Seediscussions,stats,andauthorprofilesforthispublicationat:https://www.researchgate.net/publication/303552484

Cognitivebehavioraltherapyforchronicpainiseffective,butforwhom?

ArticleinPain·May2016

ImpactFactor:5.21·DOI:10.1097/j.pain.0000000000000626

READS

136

10authors,including:

JoanEBroderick

UniversityofSouthernCalifornia

91PUBLICATIONS4,114CITATIONS

SEEPROFILE

FrancisJKeefe

DukeUniversityMedicalCenter

442PUBLICATIONS23,062CITATIONS

SEEPROFILE

AlanTKaell

StonyBrookUniversity

55PUBLICATIONS1,777CITATIONS

SEEPROFILE

DavidSCaldwell

DukeUniversityMedicalCenter

28PUBLICATIONS2,142CITATIONS

SEEPROFILE

Allin-textreferencesunderlinedinbluearelinkedtopublicationsonResearchGate,

lettingyouaccessandreadthemimmediately.

Availablefrom:JoanEBroderick

Retrievedon:11July2016

 

 

Cognitive behavioral therapy for chronic pain is effective, but for whom?

Joan E. Brodericka,b*, Francis J. Keefec,d, Stefan Schneidera,b, Doerte U. Junghaenela,b, Patricia Bruckenthalf, Joseph E. Schwartze, Alan T. Kaellg, David S. Caldwelld,, Daphne McKeec, Elaine Gouldh

aCenter for Self-Report Science bCenter for Economic & Social Research

University of Southern California

cDepartment of Psychiatry and Behavioral Sciences

Duke University Medical Center dDepartment of Medicine

Duke University Medical Center

eDepartment of Psychiatry and Behavioral Science

fSchool of Nursing gDepartment of Medicine, Rheumatology Division

hDepartment of Radiology

Stony Brook University

*Current contact information for corresponding author:

Joan E. Broderick, Ph.D.

Dornsife Center for Self-Report Science

Center for Economic & Social Research

University of Southern California

Los Angeles, California 90089-3332

Email: Joan.Broderick@usc.edu

Grant support: NIH/NIAMS R01 AR054626

ClinicalTrials.gov identifier: NCT00636454

Total number of pages: 33

Total number of tables: 4

Keywords: treatment effectiveness, pain coping skills, osteoarthritis, chronic pain,

clinical nursing research

 

 

2  

Abstract

Moderator analyses are reported for post-treatment outcomes in a large,

randomized, controlled effectiveness trial for chronic pain for hip and knee osteoarthritis (OA)

(N=256). Pain Coping Skills Training, a form of cognitive behavioral therapy, was compared

to usual care. Treatment was delivered by nurse practitioners in patients’ community doctors’

offices. Consistent with meta-analyses of pain CBT efficacy, treatment effects in this trial

were significant for several primary and secondary outcomes, but tended to be small. This

study was designed to examine differential response to treatment for patient subgroups to

guide clinical decision making for treatment. Based on existing literature, demographic (age,

sex, race/ethnicity, education) and clinical variables (disease severity, BMI, patient treatment

expectations, depression, and patient pain coping style) were specified a priori as potential

moderators. Trial outcome variables (N=15) included pain, fatigue, self-efficacy, quality of life,

catastrophizing, and use of pain medication. Results yielded five significant moderators for

outcomes at post-treatment: pain coping style, patient expectation for treatment response,

radiographically-assessed disease severity, age, and education. Thus, sex, race/ethnicity,

BMI, and depression at baseline were not associated with level of treatment response. In

contrast, patients with interpersonal problems associated with pain coping did not benefit

much from the treatment. Although most patients projected positive expectations for the

treatment prior to randomization, only those with moderate to high expectations benefited.

Patients with moderate to high OA disease severity showed stronger treatment effects.

Finally, the oldest and most educated patients showed strong treatment effects, while

younger and less educated did not.

 

 

3  

Introduction

During the last 30 years, well over 100 treatment studies for managing pain have

been conducted using cognitive behavioral therapy (CBT) and disease self-management

interventions [73]. In general, meta-analyses report small to moderate beneficial effects for

pain, disability, mood, pain catastrophizing, and self-efficacy immediately after treatment

when compared to usual care [19; 73].

Despite the large number of CBT clinical trials, very few reports of predictors

(moderators) of the treatment effects have been published. This is unfortunate, since

investigation of moderators can identify patient subgroups that exhibit different treatment

responses. Turk argued that the field needs to advance beyond conceptualizing chronic pain

as homogeneous and applying the same interventions to everyone [66]. He cited many

papers that observed important psychological and biological heterogeneity among patients

with persistent pain. Jamison conducted some of the early work on psychosocial distinctions,

identifying three patient clusters generated by the Multidimensional Pain Inventory [28].

These data suggested the possibility of tailoring treatment to address the specific patient

features to yield improved outcomes. In contrast, others have argued that multimodal

therapies deliver generic benefits irrespective of patients’ individual psychosocial profiles [14;

24].

The importance of finding predictors of treatment response in patients with chronic

pain is widely recognized. Recently, the Nijmegen Decision Tool was published to guide

recommendations for surgical or non-surgical interventions for chronic back pain [69]. At pre-

treatment, high levels of disability, unemployment, and involvement with litigation or

compensation claims were found to bode poorly for surgical outcomes. In a 2009 review,

older age and longer duration of pain as well as somatization, depression, anxiety, and poor

coping were pre-treatment factors associated with poorer outcomes for back surgery and

 

 

4  

implanted spinal cord stimulation [13]. Regarding CBT treatment for chronic pain, Vlaeyen &

Morley suggest that the next generation of research should be determining “what works for

whom” [70]. Moreover, the personalized medicine movement argues for more tailored

patient-treatment matching [44].

Based on a survey of CBT chronic pain literature, we found few outcomes of

demographic variables moderating treatment response. Education, marital status, and pain

duration were not significant predictors [50]. Higher treatment expectations were associated

with more improvement [23; 40]. The literature on depression is mixed with some studies

finding that depression is associated with greater improvements [68], while others find

depression results in poorer outcomes [51; 67]. While some studies examining pain coping

profiles found that MPI “dysfunctional” patients benefited the most from treatment,

“interpersonally distressed” somewhat less, and “adaptive” patients showed little or no

improvement [59; 63-65]; other studies found no differential treatment response. Baseline

catastrophizing, a maladaptive form of coping, was found in one study to be associated with

a poorer outcome [67]. Finally, we found no studies examining disease severity as a

predictor of treatment response.

This paper reports on a priori-specified (grant application) moderator analyses of five

demographic and three clinical variables in one of the largest randomized controlled

effectiveness trials of CBT for chronic pain [10]. Outcomes reported are for the change from

baseline to post-treatment assessment.

Methods

Study Design

This study was a multi-site, randomized controlled trial examining the

effectiveness of Pain Coping Skills Training (PCST) delivered by trained nurse

practitioners (NP) in community primary care and rheumatology offices [10]. Patients

 

 

5  

with osteoarthritis (OA) were randomized in equal numbers to one of two conditions:

PCST (treatment condition) or Usual Care (control condition). Patients in the PCST

treatment condition received 10 sessions of individual PCST designed to teach and

promote the use of cognitive-behavioral pain management coping skills. Patients in the

control condition continued with their usual care for OA. Consistent with usual care,

patients in both conditions were provided with an OA informational brochure from the

Arthritis Foundation and information on programs (support groups, arthritis education,

and aquatic exercise classes) offered in the community.

Participants

Patients with chronic pain due to OA of the knee or hip were recruited from

community primary care and rheumatology practices in New York, Virginia, and North

Carolina. Advertisements with information about the study were posted in the waiting

rooms, and participating doctors informed eligible patients of the opportunity to

participate in the clinical trial at the time of a regular office visit. Patients were told that

the purpose of the study was to evaluate the effectiveness of a 10-session program for

coping with persistent pain delivered by nurses in their doctor’s office. Patients randomly

assigned to the control group would continue with their usual care and participate in the

periodic assessments. Interested patients were invited to contact the research office for

further details and to be screened by telephone for eligibility. Eligibility criteria were (1)

physician-confirmed diagnosis of hip or knee OA, (2) 21 years of age or older, (3) usual

pain ≥ 4 on a 10-point scale for a duration of at least 6 months, (4) ability to read, write,

and understand English, (5) ability to attend 10 treatment sessions at the doctor’s office

if randomized to treatment, (6) no cognitive/mental impairment that would interfere with

participation, (7) no expected joint replacement surgery in the next two years.

Measures

Moderator variables assessed

 

 

6  

Demographic characteristics. Demographic measures included age at the time of the

baseline interview, sex, race/ethnicity, body mass index, and level of highest education.

Baseline OA disease radiographic severity ratings. The widely-used Kellgren-

Lawrence system for osteoarthritis joint damage based on radiographs was used to

grade disease severity at baseline [37]. Patients were not informed of their grade.

Severity was graded from 0 (no radiographic findings of OA) to 4 (definite osteophytes

with severe joint space narrowing and subchondral sclerosis). Scoring based on

radiographs has been shown to correlate moderately with articular surface grading

during knee arthroscopy [39]. All X-rays were graded using K-L criteria by two

independent raters, and a third rating was obtained in cases where the ratings disagreed

by 2 grades or more (n = 24; 9% of the sample). Inter-rater reliability was acceptable

among the first two raters, with linear weighted kappa = 0.74 (95% CI = 0.68 to 0.79)

and Krippendorff's [26] ordinal alpha = 0.76 (95% CI = 0.71 to 0.80). Reliability was

slightly improved by the third rating (ordinal alpha = 0.78, 95% CI = 0.75 to 0.81).

Baseline treatment expectations. A 5-item questionnaire was modified for this study

based on the Credibility/Expectation Questionnaire [18]. Patients were asked to rate on

a 10-point scale whether PCST seems logical, if they feel confident about the training,

whether the training will help to control their pain, if they expect the nurse delivering the

training to be helpful, and if they would recommend this training to others. The overall

scale score for this measure showed good internal consistency (Cronbach α=0.87) [9].

Beck Depression Inventory-II (BDI). This 21-item self-report questionnaire measures

cognitive, affective, and somatic aspects of depressed mood [5; 6]. It is widely used as a

treatment outcome measure and is sensitive to the range of depressed mood in chronic

pain patients [20; 27; 74]. The BDI has demonstrated validity and sensitivity to treatment

change [4]. The internal consistency of the BDI total score was 0.89 in the present study.

 

 

7  

Multidimensional Pain Inventory (MPI). The MPI is a 61-item instrument that

evaluates the impact of and adaptation to chronic pain. Section one addresses patients’

pain severity, pain-related interference, appraisals of social support, life control, and

affective distress. Section two measures patients’ perceptions of significant others’

positive and negative behaviors in response to patient pain. Section three assesses

patients’ general activity level [38]. Internal consistencies for the subscales assessed at

baseline ranged from 0.71 to 0.92 in the present sample. There are two scoring systems.

The classical MPI scoring system uses 9 of the 13 subscales to classify patients into 3

main adaptational styles: adaptive, dysfunctional, and interpersonally distressed patients

[38]. In addition, a more recent scoring method based on Rasch modeling yields two

dimensional composite scores: an interpersonally distressed score and a dysfunctional

score [58].

Outcome Measures

Arthritis Impact Measurement Scales (AIMS2). This 78-item questionnaire measures

the health status of patients with arthritis and has been used extensively in survey and

treatment outcomes research [25; 52]. The AIMS2 addresses pain, mobility, walking and

bending, extremity functioning, self-care, household tasks, social activities and support,

work, tension, and mood. The recall period for this instrument was changed from “in the

past month” to a 2-week period. Internal consistency subscale estimates ranged from

0.72 to 0.90 in the present study.

Brief Pain Inventory (BPI). Four items from the BPI were used to measure current pain

and “average”, “worst”, and “least” pain in the past two weeks. The inventory is reliable,

valid and has achieved widespread use among medical conditions with chronic pain [15;

17]. The internal consistency of the four-item scale was 0.89 in the present study.

Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). The

WOMAC is the most widely used outcome measure in hip and knee arthritis

 

 

8  

pharmaceutical and surgical studies. Several studies support the reliability and validity of

the WOMAC [7; 46]. The instrument has 24 items covering three domains: pain,

stiffness, and physical function experienced during the past 48 hours. Internal

consistency estimates ranged from 0.70 to 0.95 for the three subscales in the present

study.

Coping Strategies Questionnaire (CSQ). The 42–item Coping Attempts subscale of

the CSQ [35; 57] was used to assess how often a patient engages in seven different

coping strategies when they feel pain: coping self-statements, praying or hoping,

ignoring pain sensations, reinterpreting pain sensations, increasing behavioral activities,

catastrophizing, and diverting attention. This instrument has shown sensitivity to

treatment change in various chronic pain samples [21; 48] as well as good internal

consistency and construct validity [35]. Internal consistency estimates for the seven

subscales ranged from 0.77 to 0.86 in the present study. Since the catastrophizing

subscale has been shown to be a very important variable in pain research, it is

examined separately in our analyses.

Arthritis Self-Efficacy Scale. This 8-item instrument measures patients’ perceived

ability to perform specific behaviors aimed at controlling arthritis pain and disability

(ranging from 1=very uncertain to 10=very certain) [22]. The scale was adapted from the

20-item questionnaire developed by Lorig and colleagues [47] that has shown sensitivity

to increases in a sense of mastery over arthritis pain in many outcome trials [45; 61].

The 8-item version has shown adequate reliability and validity [22]. The internal

consistency of the total score was 0.92 in the present study.

Quality of Life Scale. This 16-item instrument measures quality of life across different

life domains in patients with chronic illness. The measure is reliable and content-valid;

among medical patients, internal consistency coefficients are above 0.85, and 6-week

 

 

9  

test-retest reliability is 0.76 [12]. The internal consistency of the total score was 0.91 in

the present study.

Brief Fatigue Inventory (BFI). Like the BPI after which it was modeled, the BFI was

designed to measure fatigue in cancer patients, but its use has expanded to many

medical conditions [3; 53]. Four items from the BFI were used to measure current fatigue

and “average”, “worst”, and “least” fatigue; the recall period was changed from the past

24 hours to the past two weeks. A factor analysis determined that the BFI assesses a

single fatigue dimension with good internal consistency and adequate correlations with

other fatigue scales [53; 75]. The internal consistency of the four items was 0.86 in the

present study.

End-of-day symptom diaries recorded on interactive voice recording (IVR). Several

key constructs that are central to the arthritis pain experience were measured via IVR (a

telephone computer interface) for seven consecutive days at each assessment period

(baseline, post-treatment, 6- and 12-month follow-up). These constructs included ratings

of pain intensity, interference with physical, work, and social activities due to pain,

fatigue, satisfaction with the day’s accomplishments, and pain medication usage. IVR

data capture is reliable and valid when compared to paper-and-pencil assessment, and

compliance is typically good [10; 11; 42; 54].

Creation of composite measures

Several key constructs were a priori specified as primary outcomes: pain

intensity, physical functioning, psychological distress, coping strategies, catastrophizing,

self-efficacy, and quality of life. Given the multiple scales administered for several

domains, and to reduce Type 1 error, composite measures were created for four of the

primary outcomes (pain, physical functioning, psychological distress, and coping). The

other outcomes were measured with single scales. The pain composite was comprised

of the BPI pain, AIMS2 pain, and WOMAC pain subscales (average inter-correlation

 

 

10  

across scales at baseline = 0.70). Physical functioning was composed of the AIMS2

physical and WOMAC difficulties performing activities subscales (r = 0.58).

Psychological distress was comprised of the BDI and AIMS2 affect (tension and mood)

scales (r = 0.70). A coping strategies composite was created by averaging the CSQ

subscales (average r = 0.47), excluding the catastrophizing subscale, that is important in

its own right. In each case, scale scores were first standardized based on the baseline

means and standard deviations (SDs) across all patients, and then were averaged into

composites. Thus, the composite z-scores at each assessment time point indicate where

a patient scored in relation to all patients at pre-treatment.

Procedure

All study procedures were approved by the Stony Brook University and Duke

University Medical Center Institutional Review Boards. The study was registered at

ClinicalTrials.gov (NCT00636454). Eligible patients were scheduled for their baseline

visit at the patient’s participating community clinic site. Prior to initiating study

procedures, patients provided written informed consent. During the baseline visit,

patients completed a battery of outcome questionnaires, were instructed on how to use

the Interactive Voice Recording (IVR) telephone system for the seven daily ratings

following the baseline visit and had their weight and height measured. Patients were also

sent for an X-ray of their most painful OA-diagnosed joint at no cost to them to determine

their baseline disease severity. If a recent X-ray (within the past 9 months) was already

available, the research staff obtained a copy and no new x-ray was obtained. Patients

were informed that they needed to complete their daily ratings and provide an X-ray

within 4 weeks of the baseline assessment.

Upon completion of all baseline assessment components, patients were

randomized to one of the two study conditions. Randomization to experimental condition

 

 

11  

(PCST or usual care) was done using a permuted block randomization algorithm with

block sizes of 6 and 8. The study statistician created a randomization program accessed

by site coordinators at the time of each patient’s randomization. The randomization

assignment was only provided after the patient’s unique identifier and initials were

entered into the randomization program. The study coordinator then called patients and

informed them of their assignment to study treatment group. Patients assigned to PCST

were then scheduled for their first appointment with a NP who provided 10 individual

weekly sessions at the patient’s doctor’s office (window for treatment completion 10 to

20 weeks from randomization). Patients assigned to usual care were instructed to

continue with their regular treatment for their OA. Both study groups were asked to

complete a post-treatment assessment, a six-month follow-up and a 12-month follow-up

assessment. As in the baseline assessment, research assistants met with patients for

each assessment when patients completed outcome measures, had height and weight

measures, and completed the seven daily IVR ratings. The research team maintained

assessor blinding, but patients sometimes revealed their experimental condition. Data

collection was conducted from 2008-2013.

Pain Coping Skills Training (PCST)

PCST interventions teach patients cognitive and behavioral skills to manage their

pain and enhance their perception of pain control. Four broad coping skills were taught

across the 10 30-45-minute sessions: relaxation response, attention diversion

techniques, altering activity and rest patterns as a way of increasing activity level, and

reducing negative pain-related thoughts and emotions. The sessions were outlined in

detail in a treatment manual and followed a format of review of home practice assigned

at the previous session, instruction in a new coping skill, guided practice in that skill, and

 

 

12  

a home practice assignment. Homework assignments are an integral component of

PCST followed by review and problem-solving in the subsequent session.

Consistent with the goal of testing the effectiveness of NPs delivering PCST in

the patients’ doctors’ offices, all treatment sessions were conducted in the clinics or by

telephone (phone sessions). Up to 4 sessions could be conducted via telephone with

some discretion on the part of the NP and patient. The first 3 sessions and the last

session had to be conducted in person. Patients were provided with a treatment binder

divided into sections for each session. These sections included handouts and logs to

record home practice of the skill, which were reviewed by the NP at each session.

Treatment sessions with a patient were stopped if they were not completed within 20

weeks of randomization.

Nurse practitioners (NPs) delivering the treatment

Treatment sessions were conducted by several NPs hired by the research grant.

Study nurses received 2-3 days of intensive training in PCST and individual supervision

of their cases for several months. Additional oversight for purposes of quality assurance

was provided for the duration of the study.

Analytic Strategy

Tests of moderated treatment effects were conducted using analysis of

covariance models for categorical moderator variables and using multiple regression

procedures as outlined by Aiken and West [2] for continuous moderator variables. In

each case, post-treatment scores on the outcome variable were regressed on the

baseline scores of that variable, group (treatment versus control), the moderator

variable, and the group by moderator interaction term. Clinic site was included as an

additional covariate. Thus, the interaction term tests whether the treatment effect, as the

group difference in change from pre-treatment, differs across levels of the moderator,

 

 

13  

controlling for potential site differences in outcomes. Significant interactions for

continuous moderators were probed by analyses of simple slopes. Specifically, these

analyses probed the group difference in change for high (1 SD above the mean),

average (at the mean) and low (1 SD below the mean) values of the moderator variable

[16]. To facilitate interpretation of the magnitude of the moderated treatment effects, we

report effect sizes (Cohen’s d) for high, average, and low values of the moderator,

computed as the group difference in change on the outcome relative to the standard

deviation at baseline (scaled such that positive effect sizes indicate improvement in the

treatment group relative to the control group). Unstandardized group mean changes and

moderated treatment effects in raw scale scores are provided in the supplemental

appendix. Because some patients were missing at post-treatment, analyses were

conducted using full information maximum likelihood estimation, which yields unbiased

parameter estimates and standard errors under the assumption that the data are either

Missing Completely at Random (MCAR) or Missing at Random (MAR) [60]. The

significance level was set at .05, consistent with the suggestion by Kraemer [41] that

these moderator analyses are primarily hypothesis-generating rather than hypothesis

testing activities.

Results

The treatment and control groups (N=256) were not significantly different on

demographic and health variables at baseline with the exception of employment status in

which the control group had a higher rate of employment than the treatment group (see

Table 1). Likewise, the groups did not differ on any of the outcome measures at

baseline; and comparisons of treatment effects across the two clinical sites did not yield

any differences. Overall, PCST produced significant improvements in a range of pain-

related variables including pain intensity, coping with pain, self-efficacy for controlling

 

 

14  

pain, activity interference due to pain, and reduced use of pain medication when

compared to usual care [10].

Demographics

Five demographic variables were examined for evidence of moderation of treatment

outcome. Sex, race/ethnicity, and body mass index were not significant moderators for

any of the outcomes.

Age (M = 67.2 years; range = 36-100) significantly moderated post-treatment pain

(p <.05) and daily ratings of “Quality of Days” (p = 0.004). Specifically, the youngest

patients (age = 57.7) experienced no reduction in pain from treatment, whereas the

treatment effect for the average-age (age = 67.2) was d = 0.19 and for the oldest

patients (age = 76.7) d = 0.37. More pronounced effect modification by age was

observed for Quality of Days: the youngest patients reported poorer Quality of Days after

treatment (d = -0.25), the average age patient reported a small improvement (d = 0.14),

and the oldest patients experienced a much larger improvement (d = 0.52) in the

treatment group compared to controls.

Level of education moderated post-treatment level of catastrophizing (p = 0.005)

even within a sample that tended to be more educated (up to high school: 28%; college:

51%; post-grad: 21%). A marked treatment effect for catastrophizing (d = 0.57) was

observed in the highly educated patients (post-graduate), whereas there was little

improvement for the college educated (d = 0.08) and a worsening in the high school

educated (d = -0.20).

Clinical Variables

Disease severity at baseline, as measured by Kellgren-Lawrence radiograph

ratings, moderated several outcomes: pain intensity composite (p = 0.02), fatigue

 

 

15  

(p = 0.004), quality of life scale (p = 0.05), and daily quality of days (p = 0.03). Close to

25% of the sample was classified into each of the four K-L severity groups. With a good

deal of consistency, as shown in Table 2, patients with the most severe organic disease

had a robust response to treatment on measures of pain (d = 0.51), quality of life (d =

0.40) and fatigue (d = 0.75) . Those patients with little joint damage reported no

improvement on these variables and a worsening for quality of life and daily quality of

day measures.

Baseline ratings of treatment expectation significantly moderated five outcome

variables: pain intensity composite (p = 0.03), catastrophizing (p = 0.04), self-efficacy

(p = 0.05), fatigue (p = 0.03), and daily IVR pain ratings (p = 0.03). Patients with lower

expectations for the helpfulness of treatment had no improvement in pain,

catastrophizing, and fatigue, though they did show an improvement in self-efficacy

(d = 0.37) (see Table 3). The highest expectations were associated with the greatest

improvement on these outcomes, especially for IVR pain (d = 0.59), self-efficacy (d =

0.83), and fatigue (d = 0.60). Those with “average” (still strong) expectations

experienced more moderate improvements IVR pain (d = .37), self-efficacy (d = .60),

and fatigue (d = .36).

Our measure of depression at baseline, the BDI, did not moderate treatment

response on any outcome. This may be due to very low levels of depressive symptoms

in the sample: 74% minimal, 9% mild, 2% moderate, and 2% severe.

The Multidimensional Pain Inventory (MPI) is scored using the original method of

assigning patients to three pain coping styles based on classical test theory: adaptive,

interpersonally distressed, and dysfunctional [38], and a more recent method based on

Item Response Theory (IRT) that yields two Rasch Scale composite scores:

 

 

16  

interpersonal distress and dysfunctional [58]. We examined moderation using both

methods. Using the original clusters, 46% of our patients were identified as Adaptive,

26% as Interpersonally Distressed, and 7% as Dysfunctional with identical distributions

in our treatment and control groups. The remaining 20% of patients could not be

classified into one of the three clusters, as is common with the MPI, and were not

included in this analysis. The proportion of patients classified as Interpersonally

Distressed was comparable to that observed in prior research with various

musculoskeletal disorders, including low back pain, fibromyalgia, and arthritis; in

contrast, the proportion of patients with an adaptive coping style was slightly higher, and

the proportion classified as having a dysfunctional coping style was lower than

previously observed [8; 31; 65]. All patients’ data could be analyzed for the Rasch

scoring. Our patients’ mean Dysfunctional score was, on average, lower (M = 42.1, SD =

10.33) than in the large chronic pain “normative” sample (M = 55.1, SD = 12.0) reported

in the MPI Version 3 Handbook [58]. In contrast, the patients in our sample had

somewhat higher Interpersonal Distress scores (M = 43.3, SD = 12.3) than the MPI

“normative” sample (M = 39.5, SD = 14.0).

The traditional MPI cluster groups yielded no significant moderator effects for the

primary composites and other outcome variables. The newer Rasch Scale Score for

Interpersonally Distressed, however, yielded several significant moderator effects for

treatment outcomes: the psychological distress composite (p = 0.01), self-efficacy (p =

0.05), catastrophizing (p = 0.02), and quality of life (p = 0.03). In addition, change in

aggregated daily measures of quality of day (p = 0.03), satisfaction with

accomplishments (p = 0.02), and need to take additional medication for pain (p = 0.03)

were moderated by the MPI Interpersonal Distress score. Specifically, the higher the

Interpersonally Distressed Coping score, the poorer the treatment response for these

 

 

17  

outcomes (see Table 4 showing effect sizes for various scores). The MPI Rasch

Dysfunctional score did not moderate treatment response.

Discussion

This study examined moderators of treatment response in a large RCT of

osteoarthritis patients with chronic pain [10] who received either Pain Coping Skills

Training, a form of CBT, or usual care. Overall, RCT treatment effects were significant

for several of the primary and secondary outcomes; however, they tended to be small as

has been found in meta-analyses of CBT for pain [70; 73]. Thus, the question of

differential patient response to this treatment is important. Do some patients benefit

substantially more or less than the average? Is there evidence that this treatment can be

recommended with greater confidence to patients with particular demographic or clinical

presentations? Do we need to consider revisions to the treatment or alternative

treatments for patients with other characteristics?

As noted earlier, only a few published trials have examined moderators of treatment

effects for CBT for pain. Variables that have been examined include demographics,

treatment expectation, disease severity, depression, and style of coping with pain (MPI);

and we specified these variables a priori for moderation analyses. In our study, five

variables emerged as moderators of several outcomes.

The MPI coping style variable was the strongest moderator. Prior literature

examining moderation by MPI clusters usually found that the Interpersonally Distressed

patients benefited less from treatment than Dysfunctional patients. Often, Adaptive

patients showed little treatment response due to positive baseline coping. In this trial, we

found no differences in treatment response among the three MPI clusters. In the revised

MPI scoring, the new Rasch approach assigns each patient a score for the two

maladaptive coping style. Dysfunctional scores did not moderate outcomes indicating

 

 

18  

that degree of Dysfunctional coping did not influence treatment response. However, our

patients with mid to higher Interpersonally Distressed styles of coping with pain benefited

significantly less from the treatment on the following outcomes: psychological distress

composite, quality of life, self-efficacy, catastrophizing, and daily ratings of satisfaction

with accomplishments, quality of day, and need for pain medication. In fact, with one

exception, only patients with relatively low Interpersonally Distressed ratings showed

benefit on these outcome variables. The exception was self-efficacy in which all patients

showed improvement, but the effects were much stronger for those with less

Interpersonal Distress. On a positive note, patients’ treatment responses for pain and

physical functioning did not vary by level of Interpersonal Distress coping style.

These data are consistent with prior research that usually found that patients,

classified as Interpersonally Distressed in their pain coping, benefit the least from pain

treatment [59; 63-65]. This refines and underscores the importance of Interpersonal

Distress in pain coping. Patients with a strong Interpersonally Distressed pain coping

style report a greater number of negative behaviors by their significant others in

response to their pain compared to other patients. In addition, these patients report less

social support from their significant others. As such, their experiences of chronic pain are

intertwined with problematic interpersonal relations within their immediate social

network. Specific management of interpersonal difficulties associated with pain is not

usually a focus of CBT protocols for pain, including the one implemented in the present

study. As noted by Turk, addressing social relationships (e.g., guidance for interpersonal

problem solving and assertion training) might be particularly beneficial for patients with

an Interpersonally Distressed pain coping style [65; 66]. Importantly, some research,

including our own, has demonstrated the utility of including spouses and family members

in chronic pain treatment [32-34; 49]. The emerging consistency of the association of the

 

 

19  

Interpersonally Distressed coping style with poorer outcomes suggests these patients

need additional or other treatment approaches to yield more positive outcomes.

Second, patients’ baseline expectation of the benefit of the PCST (assessed prior to

randomization) moderated several important outcomes: pain, fatigue, self-efficacy, and

catastrophizing. This was evident even in the context of overall positive treatment

expectations in the recruited sample. Patients with relatively lower (scores of 6.4 on 10-

point scale) experienced very little benefit from treatment, while patients with average to

high expectations experienced moderate to large effects. This finding is also observed in

two prior studies [23; 40]. Perhaps, some patients are not inclined toward self-

management approaches to deal with their pain; that is, they recognize that the

treatment is not a good fit for them, although, they did agree to participate and accept a

50% probability of being randomly assigned to the treatment group. Or, they may require

preliminary work using motivational interviewing to enhance their “readiness for change”

to more fully reap the benefits of treatment [29; 43].

Third, radiograph measures of disease severity predicted treatment response. The

30% of patients with the most severe joint disease (Kellgren-Lawrence ratings of greater

than 3) experienced moderate to large treatment benefits for pain, fatigue, quality of life,

and daily quality of day. In contrast, the 22% of patients with the lowest levels of

objective disease (KL ratings of 0-1) showed no benefit or worsening. Mid-level disease

severity patients (49%; KL ratings of 2-3) experienced some treatment effects, especially

for fatigue and quality of day. This result is likely of interest to rheumatologists and

primary care clinicians who are frequently involved in the management of pain in OA

patients with severe disease [36]. It is very encouraging that patients with the most

severe disease benefit from this intervention to better manage their disease. We believe

that this is the first report of the relationship of disease severity with PCST pain

outcomes.

 

 

20  

The outcomes for demographic variables were encouraging in that for both men and

women, patients of different race and ethnicity, as well as BMI, all benefited equivalently

from the treatment. This speaks to the generalizability of treatment efficacy across a

range of patient groups. However, age and education did moderate outcomes on three

variables. The oldest patients showed the most robust treatment effect for pain and daily

quality of day, whereas the younger patients did not. Our most highly educated patients

showed improvement on catastrophizing, whereas high school and college educated

patients did not; though our PCST treatment protocol did not specifically target

catastrophizing. It is possible that PCST protocols that do target catastrophizing may

yield a more universal effect. Overall, the results for moderation by demographic

variables are positive news. Across 5 demographic moderator variables by 15 outcome

variables, only 3 of 75 possible moderator relationships were detected which is within

the realm of statistical chance. Thus, our data are generally consistent with the

conclusions of a 2002 review that age, sex, and education did not moderate treatment

effects [51]. Nevertheless, the results that show older patients experience the best

improvements in pain suggest that this treatment can be provided to even the very old

with good results.

The differences in treatment effect sizes observed across the continuums of the

moderator variables are important. While the overall trial’s effect sizes are modest, for

subgroups the effects rise substantially and warrant consideration in clinical decision-

making [44]. Results from two of the moderators, pain coping style and treatment

expectations, suggest incorporation of additional psychological approaches into the

treatment. A higher score on the MPI Interpersonally Distressed dimension likely

requires examination of the social environment of the patient and the role of the patient’s

pain in those relationships. This style of coping with pain may reflect a more

generalizable pattern of problematic social interactions. Indeed, treatment effectiveness

 

 

21  

may benefit from some involvement of the patient’s significant other(s) [32-34]. As such,

these patients might benefit from PCST delivered by health professionals who can be

trained to augment PCST with interventions focused on the social context of pain [56].

Similarly, lower treatment expectations may warrant inclusion of other interventions,

such as motivational interviewing, in order to orient patients to the value of self-

management approaches for managing their condition [29; 30]. However, the factors

underlying expectations are not well elucidated. Therefore, when patients present with

low to moderate treatment expectations, this should be explored to identify treatment

preferences and barriers.

The data from this study also suggest that age and educational level impact

treatment outcomes for reasons that are not apparent. Older and very educated patients

benefited more from the treatment. As Internet-based interventions for pain management

are developed, it will be interesting to see how demographics moderate those treatment

effects compared with in-person interventions. The next generation of PCST treatment

should consider approaches that may be more relevant for younger, and perhaps busier,

patients as well as those that are less educated.

Finally, this study has several important health economic implications. First, there is

growing concern about the long-term costs and benefits of biological treatments for OA

[71; 72]. There is also growing agreement about the need to identify patients who will

and will not respond to biologic therapy in order to efficiently manage medical resources

[62; 71]. Likewise, the ability to identify patients who might respond best to behavioral

approaches may be particularly useful to clinicians working with patients who fail to

respond adequately to biologic approaches. And, finally, our results are the first to

document the high levels of PCST effectiveness among patients with the most severe

disease as assessed by imaging. Treatment options for these older patients often are

very limited, i.e., medications contraindicated, or the patient is not a surgical candidate

 

 

22  

because of co-morbid conditions [1; 55]. Thus, patients who must delay joint

replacement or who are unable to receive replacement are particularly good candidates

for this treatment.

 

 

23  

Acknowledgments

Research reported in this publication was supported by the National Institute of

Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health

under Award Number AR054626 and General Clinical Research Center Grant

#M01RR10710. The content is solely the responsibility of the authors and does not

necessarily represent the official views of the National Institutes of Health.

Conflict of Interest statement

We declare that there are no conflicts of interest.

 

 

24  

References

[1] Abdulla A, Adams N, Bone M, Elliott AM, Gaffin J, Jones D, Knaggs R, Martin D, Sampson L, Schofield P. Guidance on the management of pain in older people. Age Ageing 2013;42(Suppl 1):i1-57. doi: 10.1093/ageing/afs1200.

[2] Aiken L, West S. Multiple Regression: Testing and Interpreting Interactions. Newbury Park, CA: Sage Publications, 1991.

[3] Anderson KO, Getto CJ, Mendoza TR, Palmer SN, Wang XS, Reyes-Gibby CC, Cleeland CS. Fatigue and sleep disturbance in patients with cancer, patients with clinical depression, and community-dwelling adults. J Pain Symptom Manage 2003;25(4):307-318.

[4] Beck A, Steer R, Garbin M. Psychometric properties of the Beck Depression Inventory: Twenty-five years of evaluation. Clinical Psychology Review 1988;8(1):77-100.

[5] Beck AT, Steer RA, Brown GK. Beck Depression Inventory®—II Manual. San Antonio, Texas: Harcourt Assessment, Inc, 1996.

[6] Beck AT, Steer RA, Garbin MG. Psychometric properties of the Beck Depression Inventory: Twenty-five years of evaluation. Clinical Psychology Review 1988;8(1):77-100.

[7] Bellamy N, Buchanan WW, Goldsmith CH, Campbell J, Stitt LW. Validation study of WOMAC: a health status instrument for measuring clinically important patient relevant outcomes to antirheumatic drug therapy in patients with osteoarthritis of the hip or knee. Journal of Rheumatology 1988;15(12):1833-1840.

[8] Bergstrom C, Jensen I, Hagberg J, Busch H, Bergstrom G. Effectiveness of different interventions using a psychosocial subgroup assignment in chronic neck and back pain patients: a 10-year follow-up. Disabil Rehabil 2012;34(2):110-118. doi: 110.3109/09638288.09632011.09607218. Epub 09632011 Oct 09638211.

[9] Broderick JE, Junghaenel DU, Schneider S, Bruckenthal P, Keefe FJ. Treatment expectation for pain coping skills training: relationship to osteoarthritis patients' baseline psychosocial characteristics. Clin J Pain 2011;27(4):315-322.

[10] Broderick JE, Keefe FJ, Bruckenthal P, Junghaenel DU, Schneider S, Schwartz JE, Kaell AT, Caldwell DS, McKee D, Reed S, Gould E. Nurse practitioners can effectively deliver pain coping skills training to osteoarthritis patients with chronic pain: A randomized, controlled trial. Pain 2014;155(9):1743-1754.

[11] Brodey BB, Rosen C, Brodey I, Sheetz B, Unutzer J. Reliability and acceptability of automated telephone surveys among Spanish- and English-speaking mental health services recipients. Mental Health Service Research 2005;7(3):181-184.

[12] Burckhardt C, Woods S, Schultz A, Ziebarth D. Quality of life of adults with chronic illness: A psychometric study. Research in Nursing & Health 1989;12:347-354.

[13] Celestin J, Edwards RR, Jamison RN. Pretreatment psychosocial variables as predictors of outcomes following lumbar surgery and spinal cord stimulation: a systematic review and literature synthesis. Pain Med 2009;10(4):639-653. doi: 610.1111/j.1526-4637.2009.00632.x.

[14] Chapman SL, Pemberton JS. Prediction of treatment outcome from clinically derived MMPI clusters in rehabilitation for chronic low back pain. Clin J Pain 1994;10(4):267-276.

[15] Cleeland CS, Ryan KM. Pain assessment: Global use of the Brief Pain Inventory. Annals of the Academy of Medicine, Singapore 1994;23(2):129-138.

[16] Cohen J, Cohen P. Applied multiple regression/correlation analysis for the social sciences. Hillsdale, NJ: Erlbaum, 1983.

 

 

25  

[17] Daut RL, Cleeland CS. The prevalence and severity of pain in cancer. Cancer 1982;50(9):1913-1918.

[18] Devilly GJ, Borkovec TD. Psychometric properties of the credibility/expectancy questionnaire. Journal of Behavior Therapy and Experimental Psychiatry 2000;31(2):73-86.

[19] Eccleston C, Williams AC, Morley S. Psychological therapies for the management of chronic pain (excluding headache) in adults. Cochrane Database Syst Rev 2009(2):CD007407. doi: 007410.001002/14651858.CD14007407.pub14651852.

[20] Frank R, Beck N, Parker J, Kashani J, Elliott T, Haut A, Smith E, Atwood C, Brownlee-Duffeck M, Kay D. Depression in rheumatoid arthritis. Journal of Rheumatology 1988;15:920-925.

[21] Gil KM, Abrams MR, Phillips G, Keefe FJ. Sickle cell disease pain: relation of coping strategies to adjustment. Journal of Consulting and Clinical Psychology 1989;57(6):725-731.

[22] Gonzalez VM, Stewart A, Ritter PL, Lorig K. Translation and validation of arthritis outcome measures into Spanish. Arthritis Rheum 1995;38(10):1429-1446.

[23] Goossens ME, Vlaeyen JW, Hidding A, Kole-Snijders A, Evers SM. Treatment expectancy affects the outcome of cognitive-behavioral interventions in chronic pain. Clinical Journal of Pain 2005;21(1):18-26; discussion 69-72.

[24] Guck TP, Meilman PW, Skultety FM, Poloni LD. Pain-patient Minnesota Multiphasic Personality Inventory (MMPI) subgroups: evaluation of long-term treatment outcome. J Behav Med 1988;11(2):159-169.

[25] Haavardsholm EA, Kvien TK, Uhlig T, Smedstad LM, Guillemin F. A comparison of agreement and sensitivity to change between AIMS2 and a short form of AIMS2 (AIMS2-SF) in more than 1,000 rheumatoid arthritis patients. Journal of Rheumatology 2000;27(12):2810-2816.

[26] Hayes A, K K. Answering the call for a standard reliability measure for coding data. Communication Methods and Measures 2007;1(1):77-89.

[27] Holzberg AD, Robinson ME, Geisser ME, Gremillion HA. The effects of depression and chronic pain on psychosocial and physical functioning. Clinical Journal of Pain 1996;12(2):118-125.

[28] Jamison RN, Rudy TE, Penzien DB, Mosley TH, Jr. Cognitive-behavioral classifications of chronic pain: replication and extension of empirically derived patient profiles. Pain 1994;57(3):277-292.

[29] Jensen MP, Nielson WR, Kerns RD. Toward the development of a motivational model of pain self-management. J Pain 2003;4(9):477-492.

[30] Jensen MP, Nielson WR, Turner JA, Romano JM, Hill ML. Changes in readiness to self-manage pain are associated with improvement in multidisciplinary pain treatment and pain coping. Pain 2004;111(1-2):84-95.

[31] Junghaenel DU, Keefe FJ, Broderick JE. Multi-modal examination of psychological and interpersonal distinctions among MPI coping clusters: a preliminary study. J Pain 2010;11(1):87-96.

[32] Keefe F, Blumenthal J, Baucom D, Affleck G, Waugh R, Caldwell D, Beaupre P, Kashikar-Zuck S, Wright K, Egert J, Lefebvre J. Effects of spouse-assisted coping skills training and exercise training in patients with osteoarthritic knee pain: a randomized controlled study. Pain 2004;110(3):539-549.

[33] Keefe F, Caldwell D, Baucom D, Salley A, Robinson E, Timmons K, Beaupre P, Weisberg J, Helms M. Spouse-assisted coping skills training in the management of osteoarthritis knee pain. Arthritis Care and Research 1996;9(4):279-291.

[34] Keefe F, Caldwell D, Baucom D, Salley A, Robinson E, Timmons K, Beaupre P, Weisberg J, Helms M. Spouse-assisted coping skills training in the management

 

 

26  

of knee pain in osteoarthritis: long-term follow-up results. Arthritis Care and Research 1999;12(2):101-111.

[35] Keefe F, Caldwell D, Queen K, Gil K, Martinez S, Crisson J, Ogden W, Nunley J. Pain coping strategies in osteoarthritis patients. Journal of Consulting and Clinical Psychology 1987;55(2):208-212.

[36] Keefe FJ, Porter L, Somers T, Shelby R, Wren AV. Psychosocial interventions for managing pain in older adults: outcomes and clinical implications. Br J Anaesth 2013;111(1):89-94. doi: 10.1093/bja/aet1129.

[37] Kellgren JH, Lawrence JS. Radiological assessment of osteo-arthrosis. Annals of Rheumatic Disease 1957;16(4):494-502.

[38] Kerns R, Turk D, Rudy T. The West Haven-Yale Multidimensional Pain Inventory (WHYMPI). Pain 1985;23:345-356.

[39] Kijowski R, Blankenbaker D, Stanton P, Fine J, De Smet A. Arthroscopic validation of radiographic grading scales of osteoarthritis of the tibiofemoral joint. AJR American Journal of Roentgenology 2006;187(3):794-799.

[40] Kole-Snijders AM, Vlaeyen JW, Goossens ME, Rutten-van Molken MP, Heuts PH, van Breukelen G, van Eek H. Chronic low-back pain: what does cognitive coping skills training add to operant behavioral treatment? Results of a randomized clinical trial. J Consult Clin Psychol 1999;67(6):931-944.

[41] Kraemer HC, Wilson GT, Fairburn CG, Agras WS. Mediators and moderators of treatment effects in randomized clinical trials. Archives of General Psychiatry 2002;59(10):877-883.

[42] Kranzler HR, Abu-Hasaballah K, Tennen H, Feinn R, Young K. Using daily interactive voice response technology to measure drinking and related behaviors in a pharmacotherapy study. Alcoholism, Clinical and Experimental Research 2004;28(7):1060-1064.

[43] Kratz AL, Molton IR, Jensen MP, Ehde DM, Nielson WR. Further evaluation of the Motivational Model of Pain Self-Management: coping with chronic pain in multiple sclerosis. Ann Behav Med 2011;41(3):391-400. doi: 310.1007/s12160-12010-19249-12166.

[44] Lazaridou A, Edwards RR. Getting personal: the role of individual patient preferences and characteristics in shaping pain treatment outcomes. Pain 2016;157(1):1-2.

[45] Lefebvre JC, Keefe FJ, Affleck G, Raezer LB, Starr K, Caldwell DS, Tennen H. The relationship of arthritis self-efficacy to daily pain, daily mood, and daily pain coping in rheumatoid arthritis patients. Pain 1999;80(1-2):425-435.

[46] Lingard EA, Katz JN, Wright RJ, Wright EA, Sledge CB. Validity and responsiveness of the Knee Society Clinical Rating System in comparison with the SF-36 and WOMAC. Journal of bone and joint surgery American volume 2001;83-A(12):1856-1864.

[47] Lorig K, Chastain R, Ung E, Shoor S, Holman H. Development and evaluation of a scale to measure perceived self-efficacy in people with arthritis. Arthritis Rheum 1989;32(1):37-44.

[48] Main CJ, Waddell G. A comparison of cognitive measures in low back pain: statistical structure and clinical validity at initial assessment. Pain 1991;46(3):287-298.

[49] Martire L, Schulz R, Helgeson V, Small B, Saghafi E. Review and meta-analysis of couple-oriented interventions for chronic illness. Ann Behav Med 2010;40(3):325-342.

 

 

27  

[50] McCracken LM, Spertus IL, Janeck AS, Sinclair D, Wetzel FT. Behavioral dimensions of adjustment in persons with chronic pain: pain-related anxiety and acceptance. Pain 1999;80(1-2):283-289.

[51] McCracken LM, Turk DC. Behavioral and cognitive-behavioral treatment for chronic pain: outcome, predictors of outcome, and treatment process. Spine (Phila Pa 2002;27(22):2564-2573.

[52] Meenan RF, Mason JH, Anderson JJ, Guccione AA, Kazis LE. AIMS2. The content and properties of a revised and expanded Arthritis Impact Measurement Scales Health Status Questionnaire. Arthritis Rheum 1992;35(1):1-10.

[53] Mendoza TR, Wang XS, Cleeland CS, Morrissey M, Johnson BA, Wendt JK, Huber SL. The rapid assessment of fatigue severity in cancer patients: Use of the Brief Fatigue Inventory. Cancer 1999;85(5):1186-1196.

[54] Mundt JC, Bohn MJ, King M, Hartley MT. Automating standard alcohol use assessment instruments via interactive voice response technology. Alcoholism, Clinical and Experimental Research 2002;26(2):207-211.

[55] O'Neil CK, Hanlon JT, Marcum ZA. Adverse effects of analgesics commonly used by older adults with osteoarthritis: focus on non-opioid and opioid analgesics. Am J Geriatr Pharmacother 2012;10(6):331-342. doi: 310.1016/j.amjopharm.2012.1009.1004. Epub 2012 Oct 1012.

[56] Porter LS, Keefe FJ, Wellington C, de Williams A. Pain communication in the context of osteoarthritis: patient and partner self-efficacy for pain communication and holding back from discussion of pain and arthritis-related concerns. Clin J Pain 2008;24(8):662-668. doi: 610.1097/AJP.1090b1013e31816ed31964.

[57] Rosenstiel AK, Keefe FJ. The use of coping strategies in chronic low back pain patients: relationship to patient characteristics and current adjustment. Pain 1983;17(1):33-44.

[58] Rudy TE. Multiaxial assessment of pain: Multidimensional Pain Inventory computer program and user’s manual. Version 3 Pittsburgh, PA: University of Pittsburgh, 2004.

[59] Rudy TE, Turk DC, Kubinski JA, Zaki HS. Differential treatment responses of TMD patients as a function of psychological characteristics. Pain 1995;61(1):103-112.

[60] Schafer J, Graham J. Missing data: Our view of the state of the art. Psychological Methods 2002;7(2):147-177.

[61] Schiaffino KM, Revenson TA, Gibofsky A. Assessing the impact of self-efficacy beliefs on adaptation to rheumatoid arthritis. Arthritis Care and Research 1991;4(4):150-157.

[62] Schnitzer TJ, Marks JA. A systematic review of the efficacy and general safety of antibodies to NGF in the treatment of OA of the hip or knee. Osteoarthritis Cartilage 2015;23(Suppl 1):S8-17. doi: 10.1016/j.joca.2014.1010.1003.

[63] Strategier LD, Chwalisz K, Altmaier EM, Russell DW, Lehmann TR. Multidimensional assessment of chronic low back pain: Predicting treatment outcomes. Journal of Clinical Psychology in Medical Settings 1997;4:91-110.

[64] Talo S, Forssell H, Heikkonen S, Puukka P. Integrative group therapy outcome related to psychosocial characteristics in patients with chronic pain. Int J Rehabil Res 2001;24(1):25-33.

[65] Turk D, Okifuji A, Starz T, Sinclair J. Differential responses by psychosocial subgroups of fibromyalgia syndrome patients to an interdisciplinary treatment. Arthritis Care and Research 1998;11:397-404.

[66] Turk DC. The potential of treatment matching for subgroups of patients with chronic pain: lumping versus splitting. Clin J Pain 2005;21(1):44-55; discussion 69-72.

 

 

28  

[67] Turner JA, Holtzman S, Mancl L. Mediators, moderators, and predictors of therapeutic change in cognitive-behavioral therapy for chronic pain. Pain 2007;127(3):276-286. Epub 2006 Oct 2027.

[68] van der Hulst M, Vollenbroek-Hutten MM, Groothuis-Oudshoorn KG, Hermens HJ. Multidisciplinary rehabilitation treatment of patients with chronic low back pain: a prognostic model for its outcome. Clin J Pain 2008;24(5):421-430. doi: 410.1097/AJP.1090b1013e31816719f31816715.

[69] van Hooff ML, van Loon J, van Limbeek J, de Kleuver M. The nijmegen decision tool for chronic low back pain. Development of a clinical decision tool for secondary or tertiary spine care specialists. PLoS One 2014;9(8):e104226.

[70] Vlaeyen JW, Morley S. Cognitive-behavioral treatments for chronic pain: what works for whom? Clinical Journal of Pain 2005;21(1):1-8.

[71] Wenham C, McDermott M, Conaghan PG. Biological therapies in osteoarthritis. Current Pharmaceutical Design 2015;21:2206-2215.

[72] Wenham CY, Conaghan PG. New horizons in osteoarthritis. Age Ageing 2013;42(3):272-278. doi: 210.1093/ageing/aft1043. Epub 2013 Apr 1098.

[73] Williams AC, Eccleston C, Morley S. Psychological therapies for the management of chronic pain (excluding headache) in adults. Cochrane Database Syst Rev 2012;11:CD007407.(doi):10.1002/14651858.CD14007407.pub14651853.

[74] Wilson K, Eriksson M, D'Eon J, Mikail S, Emery P. Major depression and insomnia in chronic pain. Clinical Journal of Pain 2002;18(2):77-83.

[75] Yellen SB, Cella DF, Webster K, Blendowski C, Kaplan E. Measuring fatigue and other anemia-related symptoms with the Functional Assessment of Cancer Therapy (FACT) measurement system. Journal of Pain and Symptom Management 1997;13(2):63-74.

   

 

 

29  

Table 1.

Demographic and medical characteristics by experimental group.

Control group (N = 128)

Treatment group (N = 129)

P for diff. between groups

n a M (SD) or % n a M (SD) or %

Age 128 66.37 (10.26) 129 68.00 (8.67) .17

Years with OA 121 13.59 (9.09) 128 13.95 (10.63) .77

BMI 123 32.87 (8.00) 124 33.77 (8.24) .38

Disease severity (K-L grading) 122 125 .22

0 - 1 27.0% 16.8%

>1 - 2 20.5% 27.2%

>2 - 3 23.0% 26.4%

>3 - 4 29.5% 29.6%

Female 128 78.9% 129 74.4% .40

White race 128 85.9% 129 87.6% .69

Married/living with partner 123 62.6% 126 64.3% .78

Education 126 127 .18

High school graduate 27.0% 28.4%

College graduate 56.4% 46.5%

Master's degree 16.7% 25.2%

Currently employed 121 39.7% 128 21.1% .001

Currently on disability 125 15.2% 128 13.3% .66

Current smoker 126 5.6% 127 7.1% .62

Past smoker 123 52.9% 127 54.3% .81

Regular exercise 121 49.6% 124 45.2% .49

Treatment for psychiatric disorder

126 15.1% 128 16.4% .77

Treatment for drugs 125 1.6% 128 0.0% .15

Memory/thinking problems 125 9.6% 126 10.3% .85

 

 

30  

Table 2.

Standardized intervention effect sizes by disease severity groups

Kellgren-Lawrence disease severity ratings

0-1 (n=54)

>1-2 (n=59)

>2-3 (n=61)

>3-4 (n=73)

Pain intensity composite -.06 .22 .06 .51

Quality of life -.20 .08 .07 .40

Fatigue -.03 .13 .43 .75

IVR quality of days -.34 .29 .41 .20

Note: Intervention effects are standardized based on the pooled baseline standard

deviation. Positive values indicate treatment benefits. IVR = Interactive voice recording

(end-of-day diary reports).

 

 

 

31  

Table 3.

Standardized intervention effect sizes by baseline treatment expectations

Baseline treatment expectation scores

Low (-1 SD, X=6.4)

Average (X=7.9)

High (+1 SD, X=9.3)

Pain intensity composite .00 .20 .40

Catastrophizing -.07 .13 .33

Self-efficacy .37 .60 .83

Fatigue .11 .36 .60

IVR pain .15 .37 .59

Note: Intervention effects are standardized based on the pooled baseline standard

deviation. Positive values indicate treatment benefits. IVR = Interactive voice recording

(end-of-day diary reports).

   

 

 

32  

Table 4.

Standardized intervention effect sizes by baseline interpersonal distress levels

MPI interpersonal distress Rasch scores

Low (-1 SD, X=31.0)

Average (X=43.3)

High (+1 SD, X=55.6)

Psychological distress composite .35 .15 -.04

Catastrophizing .39 .16 -.06

Self-efficacy .86 .63 .40

Quality of life .33 .12 -.10

IVR quality of days .43 .17 -.09

IVR satisfaction with accomplishments .38 .14 -.11

IVR medication taking .29 .15 .00

Note: Intervention effects are standardized based on the pooled baseline standard

deviation. Positive values indicate treatment benefits. MPI = Multidimensional Pain

Inventory. IVR = Interactive voice recording (end-of-day diary reports).