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Publicly Available Published by De Gruyter October 1, 2017

Behavioral inhibition, maladaptive pain cognitions, and function in patients with chronic pain

  • Mark P. Jensen EMAIL logo , Ester Solé , Elena Castarlenas , Mélanie Racine , Rubén Roy , Jordi Miró and Douglas Cane

Abstract

Background and aims

Trait behavioral inhibition represents a tendency to react with negative emotions - primarily worry - to cues which signal potential threats. This tendency has been hypothesized by a two-factor model of chronic pain to have direct effects on psychological and physical function in individuals with chronic pain, as well as to influence the associations between pain-related maladaptive cognitions and function. Our aim was to test these hypothesized associations in a sample of individuals who were being screened for possible interdisciplinary chronic pain treatment.

Methods

Eighty-eight patients referred to an interdisciplinary chronic pain management program were administered measures of average pain intensity, trait behavioral inhibition, kinesiophobia, pain catastrophizing, depressive symptoms, and pain interference. We then performed two linear regression analyses to evaluate the direct effects of trait behavioral inhibition on depressive symptoms and pain interference and the extent to which behavioral inhibition moderated the associations between kinesiophobia and pain catastrophizing, and the criterion variables.

Results

In partial support of the study hypotheses, the results showed significant (and independent) direct effects of trait behavioral inhibition on depressive symptoms, and behavioral inhibition moderated the association between kinesiophobia and depression, such that there were stronger associations between kinesiophobia and depressive symptoms in those with higher dispositional sensitivity to fear-inducing stimuli. However, neither direct nor moderating effects of behavioral inhibition emerged in the prediction of pain interference.

Conclusions

If replicated in additional studies, the findings would indicate that chronic pain treatments which target both reductions in maladaptive cognitions (to decrease the direct negative effects of these on depressive symptoms) and the individual’s tendency to respond to pain with worry (as a way to buffer the potential effects of maladaptive cognitions on depressive symptoms) might be more effective than treatments that targeted only one of these factors.

Implications

Additional research is needed to further evaluate the direct and moderating effects of pain-related behavioral inhibition on function, as well as the extent to which treatments which target behavioral inhibition responses provide benefits to individuals with chronic pain.

1 Introduction

Chronic pain often has a significant negative impact on a variety of health-related quality of life domains, including psychological function [1,2] and individuals’ abilities to engage in daily activities [3]. The importance of addressing pain’s negative impact on these function domains is acknowledged by consensus recommendations, which note that physical and psychological function should be measured as outcome domains in pain clinical trials [4]. Consistent with this idea, a large and growing number of chronic pain clinical trials include measures of these domains as primary, coprimary, or secondary outcome variables (e.g., [5,6,7]). Given the growing recognition of the negative effects of chronic pain on broad areas of function, as well as the importance of these as outcome variables, knowledge concerning the modifiable variables that influence pain’s negative impact on function would be useful for identifying potential treatment targets in chronic pain treatment programs.

One factor that has been proposed to play a role in pain’s impact on function is the individual’s trait tendency to inhibit behavior in response to environmental cues which signal the possibility of negative (punishing) events [8]. The concept of behavioral inhibition tendency is based on well-established two-factor models of behavioral regulation (e.g., [9,10,11,12]), of whichJeffrey Gray’s reinforcement sensitivity theory is perhaps the most well-known [13].These models hypothesize the existence of two distinct neurophysiological systems that underlie behavioral and emotional responses: (1) a Behavioral Activation System (BAS) which underlies approach behaviors and associated emotional responses (e.g., hope, optimism) in the presence of cues for potential reinforcement and (2) a Behavioral Inhibition System (BIS) which underlies withdrawal behaviors and associated emotional responses (e.g., anxiety, fear) in the presence of cues for potential punishment (i.e., pain).

Based on a BIS-BAS model of pain and pain-related responses, Jensen and colleagues have hypothesized that having a greater tendency for BIS activation - as reflected by higher scores on the Behavioral Inhibition System subscale of the BIS/BAS scale [14] - would be a vulnerability factor that could increase the negative effects of maladaptive pain-related thoughts on function [8]. Thus, individuals with chronic pain who endorse a greater tendency to inhibit behavior in response to cues signaling potential punishment would be hypothesized to report higher levels of psychological dysfunction (e.g., anxiety and depressive symptoms) and behavioral dysfunction (e.g., pain interference) than individuals who do not endorse this tendency. In addition, higher levels of trait behavioral inhibition would be hypothesized to moderate the effects of maladaptive cognitions such as pain catastrophizing [15] and thoughts reflecting fear of pain associated with activity [16] on function. Therefore, those who report more inhibition tendencies would be expected to show stronger associations between these thoughts and poorer function [8].

Consistent with the BIS-BAS model of pain, high trait behavioral inhibition has been found to be associated with headache frequency and severity in a sample of college students [17]. In addition, trait behavioral inhibition has been shown to make unique contributions to the prediction of pain-related catastrophizing when controlling for other personality factors [18]. Furthermore, research in nonpain populations also supports a role for trait behavioral inhibition in a variety of psychological disorders associated with emotional and behavioral dysregulation (see review byJensen and colleagues [8]).

While these preliminary findings are consistent with a model proposing a role for trait behavioral inhibition in how people respond to pain, to our knowledge there has been no research examining the direct and moderating effects of trait behavioral inhibition on psychological or physical function (e.g., depressive symptoms or pain interference) in clinical samples of individuals with chronic pain. The current study sought to address this knowledge gap by testing the hypotheses that (1) trait behavioral inhibition would make an independent contribution to the prediction of function over and above the negative effects of maladaptive pain-related cognitive responses (fear of movement [kinesiophobia] and catastrophizing) and that (2) behavioral inhibition would moderate the effects of maladaptive cognitions, such that individuals with chronic pain who also endorse high levels of behavioral inhibition would evidence stronger associations between measures of maladaptive cognitions and poorer function than those who show a lower level of trait behavioral inhibition. These hypotheses are graphically illustrated in Fig. 1.

Fig. 1 
            Graphical representation of the study hypotheses.
Fig. 1

Graphical representation of the study hypotheses.

2 Materials and methods

2.1 Participants and procedures

The participants in this study came from a consecutive group of patients who had been referred to an interdisciplinary chronic pain management program in Halifax, Canada. During the intake assessments, patients were administered and completed the measures used to test the study hypotheses. In all, 88 individuals completed the study measures. Sixty-three (72%) were women, and the mean age of the sample was 52.90 years (SD = 11.35; Range = 20-76). The majority of the participants (55 or 63%) reported that they had chronic pain in more than three sites; pain at only one site was extremely rare (5 participants, 6%). Of the remaining 28 participants who reported pain in three or fewer sites, the most common site was the low back (22 participants), followed by the legs (16 participants), neck (11 participants), shoulder/arm (10 participants), and perineal/genital (10 participants) were the next most common pain locations. Twelve (14%) of the participants reported that they were working full or part-time, and 35 (40%) reported that they were receiving disability benefits. The participants tended to be highly educated, with 54 (61%) reporting that they had completed a university or college degree, and 26 (30%) reporting that they had completed high school. Institutional review board approval was obtained from the Research Ethics Board, and all participants provided written informed consent to use of these data for research purposes.

2.2 Measures

2.2.1 Average pain intensity

Average pain intensity was assessed using the 0-10 Numerical Rating Scale of pain intensity from the PROMIS-29 [19]. With this measure, respondents are asked to rate their “pain on average” in the past 7 days on a 0 (“No pain”) to 10 (“Worst pain imaginable”) scale. A great deal of support exists for the reliability and validity of the 0-10 NRS as a measure of pain intensity [20], and the 0-10 NRS has been recommended by an IMMPACT consensus group as a core outcome measure to assess pain intensity for research purposes [21].

2.2.2 Behavioral inhibition

Behavioral inhibition tendency was assessed using the seven BIS items from the BIS/BAS scale [14]. Sample items are “I worry about making mistakes” and “I feel worried when I think I have done poorly at something.” Respondents rate each item on a 4-point Likert scale, ranging from “Very false for me” to “Very true for me”. The BIS scale has been found to have acceptable internal consistency, ranging from 0.73 to 0.80 [14,17,22,23] and has also evidenced a great deal of convergent and discriminant validity in different samples [22,23,24], including samples of individuals with chronic pain [17]. The internal consistency of the BIS scale in the current sample was adequate (Cronbach’s alpha = 0.74).

2.2.3 Kinesiophobia

Kinesiophobia was assessed using the Tampa Scale of Kinesiophobia (TSK) [25]. The 17-item TSK asks respondents to indicate their level of agreement to statements related to fear of injury or re-injury associated with movement on 4-point Likert scales (with anchors ranging from “Strongly disagree” to “Strongly agree”). Sample items include “I’m afraid that I might injure myself if I exercise” and “Pain always means I have an injured body.” The TSK can be scored to yield a total score or two scale scores. The total score was used in the current analyses. It has evidence supporting its validity via its positive associations with measures of fear of pain, pain catastrophizing, and disability in individuals with both chronic low back pain and fibromyalgia [26]. Moreover, the total score has evidenced adequate to excellent internal consistency (Cronbach’s alphas range: .79 to .81) in both of these samples [26]. In the current sample, the internal consistency (Cronbach’s alpha) was .83, indicating good reliability.

2.2.4 Pain catastrophizing

Pain catastrophizing was assessed using the 13-item Pain Catastrophizing Scale (PCS) [27]. With the PCS, respondents are asked to rate the frequency with which they have 13 negative thoughts about their pain with a 5-point Likert scale ranging from “Not at all” to “All the time”. The PCS items can be scored to obtain a total score or three subscale scores reflecting three catastrophizing domains (helplessness, pain magnification, and rumination). Analyses are more commonly performed with the total score given the strong associations among the subscales; a practice that was used in the analyses presented here. A great deal of research supports the reliability as well as the construct, criterion, and discriminant validity of the PCS in individuals with chronic pain [28,29]. The PCS evidenced excellent internal consistency (Cronbach’s alpha = .92) in the current sample.

2.2.5 Depressive symptoms

We used the 4-item Depression scale from the Patient-Reported Outcomes Measurement Information System-29 (PROMIS-29) Health Profile Scale (http://www.nihpromis.org) [19] to measure depressive symptoms. We scored the items, per the usual protocol with PROMIS measures, into a T-score metric, with a normative mean value (based on a sample representative of the USA population) of 50 and standard deviation of 10 [30]. With these items, respondents are asked to rate the frequency with which they experienced four symptoms of depression (feeling worthless, helpless, depressed, and hopeless) in the past 7 days on a 5-point Likert scale (from “Never” to “Always”). The PROMIS-29 Depression scale has evidenced good internal consistency and strong convergent validity [31]. The items evidenced an excellent level of internal consistency (Cronbach’s alpha = 0.93) in the current sample.

2.2.6 Pain interference

Pain interference was assessed using the four pain interference items from the PROMIS-29 [19]. With these items, respondents indicate the extent to which pain interfered with function (day to day activities, work around home, participation in social activities, household chores) during the previous seven days on 5-point Likert scales ranging from “Not at all” to “Very much.” As with the PROMIS-29 Depression scale, we converted the items into T-scores. The PROMIS-29 Pain Interference scale has demonstrated excellent psychometric properties, as evidenced by excellent internal consistency [19], and predictive, convergent, and known-groups validity [19,32]. In the current sample, the internal consistency (Cronbach’s alpha) of the four items was .92, indicating excellent reliability.

2.3 Data analysis

We first computed means and standard deviations as well as number and percentages of the demographic and pain-related variables for descriptive purposes. We also computed Pearson correlations between the study variables to evaluate their univariate associations. To address the primary study aim - that is, to determine if trait behavioral inhibition and maladaptive pain-related cognitions (and their interaction) would both make independent contributions to the prediction of patient dysfunction - we performed two linear regression analyses predicting depressive symptoms and pain interference from the measures of trait BIS, kinesophobia and pain catastrophizing. Prior to these analyses, we examined the skew, kurtosis, and multicollinearity of the variables to ensure they met the assumptions for regression [33,34]. In the regression analyses, depressive symptoms and pain interference were the criterion variables. We entered (centered) average pain intensity in the first step to control for its potential confounding effect on both the criterion and primary predictor variables. We then entered the measure of behavioral inhibition (centered BIS scale) in step 2. In step 3, we entered centered measures of the maladaptive cognitions assessing kinesiophobia and pain catastrophizing, and in step 4 we entered the BIS (centered) × Kinesiophobia (centered) and BIS (centered) × Pain Catastrophizing (centered) interaction terms. If any interaction term emerged as significant, we planned to interpret the interaction using the visualization strategy recommended by Hayes and Rockwood [35]; that is, by computing regression lines representing the associations between the criterion measure and the cognitive measure separately for those with “high” (one SD above the mean) and “low” (one SD below the mean) on the BIS scale.

Table 1

Means, standard deviations, skew, and kurtosis of the study variables.

Variable Mean (SD) Skew Kurtosis
Pain intensity (0-10 NRS) 6.32 (1.57) –0.62 –0.03
Behavioral inhibition (BIS) 21.26 (4.00) –0.35 –0.24
Kinesiophobia (TSK) 40.47 (8.05) –0.07 –0.34
Pain catastrophizing (PCS) 24.69 (10.92) 0.19 –0.66
Depression (PROMIS-29) 58.78 (8.82) –0.23 –0.06
Pain interference (PROMIS-29) 67.27 (5.02) 0.18 –0.49

Note: NRS, Numerical Rating Scale; BIS, Behavioral Inhibition System scale; TSK, Tampa Scale of Kinesiophobia; PCS, Pain Catastrophizing Scale; PROMIS-29,29-item Patient-Reported Outcomes Measurement Information System Health Profile

Table 2

Univariate associations among the study variables.

Variable Pain intensity (0-10 NRS) Behavioral inhibition (BIS) Kinesiophobia (TSK) Pain catastrophizing (PCS) Depression (CES-D)
Pain intensity (0-10 NRS)
Behavioral inhibition (BIS) –.09
Kinesiophobia (TSK) .18 .19
Pain catastrophizing (PCS) .34[**] .15 .44[***]
Depression (PROMIS-29) .30[**] .36[**] .40[***] .64[***]
Pain interference (PROMIS-29) .46[***] .04 .13 .34[**] 38[***]

Note: NRS, Numerical Rating Scale; BIS, Behavioral Inhibition System scale; TSK, Tampa Scale of Kinesiophobia; PCS, Pain Catastrophizing Scale; PROMIS-29,29-item Patient-Reported Outcomes Measurement Information System Health Profile

3 Results

3.1 Desriptives of the study variables and their univariate associations

The means and standard deviations of the study variables are presented in Table 1. As can be seen, these data indicate that the sample reported moderate to severe pain intensity on average (Mean [SD] = 6.32 [1.57] on the 0-10 NRS scale). In addition, the scores on the measures of fear of movement, catastrophizing, depression, and pain interference were all similar to those obtained from other samples of individuals with chronic pain seeking treatment (e.g., Mean [SD or CI] scores for each domain from other samples of individuals with chronic pain: TSK, 41.2 [8.82] and 42.9 [9.1] [36]; PCS, 9.58 [11.38]; [29]; PROMIS depression, 53.2 [52.0, 54.5] [37]; PROMIS pain interference, 66.7 [6.0] [38], 64.2 [95% CI 63.2, 65.2] [39]). In short, the sample appeared to be similar to samples of individuals with chronic pain who have participated in research studies that employed the pain-related measures used here. With respect to the BIS score of the sample (Mean [SD] = 21.26 [4.00]), it was comparable to that of the original scale development sample of college students (Mean [SD] = 19.99 [3.79]) [14]; as well as samples of otherwise healthy individuals in the community (Mean [SD] ranges: 18.8-22.0 [2.8-3.7]) [23], suggesting little impact of chronic pain on the measure of trait sensitivity to punishment.

The univariate associations among the study variables are presented in Table 2. As can be seen, the study predictor variables (assessing dispositional behavioral inhibition, kinesiophobia, pain catastrophizing) showed moderate to strong associations with depressive symptoms (rs range: .36 to .64, ps<.01). However, only pain catastrophizing evidenced a significant univariate association with pain interference (r = .34, p < .01).

3.2 Assumptions testing

None of the study variables evidenced substantial skew or kurtosis with both being well below the standard cutoff of 3 (skew ranged from –0.62 to 0.18 and kurtosis ranged from –0.66 to –0.03) supporting the use of the planned parametric analyses [33]. In addition, although the study variables tended to be associated with one another in expected directions (see Table 2), none of the predictor variables (pain intensity, behavioral inhibition, kinesiophobia, and pain catastrophizing) evidenced associations so strong as to indicate that multicollinearity would bias the findings from the planned regression analyses (range of correlation coefficients, –.09 to .44; see Table 2). To confirm this, we also computed variance inflation factors for the four-predictor variables. These ranged from 1.04 to 1.35, which were well below the cutoff of 10 as an indication of substantial multicollinearity [34], providing further support for the absence of multicollinearity among the predictors. In sum, the variables met the assumptions for the planned regression analyses.

3.3 Independent effects of behavioral inhibition, kinesiophobia, and pain catastrophizing as predictors of depressive symptoms

The results of the regression analyses predicting depressive symptoms are presented in Table 3. As can be seen, the primary study hypothesis was partially supported. Specifically, we found that the measure of behavioral inhibition made a substantial (R2 change = .12; p<.001) contribution to the prediction of depressive symptoms, after controlling for pain intensity. In addition, kinesiophobia and catastrophizing contributed another 27% of the variance (p <.001) to the prediction of depressive symptoms when controlling for both pain intensity and behavioral inhibition. However, although both kinesiophobia and pain catastrophizing evidenced significant associations with depression (see Table 3), when both were entered in the same step, only pain catastrophizing showed a unique significant association with depressive symptoms (β = .51, p < .001). Both behavioral inhibition and pain catastrophizing remained significant in the final model, when all the predictor variables were controlled.

Table 3

Results of the linear regression analyses predicting depressive symptoms.

Step and variable Total R2 R 2 change Fchange β to enter β final
Step 1: Pain intensity (0-10 NRS) .09 .09 8.61[**] .30[**] .15
Step 2: Dispositional behavioral inhibition (BIS, centered) .24 .12 16.94[***] .39[***] .31[***]
Step 3: Maladaptive cognitions .51 .27 22.70[***]
Kinesiophobia (TSK, centered) .10 .13
Pain catastrophizing (PCS, centered) .51[***] .50[***]
Step 4: Interactions .55 .04 3.69[*]
BIS × Kinesiophobia .22[**] .22[**]
BIS × Pain catastrophizing –.06 –.06

Note: NRS, Numerical Rating Scale; BIS, Behavioral Inhibition System scale; TSK, Tampa Scale of Kinesiophobia; PCS, Pain Catastrophizing Scale; PROMIS-29,29-item Patient-Reported Outcomes Measurement Information System Health Profile

Fig. 2 
              
                Fig. 2. Regression lines representing the associations between kinesiophobia and depressive symptoms forthose with low (-1 SD) and high (+1 SD) trait behavioral inhibition.
Fig. 2

Fig. 2. Regression lines representing the associations between kinesiophobia and depressive symptoms forthose with low (-1 SD) and high (+1 SD) trait behavioral inhibition.

A significant Behavioral Inhibition x Kinesiophobia interaction effect also emerged (β = .22, p<.01) in the prediction of depressive symptoms. As planned, in order to understand this interaction effect, we computed the regression lines separately for those with high and low trait behavioral inhibition (see Fig. 2). As can be seen, and as hypothesized, those with higher dispositional sensitivity to fear-inducing stimuli evidenced stronger associations between kinesiophobia and depressive symptoms than those with lower dispositional sensitivity to fear-inducing stimuli. In fact, among those endorsing low behavioral inhibition, the association between kinesiophobia and depressive symptoms was very weak.

3.4 Independent effects of behavioral inhibition, kinesiophobia, and pain catastrophizing as predictors of pain interference

Consistent with the univariate associations (Table 2), neither behavioral inhibition nor maladaptive cognitions (as a group) were significantly associated with pain interference, once pain intensity was controlled (see Table 4). In addition, no significant interactions between behavioral inhibition and either of the maladaptive cognitions emerged in the prediction of pain interference.

Table 4

Results of the linear regression analyses predicting pain interference.

Step and variable Total R2 R 2 change F change β to enter β final
Step 1: Pain intensity (0-10 NRS) .21 .21 23.02[***] .46 .40[***]
Step 2: Dispositional behavioral inhibition (BIS, centered) .22 .01 0.69 .08 .06
Step 3: Maladaptive cognitions .26 .04 2.07
Kinesiophobia (TSK, centered) –.05 –.04
Pain catastrophizing (PCS, centered) .22[*] .22[†]
Step 4: Interactions .26 .01 0.30
BIS × Kinesiophobia –.05 –.05
BIS × Pain catastrophizing .08 .08
  1. Note: NRS, Numerical Rating Scale; BIS, Behavioral Inhibition System scale; TSK, Tampa Scale of Kinesiophobia; PCS, Pain Catastrophizing Scale; PROMIS-29,29-item Patient-Reported Outcomes Measurement Information System Health Profile

  2. p <.01.

4 Discussion

The key finding from this study is that trait behavioral inhibition evidenced a stronger association with depressive symptoms than with pain interference. Moreover, and consistent with the study hypotheses, behavioral inhibition tendencies appeared to play two roles with respect to depressive symptoms: (1) a direct effect such that those patients with higher dispositional behavioral inhibition are more likely to report depressive symptoms regardless of their levels of the maladaptive cognitions; and (2) a moderating effect, such that those patients with high dispositional behavioral inhibition evidence stronger associations between kinesiophobia and depressive symptoms than those with lower dispositional behavioral inhibition. However, and inconsistent with the study hypotheses, dispositional behavioral inhibition appeared to play a relatively small role in the prediction of pain interference, having neither significant direct nor moderating effects. The findings might have clinical and theoretical implications.

4.1 Behavioral inhibition and the risk for depression

The findings showing that (1) the measure of trait behavioral inhibition accounted for significant variance in depressive symptoms and (2) also moderated the association between kinesiophobia and depressive symptoms are consistent with the BIS-BAS model and with the idea that the Behavioral Inhibition System plays a role in psychological function in individuals with chronic pain. The measure of BIS used in this study asks respondents to indicate their tendency to experience negative affect (mostly worry or fear, but also feeling “upset” or “hurt”) in response to events that are potentially threatening [14]. It is perhaps not surprising that people who see themselves as responding in this way also report higher levels of depressive symptomatology, even when controlling for the frequency of pain-specific maladaptive cognitions. This finding is also consistent with research which has examined the associations between behavioral inhibition and measures of depression in a variety of other populations (e.g., [40,41,42]).

The moderating effect of trait behavioral inhibition is a new finding which has not to our knowledge been previously examined empirically, although this role is hypothesized by the BIS-BAS model of pain [8]. In the current study, the nature of the interaction was such that at least some amount of trait behavioral inhibition seems to be required in order for fear of activity (kinesiophobia) to be associated with depression symptoms. If replicated in future studies, this finding would point to another possible treatment target other than or in addition to fear of activity. Specifically, it suggests that treatments which seek to minimize BIS responding (e.g., mindfulness, hypnosis treatments that include suggestions for experiencing positive affect [“calm confidence”], or relaxation training) in the face of threats could potentially reduce the negative impact of kinesiophobia on depression [8], and therefore help to improve function. The current findings indicate that research to examine BIS reactivity as a mechanism of such treatments is warranted.

4.2 Behavioral inhibition and the risk for pain interference

The lack of a direct or moderating influence of trait behavioral inhibition on pain interference is inconsistent with the BIS-BAS model of pain as enunciated by Jensen and colleagues [8]. There are a number of possible explanations for this negative finding. First, it is possible that the BIS-BAS model applies only or mostly to outcomes related to psychological function. Ifthis was true, the BIS-BAS model would need to be adjusted to remove non-psychological function domains as outcomes hypothesized to be influenced by BIS-related processes.

A second possibility is that the BIS-BAS model may apply better to behavioral outcomes (e.g., pain interference, disability) in populations which have more variability in measures of these outcomes, such as individuals in the community. For example, in the current sample of individuals with chronic pain, trait behavioral inhibition was not significantly associated with pain intensity. However, the same measure of behavioral inhibition used in this study has been shown to be significantly associated with headache severity in a sample of college students [17]. It is possible that once pain reaches a certain level of severity or when it becomes more chronic - such as the severity needed to motivate individuals to seek chronic pain treatment - further increases in severity may no longer have a linear relationship with behavioral inhibition. Such a non-linear association between measures of BIS and pain would hinder the ability of BIS scores to evidence associations with pain or pain-related function (i.e., pain interference) in samples of patients with longstanding chronic pain.

It is also possible that there might be a ceiling effect for pain interference in the sample, resulting in a restriction of range in this variable which could attenuate the associations found. Evidence in support of this possibility can be seen in Table 1, which shows that the PROMIS Pain Interference score is not only very high (67.27, or about 1.7 standard deviations above the mean of the normative sample) but also that the standard deviation in this sample is very low (5.02, about half of that of the normative sample). The PROMIS Depression score, on the other hand, is only about 0.9 standard deviations above the mean of the normative sample and has a standard deviation of 8.82 which is very close to that of the normative sample.

A fourth possible explanation for the lack of a significant association between behavioral inhibition and pain interference in this study might be related to the specific measure of behavioral inhibition used. The BIS scale used here assesses the tendency to respond with negative affect to potentially threatening events in general [14], as opposed to negative affect in response to pain, specifically. It is possible, that stronger associations between behavioral inhibition and function would emerge in pain populations when pain-specific BIS response measures are used [43]. However, if this possibility was supported in future studies, it would have important implications for the BIS-BAS model of chronic pain. Specifically, it would suggest that there are less likely to be “generic” vulnerability characteristics that are necessarily activated in the presence of any cues for danger (e.g., acute pain) for individuals across settings and risk domains. Rather, such a finding would suggest that whether or not the BIS or BAS are activated may vary as a function of the specific cue domain; some people may be more likely to respond to pain with BIS activation or pain-specific BIS activation, while less likely to respond to cues indicating danger in other contexts (e.g., social situations for individuals with social anxiety). What constitutes “danger” likely varies from one person to the next.

4.3 Study limitations

This study has a number of limitations that should be kept in mind when interpreting the results. First, the sample size was relatively small, which can reduce the reliability of the statistics computed. It would be important to replicate the study in additional samples of individuals with chronic pain in order to establish the reliability of the results. Second, we used a cross-sectional design to evaluate the study hypotheses. Although this provided an opportunity to test hypothesized concurrent associations, such a design does not allow for the evaluation of causal associations. We do not know, for example, if levels of behavioral inhibition and maladaptive cognitions influence depressive symptoms, if depressive symptoms influence behavioral inhibition and maladaptive beliefs, or if mutual causal associations exist between maladaptive cognitions and depressive symptoms (this latter option is hypothesized by the BIS-BAS model; see [8]). The evaluation of causal associations requires longitudinal studies or, ideally, true experiments and clinical trials in which behavioral inhibition tendencies and/or individuals with such tendencies are themselves the targets of treatment, and the influence of changes in these tendencies are measured.

5 Conclusions

This study found limited support for the BIS-BAS model of chronic pain by showing significant (and independent) direct and moderating effects of trait behavioral inhibition on depressive symptoms in a sample of individuals with chronic pain. Hypothesized associations between trait behavioral inhibition and pain interference were not found, however. This latter negative effect, if replicated in future research, would indicate that modification of the proposed model is needed. However, more research is needed to evaluate the reliability of these findings in additional samples. In particular, it would be important to test the model using longitudinal and experimental designs, as well as to examine the hypothesized associations using measures ofbehavioral inhibition responses to pain, specifically. Findings from this body of research could be used to enhance the efficacy of existing pain treatments by identifying additional treatment targets, as well as inform the development of new and perhaps more effective pain treatments.

Highlights

  • Trait behavioral inhibition (BI) is hypothesized to influence patient function.

  • In a sample of individuals with chronic pain, BI was associated with depression.

  • BI also moderated the association between kinesiophobia and depression.

  • Research to study the benefits of minimizing the negative effects of BI is warranted.


DOI of refers to article: http://dx.doi.org/10.1016/j.sjpain.2017.07.020.



Department of Rehabilitation Medicine, University of Washington, Box 359612, Harborview Medical Center, 325 Ninth Avenue, Seattle, WA98104, USA

  1. Financial support: Financial support for this project was provided by grants from the Spanish Ministry of Economy and Competitiveness (PSI2015-70966-P; PSI2016-82004-REDT), Obra Social de Caixabank and RecerCaixa awarded to JM.JM’s work is supported by the Institucio Catalana de Recerca i Estudis Avangats (ICREA-Academia), and Fundacion Grunenthal. RV’s work is supported by a Beatriu de Pinos Postdoctoral Fellowship (2014 BP-A 00009) granted by the Agency for Administration of University and Research Grants (AGAUR), grant R2B from Universitat Rovira i Virgili provided travel support. EC’s work is supported by grant PSI2014-60180-JIN of the Spanish Ministry of Economy and Competitiveness. SG is supported by a doctoral grant from MINECO. MR’s work is supported by The Earl Russell Chair in Pain Research, Western University, London, Ontario and by a bequest from the estate of Mrs. Beryl Ivey to Dr. Warren R. Nielson.

  2. Ethical issues: The participants provided their informed consent for their participation, and the procedures were reviewed and approved by the Research Ethics Board.

  3. Conflicts of interest: The authors have no conflicts of interest.

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Received: 2017-05-09
Revised: 2017-07-03
Accepted: 2017-07-04
Published Online: 2017-10-01
Published in Print: 2017-10-01

© 2017 Scandinavian Association for the Study of Pain

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