Rehabilitation Psychology 2007, Vol. 52, No. 4, 443 450 Copyright 2007 by the American Psychological Association 0090-5550/07/$12.00 DOI: 10.1037/0090-5550.52.4.443 The Convergence and Predictive Validity of the Multidimensional Pain Inventory and the Personality Assessment Inventory Among Individuals with Chronic Pain Christopher J. Hopwood and Suzannah K. Creech Texas A&M University Timothy S. Clark Baylor University Medical Center Mary W. Meagher and Leslie C. Morey Texas A&M University Objective: To explore the convergence, redundancy, and validity of the Multidimensional Pain Inventory (MPI) and the Personality Assessment Inventory (PAI) in a chronic pain treatment setting. Participants: Data from intake (N 235) and follow-up (N 187) for individuals with an average of 9 years of chronic pain who participated in a 20-day integrative treatment program were analyzed. Outcome Measures: Oswestry Disability Index, Beck Depression and Anxiety inventories, Rand Short-Form Health Survey, and clinician-rated ability to stand and carry. Results: Conjoint factor analyses suggested that the MPI and PAI combine to tap five orthogonal factors: Negative Affect, Support, Externalizing, Physical Dysfunction, and Impulsivity. MPI and PAI scales significantly related to various aspects of client functioning, although these scales were more limited in predicting clinician-rated markers and change during treatment. Conclusion: Results support the combined use of the MPI and PAI to understand patient heterogeneity and predict treatment outcome in chronic pain samples. Keywords: Multidimensional Pain Inventory (MPI), Personality Assessment Inventory (PAI), chronic pain Christopher J. Hopwood, Suzannah K. Creech, Mary W. Meagher, and Leslie C. Morey, Department of Psychology, Texas A&M University, College Station, Texas; Timothy S. Clark, Department of Anesthesiology and Pain Management, Baylor University Medical Center, Dallas, Texas. Correspondence concerning this article should be addressed to Christopher J. Hopwood, M.S., Ruth H. Kirchenstein National Predoctoral Research Fellow, Department of Psychology, Texas A&M University, College Station, TX 77843-4235. E-mail: chopwood@tamu.edu The Multidimensional Pain Inventory (MPI; Kerns, Turk, & Rudy, 1985) is among the most commonly used instruments in pain settings (Piotrowski, 1998). MPI scales 1 have demonstrated sensitivity to change during treatment for a variety of pain-related disorders, including carpal tunnel syndrome (Breuer et al., 2006), arthritis (Thieme, Gromnica-Ihle, & Flor, 2003), and chronic pain (Wittink, Turk, Carr, Sukiennik, & Rogers, 2004). The MPI has also demonstrated validity in predicting treatment outcome (Michaelson, Sjölander, & Johansson, 2004), use of sick leave (Marhold, Linton, & Melin, 2002), pain-related behavior (Turk & Okifuji, 1997), pain acceptance (McCracken, 2005), future pain following automobile accidents (Olsson, Bunketorp, Carlsson, & Styf, 2002), and differentiating back from abdominal pain (Townsend et al., 2005). Several studies have shown correlations between MPI scales and those of other instruments, but they also have shown that other instruments that are commonly used in pain settings comprise scales that capture variance not measured by the MPI. For example, Mikail, DeBreuil, & D Eon (1993) factor analyzed the scale scores of a theoretically comprehensive battery of self-report measures (Williams, 1988) in a chronic pain sample. MPI scales were represented on three of five oblique factors. However, non-mpi scales were significantly represented on four of five factors, suggesting that other measures augmented the MPI, providing relevant information for all but one identified factor (Support). Malmgren- Olsson & Bergdahl (2006) showed that individuals in pain treatment settings can be differentiated from nonclinical controls on several psychological dimensions measured by the Temperament and Character Inventory (Cloninger, Pryzbeck, Svrakic, & Wetzel, 1994) that are not assessed by the MPI and suggested the incremental importance of such instruments for diagnosis and treatment planning in pain settings. Studies such as these suggest that the overlap of the MPI with other instruments and the incremental validity of other measures used in concert with the MPI are important questions. One instrument that is commonly used in pain settings (Piotrowski, 1998) and has the potential to augment the MPI in the prediction of treatment outcome or pain-related behavior is the Personality Assessment Inventory (PAI; Morey, 1991, 2007). 1 There is also a good deal of empirical support for the classification of patients into clusters based on MPI data (Rudy, Turk, Zaki, & Curtin, 1989), although the current report focuses only on the use of MPI scale scores, which, in the current sample, proved to be more valid than the cluster system in predicting a variety of concurrent and treatment-related variables (Hopwood, Creech, Clark, Meagher, & Morey, 2007). 443
444 HOPWOOD, CREECH, CLARK, MEAGHER, AND MOREY Multiscale personality/psychopathology measures such as the PAI can help practitioners target psychological factors affecting the pain experience and may have considerable utility in assessment and treatment planning among individuals with chronic pain (Bradley, Haile, & Jaworski, 1992; Malmgren- Olsson & Bergdahl, 2006). While some MPI and PAI scales conceptually overlap (e.g., MPI Support and PAI Nonsupport), others appear to tap constructs not represented on the other instrument (e.g., MPI Pain Severity, PAI Drug Problems), suggesting the possible utility of combining them in pain assessment. However, little is known about the utility of the PAI in pain populations or the extent to which it adds to the MPI for predicting variables relevant to chronic pain treatment. The purpose of this study was to examine the convergence or redundancy of the MPI and PAI in a sample of individuals referred for chronic pain treatment and to assess the validity of data drawn from these instruments to predict baseline functioning and treatment outcome. Method Participants Participants were 235 individuals representing consecutive intakes sampled from the files of a hospital-based, integrative outpatient treatment program. To be eligible for this program, individuals must have been referred by physicians for treatment-resistant chronic pain. Participants completed a packet of questionnaires including the MPI, PAI, and other measures (discussed later) as part of eligibility consideration. The average duration of pain among clients in the clinic in which data were sampled was approximately 9 years. The type of pain experienced by participants in this study varied; the most common primary diagnoses involved lumbar spine with radicular symptoms (46.4%), cervical pain (16.1%), or fibromyalgia (6.0%). Participants were funded primarily through commercial carriers (53.0%), secondarily through Medicare (20.8%), and least frequently through workers compensation (18.0%) or other means (8.2%). The average age of sampled individuals was 48.73 years (SD 10.92). The sample was 73.3% female and 26.7% male; 85.5% of the participants were White/European American, 10.1% were African American, 3.7% were Latino/Hispanic, and the rest did not report ethnicity. All participants consented to participate in this research, which was approved by the Office of Research Subject Protection of the Baylor Research Institute and the Institutional Review Board at Texas A&M University. There were 235 individuals who began the treatment and had complete baseline data (including MPI, PAI, and other functioning measures), and 187 of these completed treatment and were used in all analyses (Hopwood, Creech, Clark, Meagher, & Morey, in press). No significant differences were found on demographic variables between participants who did and those who did not complete the treatment. The program is a Commission on Accreditation for Rehabilitation Facilities (CARF) accredited 20-day treatment program that includes physical therapy, aquatics therapy, cognitive behavioral group psychotherapy, occupational therapy, individual biofeedback and counseling, and vocational services as needed. Studied Instruments Multidimensional Pain Inventory (MPI; Kerns et al., 1985). The MPI includes nine scales: Pain Severity, Interference, Life Control, Affective Distress, Support, Punishing Responses, Solicitous Responses, Distracting Responses, and General Activity Level. The median internal consistency for these scales was.77, and the median 2-week test retest reliability was.84 in initial validation studies (Kerns et al., 1985). Personality Assessment Inventory (PAI; Morey, 1991, 2007). The PAI is a multiscale self-report instrument with 344 items that are answered on a 4-point scale and that load onto 22 nonoverlapping full scales under four domains: validity (4 scales), clinical (11), treatment consideration (5), and interpersonal style (2). The median internal consistency across all PAI full scales in the clinical normative sample was.86, and the median 1-month test retest reliability in a community sample was.85 (Morey, 1991). A previous study based on data from roughly half of the sample used in the current report, as well as roughly 400 individuals from the same facility whose data were not included in the current analyses, reported a median internal consistency across PAI full scales of.80 (Karlin et al., 2005). Validating Instruments Several scores were used to measure both baseline functioning and treatment outcome. Outcome measures were obtained shortly before discharge. Beck Depression Inventory (2nd ed., BDI II; Beck, Steer, & Brown, 1996). The BDI II is a commonly used self-report measure of depression that was administered at baseline and following treatment. It has 21 items rated on a 4-point scale; previous research has attested to the reliability and validity of the instrument (Beck et al., 1996). Beck Anxiety Inventory (BAI; Beck & Steer, 1990). The BAI is a commonly used self-report measure of anxiety that was administered at baseline and following treatment. It has 21 items rated on a 4-point scale; previous research has attested to the reliability and validity of the instrument (Beck & Steer, 1990). Rand 12-Item Short-Form Health Survey (SF 12; Ware, Snow, Kosinski, & Gandek, 1993). The Physical Health Summary Measure (abbreviated PCS for physical component summary) and Mental Health Summary Measure (abbreviated MCS for mental component summary) scores from the Rand questionnaire were used to represent the extent of physical health and mental health of participants, such that higher scores indicated better health. These variables were assessed at intake and following treatment. Test retest (2-week) reliability of the SF 12 reported in the validation study was.89. This instrument has also demonstrated significant relationships to a variety of validating criteria in a pain sample (Ware et al, 1993). Oswestry Disability Questionnaire (Fairbank, Couper, Davies, & O Brien, 1980). The Oswestry Questionnaire is a measure of physical disability with a 1-day test retest reliability of.99 in the original validation study and internal consistency of.87 (Kopec et al., 1996). High scores on the Oswestry Questionnaire indicate greater levels of disability. It has been validated by a variety of methods, including correlations with concur-
MPI AND PAI CONVERGENCE AND VALIDITY 445 rently administered pain scales, differentiation of clinical groups, and sensitivity to clinical change, as reviewed by Fairbank and Pynsent (2000). Clinician ratings. Licensed physical therapists tested participants on a variety of tasks prior to, during, and following treatment to track progress. Data from two tasks were used in the current report. Standing refers to the number of times that a participant could stand in 1 min. Carrying refers to the amount of weight (in pounds) that a participant could carry in a crate for 150 ft. Procedures The content overlap of the MPI and PAI scales was examined in two stages in which all available baseline data (N 235) were used. First, scale-level intercorrelations were computed. Next, factors were extracted from a conjoint analysis of MPI and PAI scales to examine their joint structure. Principal components analysis was used, with parallel analysis (O Connor, 2000) used for factor extraction determinations (Zwick & Velicer, 1986). Factors were orthogonally rotated to enhance the interpretability of results. To assess concurrent validity, we computed correlations between the MPI/PAI scale scores and the validating variables at intake, using all data for program graduates (N 187). To assess the usefulness of MPI/PAI data in predicting change, we computed variables to reflect the proportion of baseline scores represented in posttreatment scores (i.e., [Time 1 Time 2]/Time 1). These were scaled so that higher proportions meant greater change in the positive direction. The MPI and PAI scales were then correlated with these proportions. This method was used rather than residualized change scores because the conceptual and empirical overlap between MPI/PAI scales and validating measures suggested that removing baseline variance associated with the latter would artificially limit the potential validity of the former to predict change. Results To investigate the relation between the MPI and the PAI, we computed correlations between scales from each instrument. Results are presented in Table 1. These data indicated that a number of MPI scales (e.g., Affective Distress, Life Control, and Punishing Responses) overlapped considerably with the PAI. However, other scales have more limited relations. As might be suspected, MPI Table 1 Multidimensional Pain Inventory (MPI) Personality Assessment Inventory (PAI) Scale Intercorrelations in a Chronic Pain Sample PAI scale Pain Severity Interference Life Control Affective Distress Support MPI scale Punishing Responses Solicitous Responses Distracting Responses General Activity Level Validity domain Inconsistency.10.02.13.23.00.03.11.07.03 Infrequency.08.09.09.11.07.03.00.11.08 Negative Impression Management.15.06.33.23.20.38.05.02.13 Positive Impression Management.01.15.24.28.12.31.11.03.00 Clinical domain Somatic Complaints.18.23.27.21.09.34.14.11.12 Anxiety.16.08.42.44.11.40.04.09.04 Anxiety-Related Disorders.12.10.35.35.17.35.01.10.04 Depression.11.21.45.36.12.32.06.13.16 Mania.15.01.25.29.05.21.06.19.12 Paranoia.07.01.35.30.29.41.17.04.09 Schizophrenia.10.08.40.34.22.41.08.02.17 Borderline Features.10.10.41.38.20.38.09.03.03 Antisocial Features.05.04.19.23.19.22.16.01.11 Alcohol Problems.03.07.09.05.08.08.05.02.05 Drug Problems.08.03.11.18.01.13.01.02.01 Treatment consideration domain Aggression.14.02.31.31.10.19.11.16.12 Suicidal Ideation.01.15.28.24.18.21.12.00.10 Stress.14.13.32.26.27.36.07.02.21 Nonsupport.08.08.26.18.48.43.37.18.22 Treatment Rejection.01.22.32.24.26.40.10.03.09 Interpersonal style domain Dominance.06.06.06.08.20.17.26.16.18 Warmth.11.03.14.15.25.18.25.08.17 Note. N 235. Bold coefficients are significant (p.001).
446 HOPWOOD, CREECH, CLARK, MEAGHER, AND MOREY Support and Solicitous Responses overlapped significantly with the interpersonally oriented PAI scales but not with those measuring psychiatric symptoms or emotional distress. Other MPI scales, such as Pain Severity, Interference, Distracting Responses, and General Activity Level, appear to be independent of PAI scales. PAI scales measuring externalizing behaviors (e.g., Antisocial Behavior, Alcohol Problems, Drug Problems, and Aggression) appear to be mostly unrelated to the MPI. To examine the structure of these instruments in combination, we conducted a conjoint factor analysis of the MPI (9 scales) and PAI (22 scales). This analysis yielded five factors that accounted for 59.23% of the variance in MPI and PAI scales. The relation of scales to rotated factor scores is represented in Table 2. On the basis of these coefficients, we named the factors Negative Affect, Support, Externalizing, Physical Dysfunction, and Impulsivity. The rotated factor pattern coefficients clarify the unique contributions of each instrument. Three factors, Negative Affect, Externalizing, and Impulsivity, captured variance primarily from the PAI, whereas physical dysfunction and support were mainly represented by the MPI scales. To assess concurrent validity, we correlated MPI and PAI scales with validating measures of mental (Table 3) and physical (Table 4) health. Several MPI and PAI scales related to all three measures of mental health, which is not surprising given the conceptual overlap of these indicators and their shared self-report methodology. The MPI Pain Severity, Interference, and General Activity Level scales and the PAI Somatic Complaints and Depression scales were related to both self-report physical health variables, r.21, p.001, as shown in Table 4. However, these correlations were more modest than was the case with mental health related variables, as might be expected given the constructs they represent. Only four MPI/PAI scales correlated more than.20 with the behavioral indicators (MPI General Activity Level and PAI Somatic Complaints with standing, PAI Mania and PAI Aggression with carrying), perhaps indicating that both conceptual dissimilarity and method effects attenuated these correlations. Individuals in the current sample improved significantly, on average, for every outcome variable, as shown in Table 5. As might be expected given the nature of the treatment, improve- Table 2 Correlations of Multidimensional Pain Inventory (MPI) Personality Assessment Inventory (PAI) Scale Scores With Conjoint Factor Scores Scale Negative Affect Support Externalizing Physical Dysfunction Impulsivity MPI Pain Severity.022.231.165.641.083 Interference.146.107.025.578.230 Life Control.389.026.121.636.179 Affective Distress.370.053.159.511.228 Support.149.859.025.018.108 Punishing Responses.356.506.148.286.022 Solicitous Responses.036.869.023.205.024 Distracting Responses.133.764.142.004.020 General Activity Level.143.247.248.560.064 PAI Inconsistency.051.097.059.127.696 Infrequency.047.015.041.063.565 Negative Impression Management.722.028.229.195.088 Positive Impression Management.730.073.212.034.086 Somatic Complaints.642.118.052.291.192 Anxiety.844.023.116.190.158 Anxiety-Related Disorders.819.011.161.136.107 Depression.842.069.097.217.009 Mania.313.011.775.130.036 Paranoia.606.259.335.119.310 Schizophrenia.824.089.097.151.138 Borderline Features.842.083.271.084.205 Antisocial Features.322.191.650.064.276 Alcohol Problems.103.005.144.191.615 Drug Problems.111.025.219.085.712 Aggression.437.104.630.019.232 Suicidal Ideation.609.066.054.050.079 Stress.593.154.184.276.090 Nonsupport.573.478.027.021.123 Treatment Rejection.728.116.067.115.026 Dominance.382.234.582.082.213 Warmth.382.251.147.230.212 Note. N 235. The largest factor pattern coefficient for each variable is bold.
MPI AND PAI CONVERGENCE AND VALIDITY 447 Table 3 Correlations of Pretreatment Multidimensional Pain Inventory (MPI) Personality Assessment Inventory (PAI) Full-Scale Scores With Scores on Indicators of Mental Health at Baseline and Proportion of Baseline Symptoms Present at Outcome Scale BDI-II BAI MCS Intake Change Intake Change Intake Change MPI Pain Severity.17.03.21.03.21.11 Interference.27.02.14.09.32.18 Life Control.40.07.26.01.51.22 Affective Distress.38.08.29.02.41.20 Support.18.04.12.01.12.05 Punishing Responses.40.05.30.09.30.23 Solicitous Responses.01.08.07.06.03.04 Distracting Responses.12.02.12.02.06.01 General Activity Level.18.16.07.13.27.03 PAI Inconsistency.12.15.11.10.14.07 Infrequency.09.06.09.08.02.05 Negative Image Management.51.04.44.03.38.26 Positive Image Management.40.01.36.07.34.10 Somatic Complaints.42.02.39.05.34.22 Anxiety.64.09.57.03.48.22 Anxiety-Related Disorders.54.06.42.10.43.18 Depression.71.01.47.07.53.29 Mania.13.02.18.07.13.02 Paranoia.43.09.27.08.33.25 Schizophrenia.58.01.39.09.42.21 Borderline Features.58.06.41.12.46.21 Antisocial Features.25.10.18.05.21.05 Alcohol Problems.08.15.07.05.09.01 Drug Problems.16.18.13.11.10.05 Aggression.27.03.24.11.20.07 Suicidal Ideation.49.04.29.01.31.15 Stress.43.13.31.16.36.11 Nonsupport.37.04.20.05.26.14 Treatment Rejection.52.01.36.18.39.25 Dominance.21.04.14.10.09.03 Warmth.32.11.17.12.20.01 Note. N 187 (all sample graduates). Validating variables: BDI-II Beck Depression Inventory (2nd ed.); BAI Beck Anxiety Inventory; MCS Rand 12-Item Short-Form Health Survey Mental component summary. For validating variables, high scores on the BDI-II and BAI indicate greater dysfunction, whereas high scores on the MCS indicate better functioning. Change proportions are keyed so that greater values more change in the positive direction. Significant correlations (p.001) are bold. ment was greater on physical than mental outcomes. Within the physical domain, individuals demonstrated relatively more improvement on the clinician-rated variables, in particular on carrying. To assess predictive validity, we correlated MPI and PAI scales with the proportion of change in outcome scores relative to baseline. For all variables except mental health, these results (shown in Tables 3 and 4) were quite modest, with only one coefficient exceeding.20 (PAI Mania with standing). Interestingly, several MPI and PAI variables predicted improvement in global mental health. Discussion The current article examined the content overlap and predictive validity of two multiscale self-report instruments that are commonly used in pain settings. Results suggest some overlap exists between the MPI and the PAI, but they also suggest that each offers some unique elements that may be useful in understanding individuals experiencing chronic pain, as might be expected because of their divergent theoretical underpinnings. In particular, it appears that the PAI tapped Negative Affect, Externalizing, and Impulsivity factors that were not captured by the MPI, but that nevertheless relate to pain-related functionality. Conversely, the MPI captured Support and Physical Dysfunction, factors that were largely independent of most PAI variables. The PAI-related factors were fairly similar to those observed in previous factor analyses of the PAI (Deisinger, 1995; Morey, 1991), including one using data drawn from a chronic pain sample (Karlin et al., 2005). Factor analytic studies with the MPI (e.g., Deisinger, Cassisi, Lofland, Cole, & Breuhl, 2001; Kerns et al., 1985) have tended to factor item, rather than scale scores; use Kaiser s rule or the scree test for factor extraction; and use oblique factor rotation (Mikail et al., 1993), so that previous results might not be expected to correspond to those in the current analyses.
448 HOPWOOD, CREECH, CLARK, MEAGHER, AND MOREY Table 4 Correlations of Pretreatment Multidimensional Pain Inventory (MPI) Personality Assessment Inventory (PAI) Full-Scale Scores With Scores on Self-Reported and Clinician-Rated Indicators of Physical Health at Baseline and Proportion of Baseline Symptoms Present at Outcome Scale PCS ODI Standing Carrying Intake Change Intake Change Intake Change Intake Change MPI Pain Severity.28.17.31.18.09.16.02.10 Interference.29.04.35.11.18.09.12.04 Life Control.14.13.19.13.02.10.05.13 Affective Distress.07.10.12.17.05.07.05.08 Support.09.15.05.12.06.02.13.10 Punishing Responses.04.05.11.15.08.02.04.07 Solicitous Responses.14.16.08.02.06.02.01.00 Distracting Responses.12.17.08.08.07.03.02.00 General Activity Level.17.06.36.01.27.03.06.09 PAI Inconsistency.00.03.01.05.07.01.08.12 Infrequency.02.03.10.00.02.03.00.08 Negative Image Management.12.09.20.02.09.11.03.07 Positive Image Management.18.10.13.02.01.11.08.08 Somatic Complaints.27.08.27.02.24.15.11.07 Anxiety.21.12.25.05.08.09.02.07 Anxiety-Related Disorders.12.07.20.07.06.16.04.01 Depression.22.09.31.06.13.07.03.09 Mania.03.03.03.02.01.24.24.14 Paranoia.07.06.04.08.03.04.13.06 Schizophrenia.14.11.23.03.10.11.07.06 Borderline Features.14.08.15.02.01.10.10.06 Antisocial Features.02.01.02.17.02.12.15.10 Alcohol Problems.09.06.00.07.10.01.01.01 Drug Problems.02.02.02.02.00.08.05.01 Aggression.09.14.03.05.01.12.23.14 Suicidal Ideation.10.03.07.04.03.08.06.06 Stress.12.00.27.14.09.14.09.02 Nonsupport.03.02.17.00.03.05.04.01 Treatment Rejection.15.13.09.02.01.12.07.08 Dominance.10.01.15.10.05.06.18.18 Warmth.14.02.10.06.00.04.08.08 Note. N 187 (all sample graduates). Validating variables: PCS Rand 12-Item Short-Form Health Survey Physical Component Summary; ODI Oswestry Disability Index; Standing and Carrying clinician ratings. For validating variables, high scores on the ODI indicate greater dysfunction, whereas high scores on PCS, Standing, and Carrying indicate better functioning. Change proportions are keyed so that greater values more change in the positive direction. Significant correlations (p.001) are bold. Participant functioning was assessed with several measures tapping mental and physical health domains and using self-report and clinician-rating methods. As expected, there was strong convergence between MPI/PAI scales and the mental health validating variables. Correlations between MPI/PAI indicators and physical health scales were more modest, although these correlations were higher, as expected, for indicators with shared (self-report) method variance. Whereas modest relations are not surprising with the PAI (only the Somatic Complaints scale was designed to directly measure aspects of physical health), it is somewhat surprising that the MPI scales were mostly unrelated to the behavioral markers. The patterns of relations with the two behavioral markers are also interesting. Indicators of physical dysfunction (MPI General Activity Level, PAI Somatic Complaints) were related to standing, whereas indicators of positive affect and assertiveness (PAI Mania and Aggression) were related to carrying. This perhaps indicates the importance of positive affect in activity (Cohen & Pressman, 2006), as opposed to more generalized dysfunction, which may be more directly related to physical difficulties. Correlations between MPI/PAI scales and change were quite modest, with the exception of change on the Rand MCS. However, both the MPI and PAI are commonly used, themselves, as outcome measures. Furthermore, it is very likely that clinicians used scales on the MPI and PAI to target treatment, perhaps limiting the opportunity for these markers to predict change in other domains. In any case, current data suggest that baseline MPI and PAI data are more informative regarding mental than physical health and that they are limited in their ability to predict treatment outcome in a pain setting. Overall, the nonredundancy of the MPI and PAI, as well as their significant overlap with constructs relevant to chronic pain and its treatment, indicates the value of integrating both instruments in the
MPI AND PAI CONVERGENCE AND VALIDITY 449 Table 5 Descriptive Statistics for Validating Variables at Baseline, Outcome, and Proportion of Change Scale Intake Outcome Proportion of Change Paired samples M SD M SD t M SD Mental health BDI 23.10 10.51 11.73 9.68 17.95 0.48 0.37 BAI 17.39 10.04 11.47 9.03 10.18 0.27 0.59 MCS 32.00 7.20 42.43 8.52 19.44 0.37 0.33 Physical health PCS 23.39 5.13 32.70 9.54 16.03 0.43 0.44 Disability 25.12 6.28 19.80 7.19 12.33 0.20 0.26 Standing 12.77 5.48 23.06 10.34 22.55 0.93 0.70 Carrying 4.40 4.14 12.21 5.92 29.72 2.52 1.72 Note. N 187 (all sample graduates). BDI Beck Depression Inventory; BAI Beck Anxiety Inventory; MCS Rand 12-Item Short-Form Health Survey (SF-12) Mental Component Summary; PCS Rand SF 12 Physical Component Summary; Disability Oswestry Disability Index; Standing and Carrying clinician ratings. All t tests, p.001. assessment of individuals experiencing chronic pain. However, the current study was limited by sample size considerations. 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