Abstract
Background
The fear-avoidance model of chronic pain has established avoidance as a predictor of negative outcomes in chronic pain patients. Avoidance, or deliberate attempts to suppress or prevent unwanted experiences (e.g., pain), has been studied extensively, with multiple reviews implicating this behavior as a predictor of disability, physical disuse, and depression. Despite hundreds of studies examining the associations between different components of this model (i.e., catastrophizing, fear, avoidance, depression), the association between fear-avoidance and pain intensity has remained unclear. The present study seeks to clarify this association across samples.
Method
The present analyses synthesize the literature (articles from PsycInfo, PubMed, and ProQuest) to determine if fear-avoidance and pain intensity are consistently correlated across studies, samples, and measures. Eligible studies measured pain intensity and fear-avoidance cross-sectionally in chronic pain patients. The search resulted in 118 studies eligible for inclusion. A random-effects model was used to estimate the weighted mean effect size. Comprehensive Meta-Analysis software was used for all analyses. Moderation analyses elucidate the variables that affect the strength of this association. Meta-regression and meta-ANOVA analyses were conducted to examine moderating variables. Moderator variables include demographic characteristics, pain characteristics, study characteristics, and national cultural characteristics (using Hofstede’s cultural dimensions). Publication bias was examined using the funnel plot and the p-curve.
Results
Results indicate a small-to-moderate positive association between fear-avoidance and pain intensity. The results were stable across characteristics of the sample, including mean age, gender distribution, marital status, and duration of pain. Moderation analyses indicate that the measures utilized and cultural differences affect the strength of this association. Weaker effect sizes were observed for studies that utilized measures of experiential avoidance when compared to studies that utilized pain-specific fear-avoidance measures. Studies that utilized multiple measures of fear-avoidance had stronger effect sizes than studies that utilized a single measure of fear-avoidance. Three of Hofstede’s cultural dimensions moderated the association, including Power Distance Index, Individualism versus Collectivism, and Indulgence versus Restraint.
Conclusions
The present meta-analysis synthesizes the results from studies examining the association between fear-avoidance and pain intensity among individuals with chronic pain. The positive association indicates that those with increased fear-avoidance have higher pain intensity, and those with higher pain intensity have increased fear-avoidance. Findings indicate that cultural differences and measurement instruments are important to consider in understanding the variables that affect this association. The significant cultural variations may indicate that it is important to consider the function of avoidance behavior in different cultures in an effort to better understand each patient’s cultural beliefs, as well as how these beliefs are related to pain and associated coping strategies.
Implications
The results from the current meta-analysis can be used to inform interventions for patients with chronic pain. In particular, those with more intense pain or increased fear-avoidance should be targeted for prevention and intervention work. Within the intervention itself, avoidance should be undermined and established as an ineffective strategy to manage pain in an effort to prevent disability, depression, and physical deconditioning.
1 Introduction
Chronic pain affects about 20% of the global population [1]. Those afflicted with chronic pain are at an increased risk for poor work productivity, disability, unemployment, and poor social and emotional functioning [2, 3]. Increasing concern about the prevalence of chronic pain and associated emotional suffering has driven research on the etiology and treatment of chronic pain conditions.
Across the chronic pain literature, distinct coping strategies have been identified. A well-established response to pain is fear, and a common response to fear is avoidance [2]. Avoidance is any attempt to reduce or prevent unwanted experiences. Avoidance may effectively reduce fear and pain intensity in the short-term. In the long-term, however, avoiding physiological sensations associated with chronic pain and movement may result in physical deconditioning, which may then lead to more pain when mobile.
The fear-avoidance model of chronic pain has been examined in hundreds of studies over the past 30 years [4] providing a framework for understanding the psychological factors that impact outcomes in chronic pain patients. The fear-avoidance model posits that in response to pain, catastrophizing brings about fear, followed by avoidance behaviors (see Fig. 1). Those who adopt this model refer to this construct as fear-avoidance.
Fear-avoidance model of chronic pain.
Many studies have indicated that fear-avoidance increases disability and self-reported suffering [5, 6, 7, 8]. A recent meta-analysis indicated that across studies, pain-related fear and disability were modestly associated (r = .50; [9]), and pain intensity did not significantly moderate the association. Despite several studies indicating a positive association between pain intensity and disability [10, 11, 12], the fear-avoidance model posits that within individuals who experience high pain intensity, the level of disability is heavily dependent upon avoidance [13]. Thus, pain intensity is related to disability, but behavior in response to pain is a better predictor of disability [14]. Thus, the fear-avoidance model posits fear-avoidance as a key ingredient in predicting disability, yet the association between fear-avoidance and pain intensity is less clear. Although research of the fear-avoidance model was designed to measure disability, pain intensity is an additional important variable. The current review seeks to understand how pain intensity impacts fear-avoidance to facilitate a more comprehensive picture of the psychological factors involved in chronic pain.
Several studies have indicated a positive association between reported pain intensity and fear-avoidance [15, 16]. If those with higher pain intensity are more prone to fear-avoidance, this provides an opportunity for intervention. The current literature suggests that fear-avoidance is associated with negative outcomes in chronic pain populations and predicts disability. Thus, there is a need for interventions that seek to increase willingness to experience pain and engage in valued activities in the presence of intense pain.
1.1 Objectives of the current meta-analysis
The present study synthesizes the findings from studies across different samples and measures and determines the strength of the association between pain intensity and fear-avoidance. Furthermore, moderation analyses elucidate other variables that influence the strength of this association.
2 Method
The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA; [17]) checklist was used in preparing and conducting the current meta-analysis.
2.1 Inclusion and exclusion criteria
Studies were required to include a human sample of individuals with chronic pain (≥three months duration). Studies were excluded if they included individuals with acute pain–that is, those who reported pain duration of less than three months. In the event that a study explicitly stated that the majority of participants reported chronic pain, the author was contacted to provide data with only the chronic pain patients.
Additionally, included studies must have been published in English. Furthermore, only empirical studies were examined. Studies were also required to utilize original data in order to avoid including the same effect size more than once. In the event that more than one study was published using the same data, the data were included once, utilizing the first study to report the necessary information for moderation analyses. Qualitative data were not included in the present analyses. No restrictions were placed on date of publication. Moreover, it was necessary that studies utilized a self-report measure of both fear-avoidance and pain intensity. Studies were included if they reported correlation(s) between fear-avoidance and pain intensity. In the event that the study was longitudinal or an intervention trial, only baseline measures were utilized to avoid confounding the association of interest with change over time as a result of the intervention. If a study included both a measure of fear-avoidance and pain intensity and met all other inclusion criteria, but did not report sufficient data to calculate a correlation, the authors were contacted to provide the data or relevant statistics.
2.2 Selection of predictor variables
Across the literature, multiple measures of fear-avoidance have been utilized. Most of these measures confound fear and avoidance, using items that address both constructs in the same scale (e.g., Fear-Avoidance Beliefs Questionnaire, Pain Anxiety Symptoms Scale). As a result, the current meta-analysis included all measures of fear-avoidance–those that measure avoidance exclusively as well as those that measure fear and avoidance. Although these were included in an effort to provide the most comprehensive representation of the current literature, the over-arching issue still remains. Fear and avoidance are distinct theoretical concepts, addressing two different parts of the fear-avoidance model. Fear is the physiological response to pain, as the body perceives pain to be threatening. Avoidance is the behavioral response to this pain–any attempt to deliberately reduce or prevent the unwanted experience. The combination of these two constructs, in theory or in measurement instruments, confounds associations between fear or avoidance and other variables of interest (e.g., pain intensity, disability).
Experiential avoidance is frequently measured using self-report instruments that evaluate fear or avoidance of unwanted or painful sensations, emotions, thoughts, or other experiences. Although some measures of avoidance focus explicitly on pain-specific fear and avoidance, others focus more broadly on avoidance of any stimuli [18, 19]. Furthermore, a variety of problematic outcomes are known to be associated with experiential avoidance, including increased psychiatric symptoms [20, 21]. This adds to the variability seen in the association between pain intensity and fear-avoidance. The current meta-analysis includes measures that focus specifically on pain-related fear-avoidance, as well as those that measure fear-avoidance of stimuli more generally.
Furthermore, some measures of avoidance are frequently used as measures of acceptance after being reverse-scored (e.g., Acceptance and Action Questionnaire; [22, 23]). In addition, measures of acceptance often represent lower levels of avoidance as higher levels of acceptance. For example, the Chronic Pain Acceptance Questionnaire has two subscales, one of which (pain willingness) measures avoidance and is reverse-scored so that higher scores on this subscale represent greater acceptance [24]. For this reason, studies that included measures of acceptance were included, and the effect size was reverse-coded.
Studies that included one or more measures of fear-avoidance were eligible for inclusion. In the event that more than one measure was included, all reported effect sizes were averaged in an effort to represent all measures. Composite effect sizes across all measures of fear-avoidance were utilized to form the most representative weighted effect size. Subsequently, composite effect sizes across only pain-specific fear-avoidance measures were calculated, and these were used to estimate the effect size between pain-specific fear-avoidance and pain intensity (referenced in results as “Pain-Specific Fear-Avoidance”). Effect sizes were calculated for studies that utilized measures of fear-avoidance of any stimuli (referenced in results as “General Fear-Avoidance”), and this weighted effect size represents the association between experiential avoidance and pain intensity. Furthermore, if subscale and total scores were both used in the correlation analyses, total scores were used. If only subscale scores were utilized, then all subscale-based effect sizes were averaged.
Pain intensity measures were self-report scales that asked participants to rate the intensity or severity of pain. Most measures ask participants to rate the intensity of pain currently, though others will average the ratings of worst pain, least pain, average pain, and current pain. A substantial number of included studies utilized a one-item visual analog or numerical rating scale to measure pain intensity. Table 1 presents a summary of the fear-avoidance measures included, and Table 2 presents the pain intensity measures that were included in the analyses.
Included fear-avoidance measures and domains assessed.
| Measure | Domains assessed |
|---|---|
| Pain-specific fear-ivoidance measures | |
| TSK [142] | Focus on somatic symptoms and avoidance of activities that could result in pain |
| CPAQ [24] | Willingness to experience pain and engage in activities |
| FABQ [19] | Fear-avoidance beliefs in physical and work activities |
| PASS[143] | Cognitive anxiety, physiological anxiety, escape/avoidance behaviors, fearof pain |
| PBQ[144] | Avoidance, compliance, and help-seeking behaviors used in response to pain |
| FOPQ [145] | Pain-related fears and avoidance behaviors |
| FAPS [146] | Degree of avoidance associated with situations or activities |
| POAM-P [147] | Frequency of avoidance and persistence behavior |
| FSR[148] | Avoidance, resignation, and distraction in response to pain |
| DICI [149] | Illness-related coping activities |
| ACPT [44] | Acceptance of pain |
| PBC[150] | Avoidance of activities due to pain |
| TOPS [151] | Fear-avoidance behaviors associated with chronic pain |
| PARQ [152] | Avoidance, pacing, and confronting behavior in chronic pain |
| BCPI [153] | Psychological flexibility coping strategies and other traditional coping strategies |
| PCI[154] | Retreating and resting attempts to limit pain |
| PSITQ [155] | Frequency ofavoidance ofactivities due to pain |
| PSI [156] | Perceptions of personal problem-solving behaviors and attitudes |
| General fear-avoidance measures | |
| AAQ [18] | Experiential avoidance ofthoughts, emotions, situations, or experiences |
| CISS [157] | Frequency ofengaging in various coping activities during stressful situations |
| WOC [158] | Ways of coping; escape-avoidance subscale |
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Abbreviations: TSK, Tampa Scale for Kinesiophobia; CPAQ Chronic Pain Acceptance Questionnaire; FABQ Fear-Avoidance Beliefs Questionnaire; PASS, Pain Anxiety Symptoms Scale; PBQ Pain Behavior Questionnaire; FOPQ Fear of Pain Questionnaire; FAPS, Fear Avoidance of Pain Scale; POAM-P, Patterns of Activity Mapping–Pain; FSR, Questionnaire on Pain Regulation; DICI, Dealing with Illness Coping Inventory; ACPT, Acceptance and Control in Psychological Treatment; PBC, Pain Behavior Questionnaire; TOPS, Treatment Outcomes of Pain Survey; PARQ, Pain and Activity Relations Questionnaire; BCPI, Brief Pain Coping Inventory; PCI, Pain Coping Inventory; PSITQ; Pain Situations Questionnaire; PSI, Problem-Solving Inventory; AAQ, Acceptance and Action Questionnaire; CISS, Coping Inventory for Stressful Situations; WOC, Ways of Coping.
Included pain intensity measures and domains assessed.
| Measure | Domains assessed |
|---|---|
| Pain intensity measures | |
| VAS | 100 mm line from no paintoworst pain imaginable to measure current pain |
| NRS [159] | Current pain rated on numeric scale from no pain to worst pain imaginable |
| MPQ [160] | Checklist of adjectives to describe pain; subscale to rate pain severity |
| MPI [161] | Severity of pain rated on seven-point scale, three items |
| BPI [162] | Pain severity associated with variety of activities |
| PII [163] | Average of the highest, lowest, average, and current level of pain |
| BAP [164] | Psychological and physical functioning and pain intensity |
| HI [165] | Average daily headache score |
| TOPS [151] | Pain symptoms, among other subscales |
| SF-36 [166] | Bodily pain severity, among other subscales |
| HQ [5, 6] | Severity oftypical headache |
| VRS [167] | Verbal rating scale of pain intensity |
| PINRS [168] | Pain severity over the last two weeks |
| GCPS [169] | Current pain, worst pain in last 6 months, and average pain in last 6 months |
| PIR [170] | Numeric rating scale for pain intensity |
| AIMS-2 [171] | Physical, social, and emotional functioning of RA patients |
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Abbreviations: VAS, Visual Analogue Scale; NRS, Numerical Rating Scale; MPQ McGill Pain Questionnaire; MPI, Multidimensional Pain Inventory; BPI, Brief Pain Inventory; PII, Pain Intensity Index; BAP, Brief Assessment of Pain; HI, Headache Index; TOPS, Treatment Outcomes of Pain Survey; SF-36, Short Form Health Survey-36; HQ, Headache Questionnaire; VRS, Verbal Rating Scale; PINRS, Pain Intensity Numeric Rating Scale; GCPS, Graded Chronic Pain Scale; PIR, Pain Intensity Rating; AIMS-2, Arthritis Impact Scale-2nd edition.
2.3 Moderator variables
Moderator variables included in the present analysis span demographic characteristics, pain characteristics, and study characteristics. Data were collected from available studies or provided by authors in the case that data were not reported in the article. To test for moderation within demographic characteristics, mean age, percentage of males in the sample, and percentage of married or cohabiting individuals in the sample were examined as moderator variables. The central tendency (mean or median) of pain duration in the sample was examined as a moderator as well. Lastly, study characteristics were examined. Studies that included measures of pain-specific fear-avoidance, general fear-avoidance, or both of these measures were compared. Next, the type of sample recruited (e.g., chronic back pain, fibromyalgia) was examined as a moderator if there were three or more like samples.
The country from which participants were recruited was also recorded. The Hofstede dimensions of national culture were then utilized to examine culture as a moderator. These dimensions have been used extensively as a cultural comparison tool [25], based on an initial examination of 116,000 questionnaires given in countries around the world and many subsequent replications that found these same dimensions [26]. The dimensions include the Power Distance Index (PDI), Individualism versus Collectivism (IDV), Masculinity versus Femininity (MAS), Uncertainty Avoidance Index (UAI), Long Term Orientation versus Short Term Normative Orientation (LTO), and Indulgence versus Restraint (IND).The PDI reflects the way in which a society feels and acts regarding inequality, with higher scores representing acceptance of inequalities among people. The IDV index expresses the extent to which individualism (“I” mentality) and collectivism (“we” mentality) are balanced within a culture. Higher scores on the IDV index indicate higher levels of individualism. The MAS dimension defines masculinity in society as a preference for achievement and competition, whereas femininity is defined as a preference for cooperation and consensus. Higher scores on the MAS dimension indicate more masculine preferences. The UAI reflects how comfortable a society is with uncertainty, with higher scores representing intolerance of uncertainty. The LTO index quantifies the balance between managing past challenges and handling present and future challenges. Higher scores on this dimension indicate preferences for societal change and working toward the future. Finally, the IND index represents the extent to which short-term gratification and long-term gratification are valued in a society. Higher scores on this dimension indicate greater value on indulgence and immediate gratification. Though Hofstede’s dimensions of national culture have been used considerably [25], the validity of this measure has been questioned [27].
2.4 Search strategies
Searches were conducted using PsycInfo, PubMed, and ProQuest databases. Search terms included “avoidance” and “chronic pain” for studies published in English at any time prior to the final search date (January 26, 2016). In the case of ProQuest dissertations and theses, the same search terms were used, but the search was limited to abstracts due to the database’s incorporation of all9 disciplines.
For all searches, an initial screen was completed to exclude studies that did not meet the inclusion criteria. Subsequently, the abstracts of all remaining articles were inspected, and if the article appeared to meet the inclusion criteria, the full article was obtained. Then, reference lists from each included article were examined for articles missed during the initial search. After excluding repetitions across search engines, this process identified 799 studies for consideration. Eight of these studies were not available via the Internet, hard copy, or interlibrary loan (k = 8). One study was not included because it had been retracted since publication (k =1). Four studies were excluded because the studies were not quantitative in nature (k = 4). Of all available studies for inclusion, studies were excluded because they were not empirical studies (k = 155), did not use human subjects (k = 29), or were not available in English (k = 45). Furthermore, studies were excluded because they did not recruit from a chronic pain population (k = 168) or did not include self-report, cross-sectional measures of fear-avoidance or pain intensity (k = 138). Others were excluded if they did not report sufficient information to calculate effect sizes, or reported effects sizes were confounded (k = 126). Studies were excluded if they reported non-independent data (k = 7). With these exclusions, a total of 118 studies were included in the present meta-analysis.
Considerable effort was put forth to obtain unpublished data by contacting listservs (e.g., Association for Cognitive and Behavioral Therapies, Society for Behavioral Medicine’s Pain Special Interest Group) and searching conference proceedings using all-academic.com. No unpublished data were obtained.
2.5 Coding procedures
The first author coded the studies with the help of a postbaccalaureate research assistant. To assess intercoder agreement, the first author and research assistant double coded a random sample of 30% of the included studies. The coder was trained to reach acceptable reliability. According to Fleiss’ [28] standards, a kappa of .75 or greater indicates excellent agreement. The kappa for the present meta-analysis was .87, which indicates excellent agreement. All discrepancies were resolved through discussion until 100% agreement was reached.
The following variables were coded from each study provided the information was available: (a) design of the study (crosssectional, longitudinal), (b) type of sample (e.g., chronic low back pain, fibromyalgia), (c) N of sample, (d) age of sample (M, SD), (e) percent of males in sample, (f) percent of sample married/cohabiting, (g) country where study was conducted, (h) central tendency of pain duration in months (mean, median), (i) measure(s) of fear-avoidance, pain and depression, (j) reliability coefficients for fear-avoidance, pain, and depression measures, and (k) correlation(s) between fear-avoidance and pain intensity.
2.6 Meta-analytic procedure
All effect size computations were made using Fisher’s Z transformations, with study weights of n–3 [29]. The computations were completed using Comprehensive Meta-Analysis software [30].
Preliminary analyses of effect sizes were conducted. Effect sizes were examined for outliers. Analyses to detect publication bias were conducted to determine whether studies with small effect sizes or nonsignificant results had a lower probability of getting published. Trim-and-fill analyses (executed in the Comprehensive Meta-Analysis software package; [31]) and funnel plots were examined for evidence of publication bias (see Fig. 2). A funnel plot visually displays publication bias estimates using sample size and effect size [32]. A symmetrical funnel plot indicates a lack of publication bias. Because several examinations of the funnel plot have indicated that this method falsely concludes publication bias when it is in fact not present [33, 34], the p-curve was used as an additional measure of publication bias. The p-curve is a method used to model the distribution of significant p values [35]. The trim-and-fill analyses assume that studies with small effect sizes are less likely to be published, and the p-curve assumes that studies with nonsignificant results are less likely to be published, regardless of the effect size [36]. Both approaches were utilized in order to address both perspectives. All effect size and sample size data were entered into the online application at http://p-curve.org/ to generate a p-curve and the relevant statistics.
Funnel plot indicating the relationship between effect size and standard error (publication bias) in studies examining the association between fear-avoidance and pain. The hollow diamond indicates the mean effect size, and hollow circles indicate a single effect size. There are no black diamonds, indicating no imputed effect sizes.
A random-effects model was utilized to estimate a weighted mean effect size [37]. A fixed-effects model assumes that the effect sizes across diverse samples will be the same. Given the observed diversity in the strength of the association between pain intensity and fear-avoidance across the literature, a random-effects model was most appropriate. A weighted mean effect size was computed using CMA. Effect size distributions were tested for homogeneity. Meta-regression and meta-ANOVA were used to examine moderators of the weighted mean effect size.
3 Results
3.1 Description of studies
The 118 included studies in this meta-analysis were published between 1989 and 2015. The total number of participants included in the present analyses is 25,969. Sample sizes varied between 8 and 6147 (M = 220.08, SD = 567.77). On average, the samples were made up of 34.92% males (range: 0–100%), with two studies that did not report gender distribution. The mean age of participants was 43.92 years (SD = 12.05), with three samples that did not report a mean age. All samples utilized a chronic pain population with various types of chronic pain: 69 nonspecific chronic pain, 30 chronic back pain, 4 fibromyalgia, 3 chronic headache, 4 chronic spinal pain, 2 rheumatoid arthritis, 2 temporomandibular facial pain, 1 endometriosis, 1 irritable bowel disease, 1 chronic migraine, 3 multiple sclerosis, 1 osteoarthritis, 1 work-related chronic pain, and 1 whiplash associated disorder. The average percentage of the samples that were married or cohabiting was 63.48% (range: 6–84%), with 67 studies reporting this information. The average pain duration of the samples was 84.22 months (range: 12–201.84), with 94 studies reporting the central tendency (mean or median) of pain duration. Appendix A shows the sample characteristics and effect sizes for all included studies.
A variety of measures of fear-avoidance were used in the included studies, with many of the studies (38.13%) using multiple measures. Table 1 provides a summary of the included measures, as well as the domains assessed by these measures. Pain intensity was also assessed using a variety of measures. The pain intensity measures from included studies are summarized in Table 2.
3.2 Preliminary analyses
The data indicated that there were no statistical outliers in the effect sizes. As a result, all studies were included in analyses.
3.3 Effect size analyses
Weighted mean effect sizes were computed for the crosssectional association between fear-avoidance and pain (see Table 3). The total effect size represents the weighted mean effect size calculated using the average across all reported correlations between fear-avoidance and pain intensity, including both pain-specific and general avoidance measures. The results of the effect size analyses supported a positive association between fear-avoidance and pain intensity (with a weighted mean effect size of r = .26). Thus, as pain intensity increases, fear-avoidance also increases. As shown in Table 3, the Q statistic (a measure of homogeneity) was statistically significant, indicating a large ratio of between-study to within-study variance [38], Q(118) = 710.25, p < .001. I2 analyses were used to quantify inconsistency and heterogeneity in the results. The I2 statistic represents the percentage of variation due to heterogeneity rather than chance [39]. The I2 value computed herein (I2 =83.53) indicates considerable heterogeneity and signifies that a substantial amount of variation across included studies is due to heterogeneity. Thus, moderation analyses are justified.
Effect size r estimates for association between fear-avoidance and pain intensity.
| Moderator | r | 95% CI | z | k | Q | I 2 |
|---|---|---|---|---|---|---|
| Total | .26 | .23–.30 | 15.87[***] | 118 | 710.25[***] | 83.53 |
| Pain-specific fear-avoidance | .28 | .25–.31 | 16.16[***] | 111 | 708.62[***] | 84.48 |
| General fear-avoidance | .18 | .09–.27 | 3.89[***] | 15 | 65.53[***] | 78.64 |
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Note. Estimates utilized a random effects model. Values in the Q column indicate the heterogeneity in the weighted mean effect size. CI = confidence interval.
4 Pain-specific fear-avoidance vs. general fear-avoidance effect sizes
Weighted mean effect sizes were computed for the associations between pain-specific fear-avoidance and pain intensity, as well as general fear-avoidance and pain intensity. The weighted mean effect size for pain-specific fear-avoidance and pain intensity indicates a positive association (r = .28), and the weighted mean effect size for general fear-avoidance and pain intensity also supports a positive association (r = .18).
4.1 Moderation analyses
The previous analyses indicated heterogeneity in the distribution of effect sizes. As a result, moderation analyses were conducted. First, meta-regression analyses were undertaken to determine if pain duration moderated the association between fear-avoidance and pain intensity (see Table 4). The metaregression analysis indicated that pain duration did not moderate this association, Q(93) = .47, p > .05. Thus, duration of pain does not affect the strength of the association between fear-avoidance and pain intensity.
Tests of meta-regression for the relation between fear-avoidance and pain intensity.
| Moderator | Point estimate | 95% CI | z | k | Q |
|---|---|---|---|---|---|
| Total | .26[***] | .23 to −.29 | 15.00[***] | 112 | 691.42[***] |
| Pain Duration | .0002 | −.0004 to .0003 | 0.68 | 93 | .47 |
| Percent Married | .0024 | −.0006 to .0053 | 1.59 | 66 | 2.51 |
| Age | .0024 | −.0001 to .0049 | 1.92 | 114 | 3.67 |
| Percent Male | .0005 | −.0004 to .0015 | 1.07 | 115 | 1.14 |
| PDI | .0032 | .0008 to .0055 | 2.66[**] | 116 | 7.06[**] |
| IDV | −.0026 | −.0039to −.0013 | -3.89[***] | 116 | 15.15[***] |
| MAS | −.0010 | −.0022to.0003 | -1.46 | 116 | 2.12 |
| UAI | .0011 | −.0003 to .0024 | 1.57 | 116 | 2.46 |
| LTO | .0006 | −.0007 to .0019 | .90 | 116 | .82 |
| IND | −.0025 | −.0043 to −.0006 | -2.64[*] | 115 | 6.95[*] |
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Note. Values in the Q column indicate heterogeneity in a variable.
Abbreviations: CI = confidence interval. PDI, power distance index; IDV, individualism versus collectivism; MAS, masculinity versus feminity; UAI, uncertainty avoidance index; LTO, long term orientation versus short term normative orientation; IND, indulgence versus restraint.
Other sample characteristics were examined as moderators of the association between fear-avoidance and pain intensity. The percentage of the sample that were married or cohabiting did not significantly moderate the association between fear-avoidance and pain intensity, Q(66) = 2.51, p > .05. Furthermore, the mean age of the sample did not moderate the association, Q(114) = 3.67, p > .05. The percentage of males in the sample also did not moderate the relation between fear-avoidance and pain intensity, Q(115) = 1.14, p > .05.
Hofstede’s cultural dimensions were examined as continuous moderators using meta-regression. One study was excluded from these analyses because it recruited participants from four different countries. The results of these analyses indicated three of Hofstede’s dimensions were significant moderators: Power Distance Index, Individualism versus Collectivism, and Indulgence versus Restraint. The Power Distance Index moderated the association between fear-avoidance and pain intensity, indicating that as ratings on the PDI increased (indicating increased comfort with power inequalities), the association between fear-avoidance and pain intensity was stronger (Q(116) = 7.06, p < .05). The Individualism versus Collectivism scale was also a significant moderator of this association, such that as ratings on this scale decreased (less individualist or more collectivist ratings), the association between fear-avoidance and pain intensity was stronger (Q(116) = 15.15, p < .001). Finally, the Indulgence versus Restraint scale moderated the association, indicating that as scores on this scale decreased (indulgence decreases), the association between fear-avoidance and pain intensity was stronger (Q(115) = 6.95, p < .05).
Finally, the measurement characteristics of the studies were considered as moderators (see Table 5). The first analysis examined whether the use of one or multiple measures of fear-avoidance moderated the association between fear-avoidance and pain intensity. This moderation was significant, Q(1) = 5.60, p <.05. The results indicated that for studies using multiple measures of fear-avoidance, the weighted effect size was significantly higher (r(45) = .30) than for studies using a single measure (r(73) = .24). Furthermore, a moderation analysis evaluated whether the use of pain-specific fear-avoidance measures, general avoidance measures, or both types of measures affected the strength of the association. The majority of studies (k = 99) utilized only pain-specific fear-avoidance measures. Analyses indicated that the type of fear-avoidance measure significantly moderated the association (Q(2) = 15.91, p <.05). Studies that utilized pain-specific measures of fear-avoidance had a higher weighted effect size (r(99) = .27) than those that used measures of general fear-avoidance (r(6) = .02). Studies that utilized both pain-specific and general fear-avoidance had a small-to-moderate weighted effect size (r(13) = .30). To further explore this moderation, effect sizes were calculated using the average of only pain-specific measures within a study as well as effect sizes using only general fear-avoidance measures. The weighted effect sizes are reported in Table 3.
Tests of meta-ANOVA for moderation of association between fear-avoidance and pain intensity.
| Moderator | Point estimate | 95% Cl | z | k | Q |
|---|---|---|---|---|---|
| Total | .26[***] | .23−.29 | 15.00[***] | 112 | 691.42[***] |
| Pain measure | 3.58 | ||||
| One-item pain | .24 | .21−.27 | 14.24[***] | 63 | |
| Multiple item pain | .30 | .24−.35 | 10.61[***] | 54 | |
| Sample | 3.89 | ||||
| Chronic pain | .27 | .23−.32 | 10.88[***] | 66 | |
| Chronic back pain | .24 | .21−.28 | 13.52[***] | 30 | |
| Fibromyalgia | .32 | .09−.51 | 2.68[**] | 4 | |
| Chronic spinal pain | .30 | .25−.35 | 10.17[***] | 3 | |
| Chronic headache | .24 | .09−.38 | 3.03[**] | 4 | |
| Number of fear-avoidance measures | 5.60[*] | ||||
| One measure | .24 | .19−.28 | 10.40[***] | 73 | |
| Multiple measures | .30 | .27−.33 | 18.93[***] | 45 | |
| Type of fear-avoidance measure | 15.91[*] | ||||
| Pain-specific fear-avoidance | .27 | .24−.31 | 14.49[***] | 99 | |
| General fear-avoidance | .02 | −.12to .15 | .21 | 6 | |
| Pain-specific and general fear-avoidance | .30 | .25 to .35 | 12.14[***] | 13 |
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Note. Values in the Q column indicate heterogeneity in a variable.
All analysis used a mixed effects approach.
4.2 Publication bias
Trim-and-fill analyses indicated no evidence of publication bias. As shown in Fig. 2, the funnel plot appears to be relatively symmetrical, with no imputed studies to the left of the mean. This provides no evidence of publication bias.
A histogram of p-values for studies with significant correlations is shown in Fig. 3. Over 80% of the statistically significant effects included in the analyses were significant at p < .01. p-curve analyses indicated that the studies did not suggest evidence of p-hacking (p > .999). The p-curve graph is shown in Fig. 4. The p-curve analyses suggest that there is no evidence of a bias toward reporting only significant effects (Tables 4 and 5).
Histogram representing the frequency of p-values for all statistically significant effects.
P-curve depicting distribution of p values forall statistically significant effects (p < .05).
5 Discussion
This is the first meta-analysis to compute an estimate of the association between fear-avoidance and pain intensity in chronic pain patients. The results indicate that fear-avoidance and pain intensity are positively associated, and this link is of small-to-moderate magnitude. This finding suggests that those with higher pain intensity may have a greater propensity to avoid activities that worsen pain, or those who are more avoidant report higher pain intensity. Importantly, the magnitude of the association between fear-avoidance and pain intensity varied as a function of several moderating variables. Specifically, the association between pain intensity and fear-avoidance varied as a function of cultural characteristics (using Hofstede’s dimensions) and characteristics of the measures employed in the studies. The association did not vary in strength based on type of sample, pain duration, age, gender, or marital status.
The association between fear-avoidance and pain intensity varied significantly as a function of Hofstede’s cultural dimensions. The results indicated that the association between fear-avoidance and pain intensity was stronger in cultures where individuals are comfortable with an unequal distribution of power in society, in cultures where collectivist ideals are valued, and in cultures where restraint is more important than short-term gratification. These findings indicate that cross-cultural comparisons within the chronic pain literature are critically important. For example, previous research has explored the role of health care providers’ beliefs about the extent to which chronic pain has a psychological or physical cause, finding that treatment of chronic pain as a strictly physical condition adds stress to chronic pain patients [40]. A cultural and medical focus on autonomy isolates the individual from their environment, which focuses on the individual’s role in the disease process, rather than lifestyle changes or preventive factors that might improve quality of life [41]. The current findings suggest that cultural norms and beliefs may impact the extent to which pain intensity is associated with fear-avoidance. It is hypothesized that in cultures where collectivism and restraint are valued, avoidance may serve a broader function. When in pain, it may be more culturally appropriate to avoid pain, as the pain may draw attention to the individual or indicate a lack of restraint. However, this is a preliminary hypothesis, and future research should explore the sociological and cultural influences that affect fear-avoidance and pain intensity in chronic pain patients to better understand the impact of societal variations in values and normative behavior. In addition, Hofstede’s cultural dimensions are applied at a national level, but many cultures exist within nations, and other concerns about the validity of these dimensions have been raised [27]. For this reason, future research should examine culture at an individual level to increase the specific dimensions of culture’s role in the association between pain intensity and fear-avoidance.
The results indicated that studies using multiple measures of fear-avoidance had a higher weighted mean effect size than studies that used one measure of fear-avoidance. This finding could be due to increased precision of measurement that comes with more items to measure a similar construct [42].
An additional measurement-related finding was that studies utilizing pain-specific measures or both pain-specific and general measures had a higher weighted effect size than those using only general fear-avoidance measures. Interpretation of this finding is limited by the fact that there were few k = 6) studies that solely utilized general fear-avoidance measures. The analyses indicated that the correlation between general fear-avoidance and pain intensity is of lesser magnitude than the correlation between pain-specific fear-avoidance and pain intensity. Pain-specific stimuli may be inherently more salient with chronic pain patients, so measures that target pain-specific fear-avoidance may resonate more with this population. In sum, general measures of fear and avoidance should only be used in conjunction with pain-specific measures–never alone.
The fear-avoidance model of chronic pain is a well-established conceptualization of the factors that impact the development and maintenance of chronic pain, as well as the possible consequences of fear and avoidance. The current analyses indicate a small-tomoderate association between fear-avoidance and pain intensity. Those with higher reported pain intensity may be more prone to avoid pain. Alternatively, individuals who report more fear-avoidance may also experience increased pain intensity. The avoidance of pain, though successful in the short-term, may increase pain intensity by physical deconditioning or by reinforcing the notion that pain should be avoided. Future research should explore the mechanisms by which fear-avoidance and pain intensity are associated using mediation analyses and/or prospective study designs.
Despite the significant body of research supporting the fear-avoidance model of chronic pain, it remains a problematic construct. The term fear-avoidance suggests that fear and avoidance co-occur simultaneously. These two processes can certainly occur independently because an individual can experience fear and choose to approach a stimulus, or an individual can avoid a stimulus but not self-report fear. This conceptual issue also has methodological significance. Many of the fear-avoidance measures included in the current meta-analysis confound fear and avoidance, which prevents the separation of these two constructs. This not only confounds the measurement instruments, but also introduces error into the correlation between fear-avoidance and pain intensity. Future research should focus on the development of instruments that separate fear and avoidance to better understand the differential predictive value of these variables.
The current study represents the first meta-analysis to estimate the association between fear-avoidance and pain intensity. However, several limitations should be considered. First, only cross-sectional data were considered in the analyses to avoid including confounding variables with the effects of interventions. Thus, no causal inferences can be drawn from the results. Second, many studies did not collect or report adequate information to be included in all moderation analyses, limiting the conclusions that can be drawn. Third, the majority of the included studies did not report reliability coefficients for the sample; thus, corrections for artifacts in each sample could not be applied. As a result, the correlations may include error that is specific to the measure and the sample. Fourth, though the International Association for the Study of Pain has indicated that three or more months qualifies as chronic pain, the inclusion criteria across studies varied considerably [43]. Therefore, the test of pain duration as a moderator was limited by the restricted range of average pain duration in the included studies. The lowest reported mean pain duration included was 12 months, which limits the ability of these results to generalize to all chronic pain patients. Some moderation analyses were not conducted because not enough studies reported data (e.g., attention to pain, negative affect). Future research should explore other variables that may affect the strength of the association between fear-avoidance and pain intensity in chronic pain patients. Fifth, several acceptance measures were included in the current analyses. Most commonly, the CPAQ [24] was included, as it was originally developed [44] based on the AAQ [18], a measure of experiential avoidance. The AAQ has been used as an indicator of acceptance when reverse-scored [22, 23]. The inclusion of both acceptance and avoidance measures in the current analyses is a limitation that is reflective of measurement imprecision in the broader literature. Nevertheless, these measures were included in an effort to provide the most comprehensive picture of the literature. Finally, the use of Hofstede’s dimensions of national culture have been criticized for lacking validity [27]. As such, the current findings should be interpreted with caution, and more idiographic measures should be utilized in future research.
This study has significant implications for future research. The directionality of the relation between fear-avoidance and pain intensity needs to be further explored. If fear-avoidance is a risk factor for increased pain intensity, then measures of fear-avoidance should be utilized in the identification of individuals who would benefit from behavioral interventions focused on engaging in approach behaviors. If higher pain intensity places an individual at risk to fear pain and avoid activities that could increase pain, then those that report high pain intensity should be targeted for interventions in which movement and exposure to pain is emphasized. Furthermore, in those with higher pain intensity, acceptance may be a central component of intervention work. Although the intensity of pain may dissuade an individual from movement, the extent to which this limits an individual from engaging in a meaningful life is critical. In addition, gaining a better understanding of cultural differences in coping with chronic pain is an essential next step. Health behaviors surrounding both acute and chronic pain must be considered, along with health care policies and beliefs within different countries and cultures.
In summary, the results of the present meta-analysis indicated a small-to-moderate positive association between fear-avoidance and pain intensity. The results were robust to demographic and pain characteristics.These findings have significant implications for behavioral treatments in chronic pain populations. In-vivo exposure [45] and cognitive-behavioral therapy have shown success in improving psychological and physical functioning among chronic pain patients [46]. Acceptance and Commitment Therapy, a third-wave cognitive-behavioral therapy, has also improved quality of life and functioning in a variety of chronic pain samples [47]. Through these treatments, avoidance is targeted as the key process of change through exposure to feared stimuli and promotion of approach behavior. Future research should focus on understanding the demographic characteristics, pain characteristics, and other biopsychosocial variables that affect the success of behavioral treatments in improving quality of life and functioning among chronic pain patients. This will allow for tailored treatments, with the hope of optimizing treatment outcomes for those experiencing chronic pain.
Highlights
Across studies, fear-avoidance and pain intensity were positively associated.
Cultural differences were a significant moderator of the association.
Findings can inform intervention and prevention work with chronic pain patients.
DOI of refers to article: http://dx.doi.org/10.1016/j.sjpain.2016.08.010.
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Ethical issues: Informed consent was not required, and approval by an ethics board was not obtained, as only aggregated secondary data was examined. Study protocol is not registered.
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Conflict of interest: No conflict of interest.
Acknowledgments
Many thanks to Dr. Michael O’Hara, Dr. Kristian Markon, Dr. James Marchman, Cara Solness, Dr. Teresa Treat, Dr. Ernest O’Boyle, and Dr. Mark Vander Weg for the helpful feedback on this manuscript.
References marked with an asterisk indicate studies included in the meta-analysis.
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Appendix A
Major Characteristics of Included Studies.
| References | n | Sample | r | F-A measure(s) | Pain intensity measure(s) | Pain Duration | %Married/ Cohabiting | Age | %Male | Country |
|---|---|---|---|---|---|---|---|---|---|---|
| Asghari and Nicholas [48] | 145 | Chronic pain | .2 | PBQ | NRS | 188.8 | 71 | 50.3 | 41 | Australia |
| Asmundson et al (1999) | 72 | Chronic headaches | .39 | PASS | HQ | 193.1 | – | 43.1 | 15 | Canada |
| Barke et al. (2015) | 182 | CBP | .24 | CPAQ | NRS | 152.4 | – | 51 | 29.67 | Germany |
| Bendayan et al. [49] | 315 | Chronic pain | .41 | CPAQ, AAQ | NRS | 127 | 69.6 | 53 | 25.1 | Spain |
| Boersma and Linton [50] | 141 | Chronic back/neck pain | .21 | TSK | NRS | – | – | 47.7 | 20 | Sweden |
| Callahan [51] | 84 | TMD facial pain | .09 | WOC | VRS | – | 71 | 37 | 7 | USA |
| Cannon [52] | 333 | Chronic pain | .21 | PBC | MPI | 79 | – | 43.5 | 46 | Canada |
| Carleton [53] | 31 | Chronic pain | .42 | PASS, FABQ | MPQ-SF | – | – | 46.19 | 41.9 | Canada |
| Carleton et al. [54] | 68 | CLBP | .29 | PASS-20, FABQ | VAS | 23.93 | 56 | 40.2 | 65 | Canada |
| Carleton et al. [55] | 15 | Fibromyalgia | .01 | PASS-20 | VAS | 147.6 | – | 52.5 | 6 | Canada |
| Carranza [56] | 48 | Chronic headache | .08 | PASS, PBQ | HI | – | 6 | 22 | 6 | USA |
| Cascarilla [57] | 117 | Chronic pain | .03 | CPAQ | NRS | 131.76 | 62.4 | 45.7 | 36.8 | USA |
| Chiu [58] | 41 | Chronic pain | .5 | TOPS-FA | TOPS-Pain | – | 75.61 | 43.1 | 36.6 | USA |
| Cho et al. [59] | 166 | Chronic pain | .5 | PASS-20 | NRS | 36 | 74.1 | 48.7 | 29.5 | Korea |
| Cho et al. [60] | 142 | Chronic pain | .38 | PASS-20 | SF-36 BP | 32 | 58 | 44.5 | 36.6 | Korea |
| Combs & Thorn (2015) | 102 | CBP | .43 | TSK | NRS | 143.58 | – | 50.5 | 39.2 | USA |
| Cook et al. [61] | 469 | Chronic pain | .3 | TSK | MPQ | 74.4 | 51 | 46.3 | 36 | USA |
| Crombez et al. [62], | 38 | Chronic pain | .22 | TSK | VAS | 78.72 | 78.95 | 36.84 | 28.95 | Belgium |
| Crombez et al. [63] | ||||||||||
| Crombez et al. [63]. | 35 | CBP | .19 | FABQ, TSK | VAS | 80.4 | – | 36.1 | 32 | Belgium |
| Sample 1 | ||||||||||
| Crombez et al. [63]. | 38 | CBP | .38 | TSK, FABQ | VAS | 76.2 | – | 40.84 | 34 | Belgium |
| Sample 2 | ||||||||||
| Crombez et al. [63]. | 31 | CBP | .24 | TSK, PASS | VAS | 121.2 | – | 41.61 | 48 | Netherlands |
| Sample 3 | ||||||||||
| Crowley and Kendall | 53 | Chronic pain | .28 | FAPS | VAS, MPI | 48.8 | 32 | 35.7 | 33 | New Zealand |
| [64] | ||||||||||
| Cui et al. [65] | 63 | Chronic pain | -.29 | CISS-A | VAS | 46.1 | 34.92 | 59.3 | 34.92 | Japan |
| Dehghani et al. [66] | 161 | Chronic pain | .41 | TSK | MPI | 74 | 61 | 44.56 | – | Australia |
| Dehghani et al. [15] | 207 | Chronic pain | .37 | TSK | MPI | 75 | 56 | 45.2 | 46.9 | Australia |
| Denison et al. [67]. Sample 1 | 210 | Chronic pain | .23 | TSK | NRS | 12 | 74 | 45 | 24 | Sweden |
| Denison et al. [67]. Sample 2 | 218 | Chronic pain | .23 | TSK | NRS | 12 | 69 | 47 | 35 | Sweden |
| Dyson [68] | 206 | CBP | .06 | PASS | VRS | – | – | 42.8 | 67 | USA |
| Endler et al. [69] | 147 | Chronic pain | .19 | CISS-A | MPI, MPQ | – | – | – | 31.93 | Canada |
| Esteve and Ramirez-Maestre [70]. Sample 1 | 128 | Irritable bowel syndrome | .38 | AAQ, PASS, CPAQ | PII, SF-36 BP | 63.88 | 51 | 37.91 | 45.31 | Spain |
| Esteve and Ramirez-Maestre [70]. Sample 2 | 141 | CBP | .36 | AAQ, PASS, CPAQ | PII, SF-36 BP | 53.05 | 69 | 45.75 | 45.39 | Spain |
| Esteve and Ramirez-Maestre [70]. Sample 3 | 137 | Chronic pain | .46 | AAQ, PASS, CPAQ | PII, SF-36 BP | 107.16 | 76 | 53.2 | 43.8 | Spain |
| Esteve et al. [71] | 299 | CBP | .2 | AAQ, FABQ | PII | 25.21 | 61.5 | 44.18 | 46.2 | Spain |
| Esteve et al. (2013) | 468 | CBP | .24 | FABQ, PASS, CPAQ | PII | 70.11 | 69.8 | 46.54 | 40.6 | Spain |
| Esteve et al. (2015) | 111 | Chronic pain | .19 | POAM | NRS | 176.28 | 79.3 | 53 | 30 | Spain |
| Farin et al. [72] | 688 | CBP | .26 | FABQ | VAS | – | 74.8 | 51 | 42.8 | Germany |
| Fersum et al. [73] | 94 | CBP | .27 | FABQ | PINRS | 132.58 | – | 41.87 | 49.94 | Norway |
| Fish et al. [74]. | 428 | Chronic pain | .38 | CPAQ | BPI | 147.61 | 69.86 | 46.99 | 19.39 | Ireland, UK, US, Australia |
| Foote [75] | 103 | Migraine | .37 | PASS-20, CPAQ | HS | – | – | 41.45 | 11.8 | USA |
| French et al. [76] | 200 | CBP, Neck pain | .26 | TSK, FABQ | VAS | 15.41 | – | 40 | 46 | Canada |
| Geiser [44] | 65 | Chronic pain | .03 | APQ | BAP | 31 | – | 41.5 | 46.2 | USA |
| Gomez-Perez et al. [77] | 125 | Chronic pain | .27 | TSK | PII | 140.64 | – | 58.26 | 19.2 | Spain |
| Goubert et al. [78] | 122 | CBP | .29 | TSK | MPI | 97.98 | 84.2 | 43.72 | 39.3 | Netherlands |
| Goubert et al. [79] | 39 | CBP | .24 | TSK, PASS | MPI | 93.01 | 79.5 | 43.49 | 25.6 | Belgium |
| Grotle et al. [80] | 233 | CLBP | .22 | FABQ | VAS | – | – | 42 | 46 | Norway |
| Harrison et al. (2015) | 608 | Multiple sclerosis | .24 | AEQ | BPI | 153.6 | – | 52.4 | 25.7 | England |
| Jacox [81] | 293 | Endometriosis | .15 | DICI | VAS | 61.65 | – | 34.18 | 0 | USA |
| Jeffrey [82] | 96 | Rheumatoid arthritis | -.11 | WOC | VAS | 12.2 | 56.3 | 48.1 | 12 | USA |
| Kindermans et al. [83] | 132 | Chronic pain | .28 | POAM, PARQ | VAS | 146.82 | – | 45.62 | 34.01 | Netherlands |
| Knaster et al. [84] | 121 | Chronic pain | .04 | PASS-20 | VAS | 48 | 60 | 47.9 | 38 | Finland |
| Koho et al. [85] | 51 | Chronic pain | .41 | TSK | VAS | 69.7 | – | 44.6 | 47.06 | Finland |
| Lamoth et al. [86] | 19 | CBP | .58 | TSK | VAS | 14.4 | – | 38 | 42.1 | Netherlands |
| Larsen et al. [87] | 259 | Chronic pain | .22 | PASS | MPQ | 21 | – | 38.2 | 58 | Canada |
| Larsson et al. [88] | 433 | Chronic pain | .31 | TSK | NRS | 122.4 | 54.3 | 74.8 | 36.5 | Sweden |
| Laufer et al. [89] | 63 | Chronic pain | .36 | FABQ | VAS | – | – | 46.3 | 56 | Israel |
| Lee [90] | 168 | Chronic pain | .34 | FABQ | NRS | – | – | 42.81 | 36.4 | China |
| Lopez-Martinez et al. [91] | 149 | Chronic pain | .27 | PASS-20 | PII | 59.38 | 55 | 46.83 | 25.5 | Spain |
| Luedtke et al. [92] | 135 | CBP | .14 | FABQ | VAS | 96 | – | 45 | 54.07 | Germany |
| Lundberg et al. [93]. Sample 1 | 73 | CBP | .29 | TSK | VAS | – | 57.53 | 46.4 | 42.47 | Sweden |
| Lundberg et al. [93]. Sample 2 | 74 | CBP | .2 | TSK | VAS | – | 55.41 | 46.7 | 47.3 | Sweden |
| Macleod [94] | 117 | TMJ pain | .2 | PASS | GCPS | 87.8 | 62.4 | 39.4 | 11 | USA |
| Martin et al. [95] | 208 | Chronic pain | .2 | PASS-20 | NRS | 66.72 | – | 47.18 | 40.01 | Canada |
| Martin et al. [96] | 21 | Chronic pain | -.04 | PASS-20 | NRS | – | – | 14.2 | 23.81 | Canada |
| McCracken [97] | 235 | Chronic pain | .19 | CPAQ, PASS | NRS | 119.5 | 65.2 | – | 38.2 | Britain |
| McCracken et al. [98] | 122 | Chronic pain | .08 | CPAQ | NRS | 54 | – | 15.2 | 24.6 | UK |
| McCracken and Eccleston [99] | 230 | Chronic pain | .26 | CPAQ, PASS | VAS | 32.5 | 56.1 | 46.4 | 33.5 | Britain |
| McCracken and Keogh [100] | 125 | Chronic pain | .34 | CPAQ, PASS-20 | NRS | 96 | 63.2 | 46.6 | 35.2 | Britain |
| McCracken and Velleman [22] | 239 | Chronic pain | .41 | AAQ, CPAQ | SF-36 | 120 | 61.8 | 61.5 | 41.8 | Britain |
| McCracken and Zhao-O’Brien [23] | 144 | Chronic pain | .15 | AAQ-II, CPAQ, PASS-20 | NRS | 139 | 58.4 | 42.4 | 36.1 | Britain |
| McCracken et al. [101] | 105 | Chronic pain | .29 | CPAQ, PASS-20 | NRS | 96 | 61.9 | 46.9 | 40 | UK |
| McCracken et al. [8] | 45 | Chronic pain | .42 | FABQ, PASS, PBC | MPQ | 27 | 68.9 | 46.3 | 46.7 | USA |
| McCracken et al. [102] | 190 | Chronic pain | .23 | CPAQ, PASS | MPI | 36 | 56.8 | 47.1 | 33.7 | Britain |
| Meyer [103] | 139 | Chronic pain | .24 | CPAQ, AAQ-II | VAS | 83.28 | 20.9 | 45.43 | 38.8 | USA |
| Meyer et al. [104] | 111 | CBP | .43 | FABQ | NRS | – | – | 49 | 32 | Switzerland |
| Michalski et al. [105] | 38 | Multiple sclerosis | .12 | FSR | NRS | 103.2 | – | 42 | 18.4 | Germany |
| Newton-John et al. [106] | 101 | Chronic pain | .32 | TSK | NRS | 33.91 | – | 43 | 56.4 | Australia |
| Nicholas and Asghari [107] | 252 | Chronic pain | .24 | TSK, CPAQ | MPI | 93.7 | – | 50.33 | 37.7 | Australia |
| Nicholas et al. [108] | 567 | Chronic pain | .36 | TSK | MPI | 31.2 | 55.5 | 44 | 48 | Australia |
| O’Connell [109] | 91 | Chronic pain | .09 | WOC | MPI | 61.2 | 70 | 45 | 42.86 | USA |
| Ojala et al. [110] | 81 | Chronic pain | .25 | CPAQ | VAS | 51.6 | 64 | 49 | 37 | Finland |
| Perry and Francis [111] | 68 | Chronic pain | .48 | TSK, FABQ | BPI | 116.04 | – | 43.47 | 26 | Australia |
| Plumb Vilardaga [112] | 28 | Chronic pain | .46 | PIPS, BPCI | PIR | 17.14 | 58 | 52.59 | 25 | USA |
| Ramírez-Maestre and Esteve [113]. | 190 | Chronic spinal pain | .33 | CPAQ, PASS | PII | 70.58 | 72.2 | 46.28 | 100 | Spain |
| Ramírez-Maestre and Esteve [113]. Sample 1 | 210 | Chronic spinal pain | .34 | CPAQ, PASS | PII | 63.26 | 69.5 | 46.17 | 0 | Spain |
| Ramírez-Maestre et al. [16] | 686 | Chronic spinal pain | .28 | AAQ, FABQ, CPAQ | PII | 48.7 | 60.3 | 45.4 | 41 | Spain |
| Riecke et al. [114] | 111 | CBP | .08 | PASS, TSK | NRS | 169.92 | 71.2 | 51.92 | 45.5 | Germany |
| Rodero et al. [115] | 205 | Fibromyalgia | .45 | CPAQ | VAS | 145.2 | 73.6 | 50 | 9.3 | Spain |
| Ruiz-Parraga & Lopez-Martinez (2015) | 229 | CBP | .27 | CPAQ | PII | 55.32 | 66.8 | 45.53 | 28.8 | Spain |
| Ruiz-Parraga et al. (2015) | 150 | Chronic pain | .55 | AEQ | PII | 92.4 | 62 | 48.27 | 38 | Spain |
| Samwel et al. [116] | 181 | Chronic pain | .22 | TSK, PCI | VAS | 64.1 | 76.9 | 48.7 | 35.9 | Netherlands |
| Samwel et al. [117] | 169 | Chronic pain | .24 | TSK | VAS | 59.9 | 79.1 | 47.1 | 36.1 | Netherlands |
| Schutze et al. [118] | 104 | Chronic pain | .32 | TSK | BPI | 125.7 | – | 54.5 | 31.7 | Australia |
| Sil et al. [119] | 8 | Fibromyalgia | .65 | TSK | VAS | – | – | 15.88 | – | USA |
| Simons and Kaczynski [120] | 275 | Chronic pain | .24 | FOPQ | NRS | 13 | – | 13.73 | 25.2 | USA |
| Simons et al. [121] | 181 | Chronic pain | .2 | FOPQ | NRS | 19 | – | 13.6 | 27.2 | USA |
| Simons, Smith, Kaczynski, & Basch (2014) | 312 | Chronic pain | .15 | FOPQ | NRS | 14 | – | 13.7 | 19.4 | USA |
| Strahl et al. [122] | 154 | Rheumatoid arthritis | .19 | PASS | AIMS-2 | 177.84 | 55 | 54.07 | 13.64 | USA |
| Sullivan et al. [123]. | 226 | Chronic pain | .33 | TSK | MPQ | – | – | 39.78 | 40.6 | Canada |
| Suttenfield [124] | 95 | Chronic pain | .46 | PSITQ | MPQ | 133 | – | 48 | 30.5 | USA |
| Thomas [125] | 148 | Chronic pain | .23 | CPAQ, AAQ | MPI | 201.84 | 68.9 | 47 | 15 | USA |
| Tkachuk and Harris [126] | 276 | Chronic pain | .27 | TSK, CPAQ | MPI | 102 | – | 47.76 | 35 | Canada |
| Trompetter et al. [127] | 428 | Chronic pain | .25 | PIPS | NRS | – | 69.3 | 43.7 | 27.8 | Netherlands |
| Trost et al. [128] | 60 | CBP | .19 | TSK | MPQ | 98.4 | – | 31.9 | 38.33 | USA |
| Turk et al. [129] | 233 | Fibromyalgia | .2 | TSK | MPI | 123.6 | 53.6 | 43.79 | 0 | USA |
| Viane et al. [130]. Study 1 | 120 | Chronic pain | .12 | CPAQ | MPI | 87.56 | 56.7 | 41.46 | 18.33 | Belgium |
| Viane et al. [130]. Study 2 | 66 | Chronic pain | .45 | CPAQ, ICQ | MPI | 125.98 | 81.1 | 46.77 | 24.24 | Belgium |
| Vitiello et al. [131] | 354 | Osteoarthritis | .22 | TSK | GCPS | – | – | 73.1 | 21.75 | USA |
| Vlaeyen et al. [132]. | 33 | CBP | 0 | TSK | VAS | 91.2 | – | 37.4 | 48.48 | Netherlands |
| Vlaeyen et al. [133]. Study 1 | 103 | Chronic pain | .25 | TSK | VAS | 122.07 | – | 40.7 | 43.69 | Netherlands |
| Vlaeyen et al. [133]. Study 2 | 129 | CBP | .21 | TSK | MPQ | 118.8 | – | 40.1 | 38.76 | Netherlands |
| Vowles [134] | 74 | Chronic pain, worker’s comp | .43 | CPAQ, PASS | MPQ | 20.9 | 67.6 | 38.5 | 70.3 | USA |
| Vowles et al. [135] | 334 | Chronic pain | CPAQ, PASS | NRS | 96 | 61.4 | 46.2 | 37.8 | UK | |
| Wicksell et al. [136] | 611 | Whiplash associated disorder | .34 | CPAQ, TSK | NRS | 120 | 79.8 | 49 | 25.2 | Sweden |
| Wilson et al. [137] | 42 | Chronic pain | .22 | FABQ | NRS | – | – | 14.9 | 26.2 | USA |
| Wittink et al. [138] | 6147 | Chronic pain | -.03 | TOPS | TOPS, SF-36 | – | 57.33 | – | 34.39 | USA |
| Witty [139] | 78 | CBP | .05 | PSI | MPQ | – | 65 | 48.39 | 93.6 | USA |
| Wong et al. [140] | 242 | Chronic pain | .54 | PASS-20, TSK | CPGQ | 47.52 | 66.9 | 45.67 | 41.9 | Hong Kong |
| Woods [141] | 44 | CBP | .42 | TSK, FABQ, PASS | MPQ | – | – | 46.45 | 34.1 | Canada |
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Note. Pain duration is reported in months. Age is reported in years.
Abbreviations: AAQ Acceptance and Action Questionnaire; ACPT, Acceptance and Control in Psychological Treatment; AIMS-2, Arthritis Impact Scale-2nd edition; BAP, Brief Assessment of Pain; BCPI, Brief Pain Coping Inventory; BPI, Brief Pain Inventory; CBP, Chronic back pain; CISS, Coping Inventory for Stressful Situations; CPAQ Chronic Pain Acceptance Questionnaire; DICI, Dealing with Illness Coping Inventory; FABQ Fear-Avoidance Beliefs; FAPS, Fear Avoidance of Pain Scale; FOPQ Fear of Pain Questionnaire; FSR, Questionnaire on Pain Regulation; GCPS, Graded Chronic Pain Scale; HI, Headache Index; HQ, Headache Questionnaire; MPI, Multidimensional Pain Inventory; MPQ McGill Pain Questionnaire; NRS, Numerical Rating Scale; PARQ Pain and Activity Relations Questionnaire; PASS, Pain Anxiety Symptoms Scale; PBC, Pain Behavior Questionnaire; PBQ Pain Behavior Questionnaire; PCI, Pain Coping Inventory; PII, Pain Intensity Index; PINRS, Pain Intensity Numeric Rating Scale; PIR, Pain Intensity Rating; POAM-P, Patterns of Activity Mapping–Pain; PSI, Problem-Solving Inventory; PSITQ; Pain Situations Questionnaire; SF-36, Short Form Health Survey-36; TOPS, Treatment Outcomes of Pain Survey; TSK, Tampa Scale for Kinesiophobia; VAS, Visual Analogue Scale; VRS, Verbal Rating Scale; WOC, Ways of Coping.
© 2016 Scandinavian Association for the Study of Pain
