What do the numbers mean? Normative data in chronic pain measures

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1 Pain 134 (2008) What do the numbers mean? Normative data in chronic pain measures Michael K. Nicholas a, *, Ali Asghari a,b, Fiona M. Blyth a,c a Pain Management Research Institute, University of Sydney at Royal North Shore Hospital, Sydney, Australia b School of Psychology, University of Shahed, Tehran, Iran c School of Public Health, University of Sydney, Sydney, Australia Received 21 November 2006; received in revised form 27 March 2007; accepted 9 April 2007 Abstract Although self-reported measures play a central role in the assessment of pain and its treatment, it has long been recognized that interpretation of these measures is severely limited by the absence of normative data. Despite that, relatively few of the measures used in pain clinics or research studies have normative data for reference. Using a pain centre sample (n = 6124), this paper describes the development of a normative dataset on a number of commonly used pain-related measures. The measures cover many of the key dimensions in pain assessment, including pain severity/quality, disability (physical functioning), and mood (emotional functioning). Measures of different cognitive and coping constructs are also included. Mean scores are reported for each measure according to age group, gender, pain site, as well as percentiles for different scores for patients with chronic low back pain. The potential uses for datasets of this type include the assessment and evaluation of individual cases, as well as the interpretation of published clinical trials. It is also argued that future systematic reviews of pain treatments should include consideration of such patient characteristics as pain levels, disability and mood in the studies reviewed rather than pain site and chronicity alone. Ó 2007 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved. Keywords: Chronic pain; Normative data; Assessment; Self-report 1. Introduction Self-reported measures play a central role in the assessment of chronic pain. As the name implies, normative data represent the performance on a measure or test by a standardization sample against which other performances on the measure can be compared (Anastasi and Urbina, 1997). Lack of normative data limits the interpretation of scores in individual cases as well as in treatment outcome research (as we cannot know if a score is typical, high or low for the population being studied) (Kendall et al., 1999). This * Corresponding author. Tel.: ; fax: address: (M.K. Nicholas). has implications for our ability to assess the clinical significance of a score (or change in a score). Although data on some measures with different pain samples are now available (e.g. Kerns et al., 1985; Von Korff et al., 1992; Chibnall and Tait, 1994; Wittink et al., 2006), there is little evidence that the repeated calls for normative data on commonly used measures have been widely heard (e.g. Turk and Melzack, 1992). In Turk and Melzack s (2001) text on pain assessment few measures reported normative data. More recently, in the IMMPACT recommendations on core outcome domains for chronic pain clinical trials (Turk et al., 2003) it was noted that the next step would be to select measures that meet appropriate psychometric standards (i.e. reliability, /$32.00 Ó 2007 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved. doi: /j.pain

2 M.K. Nicholas et al. / Pain 134 (2008) validity, responsiveness, appropriate, normative data) (p. 338) (italics added). So the quest for normative data is still an active one. In recent years there been an extensive discourse in the field of clinical epidemiology on the interpretation of test results, including the use of normative data. However, many different views on the nature of norms have been expressed. Sackett et al. (1991) identified six definitions of normal in common clinical use. Definitions of normal have also been queried in the pain field (Cronje and Williamson, 2006). In depression research, there have been repeated calls for normative data on commonly used measures (Kendall et al., 1999). In part, the call for normative data for self-report measures in chronic pain reflects a wider concern with how test data are used in clinical and research settings. It was in this context that we created a normative dataset derived from patients with chronic noncancer pain presenting for assessment and treatment at a tertiary-referral multidisciplinary pain management centre over 10 years from mid The dataset includes measures of pain severity, disability (physical functioning), mood (emotional functioning), quality of life, cognitions (beliefs), and coping strategies. These measures cover the first three domains recommended by the IMMPACT groups (Turk et al., 2003; Dworkin et al., 2005), as well as others relevant to the assessment of pain, and they all meet the basic criteria of adequate psychometric properties. The aims of this initial report are: (1) To describe the process of establishing this normative dataset. (2) To provide normative data, according to gender, age and pain site, for a range of self-report measures. (3) To illustrate, with case studies of patients and selected areas of the pain treatment literature, how a normative dataset might be used in clinical practice and research. 2. Method 2.1. Subjects From June 1994 to May 2004, a total of 6124 pain patients referred by their treating physicians from across the Australian state of New South Wales (population about 6 million) were seen at the Pain Management and Research Centre at the Royal North Shore Hospital, Sydney, for assessment and treatment. All patients attending this tertiary-referral centre were asked to complete the measures and the normative data set is based on this population. Of these 6124 patients, 5941 (97%) patients in the sample completed a series of commonly used questionnaires on different dimensions of pain. The numbers of patients varied for different measures as changes were made in the measures used over the data collection period and not all patients completed all measures in the assessment booklet. The remaining183 (3%) did not provide any data for the present study. The main reasons were refusal to complete questionnaires and lack of adequate English proficiency to understand the content of the questionnaires. If more than 10% of items in one questionnaire were not completed then that questionnaire was not included in the normative database. If 10% or less of the items in one questionnaire were not completed the scores were pro-rated (from the 90+% that were completed). Data on age and gender were collected for all patients. The use of the (de-identified) dataset for this study was approved by the Hospital s Ethics Committee Measures All measures described in this section are self-report scales Numerical rating scale (NRS) Patients reported their present pain intensity as well as their highest, lowest and usual pain intensity over the last week, using NRS. The NRS asks patients to rate their pain intensity on a 0 10 (11-point) scale where 0 indicates no pain and 10 means pain as bad as it could be. The validity of the NRS and its sensitivity to treatment effects have been well documented (Jensen and Karoly, 1992a) The McGill Pain Questionnaire (MPQ) The MPQ (Melzack, 1975) measures three dimensions of pain experience: the sensory, the affective and the evaluative dimensions of pain. The sensory dimension (42 items) of pain describes the sensory qualities of the experience in terms of temporal, spatial, pressure, thermal and other properties. The affective dimension (14 items) describes affective qualities of pain in terms of tension, fear and autonomic properties that are part of pain experience. The evaluative dimension (5 items) of pain consists of words that describe the overall intensity of the total pain experience. A further 17 adjectives have been classified under the category of miscellaneous, but these are not included in the present study. The MPQ can be scored in several different ways but the most common method is the Pain Rating Index (PRI). The PRI is based on the order of each adjective in a group. In this scoring system, the first word in each group is given a value of 1, the next one is given a value of 2, and so on. The rank values of the words chosen by a patient are summed to obtain a separate score for each dimension. The maximum possible scores are: Sensory (42), affective (14), and evaluative (5). Higher scores indicate more severe pain. Several studies have documented the reliability and validity of the three dimensions of the MPQ (see Melzack and Katz, 2001) Roland and Morris Disability Questionnaire (RMDQ) A slightly modified form of the RMDQ, an instrument developed and validated by Roland and Morris (1983) for assessing the functional impact of back pain, was used to measure current physical disability. As the present study involved a heterogeneous group of chronic pain patients, references to back pain were removed and subjects were asked

3 160 M.K. Nicholas et al. / Pain 134 (2008) to relate the items to their pain, regardless of site. This modified version of the RMDQ has been reported to have good psychometric properties (Jensen et al., 1992b; Asghari and Nicholas, 2001) and in this study Alpha = SF-36 Health Survey Questionnaire The SF-36 Health Survey Questionnaire (Ware and Sherbourne, 1992) is a general health-related quality of life questionnaire. Thirty-five of the 36 items of the SF-36 measure eight scales representing generic health concepts, considered to be universal and representing basic human functions and well-being. These eight health concepts (and their Alpha values in this study) are Physical Functioning (0.89), Role Function- Physical aspects (0.80), Bodily Pain (0.70), General Health (0.81), Vitality (0.75), Social Functioning (0.84), Role Function-Emotional aspects (0.85), and Mental Health (0.82). The score for each of the eight scales ranges from 0 to 100. A higher score indicates better health on that aspect. SF-36 has been used in several studies among chronic pain patients and has been found to be reliable, valid and responsive (Kavien et al., 1998; Schlenk et al., 1998). It is one of the most widely used generic measures of health-related quality of life in the world West Haven Yale Multidimensional Pain Inventory (MPI) The MPI was developed using the cognitive behavioural model of chronic pain (Turk et al., 1983) to assess psychosocial variables relevant to the experience of chronic pain (Kerns et al., 1985). The MPI has been widely used in research and clinical practice, and has been shown to have good reliability and validity (Jacob and Kerns, 2001). For the purpose of this research only the first two sections, containing 42 items (forming eight subscales), were included. The five subscales in Section One (and their Alpha values in this study) are: pain severity (0.78), level of interference (0.92), life control (0.78), affective distress (0.64), and support from significant other (0.84). The three subscales from Section Two measure patients perceptions of how their significant other responds to their pain. They (and their Alpha values in this study) are: punishing responses (0.82), solicitous responses (0.81) and distracting responses (0.71). Each subscale is scored on a 0 6 scale with higher scores indicating higher levels of each dimension Beck Depression Inventory (BDI) Version 1 of the BDI (Beck et al., 1979) was used to measure mood. The inventory consists of 21 categories of symptoms. A total score is obtained by summing scores on each category, and it can range from 0 to 63. Higher scores reflect higher levels of depression (Beck et al., 1979). The BDI has been widely used in chronic pain populations and has wellestablished reliability and validity (Morley et al., 2002) (Alpha = 0.84 in this study) Modified Zung Self-Rating Depression Scale (M-ZSDS) A modified version of the original Zung Self-Rating Depression Scale (Zung and Durham, 1965) was used to measure depression in this study (Main and Waddell, 1984). The M-ZSDS has 18 items, each item is presented as a statement and the respondent indicates the extent to which each symptom is true for him/her. The M-ZSDS index score (based on 18 items) was derived by using 72 (rather than 80) as the denominator and then multiplying by 100, as usual. Scores range between 25 and 100, with higher scores indicating higher levels of depression. The modified ZSDS has been used with chronic pain patients with good internal consistency and validity (Taylor et al., 2005) (Alpha = 0.84 in this study) Depression Anxiety and Stress Scales (DASS) This 42-item questionnaire measures depression, anxiety and stress in adults (Lovibond and Lovibond, 1995). It has three subscales, identified with factor analysis (Brown et al., 1997): The Depression scale measures dysphoric mood, including inertia and hopelessness (e.g. I felt that I had nothing to look forward to ). Importantly, this scale has no somatic items and its validity with chronic pain patients has been reported by Taylor et al. (2005). The Anxiety scale measures symptoms of fear, panic and physical arousal (e.g., I experienced trembling (e.g. in the hands ). The Stress scale measures symptoms like irritability and tension (e.g. I found it difficult to relax ). The DASS has strong psychometric properties. All three scales have good internal reliability (Alphas in this study: stress, 0.95; depression, 0.96; and anxiety, 0.89). The scale has also been shown to discriminate reliably between clinical and non-clinical subjects (Antoney et al., 1998). The scores on each subscale range between 0 and 42, with higher scores indicating higher severity Pain Self-Efficacy Questionnaire (PSEQ) (Nicholas, 1989) is based on Bandura s (1977) concept of self-efficacy. The PSEQ is a 10-item inventory which measures both the strength and generality of a patient s beliefs about his/her ability to accomplish a range of activities despite his/her pain. Scores on the PSEQ may range from 0 to 60, with higher scores indicating stronger self-efficacy beliefs (Nicholas, 2007). The psychometric properties of this measure are sound and have been reported in a number of studies (e.g., Gibson and Strong, 1996; Asghari and Nicholas, 2001; Nicholas, 2007) and the Alpha value in this study was Pain Response Self-Statements Scale (PRSS) Pain Response Self-Statements Scales (Flor et al., 1993) assess situation-specific cognitions that either promote or hinder the individual s attempts to cope with pain. Individuals are asked to rate on a six-point scale (with 0 = almost never) and (5 = almost always) how often they think in such a way when they experience severe pain. There are two nine-item subscales, which are catastrophising and coping. Both subscales are scored on a 0 5 scale with higher scores indicating more frequent catastrophising or use of adaptive coping statements, respectively. The PRSS was originally validated by Flor et al. (1993), and has been shown to have good psychometric properties (in this study, Alpha = 0.86 for the catastrophising scale and 0.76 for the coping scale).

4 M.K. Nicholas et al. / Pain 134 (2008) Tampa Scale for Kinesiophobia (TSK) The Tampa Scale for Kinesiophobia (TSK) was designed to assess fear of movement/(re)injury in individuals with pain (Kori et al., 1990). The TSK is a 17-item questionnaire, which asks individuals to rate the extent to which they agree with statements such as pain always means that I injured my body on a 4-point rating scale, with 1 = strongly disagree and 4 = strongly agree. Four items are reverse-scored and these were included in this study. Although factor analysis has revealed four subscales (harm, fear of [re]injury, importance of exercise, and avoidance of activity), the total score has been recommended as the most valid and reliable measure (Vlaeyen et al., 2002). The TSK has been shown to have good reliability and validity (Vlaeyen et al., 1995) (Alpha = 0.83 in this study) Other health-related variables Self-reported data were also collected on a range of demographic characteristics. Of these the following were extracted for the purpose of the present study: age, sex, education, occupation, marital status, pain duration, the main site(s) of persisting pain and identified cause(s) of pain. The main pain site categories are: head/face; neck; shoulders/arms; lower back; lower back and lower limbs; lower limbs; and those with two or more main pain sites (except for combined lower back and lower limbs). These sites are based on those used by the International Association for the Study of Pain (IASP) in the Axis I in the regional classification of pain (Merskey and Bogduk, 1994). A pain diagram was used to confirm the sites Descriptive statistics The demographic characteristics of the study sample are reported. Means and standard deviations of the study variables are presented for the whole sample, and by gender, age group and individual pain sites as well as for patients who reported pain in 2 or more sites (i.e. multiple sites). As investigation of possible effects of age, gender and pain site on these variables was not the intention of this study, no comparisons are presented. To illustrate the potential uses of these data we have also presented percentile scores on all measures for those reporting low back pain as their main complaint. Table 1 Demographic characteristics of the heterogenous chronic pain patient sample Age (n = 5941) Age groups n (%) Up to 20 years 98 (1.6) (12.0) (20.9) (25.0) (17.5) (11.0) (9.2) 81 years and more 174 (2.9) Gender (n = 5941) n (%) Male 2528 (42.6) Female 3413 (57.4) Marital status (n = 4508) a n (%) Married/de facto 2886 (64.0) Never married 800 (17.7) Separated/divorced 544 (12.1) Widowed 278 (6.2) Educational status (n = 4377) a n (%) Post high school qualification 1529 (34.9) Completed secondary schooling 453 (10.3) Between 9 and 11 years of education 1678 (38.3) Less than 9 years of education 717 (16.4) Birthplace (n = 4564) a n (%) Australia 3335 (72.1) Other countries b 1229 (27.9) Current work status (n = 4438) a n (%) Full-time/Part-time work 1348 (30.4) Home duties 462 (10.3) Unemployed due to pain 1430 (32.2) Retired 804 (18.1) Other c 394 (8.9) a Not all patients reported their marital and educational status, birth place, current work status, work restriction due to pain and compensation status. b UK (363), Europe (347), Asia and Middle East (291), New Zealand (89), Africa (45), Pacific Islands (34), North America (30) and South America (30). c Re-training (98), unemployed due to reasons other than pain (93), students (99), voluntary work (103). 3. Results The 5941 chronic pain patients who provided data for the study were compared with the 183 who did not, using t-tests for age and chi-square analyses for gender. The results revealed that those who participated in the study were significantly younger than those who did not provide data ( = 48 years, SD = 16, vs. = 56 years, SD = 15, t (df = 2,5122) = 3.03, p = 0.002). No significant gender difference was found between the two groups (v = 0.08, df = 1, p = 0.77). The demographic characteristics of the sample are shown in Table 1. The mean age of the patients was 48.4 (SD: 16.2) years. The bulk of the sample (4795 or 80.7%) was in the working age group (18 65 years) and 1106 (18.6%) were 65 and over. The majority (57.4%) were female, most (64.0%) lived with a partner, almost 35% had a post high school qualification (i.e. attended university or technical college after leaving high school) and most (72%) were born in Australia. Pain-related demographic variables are presented in Table 2. Almost 30% of the patients reported their work status as full-time or part-time, 32.1% were unemployed due to their pain, and the majority (almost 80%) of those who were working at the time of the study reported that pain interfered with their ability to work. Traumatic (or sudden) onset of pain

5 162 M.K. Nicholas et al. / Pain 134 (2008) Table 2 Pain-related characteristics of normative chronic pain patient sample Pain duration (n = 5285) a Mean (SD) (months) 80.2 (111.2) Number of specialists have been visited for pain (n = 4451) a Mean (SD) 6 (3) Mode of onset of pain (n = 4635) a n (%) Accident at work 1245 (27.0) At work but not involving an accident 318 (6.9) Accident at home 166 (3.6) Car accident 589 (12.7) After surgery 546 (11.8) After illness 185 (4.0) Pain just began, no obvious reason 1028 (22.1) Other reasons 558 (12.0) Pain site (n = 4932) a n (%) Head, face, mouth 364 (7.4) Cervical region 146 (3.0) Upper shoulder and upper limbs 566 (11.5) Lower back, lower spine, sacrum 641 (13.0) Lower limbs 391 (7.9) Lower back and lower limbs 701 (14.2) 2 or more major pain sites (generalised pain) 1816 (36.8) Other b (6.2) Work restricted due to pain (n = 1543) a n (%) Yes 1233 (80) No 310 (20) This visit is related to compensation claim (n = 4467) a n (%) A workers compensation claim 1429 (32) A third party accident compensation claim 318 (7) Some other legal cases 78 (1.7) None of the above 2642 (59) a Not all patients reported their pain duration, their number of visits, the mode of onset of their pain and their pain sites. b Thoracic region (102 patients), abdomen (92 patients), pelvic region (53 patients) anal, peri-anal and genital areas (60 patients). (i.e., accident at work, accident at home or car accident) was more common than insidious onset (43.3%vs. 22.1%). The location of pain was diverse, with about one-third (36.8%) reporting pain in 2 or more main sites. This group could be considered under the heading of multiple pain sites Normative data The data are organised according to categories commonly evaluated in pain populations (see Tables 3 5). These include: pain severity, disability (physical functioning), mood (emotional functioning), general health status, and pain-related beliefs/cognitions. Mean scores (and standard deviations) are presented for each category according to age group, gender and main pain site. The proportions of patients (presented as percentiles) with different score levels on each measure for low back pain are reported in Table 6. Due to space limitations, the data on the other sites mentioned in this paper are available on the centre s website (http://www.pmri.med. usyd.edu.au/clinical/ index.php) Examples of possible uses of norms in clinical and research contexts Clinical application of normative data This section describes how the normative data were employed in the assessment of two patients (Patient A, a female, and Patient B, a male) with failed back surgery and persisting low-back pain who were assessed for treatment in this multidisciplinary pain centre. After initial medical examination, both were thought suitable for a spinal cord stimulator (SCS), but their profiles on the psychometric questionnaires were quite different. Their pretreatment scores are presented in Table 7. The clinically important differences between the two patients become clearer after comparison with the clinic s normative dataset for other chronic pain patients with pain in the same anatomical region (lower back). While both were more disabled than 70% of the normative sample (on the RMDQ) and their usual pain scores (7/10) were worse than 60 70% of the normative sample, patient A was much less depressed than most of the normative sample (better than 75%, versus Patient B who was worse than 80 85% of the normative sample). Patient A was close to the median of the normative sample on pain selfefficacy (on this measure higher scores are better), while patient B was worse than 80% of the normative sample; and Patient A s level of catastrophising was better than 80 85% of the normative sample (versus worse than 90% for patient B). These findings indicate that despite the same pain diagnosis (failed back surgery syndrome/ lumbar spinal or radicular pain after failed spinal surgery: Merskey and Bogduk, 1994) these patients were substantially different in their mood state, beliefs and perceptions in relation to their pain. This suggested that Patient B required more help with these domains than Patient A. Accordingly, different treatment plans were developed for each patient. Both were to undergo implantation of a SCS, but Patient B was also recommended for our cognitive-behavioural pain management program to address the problems identified above. As can be seen in Table 7, following the implantation of the SCS (in both patients), Patient A reported improvement on all measures and required no further treatment (scores now in percentile range: 5 15, or better than 85 95% of the normative sample). In contrast, Patient B (who reported substantial initial pain reduction with the SCS) had changed little on most measures a few weeks later and continued to take strong analgesic medication. He was then admitted

6 M.K. Nicholas et al. / Pain 134 (2008) Table 3 Mean (SD) of study variables for the total sample and for males and females Total sample Male Female Disability/interference Physical disability (RMDQ) (0 24) 12.3 (5.7) 12.7 (5.8) 11.9 (5.7) N Interference (MPI) (0 6) 4.3 (1.2) 4.4 (1.3) 4.2 (1.3) N Physical functioning (SF-36) (0 100) 39.9 (25.3) 40.8 (25.3) 39.1 (25.2) N Role functioning-physical (SF-36) (0 100) 15.1 (28.5) 14.3 (28.1) 15.6 (28.8) N Social Functioning (SF-36) (0 100) 43.0 (27.0) 41.4 (26.3) 44.2 (27.4) N Distress Affective distress (MPI) (0 6) 3.4 (1.3) 3.5 (1.2) 3.4 (1.3) N Depression (DASS) (0 42) 14.3 (11.9) 15.9 (12.2) 13.1 (11.6) N Depression (BDI) (0 63) 16.7 (10.0) 16.9 (9.9) 16.5 (10.0) N Depression (Mod-Zung) (18 72) 57.2 (13.4) 58.4 (13.4) 56.4 (13.3) N Anxiety (DASS) (0 42) 9.3 (8.7) 9.6 (8.7) 9.1 (8.6) N Stress (DASS) (0 42) 16.3 (11.2) 17.5 (11.6) 15.3 (10.9) N Vitality (SF-36) (0 100) 35.4 (20.9) 36.8 (20.7) 34.2 (21.0) N Role functioning-emotion (SF-36) (0 100) 43.5 (43.6) 41.0 (43.2) 45.5 (43.8) N Mental health (SF-36) (0 100) 55.5 (21.2) 45.2 (21.5) 56.8 (21.0) N Pain-related beliefs/cognitions Pain self-efficacy (PSEQ) (0 60) 25.5 (13.8) 24.3 (13.7) (13.8) N Catastrophising (PRSS) (0 5) 2.7 (1.2) 2.7 (1.1) 2.7 (1.2) N Active coping (PRSS) (0 5) 2.7 (1.0) 2.6 (1.0) 2.8 (1.0) N Fear avoidance (TSK) (17 68) 41.2 (9.4) 43.1 (9.2) 39.7 (9.4) N Pain intensity Pain intensity (MPI) (0 6) 4.2 (1.1) 4.2 (1.2) 4.2 (1.1) N Bodily pain (SF-36) (0 100) 26.0 (18.4) 25.8 (18.1) 26.1 (18.5) N Highest pain intensity-last week (NRS) (0 10) 8.3 (1.7) 8.2 (1.6) 8.3 (1.7) N Lowest pain intensity-last week (NRS) (0 10) 4.0 (2.5) 3.9 (2.4) 4.1 (2.6) N Average pain intensity-last week ((NRS) (0 10) 6.4 (2.1) 6.2 (2.0) 6.5 (2.1) N Pain intensity-sensory (MPQ) (0 42) 16.4 (7.8) 16.7 (7.9) 16.3 (7.7) N Pain intensity-affective (MPQ) (0 14) 3.9 (3.1) 4.0 (3.1) 3.9 (3.0) N Pain intensity-total (MPQ) (0 78) 29.7 (13.1) 30.2 (13.3) 29.4 (12.7) N to our 3-week cognitive-behavioural pain management program (for more details see Molloy et al., 2006). As can be seen in Table 7, following the program Patient B largely caught up with Patient A on most variables (scores now in percentile range: 5 35, or better than 65 95% of the normative sample on

7 Table 4 Mean and standard deviations of study variables by age groups Age group >81 Disability/interference Physical disability (RMDQ) (0 24) 11.5 (6.1) 11.9 (5.9) 12.5 (5.8) 12.6 (5.7) 12.1 (5.6) 11.7 (5.7) 12.4 (5.9) 13.2 (5.5) N Interference (MPI) (0 6) 4.1 (1.2) 4.4 (1.2) 4.6 (1.1) 4.5 (1.1) 4.3 (1.2) 3.9 (1.4) 3.9 (1.4) 3.8 (1.3) N Physical functioning (SF-36) (0 100) 43.5 (25.7) 43.2 (25.7) 40.9 (24.9) 39.5 (25.2) 41.3 (24.4) 40.3 (26.2) 31.4 (24.5) 30.1 (25.5) N Role functioning-physical (SF-36) (0 100) 15.3 (29.3) 15.3 (28.8) 12.7 (25.9) 14.3 (28.0) 15.4 (27.9) 19.1 (32.3) 16.0 (29.8) 19.5 (33.7) N Social functioning (SF-36) (0 100) 38.7 (25.9) 40.3 (26.5) 38.1 (24.8) 40.8 (26.1) 46.5 (26.1) 51.1 (28.3) 49.8 (29.8) 47.6 (29.8) N Distress Affective distress (MPI) (0 6) 3.5 (1.2) 3.7 (1.6) 3.7 (1.2) 3.5 (1.2) 3.3 (1.3) 3.1 (1.3) 3.0 (1.3) 2.6 (1.3) N Depression (DASS) (0 42) 13.9 (11.4) 16.1 (12.8) 17.1 (12.5) 15.6 (11.9) 12.6 (11.3) 10.8 (10.8) 10.6 (10.6) 7.8 (8.9) N Depression (BDI) (0 63) 14.2 (8.84) 19.1 (10.9) 18.8 (9.9) 17.4 (9.8) 16.2 (10.1) 13.1 (8.9) 12.1 (7.7) 11.4(5.1) N Depression (Mod-Zung) (0 72) 58.1 (13.3) 59.2 (10.1) 60.4 (13.3) 58.6 (13.4) 54.5 (13.2) 52.2 (11.9) 50.7 (11.3) 51.3 (9.1) N Anxiety (DASS) (0 42) 10.0 (8.9) 10.8 (9.4) 10.9 (9.5) 9.3 (8.5) 8.9 (8.3) 6.6 (7.0) 7.5 (7.3) 6.3 (5.4) N Stress (DASS) (0 42) 16.1 (11.4) 18.6 (11.4) 19.0 (11.4) 17.3 (10.9) 15.5 (11.1) 12.2 (10.1) 11.3 (9.7) 8.7 (8.1) N Vitality (SF-36) (0 100) 31.6 (20.5) 33.6 (21.4) 32.6 (20.8) 33.9 (20.6) 37.7 (20.3) 40.1 (20.8) 38.8 (21.6) 38.7 (19.9) N Role functioning-emotional (SF-36) (0 100) 39.2 (43.1) 41.0 (43.2) 37.7 (41.9) 40.9 (43.4) 48.7 (43.9) 53.5 (43.8) 46.8 (44.1) 54.1 (44.6) N Mental health (Sf-36) (0 100) 52.9 (20.6) 51.0 (21.8) 50.5 (21.4) 53.9 (20.4) 58.5 (20.8) 61.7 (19.7) 63.0 (20.0) 67.2 (19.2) N Pain-related beliefs/cognitions Pain self-efficacy (PSEQ) (0 60) 24.3 (13.6) 24.3 (13.7) 23.4 (13.2) 24.7 (13.5) 27.4 (13.5) 28.5 (14.4) 27.0 (14.9) 26.7 (14.8) N Catastrophising (PRSS) (0 5) 2.8 (1.1) 2.9 (1.1) 2.9 (1.1) 2.8 (1.1) 2.6 (1.4) 2.4 (1.1) 2.33 (1.24) 2.4 (1.3) N Active coping (PRSS) (0 5) 2.4 (1.0) 2.5 (1.0) 2.6 (0.9) 2.8 (9.0) 3.0 (0.9) 2.8 (1.0) 2.7 (1.1) 2.7 (1.3) N Fear avoidance (TSK) (17 68) 39.1 (11.2) 42.3 (9.9) 42.1 (9.2) 41.8 (9.4) 40.4 (9.2) 39.8 (9.1) 40.2 (9.7) 39.1 (7.4) N Pain intensity Pain severity (MPI) (0 6) 4.1 (1.1) 4.2 (1.3) 4.3 (1.1) 4.2 (1.1) 4.1 (1.1) 4.1 (1.1) 4.1 (1.2) 4.1 (1.2) N Bodily pain (SF-36) (0 100) 24.4 (21.8) 26.4 (18.5) 25.1 (17.0) 25.1 (18.2) 26.8 (17.8) 28.5 (18.9) 26.2 (18.8) 26.1 (19.7) 164 M.K. Nicholas et al. / Pain 134 (2008)

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