ⫺ VAS, McGill Pain Questionnaire ⫺ Visual Analog Scale34; MPI, Multidimensional Pain Inventory ⫺ Item 1 (0 ⫽ no pain to 6 ⫽ very intense pain).22 NOTE. Standard deviations shown in parentheses.
in the urban area of Regina, Canada. The patients with headache (n ⫽ 169) were recruited from a neurology clinic in the same region. Demographic information for each of these samples is provided in Table 1.22,34 The majority of the participants (58.9%) were female, their age ranged from 13 to 82 years (mean ⫽ 39.8 years, SD ⫽ 13.2 years), and their mean duration of pain was 18.1 months (SD ⫽ 41.1; range ⫽ 0 –348 months). The most commonly reported primary pain sites, coded using the International Association for the Study of Pain18 primary pain site coding system, included the head (27.5%), lower back (21.5%), back (12.3%), upper limbs (11.8%), and lower limbs (10.0%). Other reported areas included the neck (4.9%) and groin (2.0%). Only 1.8% of the sample reported primary pain located in multiple areas. Participants were primarily Caucasian, and all spoke English fluently.
Measures The PASS29,30 is a self-report instrument developed to measure FOP. It comprises 40 items distributed equally on 4 10-item subscales that measure factorially distinct dimensions of FOP,25,29,37 including cognitive anxiety/ interference related to pain (eg, “I find it hard to concentrate when I hurt”), fearful appraisals of pain (eg, “Pain sensations are terrifying”), escape and avoidance behavior in response to activities that are associated with pain (eg, “I try to avoid activities that cause pain”), and physiological symptoms arising from pain (eg, “Pain seems to cause my heart to pound or race”). Respondents are asked to indicate, on a 6-point scale anchored from 0 (never) to 5 (always), the extent to which they engage in the thoughts and activities represented by the items when they experience pain. Five items (ie, items 2, 8, 16, 31, and 40) are reverse scored. The PASS subscale scores can range between 0 and 50 and the total score can range between 0 and 200, with higher scores indicating greater FOP. The PASS has demonstrated good to excellent reliability and validity.2,27
Procedure Participants completed the PASS under generally similar conditions regardless of the site of recruitment. Spe-
ORIGINAL REPORT/Asmundson et al cifically, all participants received the PASS in an individual format as part of a larger questionnaire package that they were asked to complete. Those recruited from the tertiary care rehabilitation setting were permitted to complete the package while waiting for appointments, whereas those recruited from the physiotherapy and neurology clinics were allowed to take the package home and return it by mail. To address missing values, the mean score of the relevant PASS subscale was used to fill in the item value. Missing data were only addressed if less than 20% of the items on a given subscale were missing. The proportion of missing values was 1.0%.
Analytic Approach: Taxometric Analyses Indicator Selection Four theoretically and quantitatively based itemdomains of the 40-item PASS were identified and used to build item-parcel manifest indicators of FOP: (1) cognitive anxiety, (2) escape and avoidance, (3) fearful appraisals of pain, and (4) physiological anxiety. Each of the 4 indicators bore item compositions identical to a wellestablished factor structure of the PASS (ie, cognitive anxiety items 2, 6, 10, 14, 22, 26, 30, 34, 37, 40; escape and avoidance items 3, 7, 11, 15, 19, 23, 27, 31, 35, 39; fearful appraisals of pain items 1, 5, 8, 13, 16, 18, 21, 25, 29, 33; physiological anxiety items 4, 9, 12, 17, 20, 24, 28, 32, 36, 38).29,37 As noted above, these 4 indicators correspond closely to the multiple response modalities identified in the 3-system model of fear (ie, cognitive, physiological, and motivational/behavioral shifts that increase the chance of surviving a perceived threat)17,24 and are consistent with fear-avoidance models of chronic pain.3,35,48 Although there are various means of selecting manifest indicators for taxometric procedures, factor analytically derived indicators may produce optimal taxometric results.50 Factor analytically derived indicators limit artifactual nuisance correlations, provide meaningful sample indicators from all facets of a construct, and optimize internal consistency and distinctiveness of the indicators.43
MAXEIG-HITMAX MAXEIG, or maximum eigenvalue analyses,50 were conducted in line with recent recommended guidelines.42 MAXEIG derives the maximum eigenvalues of the covariance matrix of a set of output indicators at varying levels of an input indicator. In so doing, on a rotating basis, 3 indicators served as output indicators, whereas each remaining single indicator formed an input variable. Furthermore, MAXEIG divides the input variables with overlapping intervals (ie, windows with a conventional default value of 90% overlap). Overlapping windows enable investigators to detect very low base rate taxa. MAXEIG affords a powerful and unique internal consistency test. Consistency tests, which characterize the taxometric approach, are intended to rule out the detection of a pseudo-taxon by requiring convergence of data on a common conclusion and specific values that are un-
389 likely to result in the absence of a taxon. Similarly, consistency tests are important to guard against conclusions regarding pseudo-continuity.16 This inchworm test is achieved by repeatedly conducting the MAXEIG analysis while systematically increasing the number of overlapping windows, thereby systematically decreasing the size of the subsamples used to calculate maximum eigenvalues in each overlapping window. Plots of taxonic data yield an increasingly better defined unimodal peak, as the number of windows is increased and the size of each window is decreased. In the instance of continuous data, in contrast, the systematic increase of the number of overlapping windows will produce flat or cusped plots that do not systematically peak.50 The specific criteria used herein to conduct the covariance plot “nose-count” were based on the guidelines provided by Waller and Meehl.50 For example, only peaked MAXEIG plots were interpreted as evidence of latent taxonicity. Although in some instances right-end cusped MAXEIG plots also may represent latent taxonicity,43 such cusped plots may equally represent positively skewed, latently continuous data. Fifty internal replications were used to divide cases into overlapping windows by repeatedly resorting cases along the input indicator at random. Internal replications are particularly useful when MAXEIG input variables are divided by fixed intervals and placed arbitrarily between equal-scoring cases. Thus, use of these replications is intended to minimize sampling error and bolster the reliability of the results.40 At each replication, the maximum eigenvalue of the 2 output indicators within each window was calculated, and these calculations were averaged across all replications. In the present study, we conducted the inchworm test by generating 3 sets of MAXEIG plots (50 windows, 150 windows, 250 windows). These specific values were selected on an a priori basis to produce 4 sets of MAXEIG analyses that assign n ⫽ 25 cases per window in the final inchworm test, so that a sufficiently wide range of windows is used to detect a low base rate taxon, were it present, while guarding against sampling error. In addition, the nosecount and the coherency of the standard deviation of the base rate estimates served as additional internal consistency tests. Finally, after taxonicity was tested, the General Covariance Mixture Theorem50 was used to estimate the taxon base rate for each MAXEIG plot. If the MAXEIG analyses indicated a taxonic latent structure, then individual participants were assigned to the taxon and complement classes and latent parameters were subsequently estimated.
MAMBAC A nose-count of MAMBAC,33 or mean above minus below a cut, plot was used as an external consistency test of the MAXEIG procedure. External consistency tests are premised on the same notion as internal consistency tests and are specifically intended to rule out pseudo-taxa or pseudo-continua that result from artifact(s) of a taxometric procedure under particular methodological or
390
Taxometric Analysis of the Pain Anxiety Symptoms Scale 43,50
statistical conditions. MAMBAC d (ie, difference) plots are constructed by plotting the difference between the mean of each output indicator above and below each cut score evenly spaced along each input indicator. MAMBAC d plots were generated, 1 for each possible combination of indicators, until all combinations were exhausted. Like MAXEIG, the MAMBAC procedure was conducted with 50 internal replications and by using 30 evenly spaced cuts so as to ensure at least 20 cases per cut. The MAMBAC procedure was conservatively used as an external consistency test of the latent structure of FOP exclusively and was not used to derive parameter estimates.7
L-Mode L-mode, or latent mode analysis,50 was conducted as a second external consistency test to ensure maximal independent testing of the latent structure of the FOP. Lmode derives the underlying factor score distribution by means of conducting a factor analysis with all available indicators and plots the distribution of scores on the first unrotated factor. L-mode infers taxonic latent structure from a bimodal distribution of the observed factor scores and continuous latent structure from a unimodal distribution (ie, 1 factor). Finally, if the L-mode analysis indicated a taxonic latent structure, then individual participants were assigned to the taxon and complement classes, based on the values of the factor score distribution modes, and latent parameters were subsequently estimated.
Parameter-Matched Monte Carlo Simulations By following recently recommended guidelines for taxometric analyses,40,42 parameter-matched Monte Carlo simulated continuous and taxonic data were derived. Simulated continuous data were reproduced by bootstrap and thereby matched to the number of indicators, sample size, and, importantly, the observed indicator correlation matrix, and the distributions of all indicators including their skew, kurtosis, and number of levels. Simulated taxonic data were similarly matched to the parameters of the research data. Ten separate sets of
simulated continuous and taxonic data were derived to bolster the reliability and precision of the simulated data and the taxometric analyses conducted with these simulated data.40 Monte Carlo simulated data were derived for 2 primary purposes. First, for the purpose of a priori suitability testing,42 and, as conducted in previous investigations,7 (and Zvolensky MJ, Forsyth JP, Bernstein A, Leen-Feldner EW, A concurrent test of the anxiety sensitivity taxon: Its relation to bodily vigilance and perceptions of control over anxiety-related events in a sample of young adults. J Cogn Psychother [in press]) all proposed taxometric analyses were conducted on each of the 10 simulated continuous and each of the 10 simulated taxonic data, before taxometric analyses of the research data were interpreted. By examining the degree to which it is possible to distinguish the taxometric plots of the simulated continuous and taxonic data under the parametermatched conditions of the research data, the simulations demonstrate the capacity of the research data to afford meaningful taxometric analyses. Thus, only if each taxometric procedure could distinguish between the simulated continuous and taxonic data could the data be interpreted meaningfully. If the simulated data pass the suitability tests, then the simulated data permit a second primary function. Specifically, the simulations allow investigators to compare the shape of the research data plots with the plots of the simulated taxonic and continuous data. If simulated continuous and taxonic data are discernible, then a nontaxonic pattern of findings in the research data may be reliably interpreted as a marker of latent continuity or nontaxonicity, whereas a taxonic pattern of findings in the research data can be reliably interpreted as evidentiary of taxonicity that probably is neither artifactual nor pseudo-taxonic. Thus, in addition to contrasting the taxometric plots of the research data to predefined criteria,50 these parameter-matched simulations are useful because they provide an additional comparative idiographic benchmark for interpreting the data with respect to their unique parameters and thereby preclude pseudo-taxonic or pseudo-continuous conclusions.
Figure 1. MAXEIG-HITMAX plots of the manifest item-parcel indicators of the fear of pain: Parameter-matched research, simulated taxonic, and simulated continuous (dimensional) data. The research data do not demonstrate a peaked plot consistent with taxonic latent structure or the simulated taxonic data, and instead demonstrate a cusp consistent with dimensional structure and the simulated dimensional data.
ORIGINAL REPORT/Asmundson et al
391
Figure 2. MAMBAC plots of the manifest item-parcel indicators of the fear of pain: Parameter-matched research, simulated taxonic, and simulated continuous (dimensional) data. The research data do not demonstrate a peaked plot consistent with taxonic latent structure or the simulated taxonic data, and instead demonstrate a cusp consistent with dimensional structure and the simulated dimensional data.
Results Suitability Testing Before the interpretation of taxometric analyses of the research data, the suitability of the data for MAXEIG, MAMBAC, and L-mode analyses was tested by using the parameter-matched, simulated continuous and taxonic data (Figs 1 and 2, simulated plots). First, the MAXEIG inchworm consistency test of the simulated continuous data failed to yield unimodally peaked plots as the number of overlapping windows was increased but instead yielded cusped plots, whereas the taxonic data yielded unimodally peaked plots resembling the head of an inchworm.50 Second, MAMBAC plots of the simulated data were not as conspicuously distinguishable as the MAXEIG plots of the simulated data; nevertheless, MAMBAC plots of the simulated continuous data yielded fully concave or parabolic plots, whereas the taxonic data demonstrated markedly more linear cusped plots consistent with a small base-rate taxon.33 Finally, of particular importance due to the somewhat ambiguous suitability of the research data for MAMBAC analysis, L-mode suitability was examined. Specifically, the L-mode analysis of the simulated continuous data yielded a unimodal factor score distribution, whereas the taxonic data yielded a bimodal factor score distribution. Because MAXEIG, MAMBAC, and L-mode analyses of the parametermatched simulated data each passed suitability tests, all
3 analyses of the research data are likely to produce systematically meaningful and reliable conclusions with respect to the latent structure of FOP.
MAXEIG Evidentiary of latent nontaxonicity of FOP, flat, cusped, or multipeaked plots did not unimodally peak even as the number of overlapping windows was increased (Fig 1).42,50 Thus, the MAXEIG analysis and the inchworm consistency test of the research data support the nontaxonic conjecture, as all 4 plots were characteristic of latent continuity.43,44 In addition, the MAXEIG plots of the research data were dissimilar to the matched simulated taxonic data, and, moreover, all were flat, cusped, or multipeaked and so were more similar to the parameter-matched simulated continuous data (Fig 1). Finally, parameter estimates are not meaningful because taxonicity was not observed; the proposed parameter estimates are, by definition, based on class assignment valid only when taxonicity is observed.
MAMBAC MAMBAC analyses of the 4 indicators produced a total of 12 d plots, 11 of which were concave and cusped (ie, not peaked), consistent with a nontaxonic structure (Fig 2).33,43 Furthermore, the MAMBAC plots of the research data were dissimilar to the matched simulated taxonic data and, moreover, most plots were concave and
Figure 3. L-mode factor score distribution plot of the manifest item-parcel indicators of the fear of pain: Parameter-matched research, simulated taxonic, and simulated continuous (dimensional) data. The research data do not demonstrate a second mode consistent with taxonic latent structure or the simulated taxonic data, and instead demonstrate a unimodal plot consistent with dimensional structure and the simulated dimensional data.
392 cusped and so were more similar to the parametermatched simulated continuous data (Fig 2). MAMBAC nose count results were therefore consistent with the MAXEIG results.
L-Mode L-mode analysis of the 4 indicators produced a single unimodal factor score distribution (ie, not bimodal), consistent with a nontaxonic (ie, continuous) structure (Fig 3).43,50 Furthermore, the L-mode plot of the research data was dissimilar to the parameter-matched simulated taxonic data and, moreover, similar to the parametermatched simulated continuous data (Fig 2). Because taxonicity was not observed, parameter estimates cannot be derived from the L-mode analysis; L-mode relies on the observation of a bimodal distribution to conduct class assignment and derive parameter estimates, neither of which was justified under the observed unimodal factor score distribution. Thus, convergence of evidence from MAXEIG, MAMBAC, and L-mode analyses of the research and Monte Carlo simulated data strongly supports the nontaxonic, or continuous, conjecture of FOP.
Discussion The FOP construct has a central role in contemporary fear-avoidance models of chronic pain.3,35,48 The latent structure of the construct has been assumed to be multidimensional and continuous.5 There is factor analytic evidence that FOP is indeed multifaceted25,31,37,38; however, despite several researchers using various methods to allocate participants to FOP categories,1,11,13,20,21,52 there have been no empiric tests to establish whether it is best conceptualized as a continuous or discontinuous (ie, categorical) construct. The present study addressed this issue through application of a taxometric analysis of a common measure of FOP, the PASS.29,30 Results from the MAXEIG-HITMAX internal consistency test, the MAMBAC and L-mode external consistency tests, and comparisons to simulated taxonic and continuous data all indicated that FOP is nontaxonic, that is, characterized by latent continuity. Specifically, the covariance plots generated by MAXEIG-HITMAX did not unimodally peak even as the number of overlapping windows was increased, d plots generated by MAMBAC procedures were almost all concave and cusped, and Lmode analysis of indicators produced a single unimodal factor score. These findings, indicative of latent continuity, are further bolstered by comparison of the research data with Monte Carlo simulated taxonic and continuous data showing dissimilarity and similarity, respectively. These results are unique in that they provide preliminary empiric evidence that FOP is continuous, not taxonic, in nature. The present findings hold important implications for understanding FOP. The first implication is that the nontaxonicity of FOP supports the assumption that FOP is a ubiquitous individual difference variable that varies between people only in degrees along a continuous latent dimension.5 That is, these data from a sample of patients
Taxometric Analysis of the Pain Anxiety Symptoms Scale seeking treatment for musculoskeletal or headache pain, suggest that FOP does not exist in qualitatively distinct pathological and normative forms (ie, it is not taxonic) but rather along a quantitative continuum ranging from very low to very high. These are the first such findings and thus warrant replication using independent clinical samples as well as other measures of FOP. Because the base rate of a hypothetical taxon may be disproportionately represented in clinical samples of patients with pain, such as the 1 used in the present investigation, it remains possible that FOP may be taxonic among nonclinical populations; thus, future research efforts are also needed to determine the latent structure of FOP in nonclinical samples. The second implication, somewhat related to the above, pertains more specifically to understanding the mechanisms associated with the experience of high, seemingly pathological levels of FOP. Indeed, it has been suggested that a clear understanding of the latent structure of a construct can aid in focusing research efforts directed toward discovery of the origin of such constructs.41 Given that there are multiple empirically supported conceptualizations of FOP, this seems particularly propitious. One view is that FOP is similar to specific phobias (eg, fear of spiders),23,48 wherein it is believed that the fear of pain sensations underlie avoidance and other negative pain-related behaviors. A somewhat different view is that FOP is a manifestation of a fundamental predilection to be generally fearful of anything that produces anxiety symptoms, including pain and its consequences; that is, FOP may be more appropriately conceptualized as a manifestation of anxiety sensitivity.3,14 A fundamental difference between these views is that the latter is rooted in the premise that anxiety sensitivity is a risk factor for experiencing high FOP and its behavioral consequences (eg, avoidance, physical inactivity, disability). The present findings do not unequivocally indicate which of these views is most accurate; however, combined with a growing body of evidence that the anxiety sensitivity construct is taxonic,7,8 the findings reported herein provide additional empiric evidence that FOP and anxiety sensitivity are distinct in nature.4,19 Indeed, insofar as FOP is a lower-ordered form of anxiety sensitivity, as suggested in the view that elevated anxiety sensitivity puts one at risk for high FOP, it remains possible that FOP may be fully continuous, whereas anxiety sensitivity is taxonic. The present findings also hold implications for treating pathological expressions of pain-related fear in patients with disabling pain. As mentioned previously, continuous and categorical variables imply different underlying mechanisms and perhaps require different approaches to facilitate recovery. These variable types also imply different expectations regarding patterns of change, with continuous variables changing gradually over time and taxa changing in a relatively all-or-none fashion.44 Partial change is more likely with a continuous variable because a continuum has multiple and intermediate levels. On the other hand, although it may be more difficult to initiate change in a taxonic variable, this change may be
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44
more complete once initiated. The success of in vivo exposure for treating patients with chronic pain with high FOP—showing variable but most often graduated patterns of decline in FOP over treatment sessions in those with chronic back pain (Fig 1 of Vlaeyen et al47 and Fig 2 of Boersma et al9) and complex regional pain syndrome type I (Figs 1 through 4 of de Jong et al12)—may be further augmented by including interoceptive exposure techniques proven successful in patients with anxiety disorder with elevated anxiety sensitivity.45,46 It remains to be determined if anxiety sensitivity is taxonic in patients with acute or chronic pain and, if so, whether FOP provides an index of latent continuity within these potential qualitatively latent taxon and nontaxon classes. Notwithstanding, consistent with the view FOP is a lower-ordered form of anxiety sensitivity, preliminary findings indicate that interoceptive exposure is effective in reducing FOP in people with high pretreatment anxiety sensitivity.51 A study incorporating both in vivo exposure for FOP and interoceptive exposure as well as close monitoring of change processes related to FOP and anxiety sensitivity holds promise for yielding important clinical and conceptual data. There are several limitations of the present study that warrant mention and may serve to guide future research efforts. First, the sample comprised patients seeking treatment for pain related to the musculoskeletal system, most often the back, or headache, and excluded people without significant pain experiences. This was a necessary exclusion to avoid the threat of pseudo-taxonicity secondary to sampling for bimodality. Further evaluation of the latent structure of FOP is warranted in samples of patients with other acute and chronic pain conditions, and, as noted above, in people drawn from nonclinical samples as well as samples representative of the general population. Second, only one measure of FOP—the PASS—was used in the current study. Although
the PASS corresponds closely with the 3-system model of fear17,24 and provides measurement of the FOP construct consistent with contemporary fear-avoidance models of chronic pain, future studies aimed at replication of the present findings might incorporate the PASS, or its psychometrically sound abbreviated version (ie, PASS20),10,26 along with other self-report (eg, Tampa Scale for Kinesiophobia,23 Fear-Avoidance Beliefs Questionnaire,49 Fear of Pain Questionnaire-III31) and behavioral (eg, laboratory-based fear-relevant challenge procedures) indicators of the FOP construct. Such a multimethod approach may serve to improve the accuracy of parameter estimates43 and aid in refining the assessment of the latent structure of FOP through application of bootstrapped taxometric procedures.32 Finally, given the relatively limited size of the present sample, we did not explore the extent to which the taxometric results vary as a function of sex, age, or culture. Given evidence that sex and emotionality may interact to moderate (and, perhaps, mediate) FOP, taxometric analyses of FOP within the context of sufficiently large samples of male and female subjects is warranted. We are currently in the process of evaluating whether the latent structure of FOP observed in the present study is similar in seniors and across various cultures. Overall, the present findings provide important preliminary evidence for latent continuity of the FOP construct as measured by the PASS. Future research efforts, including those outlined above, are needed to further advance understanding of FOP, its measurement, and its conceptualization. To the extent that it is possible to understand the mechanisms that make some people vulnerable to pathological expressions of FOP, we will be better positioned to develop and evaluate appropriately targeted interventions and, perhaps, even develop strategies to inoculate against the effects of these mechanisms.
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