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Improving chronic pain patient profiling

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Title:
Improving chronic pain patient profiling an acceptance-based approach using an online sample
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Payne-Murphy, Jessica C
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Denver, CO
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University of Colorado Denver
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Acceptance and commitment therapy ( lcsh )
Behavior therapy ( lcsh )
Acceptance and commitment therapy ( fast )
Behavior therapy ( fast )
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non-fiction ( marcgt )

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Thesis:
Thesis (M.A.)--University of Colorado Denver. Clinical health psychology
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Includes bibliographic references.
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Department of Psychology
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by Jessica C. Payne-Murphy.

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IMPROVING CHRONIC PAIN PATIENT PROFILING: AN ACCEPTANCE-BASED
APPROACH USING AN ONLINE SAMPLE
by
Jessica C. Payne-Murphy
B.A., Smith College, 1999
A thesis submitted to the
Faculty of the Graduate School of the
University of Colorado in partial fulfillment
of the requirements for the degree of
Masters of Arts
Clinical Health Psychology Program
2012


This thesis for the Master of Arts degree by
Jessica C. Payne-Murphy
has been approved for the
Clinical Health Psychology Program
by
Abbie O. Beacham, PhD, Chair and Advisor
David Albeck, PhD
Josette G. Harris, PhD
Date: 10/26/12


Payne-Murphy, Jessica C. (M.A., Clinical Health Psychology Program)
Improving Chronic Pain Patient Profiling: An Acceptance-Based Approach Using an
Online Sample
Thesis directed by Visiting Associate Professor Abbie O. Beacham, Ph.D.
ABSTRACT
Over 116 million Americans experience chronic pain (CP) incurring an annual
cost of $635 billion in healthcare and lost work. Acceptance-based therapies have gained
increasing recognition for improving functional outcomes. In our online CP patient
support group sample, we predicted 1) CP patients would cluster into low, medium and
high groups of CP Acceptance and 2) self-reported scores of Positive Affect (PA),
Negative Affect (NA) and Perceived Disability (PD) would differ overall by cluster, with
the most positive outcomes found in the high cluster and the least found in the low. The
total sample (N= 300; Mean age = 44.7 years, SD = 11.2) was primarily female (85.6%),
Caucasian (82%), married/partnered (53.1%), and well educated (M= 14.8 years, SD =
2.4). Years with chronic pain was 14.4 (SD = 11.6) and average pain intensity was 7.4/10.
Participants completed demographic, medical history, the Chronic Pain Acceptance
Questionnaire (CPAQ), Positive and Negative Affect Scales (PANAS), and the Pain
Disability Index (PDI). A k-means cluster analysis was conducted using Activity
Engagement (AE) and Pain Willingness (PW) totals from the Chronic Pain Acceptance
Questionnaire (CPAQ). As predicted, cluster analysis with a maximum of 10 iterations
specified three clusters: Low AE/Low PW (Low-Low; n = 81); High AE/High PW
(High-High; n = 50); and Medium AE/Medium PW (Med-Med; n = 71). A MANCOVA
was then conducted to examine differences in PA, NA and PD within each cluster group,
covarying for age, number of surgeries, years of education, and current pain level.
m


Significant MANCOVA results were obtained according to Wilks X, (.55), F(6, 266) =
15.39,p< .01. Follow-up ANCOVAs revealed mean differences in the predicted
directions: the High-High group showed the most PA (M = 32.03 (SD = 6.49)); and the
least NA (M = 17.57 (SD = 5.81)) and PD (M = 32.28 (SD = 15.64)). Conversely, the
Low-Low group displayed the least PA (M= 20.28 (SD = 7.86)); and the most NA (M =
28.05 (SD = 9.33)) and PD (M= 49.57 (SD = 9.46)). Findings support utility of online
tailored interventions targeting CP Acceptance subgroups to improve functional status.
The form and content of this abstract are approved. I recommend its publication.
Approved: Abbie O. Beacham, Ph.D.
IV


TABLE OF CONTENTS
CHAPTER
I. INTRODUCTION........................................................... 1
II. REVIEW 01 THE LITERATURE................................................3
Medical Manifestations and Functional Disability in Chronic Pain......3
Demographic Factors Prevalent in Chronic Pain.........................4
Psychological Manifestations of Chronic Pain..........................5
Positive and negative affectivity and pain.....................5
Mood disorders, functional disability and pain.................9
Psychosocial Treatment Approaches.....................................9
Operant model of pain........................................ 10
Cognitive and cognitive-behavioral therapy................... 10
Third wave behavioral treatment approaches: Acceptance
and Commitment Therapy................................. 15
Profiling Chronic Pain Patients to Improve Treatment Efficacy....... 18
Profiling Chronic Pain Patients: Acceptance-Based Approaches.........21
Improving Access to and Dissemination for Chronic Pain Populations:
Online Interventions.............................................25
Purpose of the Present Study.........................................33
Study Hypotheses.....................................................34
III. METHOD.................................................................35
Participants.........................................................35
Materials and Procedures.............................................36
v


Measures
37
Data Analysis...........................................................39
IV. RESULTS....................................................................42
Characteristics of the Participant Sample Compared to Population Means.42
Demographic Characteristics of the Participant Sample...................46
Pain Characteristics of the Participant Sample..........................50
Hypothesis One..........................................................54
Hypothesis Two..........................................................56
VI. DISCUSSION..................................................................61
Sample characteristics..................................................61
Hypothesis One..........................................................65
Hypothesis Two..........................................................67
Limitations.............................................................68
Future Directions.......................................................69
Summary.................................................................71
REFERENCES.....................................................................73
APPENDIX.......................................................................91
A. Invitation to Participants: Wave 1..................................91
B. Invitation to Participants: Wave 2..................................92
C. Participant Questionnaire Battery...................................93
D. Chronic Pain Acceptance Questionnaire..............................131
E. Pain Disability Index............................................. 132
F. Positive and Negative Affect Scale.................................134
vi


LIST OF TABLES
TABLE
1. Demographic and Pain Characteristics of Clinic Samples vs.
Current Online Sample........................................................42
2. Demographic Characteristics of Sample by Data Collection Wave.....................48
3. Demographic Characteristics of Sample by Cluster Group............................49
4. Pain Characteristics of Sample by Data Collection Wave............................51
5. Pain Characteristics of Sample by Cluster Group...................................53
6. Pain Characteristics by Gender....................................................55
7. CPAQ Score Means of the Patient Clusters..........................................56
8. MANCOVA Results by Perceived Disability, Positive Affect
and Negative Affect..........................................................58
9. ANCOVA Results, Means and Standard Deviations for
Positive Affect, Negative Affect, and Perceived Disability by Cluster Group.... 60
vii


LIST OF FIGURES
FIGURE
1. CP Activity Engagement High, Medium and Low score groups and
MPI Subscale score plots.................................................24
2. CP Willingness High, Medium and Low score groups and
MPI Subscale score plots.................................................24
vm


CHAPTER I
INTRODUCTION
Chronic pain (CP) is a debilitating and prevalent health concern that is a
tremendous financial and emotional burden for over 116 million Americans and their
families (Institute of Medicine of the American Academies, 2011). CP is defined by
three months or more of an unpleasant sensory and emotional experience associated
with actual or potential tissue damage, or described in terms of such damage
(International Association for the Study of Pain, 2011). Disability compensation,
healthcare costs and lost work productivity are significant problems in CP populations.
The prevalence of CP in most industrialized countries is 10-13% of the
population (Croft, 1993; Harstall & Ospina, 2003; Wolfe, 1995). The expense of CP to
the consumer and healthcare system is considerable with an approximate cost of $635
billion in litigation, compensation, healthcare, and lost productivity (IOM, 2011). Loss
of daily activity and work productivity due to pain are significant: a 2001 study indicated
that 13% of nearly 30,000 randomly sampled working Americans missed productive
work time due to pain conditions in a proscribed two-week period (Stewart, Ricci, Chee,
Morganstein, & Lipton, 2003).
The dynamics among the initial cause of pain, the onset of CP, and resulting
functional disability are complex. Malingering and workers compensation studies
specific to chronic pain have shown mixed results; however, Waddell et al. (1993)
suggest that fear avoidance of work tasks and activities that may cause pain are more
predictive of disability and work loss than physiological factors. In general, evidence
suggests that chronic pain patients receiving disability compensation have higher levels
1


of pain and depression, and exhibit decreased productivity and poorer prognosis overall
(Gatchel & Gardea, 1999).
Currently, comprehensive pain programs have not proven to be cost-effective or
therapeutically effective (Gatchel & Okifuji, 2006). New CP treatments are needed to
foster more positive medical and functional outcomes and lower cost burden on both
patients and consumers.
2


CHAPTER II
REVIEW OF THE LITERATURE
Medical Manifestations and Functional Disability in Chronic Pain
It was once believed that the cause and experience of pain were almost exclusively
based in physiology. However, empirical findings have now shifted this thinking in the
field to expand this etiology to a combination of physiological, psychological, social,
cultural and behavioral factors (Turk & Okifuji, 2002). The experience and report of pain
is a subjective and private one comprised of ones perception of not only the pain severity,
but also the perception of his/her suffering related to the pain (Gatchel, 2004). Higher
pain ratings and perceptions of suffering have been found to impact medical and
functional outcomes in numerous studies (Amstein, Caudill, Mandle, Norris, & Beasley,
1999; Stroud, Thorn, Jensen, & Boothby, 2000; Turner, Holtzman, & Mancl, 2007;
Turner, Jensen, & Romano, 2000).
Cross-sectional studies suggest that pain ratings and perceptions of suffering also
impact perceived disability, or ones perception of his or her ability to perform tasks, in
those with either acute or chronic pain (Feuerstein & Thebarge, 1991; Koho, Aho,
Watson, & Hurri, 2001; Roth & Geisser, 2002; Swinkels-Meewisse, Roelofs, Oostendorp,
Verbeek, & Vlaeyen, 2006; Turner, Jensen, & Romano, 2000). Across multiple studies,
pain intensity ratings are found to correlate significantly with Perceived Disability, but
not objectively-rated disability (Alschuler, Theisen-Goodvich, Haig, & Geisser, 2008;
Geisser, Haig, & Theisen, 2000; Millard, Wells, & Thebarge, 1991). Cross-sectional
studies examining the effects of pain-related beliefs on Perceived Disability suggest that
fear of pain is a stronger predictor of higher Perceived Disability rather than actual
3


functional ability (Crombez, Vlaeyen, Heuts, & Lysens, 1999; Waddell, Newton,
Henderson, Somerville, & Main, 1993). More recently, studies suggest that fear
avoidance, lower self-efficacy and depression significantly contribute to both higher
perceived and actual disability (Alschuler et al., 2008; Denison, Asenlof, & Lindberg,
2004; Geisser et al., 2000; Geisser, Robinson, Miller, and Bade, 2003). These studies
suggest that Perceived Disability is a significant factor in ones personal expression of
pain, regardless of the primary cause or nociceptive origin, duration, and severity of pain.
Demographic Factors Prevalent in Chronic Pain
A review of 15 prevalence studies of non-malignant (non-cancer) pain suggests
that female gender, lower socioeconomic status (SES) and middle age were more
common in both primary care and in the general population (Verhaak, Kerssens, Dekker,
Sorbi, & Bensing, 1998). Peak prevalence was found to range between ages 45 and 65
and CP prevalence increasingly rose by age. In a 2000 epidemiological study of over
17,000 surveyed, CP patients were more often women (20% vs. 17.1% in men), older (55
to 69 years of age), and of lower SES and education level as well (Blyth et al., 2000).
Large epidemiological studies suggest the most common pain regions within CP
populations include osteoarthritis/arthritis, rheumatoid arthritis, back pain, headache,
limb pain, and gastrointestinal pain (Breivik, Collett, Ventafridda, Cohen, & Gallacher,
2006; Elliott, Smith, Penny, Smith, & Chambers, 1999; Ohayon & Schatzberg, 2003).
Pain intensity ratings in the general population generally fall above 5 on a 10-point Likert
scale (10 = worst pain) and the most frequently reported pain durations include: two to
five years (22%); five to 10 years (20%); and 20 years or more (21%) (Breivik et al.,
2006).
4


Psychological Manifestations of Chronic Pain
The influence of social, psychological and behavioral factors which exacerbate or
minimize ones pain experience, has received considerable attention in the literature.
Turk and Nash (1996) report that pain is rated more strongly when people are feeling
depressed and hopeless. Other findings indicate that depression is 3 to 4 times more
prevalent in lower back pain patients than in the general population (Sullivan, Reesor,
Mikail, & Fisher, 1992). Several other studies have suggested that those with chronic
lower back pain have higher rates of depression and diagnosed personality disorders and
are more likely to abuse drugs and alcohol in comparison to those with acute pain
(Brewer & Karoly, 1992; Kinney, Gatchel, Polatin, Fogarty, & Mayer, 1993; Sullivan, et
al., 1992).
Positive and negative affectivity and pain. Both positive and negative affect
have also been examined and are found to alternately improve and exacerbate CP
symptoms (Affleck, Tennen, Urrow, & Higgins, 1992; Litt, Shaffer, & Napolitano, 2004;
Smith & Zautra, 2002; Zautra, Smith, Affleck & Tennen, 2001). Watson and colleagues
Positive and Negative Affect Scales (PANAS) (1988) have been utilized to measure
positive and negative affect in many studies, including those examining chronic pain.
Watson et al.s underlying theory suggests that positive and negative affect are
orthogonal, and are measured as such: both affect types lie on two different spectra,
varying from high to low levels on each scale (Watson, Clark & Tellegen, 1988).
Negative affect (NA) includes a variety of aversive mood states including anger,
contempt, disgust, guilt, fear, and nervousness whereas positive affect (PA) has been
defined as the extent to which a person feels enthusiastic, active and alert (Watson,
5


Clark & Tellegen, 1988 p. 1063.) High PA as characterized by high energy, full
concentration, and pleasurable engagement and low PA is defined as sadness and
lethargy (Watson, Clark & Tellegen, 1988, p. 1063). Several correlational studies
suggest that both high NA and low PA are found to best characterize depressive
symptomatology, as they correlate significantly with Beck Depression Inventory self-
ratings (Jolly, Dyck, Kramer, & Wherry, 1994; Watson, Clark & Carey, 1988; Watson,
Clark & Tellegen, 1988). Although the PANAS is not employed as a diagnostic measure,
those who score both high on the NA scale and low on the PA scale are likely to exhibit
symptoms of, if not diagnoses of depression.
Few studies exist examining affective symtomatology and CP; however, several
studies suggest higher ratings of NA are found in CP samples and that both NA and PA
significantly contribute to pain levels and mood in those with CP. Specifically, current
findings suggest that PA may significantly dampen the effect of NA and lower pain
intensity ratings, as these results were consistently found over a several week to six-
month time span (Strand et al., 2006; Lift, Shafer and Napolitano, 2004; Zautra et al.,
2005).
High NA appears to be a significant mood indicator among those with CP. Smith
and Zautra (2002) compared personality factors, disease activity and NA among those
with osteoarthritis and rheumatoid arthritis (n = 175) and normal healthy controls (n =
80) over a 12-week period. Findings suggest the arthritis groups had significantly higher
NA compared to healthy controls. High NA in both disease groups was correlated with
higher interpersonal stress, interpersonal sensitivity, and measured neuroticism using the
NEO-Personality Inventory (Costa & McCrae, 1992). Furthermore, those with higher
6


neuroticism and interpersonal stress in the arthritis groups showed significant increases in
self-reported disease activity, as indicated by higher pain levels, joint tenderness, overall
arthritis symptoms, and activity limitations (Smith & Zautra, 2002).
Zautra et al.s 2005 study highlights the effects of PA and NA on stress and pain
over a 10-12 week period in 124 women with osteoarthritis or fibromyalgia. Weekly
measures included: the PANAS, an average level of pain self-report, a questionnaire
rating perceived interpersonal stress across multiple domains, and an eight-item
neuroticism measure derived from the Big Five Inventory (John, Donahue, & Kentle,
1991). The Big Five Inventory is a 44-item questionnaire, similar to Costa and McCraes
(1992) 240-item NEO Personality Inventory, Revised (NEO-PI-R), such that both
measure personality traits among five categories: extroverted, agreeable, conscientious,
emotionally stable, and openness to new experiences (Gosling, Rentfrow, & Swann,
2003). As hypothesized, multilevel modeling results revealed that higher levels of
weekly stress and pain were associated with weekly increases in NA. Weekly increases
in NA also correlated with higher levels of pain in future weeks. Interestingly, weekly
PA was associated with decreased NA, stress and pain, and increased PA appeared to
contribute to significantly lower pain over subsequent weeks. One limitation of this
study is that participants were only asked to retrospectively recall their average pain level
over the past week: currently experienced NA or PA could significantly bias this rating.
Regardless, results highlight the complex dynamics among PA, NA, stress and pain
levels in CP patients.
Litt, Shafer and Napolitano (2004) examined momentary PA, NA, pain levels,
and coping styles and their results give further credence to Zautra et al.s (2005) findings.
7


Multiple daily self-ratings were taken over a six-month period, thus improving the
internal validity of Zautra et al.s design. Participants (N= 30) with temporomandibular
dysfunction pain (TMD) were asked to give ratings on a hand-held computer four to
seven times per day for six months. Multiple measures assessed PA and NA (i.e. mood
states such as depressive and anxious mood), self-efficacy, pain ratings, physical and
emotional sensitivity, and appraisal and coping styles. High NA (particularly anger,
anxiety, boredom and sadness) was found to be the most significant predictor of higher
current and subsequent pain levels, followed by depressive mood. Conversely, coping
self-efficacy and PA (happy and peppy) were correlated with lower momentary pain
ratings. Similar to Zautra et. als 2005 findings, Litt et al.s results indicate that current
high NA correlated with higher pain ratings at the very next time point and current high
PA was related to lower pain at the next time point. From these two studies, it appears
that higher PA leads to current and subsequent lower pain ratings.
Findings from Strand et al.s 2006 study further suggest these relationships exist
in CP patients and imply that PA directly moderates the effect of NA on pain. Strand and
colleagues replicated Zautra et al.s 2005 study design and included a sample of 43
rheumatoid arthritis patients living in Norway. Results again suggest higher NA
correlated with more weekly pain and stress, but not PA; and those with higher NA
experienced higher interpersonal stress and depression, but not PA. Also, those with
higher levels of depression had significantly higher NA. When participants experienced
increased pain, NA was more prevalent; however, weekly PA moderated the effect of NA
on pain. Authors also compared weekly versus overall PA and found that weekly or
8


state levels only significantly contributed whereas trait PA, or overall PA, did not
(Strand et al., 2006).
High NA and low PA are also found to be correlated with increased functional
disability in those with rheumatoid arthritis (Zautra et al., 1995). In this study examining
three independent samples (n = 179; 177 and 134), participants with more functional
disability had both higher NA and conversely, lower PA. Confirmatory factor and
mediation analyses revealed the same significant relationships among the three groups:
coping mediates the relationship between affect (both negative and positive) and disease
(pain and activity limitation). Most notably, findings indicated that those with higher PA
used more adaptive and less maladaptive coping (catastrophizing), while those with more
NA used more maladaptive coping and had more pain and activity limitations.
Furthermore, high NA again correlated with higher pain levels, as well as more
maladaptive coping styles (Zautra et al., 1995).
Mood disorders, functional disability and pain. Many additional studies also
suggest psychological factors contribute to functional disability and may predispose
individuals to the laying of the foundation for long-term chronic pain. In a 2001 World
Health Organization study of nearly 5,500 CP primary care patients, 555 were found to
have a persistent pain disorder and a preexisting depressive and anxiety disorder
significantly predicted chronic pain onset (Gureje, Simon, & Von Korff, 2001).
Additionally, depression and anxiety contribute to increased functional disability and
self-reported higher levels of pain at 6 and 12 months post-injury in those with lower
extremity trauma (N= 601) (Wegener et al., 2011).
Psychosocial Treatment Approaches
9


Due to these complex contributing factors, it is the dominant belief that CP is a
biopsychosocial condition that responds most favorably to behavioral and
multidimensional treatment approaches.
Operant model of pain. Fordyces Operant Model of Pain was a prevailing
theory of chronic pain maintenance in the 1970s, following Melzack and Walls 1965
Gate Control Theory (Melzack & Wall, 1965). This behavioral approach involves the
concept of pain behaviors or things that people say or do when they are suffering or in
pain, such as avoiding activities or exercise for fear of reinjury (Gatchel, Peng, Peters,
Fuchs, & Turk, 2007, p. 582). Turk and Rudy (1986) explain that Fordyces model is
primarily based on these pain behaviors or behavioral manifestations (p. 761). These
are communications from CP patients to providers, family members and others, which are
influenced by consequences, and are therefore subject to reinforcement in this model
(Turk & Rudy, 1986, p. 761). Behavioral treatment, therefore, specifically targets pain
behaviors and works to change the antecedents and the consequences to the behavior to
modify or shape these behaviors.
Fordyce asserts there are three components necessary to change behavior through
operant conditioning: 1) identify the behavior to be changed; 2) decide on which types of
reinforcers would be most effective; and 3) establish enough control over ones
environment in order to influence behavior via consequences and schedules of
reinforcement (Fordyce, 1968, p. 181). Although there have been many treatment
advances in CP since this time, these operant conditioning principles continue to be
regarded as effective in the treatment of CP.
Cognitive and cognitive-behavioral therapy. Cognitive and cognitive-behavioral
10


therapy (CBT) approaches to CP management began to gain considerable attention in the
1990s and have become the primary non-medical intervention for CP. Negative
cognitions specific to pain are correlated with higher pain, disability and general distress
and increased healthcare and pain medication use: therefore, they are considered key
intervention targets (Gil, Abrams, Phillips, & Keefe, 1989; Jensen, Turner, Romano, &
Karoly, 1991; Stroud, Thorn, Jensen, & Boothby, 2000). The largest area in cognitive-
based approaches focuses primarily on changing maladaptive pain beliefs or negative
cognitions by using either cognitive restructuring, active or problem-solving coping, or
emotion-focused coping strategies (Geisser, Robinson, Riley, 1999; Thorn, Cross &
Walker, 2007). Problem-solving coping is defined as changing ones physical
environment to remove or alter the adverse effects of the stressor whereas emotion-
focused coping is characterized by changing the way one attends to or interprets] the
stressor (Lazarus, 1993, p. 8). Cross-sectional CP studies suggest active or problem-
solving coping is more effective than emotion-focused coping strategies in maintaining
positive mood, activity level and generally positive pain adjustment (Geisser, Robinson,
& Riley, 1999; Snow-Turek, Norris, & Tan, 1996).
CP researchers have focused specifically on catastrophizing, and those who
expect the worst from their pain problem, ruminate about pain sensations, and feel
helpless about controlling their pain are said to catastrophize as a result of their pain
experience (Thorn, Boothby, & Sullivan, 2002, p. 128). Catastrophizing has also been
correlated with increased disability, pain intensity, and depression in those with CP
(Sullivan et al., 2001). Multiple intervention studies that focus on decreasing
catastrophizing and negative cognitions utilize both cognitive restructuring and
11


behavioral techniques. Multiple cognitive behavioral intervention studies that target
catastrophizing and negative cognitions suggest improvements in these areas as well as
decreases in pain intensity, psychosocial symptoms, and physical disability (Jensen,
Turner, & Romano, 1994; Keefe, et al., 1991; Parker et al., 1989; Sullivan et al., 2001;
Thom, Boothby, & Sullivan, 2002; Turner & Clancy, 1986). More recently, researchers
suggest that catastrophizing is really a more complex combination of emotion and
cognitive appraisal than once thought and although this remains an important treatment
target, this concept requires further investigation (Jones, Rollman, White, Hill, & Brooke,
2003).
The efficacy of cognitive behavioral therapy is undisputed for many
psychological disorders and there have been over 325 intervention studies published on
CBT since 2006. CBT for chronic pain is based on Melzack and Walls Gate Control
Theory and on the belief that behavioral improvements can be made by modifying
dysfunctional thinking and beliefs (Dozois & Dobson, 2001; Melzack & Wall, 1965).
Although the exact mechanisms of CBT are unknown, the structure of CBT interventions
for chronic pain is designed to educate and train patients to identify and reconceptualize
both maladaptive cognitions and behaviors that serve to maintain their pain experience.
Teaching the patient how to conceptualize both his or her pain and their situation in
addition to educating them how to more effectively manage their problems helps to
improve their perception and provides hope. Encouraging the patient to take an active
role in their pain management; teaching them how to self-monitor thoughts, feelings, and
behaviors; and providing training on how to give self-praise for achievements boosts self-
confidence and fosters a sense of control over the pain experience. Lastly, creative
12


problem-solving strategies are introduced to anticipate obstacles and to maintain
treatment gains (Turk & Melzack, 2001).
Meta-analyses of CBT for chronic pain demonstrate its efficacy to reduce distress
and pain experience in general; increase positive/active coping; and improve daily
functioning and overall quality of life (Morley, et al., 1999; Hoffman et al., 2007).
Hoffman et ahs 2007 review of psychological interventions for chronic low back pain
found consistent significant support for CBT for chronic pain among 22 research studies.
Again, randomized controlled trials of CBT and other interventions were found to show
significant improvements over attention-control and wait-list groups, with CBT touted as
the most effective. Significant and moderate to large effects were found on pain intensity,
health-related quality of life, and depression for CBT. In addition, multidisciplinary
programs, including those with CBT, were suggested to be superior to other active
treatments in producing improved work-related outcomes at both short and long-term
follow-up (Hoffman et ah, 2007).
Although empirical evidence is robust for CBT for CP, systemic weaknesses in
research design and generalizability to all CP patients question the true efficacy of these
treatments for all individuals. More importantly, the exact mechanism of CBT for CP is
still unknown and the majority of effect sizes are moderate, thus suggesting that more
research is needed to further sift out the core ingredients mediating and sustaining the
more positive outcomes.
As mentioned previously, the empirical efficacy of CBT is undisputed for many
psychological disorders. Meta-analyses conducted for a range of conditions demonstrate
that the largest mean effect sizes are found for unipolar depression, generalized anxiety
13


disorder, panic disorder with or without agoraphobia, social phobia, posttraumatic stress
disorder, and childhood depressive and anxiety disorders (comparison-weighted grand
mean effect size = .95, (SD = .08)) (Butler, Chapman, Forman, & Beck, 2006). Chronic
pain, as well as marital distress, anger, and childhood somatic disorders, were found to
have only moderate effect sizes (ES = .62 (SD =.11)), with CP outcomes showing a
range of modest effect sizes of -. 14 to .40 when compared to active treatment controls
(Butler et al., 2006; Morley et al., 1999). Additionally, evidence of long-term effects is
limited for CBT for CP, thus suggesting that more research is needed (Morley et al.,
1999).
Morley et al.s 1999 review similarly notes weaknesses in the current research,
drawing attention to the underpowered analyses and a wide range in the quality of
treatment and attention-control group protocols across these 25 randomized controlled
studies (RCTs). The majority of these RCTs depend primarily on self-report
questionnaires and lack multimethod assessment approaches (Morley et al., 1999). These
studies also omit intervention provider training details and competency measures: in
those that did discuss trainer competency, only 60% were found to be qualified, and
treatment fidelity was not accounted for or discussed (Morley et al., 1999). Internal
validity is also questioned due to lack of consideration of patient expectations of the
treatment (Morley et al., 1999). The variable of patient expectation has been found to be
a significant predictor of improved outcomes for several CBT for CP interventions and is
generally not accounted for in most of these studies: for example, study participants who
believed their treatment to be effective were fives times more likely to improve
regardless of which treatment arm they were assigned (Kole-Snijders, Vlaeyen, Goossens,
14


Rutten-van Molken, Heuts, van Breukelen, & von Eek, 1999; Kalauokalani, Cherkin,
Sherman, Koepsell, & Deyo, 2001).
CBT for CP studies additionally demonstrate that a large proportion of individuals
suffering from CP do not benefit from this treatment, nor any other therapies irrespective
of modality type. Attrition rates average five to 26% for these CBT intervention studies,
with higher dropouts in depressed individuals, a common comorbidity in CP samples.
An additional confounding factor is comparison across studies with samples that differ in
pain location (e.g. lower back vs. temporomandibular pain) and etiology (Turk & Rudy,
1990). It has been suggested that better categorization of participants by pain location
may improve definitive findings respective to pain type and location, thus improving
treatment efficacy (Turk, 2005). Continued efforts to improve CP outcomes are focused
primarily in varying CBT protocols; however, leaders in this field suspect that there is
ample room for improvement in the efficacy of CBT in CP (Vlaeyen & Morley, 2005,
p. 4).
Third wave behavioral treatment approaches: Acceptance and Commitment
Therapy. A recent perspective in behaviorally-based interventions has received
increasing attention in the literature since 1994 (Hayes, 1994): Acceptance and
Commitment Therapy (ACT). This third wave behavioral therapy stems from
Relational Frame Theory (RFT) (Hayes, Bames-Holmes, & Roche, 2001). RFT is an
extension of behavioral theory involving both language and cognition. This theory
essentially states that individuals develop operantly conditioned responses to stimuli that
are not directly conditioned to those responses, but are somehow similar and are therefore
generalized. This process, in behavioral learning theory has been described as
15


abstraction; however, Hayes et al. expand on this theory by stating that this process
may be arbitrarily appli[ed] based on the individual and contextually dependent in
which they are exposed to the stimulus (Mcllvane, 2003, p. 29-30).
For example, those with CP may develop generalized conditioned responses to
particular stimuli, thus altering their behavior, limiting their experiences, and invariably,
leading to lower levels of function. A common cycle found in CP is avoiding tasks that
may invoke more pain; however, as the negative experience of pain continues, the range
of activity becomes more and more limited for fear of exacerbating their pain condition.
To illustrate, a person may avoid lifting heavy books after re-injuring their back at the
library but he or she may also avoid lifting any books or even more simply, avoid the
library, due to the similarities to the conditioned stimuli. Additionally, social factors such
as enabling or alternately, criticizing behaviors from family or friends further shapes
behavior such that ones world becomes more and more restricted. Negative
interpretation of the pain experience and themselves often leads to justification for
disengaging from adaptive and daily life activity in an effort to control or avoid painful
experiences and thus mov[es] them away from healthy life functioning (McCracken &
Eccleston, 2005, p. 164.)
Acceptance of chronic pain has been defined as living with pain without reaction,
disapproval or attempts to avoid it (McCracken & Eccleston, 2003, p. 198.) Specifically,
CP Acceptance requires a disengagement from struggling with pain, a realistic approach
to pain and pain-related circumstances, and an engagement in positive everyday activities
(McCracken & Eccleston, 2003, p. 198.) This approach differs from more traditional
CBT approaches because there is little emphasis.. .on changing the content o/thoughts;
16


rather, the emphasis is on changing awareness of and relationship to thoughts (Segal,
2001).
The definition of Acceptance is two-fold: Pain Willingness and Activity
Engagement. The Chronic Pain Acceptance Questionnaire, originally developed by
Geiser (1992), has been found to yield this two-factor structure: Pain Willingness (PW)
and Activity Engagement (AE). Of the PW items, several include I would gladly
sacrifice important things in my life to control this pain better and I need to concentrate
on getting rid of my pain (McCracken, Vowles, & Eccleston, 2004, p. 165). PW has
been defined as essentially ones degree of willingness to experience pain as well as
related thoughts and feelings (McCracken & Eccleston, 2005). The AE items include
Despite the pain, I am now sticking to a certain course in my life and Its not
necessary for me to control my pain in order to handle my life well (McCracken,
Vowles, & Eccleston, 2004, p. 165). AE is ones degree of willingness to engage in lifes
activities, despite the existence of pain (McCracken, Vowles, & Eccleston, 2004).
Both cross-sectional and prospective studies examining Acceptance and ACT show
significant positive chronic pain outcomes. Higher levels of AE and PW in CP samples
are associated with significantly lower levels of pain-related anxiety, depression-related
interference with functioning, and physical and psychosocial disability. Higher AE and
PW were also positively correlated with work status (McCracken & Eccleston, 2003).
PW also negatively predicted number of pain medications whereas AE was positively
correlated with uptime and work status (McCracken & Eccleston, 2005).
Intervention studies comparing ACT and treatment-as-usual groups with CP
outpatients also suggest promising findings, with treatment efficacy sustained over three-
17


month follow-up. Significant improvements were seen in physical and psychosocial
disability outcomes, depression, pain intensity, pain-related anxiety and number of
medical visits, school and work absences (McCracken, MacKichan, & Eccleston, 2007;
McCracken, et al., 2005; Vowles & McCracken, 2008; Dahl, Wilson, & Nilsson, 2004).
Taken together, these findings suggest that Acceptance-based approaches may offer
considerable clinical utility in treatment of CP.
Behavioral, cognitive, cognitive behavioral and acceptance-based treatment
approaches have contributed substantially to our understanding of the maintenance of CP
and provide moderately effective treatment options. However, studies continue to
suggest there is heterogeneity within CP samples such that different treatments work
more effectively for different individuals (Turk, 2005). Researchers have suggested that
patients may find more effective therapeutic responses if treatment is tailored to
particular characteristics they may hold (Costa & Pinto-Gouveia, 2010; Vowles,
McCracken, McLeod, & Eccleston, 2008). Profiling CP patients by specific
characteristics, namely those that are influential in promoting more positive outcomes,
may improve the efficacy and effectiveness of behavioral, cognitive, cognitive behavioral
and acceptance-based treatment approaches.
Profiling Chronic Pain Patients to Improve Treatment Efficacy
Characterizing CP patients based on single physiological or psychological factors
have been the primary means of grouping patients to create more effective CP treatments;
however, it may be useful to group patients based on dimensions of patient characteristics
(Turk, 2005). Due to the complexity of CP experience and varied treatment response, a
more multidimensional approach has been suggested (Dworkin & LeResche, 1992; Turk
18


& Rudy, 1988). To date, only one grouping method based on cognitive, affective and
behavioral variables has been examined. The most widely known application is the West
Haven Yale Multidimensional Pain Inventory (MPI; Kerns, Turk, & Rudy, 1985). This
measure has known statistical and clinical utility (Hunsley & Mash, 2008). The MPI
measures severity and impact of pain on work, social and other life activities in this
population across twelve domains of functioning (Wehmer, 1990) and subjective feelings
of self-control, problem-solving abilities and patients perceptions of themselves (Kerns,
Turk, & Rudy, 1985). Currently, it is the most commonly used method of profiling
chronic pain patients by their varying beliefs and behaviors.
The MPI includes several subscales and cluster analysis further differentiates
patients (Turk & Rudy, 1988). Among the multidimensional scales are 1) pain severity
and suffering; 2) perceptions of how pain interferes with their lives, including
interference with family and marital/couple functioning, work and social and recreational
activities; 3) dissatisfaction with present levels of functioning in family relationships,
marital/couple relationship, work and social life; 4) appraisals of support received from
significant others; 5) perceived life control, incorporating perceived ability to solve
problems and feelings of personal mastery and competence; 6) affective distress
including depressed mood, irritability, and tension; and 7) activity levels (Turk &
Melzack, 2001).
Cluster analysis produces more concise conceptualizations of patients and groups
them into three distinct profiles: Dysfunctional, Adaptive Copers and
Interpersonally Distressed (Turk & Rudy, 1988). Patients displaying the profile of the
Dysfunctional group are characterized by high pain severity, marked interference with
19


everyday life due to pain, high affective distress, low perception of life control, and a low
activity level. Patients in the Adaptive Coper group report lower pain severity, lower
interference, lower levels of affective distress, higher degree of life control and a higher
activity level. Patients in the Interpersonally Distressed group display lower reported
levels of social support, lower scores on solicitous and distracting responses from
significant others, and higher scores on punishing responses (Rudy, Turk, Zaki, & Curtin,
1989).
Empirical support has emerged to suggest that these profiles may predict
outcomes for chronic pain patients. Differences between the Dysfunctional and Adaptive
Coper groups in particular have been found to show discrepancies in the utilization of
pain medication, work status, time spent in bed, and scores indicating pain behaviors such
as affective distress and seeking help, with Adaptive Copers using fewer pain
medications and negative coping behaviors and demonstrating more active involvement
in work and life activities (Turk & Melzack, 2001). Also, some profiles are more likely
to benefit from treatment and predict treatment completion (Rudy, Turk, Kubinski, &
Zaki, 1995). Following a six week intervention including biofeedback and cognitive
behavioral therapy for CP, Dysfunctional groups significantly improved and had larger
reductions in pain intensity, perceived interference of pain symptoms in their lives,
depression, and negative cognitions in comparison to Adaptive Copers and
Interpersonally Distressed Groups at six-month follow-up (Rudy et al., 1995).
Three additional groups of patient profiles have been identified; however,
research findings suggest that these groups have not been found to be prevalent and in
general, have not shown high clinical utility for this population (Ravani, 2005). Despite
20


the theoretical advantages to grouping CP patients based on psychosocial and behavioral
characteristics, the empirical support and clinical application of profiles for use in clinical
conceptualization and treatment have been sparse.
However, given the existing positive results and the complexity of CP conditions,
finding new ways to characterize patients for purposes of tailoring effective treatment
interventions is a worthwhile pursuit. As stated previously, leading researchers in the CP
field have suggested that there is ample room for improvement in the efficacy of CBT in
chronic pain; therefore, opening the door for additional approaches and interventions
(Vlaeyen & Morley, 2005, p. 4). New methods of profiling CP patients based on key
defining characteristics may inform the development of more specific, effective and
tailored interventions for CP.
Profiling Chronic Pain Patients: Acceptance-Based Approaches
Recent studies have examined ways of grouping CP patients using the Acceptance
construct and specific clusters and characteristics that differ within samples. Vowles,
McCracken, McLeod, & Ecclestons 2008 study examined cluster analysis results within
a sample of CP outpatients seen at a specialty treatment clinic (N= 641) and found three
distinct clusters: 1) both low AE and PW, 2) both high AE and PW, and 3) high AE and
low PW. Subsequent comparisons demonstrated that the high AE and PW group reported
significantly lower pain, depression, pain-related anxiety, physical and psychosocial
disability, medical visits, medications, daily rest.. .and daily activity than the low AE
and PW group (Vowles, McCracken, McLeod, & Eccleston, 2008, p. 288). The third
group (high AE and low PW) was reported to significantly differ from both other groups
on depression, pain-related anxiety, daily rest, and psychosocial disability. This third
21


group also differed significantly from either the Low or High group in the five remaining
dependent variables: pain, physical disability, medical visits, daily activity, and classes of
medicine (Vowles, et al., 2008).
A 2010 study conducted by Costa and Pinto-Gouveia further suggests the validity
and reliability of these three clusters. The study findings show that three similar clusters
emerged in a mixed CP outpatient primary care and tertiary care sample (N= 103): 1)
both low AE and PW, 2) both high AE and PW, and a slightly varied third group, 3)
medium AE and low PW. The authors labeled this third group High AE and Low PW;
however, they also state that AE scores fell closer to the mean and therefore Medium
AE is a more accurate title. Post-hoc analyses demonstrated that among the subgroups,
those in the High cluster showed lower levels of anxiety, depression, stress and self-
compassion in comparison to the Low Acceptance group. In addition, this medium
Acceptance group showed significantly higher levels of depression, stress, self-judgment,
and over-identification (versus mindfulness) in comparison to the high Acceptance group.
It is interesting to note that this medium group (with relatively similar mean scores on
Pain Willingness but scores that are closer to the mean for Activity Engagement when
compared to the low AE of the Low Acceptance group) also showed significantly lower
levels of depression and stress, in comparison to the low Acceptance group.
Both author groups suggest this medium group displays good enough
functioning but self-report poorer emotional and social well-being and perhaps have more
attachment to finding pain relief (low PW) than the High Acceptance group. Therefore,
Costa and Pinto-Gouveia (2010) add that AE may be a buffer characteristic that
contributes to fewer adverse affects of stress, such as not amplifying negative emotions
22


and sensations that contribute to depression. Likewise, PW may be an important
characteristic to supplement AE such that individuals are not simply grinning and
bearing pain through increased AE but are truly willing to accept their pain as well
(Costa & Pinto-Gouveia, 2010; Vowles et al., 2008). Regardless, future studies would
benefit from further investigating this medium group to better understand the associations
among AE, PW and important psychosocial characteristics.
Subsequent studies examining 300 study participants in online CP support groups
similarly suggest the emergence of these Acceptance groups. One study examining CP
Acceptance found similar CP Acceptance subgroups using a different methodology:
tertile cutoffs based on levels of CP Acceptance. More specifically, subgroups were
formed by creating tertiles of simply high, medium and low scores on both of the CPAQ
subscales (AE and PW). The resulting two sets of three subgroups were: High AE,
Medium AE, Low AE, High PW, Medium PW, and Low PW. Despite this difference in
grouping method from Vowles et al., 2008s clusters ((1) both low AE and PW, 2) both
high AE and PW, and 3) high AE and low PW), results were remarkably similar. Payne-
Murphy et al.s results suggest that both the High AE and PW tertile groups reflected
scores that were very similar to that of the MPI Adaptive Coper profile. Conversely,
both the Low AE and PW tertile groups reflected scores that were very similar to that of
the MPI Dysfunctional profile (Payne-Murphy, Lillis, Brown, Herbst & Beacham,
2010; Payne-Murphy, Beacham, & Brown, 2011). These results demonstrate a consistent
pattern that can be seen in Figures 1 and 2.
23


MPIPain MPI Life MPI Negative MPI Life MPI Total
Severity Interference Mood Control Activity
NOTE: All subscale scores converted to T-scores
Figure 1. CP Activity Engagement High, Medium and Low score groups and MPI
Subscale score plots
MPIPain MPI Life MPI Negative MPI Life MPITotal
Severity Interference Mood Control Activity
NOTE: All subscale scores converted to T-scores
Figure 2. CP Willingness High, Medium and Low score groups and MPI Subscale score
plots
24


Another study conducted with preliminary data analyses in the same online CP
sample (N= 255), demonstrated that High, Medium and Low Acceptance subgroups
differed on ratings of perceived disability of functioning. Both High AE and High PW
subgroups scored significantly lower on the following Pain Disability Index (Pollard,
1984) scales in comparison to the Low AE and Low PW subgroups: family/home
responsibilities, recreation, social activities, occupation, sexual behavior, self care, and
life support (Kinman, Beacham, Payne-Murphy, Lillis, & Brown, 2010). Findings from
these studies provide further support for Acceptance as a key factor in CP patients
functional and affective outcomes.
Improving Access to and Dissemination for Chronic Pain Populations: Online
Interventions
Healthcare consumers, particularly those with CP and chronic illnesses, are looking
to online resources with increasing frequency for social support, psychoeducation, and
treatment options. The most recent Harris Poll (2011) found that 74% of American
adults, or 173 million people, look to the Internet for health care information; 60% have
searched within the past month; and 39% search often for this information (Taylor,
2011). The numbers of individuals who have ever searched for health information has
risen 47% in the past 13 years and continues to climb (Taylor, 2011). With the popularity
of such sites as WebMD (http://www.webmd.com/) and the Mayo Clinic website
(http://www.mayoclinic.com/), more patients are seeking drug interaction, symptom, and
diagnoses information, as well as peer support group forums and patient-doctor
communication sites and email portals (Nguyen, Carrieri-Kohlman, Rankin, Slaughter, &
Stulbarg, 2003). In a study of over 4700 consumers, almost 50% reported that online
25


health information improved their understanding of their condition and corresponding
treatments, and 30% stated this information improved [their] ability to manage their
health care needs without visiting a doctor or healthcare provider (Baker, Wagner,
Singer, & Bundorf, 2003, p. 2404). Some assert that online support groups also have an
advantage over in-person resources such that they reach those with restricted mobility,
geographic limitations, and/or who desire anonymity (Nguyen et al., 2003).
Due to this increasing popularity and the ease with which researchers can contact
online participants, Internet-administered psychosocial interventions for both
psychological and health conditions are becoming more common as well. Approximately
10 randomized control trials of Internet self-management interventions for adults (18
years and older) with chronic pain, including headache, have been published since 2000
and one systematic review and meta-analysis. All randomized control studies required
participants to login and submit responses at least once weekly via home-based
computers. Over half of these studies (n = 6) followed CBT-based protocols and almost
all studies included some form of relaxation training, stress management, problem
solving, and/or psychoeducation on chronic pain and/or exercise with an emphasis on
improved pain self-management. The length of intervention for the majority of the
studies was six weeks with one to three month follow-up; however, four studies extended
follow-up to six or 12 months. Intervention designs and findings from these studies are
reviewed below.
Lorig et al.s group has contributed significantly to this literature with three
randomized control studies examining CBT-based Internet interventions for CP and
disease. The first, published in 2002, examined a six-week closed medical provider-
26


moderated email discussion group that included a copy of a CP treatment book and a
videotape modeling how to maintain an active life with pain for participants (n = 202 vs.
n = 252 waiting list control) with chronic low back pain. Despite only a mean of eight
emails submitted per participant, one-year follow-up findings suggest significant
improvements in pain intensity, disability, role functioning, and health distress (time
spent worrying about ones health) as measured by the Roland-Morris Scale, Illness
Intrusiveness Scale and two other self-developed scales. Significant decreases in
physician visits for the treatment group were also reported: 1.5 visits less in the past six
months vs. .65 visits for the control group (Lorig, et al., 2002).
Lorig et al. expanded their intervention approach to more comprehensive CBT-
based protocols in 2006 and 2008 to examine changes in health behaviors and outcomes
in those with rheumatoid arthritis, osteoarthritis, fibromyalgia, type 2 diabetes or heart or
lung disease. The authors 2006 study examined the effects of a six-week intervention in
457 treatment participants (compared to 501 usual care control patients) with arthritis,
type 2 diabetes, or heart or lung disease. Key web-based intervention features included:
interactive didactics and psychoeducation on participant-specific chronic diseases,
medication and exercises; cognitive symptom management such as negative emotions,
self-talk and relaxation; physician-patient communication; healthy eating; action
planning; solving problem training; a discussion board; and a hardcopy book similar in
content. Moderators led participants (n = 25 per group) each day to support utilization
and offer encouragement and could be contacted by email for questions. Pertinent
findings at one year post-intervention suggest intervention participants logged in an
average of 26.5 times over the course of the study and significant changes were found in
27


1) pain, fatigue, health distress, shortness of breath; 2) stretching and strength exercise;
and most notably, 3) self-efficacy (Lorig, Ritter, Laurent, & Plant, 2006).
Similar findings were reported in Lorig, Ritter, Laurent, and Plants 2008 study
investigating a six-week online CBT-based protocol in patients with rheumatoid arthritis,
osteoarthritis, and fibromyalgia (n = 433 versus n = 422 usual care control), thus
garnering further support for online CBT interventions for chronic pain. Online
intervention features were almost identical to the 2006 study, but 2008 participants
received tailored exercise activities based on self-report questionnaire responses. Study
findings showed participants averaged higher weekly log-ins (31.6) than 2006
respondents and significant improvements using an intent-to-treat analysis were also
found at one year follow-up in 1) self-reported pain, health distress, activity limitation,
and global health; and 2) self-efficacy. Notably, no significant changes in healthcare
utilization were found and fibromyalgia patients showed fewer improvements over time
compared to those with rheumatoid arthritis and osteoarthritis (Lorig, Ritter, Laurent, &
Plant, 2008). It is also worthwhile to note significant attrition rates were found in all
three of Lorig et al.,s studies: the 2008 study reports a 40% dropout rate (204 out of
855); 22% in the first six months and 7% in the second six months of the 2002 study (159
of 580); and 19% (178 of 958) in the 2006 study (Macea, Gajos, Calil, & Fregni, 2010).
The remaining CBT-based online treatments for CP are similar in design and also
report favorable and equivocal findings; however, treatment length and type and duration
of research staff interaction varied. Burhman, Falthenhag, Strom, and Andersson (2004)
examined the effect of an online six-week CBT therapy in a randomized sample (n = 22
treatment; n = 29 control) of adults with chronic back pain. Web-based intervention
28


features mirrored previous studies (applied relaxation, tailored exercise instruction,
cognitive reconstruction, problem solving, adaptive coping, daily diary submissions, and
psychoeducation on CP and exercise) but followed a CP-specific CBT model published
by Vlaeyen and Linton (2000). In addition to email contact by a CBT-trained therapist,
participants also received one structured telephone call from their therapist weekly to
promote motivation. Telephone contact may have improved attrition rates in comparison
to other non-telephone support studies: only five participants (9%) were lost to follow-up.
Clinically significant improvements at three month follow-up include: 1) control of pain,
ability to decrease pain, catastrophizing, and praying and hoping as self-reported in the
Coping Strategies Questionnaire (CSQ); 2) increased life control and a decrease in
punishing responses as measured by the MPI; 3) improved beliefs and attitudes towards
ones pain and ability to function per the Pain and Impairment Relationship Scale
(PAIRS) and 4) fewer depressive symptoms per the Hospital Anxiety and Depression
Scale (HADS) (Burhman, Falthenhag, Strom, & Andersson, 2004).
Expanding on Buhrman et al.s design, Guttberg (2006) examined a 20-week web-
based CBT-based therapy in which chronic pain and burnout (on sick leave for six
months or more) participants (n = 30 versus n = 30 wait list controls) watched 19 videos
and read handouts; responded to related questions; and participated in weekly online
Socratic discussion moderated by a CBT-trained therapist and a staff member who had a
good quality of life despite a fibromyalgia diagnosis. The videos focused on teaching
cognitive self-treatment of chronic pain and burnout and included such topics as
Down in the Dumps or Depressed, Obstacles to Change, and Setting Limits (p.
222-223). This treatment approach also differed from others of this type such that
29


participants first met study staff in-person for one-half day prior to intervention-related
online exchanges. Follow-up findings at 23 weeks post-baseline showed significant
favorable changes in 1) depression as measured by the HADS; 2) vitality, social
functioning, role-physical, and bodily pain according to the Medical Outcomes 36-item
Short-Form Health Survey (SF-36) (Sullivan et al., 1995); and 3) 13 of 23 participants
who were not on disability pensions increased their work capacity. Only the significantly
improved work capacity of 12 of 23 non-disability-receiving participants was maintained,
however, at one-year follow-up (Guttberg, 2007). Lastly, only 13% (n = 5) were lost to
attrition over the course of the study (Guttberg, 2007).
Non-CBT web-based randomized control trial interventions for CP have shown
promising, yet less conclusive findings. Berman, Iris, Bode, and Drengenbergs 2009
online mind-body intervention compared changes in pain intensity, self-efficacy,
depression, anxiety, and responses to pain in treatment (n = 41) and control (n = 37)
participants aged 55 and older with CP. Over a six-week period, participants were
required to log in at least once each week to the study site to access 1) the initial module,
which educated users on problem-solving around change according to Prochaska,
Norcross and DiClementes Six-Stage Model Program (1994), and 2) six remaining self-
care tutorials that could be completed in any order: relaxation, breathing, positive
thinking, writing about positive experiences, writing about negative experiences, and
creative visual expression. Participants also received additional information on CP and
how to communicate with others about pain, and daily email support and encouragement
from study staff. Although attrition was lower than other studies described herein (12%),
only improvement in participants awareness of their responses to their pain was found
30


significant, and pain intensity between login and logoff were notable but non-significant
(Berman, Iris, Bode, & Drengenberg, 2009).
Lastly, results from a five-month online pilot intervention examining CP self-
management for chronic low back pain (n = 20;n= 15 control) suggests improved pain
intensity and physical activity, and decreased medical visits and opioid use at post-
treatment. Participants received access to an online portal containing psychoeducation on
chronic low back pain; videos and descriptions of specific exercises; videos and other
materials written by health professionals; a forum and chat room; and a Tell a Story
page in which to discuss personal histories with other patients. The authors state
participants also completed the treatment with improved procedural and declarative
knowledge of course material. More generally, reported findings lacked significance
levels, attrition, pain intensity ratings and other important measures which unfortunately
do not lend to the validity and reliability of results (Schulz, Rubinell, & Hartung, 2007).
Web-based treatment approaches differ for headache treatment: a combination of
relaxation and autogenic training (passive concentration of bodily perceptions (e.g.,
heaviness and warmth of arms...) that are facilitated by self-suggestions) has shown
efficacy in two randomized control trials for adults with headache (Stetter & Kupper,
2002, p. 45). Strom, Pettersson and Anderssons team (2000) was one of the first groups
to conduct a randomized control trial of an Internet-based self-help treatment for a CP
condition (headache). Over a six-week period, adult participants (post-treatment n = 20)
with recurrent non-cluster headaches received an Internet-delivered relaxation program
based on Osts (1987) applied relaxation and autogenic training (DeGood, 1996).
Participants had minimal contact with experimenters; received training weekly; and
31


submitted weekly self-report measures online regarding problem solving and relaxation
training practice. Headache diaries, including pain ratings, were submitted daily.
Findings suggest exactly half of those in the treatment group (10 of 20) showed
significant improvement over waiting-group controls (n = 25) in the areas of headache
frequency and pain intensity; however, no differences were found in depression
symptoms from pre to post-treatment. Attrition was a significant challenge with 56%
leaving the study (Strom, Pettersson, & Andersson, 2000).
Devineni and Blanchard (2005) then streamlined Strom et al.s design into a four-
week online intervention but expanded treatment to include cognitive stress coping
therapy for those with tension-type headache (n= 12; n = 20 symptom-monitoring
control), whereas those with migraine and mixed headache (n = 27; n = 35 symptom-
monitoring control) received autogenic training. Both treatment groups also received
progressive muscle relaxation training using downloadable audio with guided instruction;
practiced a limited biofeedback exercise; completed baseline and two week post-
treatment self-report measures; and maintained a daily headache diary for eight weeks.
Participants had minimal email contact from study staff. Results were generally positive
with 38.5% of the treatment group having a 50% reduction in disability scores due to
headache (and without increased medication use); however, 11.6% of this group also
experienced worse headache symptoms at post-treatment. Attrition was high as well,
with 38.5% lost to follow-up from baseline to post-treatment, a span of eight weeks
(Devineni & Blanchard, 2005).
Given these studies findings, randomized control trials for Internet interventions
for chronic pain and chronic disease offer a promising and more cost-effective alternative
32


to in-person therapies. Significant weaknesses in these studies include high attrition, with
an estimated average rate of 26.6%; and exclusion of adults with chronic pain who do not
have home-based computers or who cannot afford Internet services (Macea, et al., 2010).
Macea et al.s (2010) meta-analysis and systematic review reports a .285 pooled effect
size using nine of the described studies herein and two additional online therapies for
adolescents: therefore, these therapies had an approximately small to medium effect (.29)
when intervention groups were compared to controls.
More specifically, these therapies improved health outcomes (lower pain intensity)
in several studies as well as contributed to significant behavioral changes such as reduced
medical utilization and medication use; improved functional outcomes including less
work disability, and increased self-efficacy to pursue life activities despite the presence
of pain and symptoms, in general. Additionally, only two studies reported approximate
intervention cost ($115 (Lorig, et al., 2002) and $243 per patient (Lorig, et al., 2008)) but
both are less costly than in-person care (Macea, et al., 2010). Despite current
weaknesses, the efficacy and effectiveness of these online therapies and the increased
frequency of Internet education resources make online health care consumers an
important target population for clinical research and, ultimately, behavioral and
psychosocial intervention.
Purpose of the Present Study
The purpose of the present study is to replicate Vowles et al.s (2008) group
cluster methodology using an internet-based support group sample of self-identified
chronic pain patients. Replicating this methodology in this online sample may further our
understanding of the role of Acceptance in those with CP and may provide credence to a
33


more parsimonious (as compared to MPI profiles) approach of grouping CP patients
based on these characteristics. Furthermore, simplified methods may lead to the
development of tailored interventions based on these groupings to maximize positive
outcomes in chronic pain patients. Findings from this study and subsequent development
of tailored approaches to intervention with CP subgroups may likewise inform new
modalities of intervention delivery (i.e., electronic, telehealth, internet based).
Study Hypotheses
Hypothesis 1: It is hypothesized that there will be no differences in the cluster groups
identified in this online CP support group sample compared to the cluster groups
identified in Vowles et al.s (2008) clinical sample using the same methodology outlined
in that study. Specifically, the following group clusters are predicted:
1) Low Activity Engagement Low Pain Willingness
2) High Activity Engagement High Pain Willingness
3) High Activity Engagement Low Pain Willingness
Hypothesis 2: It is hypothesized that, in the online support group sample, self-reported
scores of Positive Affect, Negative Affect and Perceived Disability will differ overall by
group, when controlling for demographic and pain characteristics as indicated.
Specifically, cluster group 1 will be expected to show lower levels of Negative Affect
and Perceived Disability and higher levels of Positive Affect. Conversely, cluster group
3 will be expected to show higher levels of Negative Affect and Perceived Disability and
lower levels of Positive Affect. Group 2 will show moderate levels of Positive Affect,
Negative Affect and Perceived Disability. Groups will be identified via cluster analysis
as described in hypothesis one.
34


CHAPTER III
METHOD
The proposed study is an analysis of archival data from a previous data collection
conducted at Spalding University in Louisville, Kentucky and a subsequent variation of
that study conducted at the University of Colorado Denver in Denver, Colorado. The
study procedure and data collection was reviewed and approved by the both the Spalding
University Research Ethics Boards and University of Colorado Denver Colorado
Multiple Institutional Review Board for their respective data collection waves.
Participants
Self-identified chronic pain patients were recruited via online chronic pain
support groups. These groups were inclusive of individuals with non-malignant (i.e., not
related to cancer) chronic pain (pain lasting > 3 months), were 18 years of age or older,
and were able to read English. The following groups were excluded from the studies: a)
12 step; b) biofeedback; c) intervention-based; d) prayer/religious; e) medication focused
(e.g., Opioid, Oxy-Contin); f) malignant pain (cancer); and g) litigious groups.
Three waves of data collection were conducted between September and
November 2008, from March to July 2009 and from July 2010 to March 2011. Data from
two sample groups of participants were collected: targeted adult members that sought
support only (versus intervention-seeking) and general public group participants that did
not subscribe to group membership. For specific details of collection procedure and
wave variations, refer to Materials and Procedure. The total sample (N= 300; Mean age
= 44.7 years, SD = 11.2) was primarily female (85.6%), Caucasian (82%),
married/partnered (53.1%), and well educated (M= 14.8 years, SD = 2.4), with an
35


average income between $30k and $40k. Most common pain locations were lower limbs
and back (n = 132 and 134, respectively). Mean years with chronic pain was 14.4 (SD =
11.6) and average pain intensity was 7.4/10.
Materials and Procedure
Chronic pain group selection for the first two initial data waves was derived from
the Yahoo! Groups search engine by searching under chronic pain support group.
Due to the identical procedure and materials followed in these first two data waves that
were completed by the same research group, these two waves are henceforth referred to
as Wave 1 in this document. Appropriate group facilitators/moderators were then
contacted individually to propose research involvement. Following approval from group
moderators to approach members, an announcement was posted to invite participation in
a brief online study on the group website (Appendix A). Responding members were then
directed to a link that produced instructions for survey completion, informed consent, a
page requesting demographic information to become eligible for the gift card incentive,
and the multi-page survey (Appendix C). Moderators were then contacted two weeks and
one month following to repost the study recruitment announcement.
The second data collection wave was recruited via Facebook, a popular social
networking site, on chronic pain-related group web pages and included posting
invitations to participate in an online research survey. Searches for chronic pain
produced 147 groups, of which several closed groups required permission from study
staff to post study survey invitations. Once permission from these groups was granted,
three waves of posting to both open and closed groups began over the course of five
weeks (Appendix B). Responding group members were directed to the Survey Monkey
36


survey site, which was nearly identical in structure and material in both the new wave one
and wave two of data collection. This entailed an introductory page, informed consent,
demographic-related questions, the multi-page self-report questionnaires, a page seeking
demographic information to enroll them in the gift card incentive lottery, and a
concluding page. For both waves of data collection, participants were encouraged to
contact study staff by electronic mail if they had any questions or concerns.
There was only one difference between the two collection waves that is pertinent
to the present study: Wave 1 was given the option to select pain location: other for the
question, Please indicate the location of your pain: (Check all that apply), whereas
wave 2 was not. Other changes made from wave 1 to wave 2 that do not apply to the
present study include the following. Wave 1 participants completed the Mindfulness
Attention Awareness Scale (MAAS) (Brown & Ryan, 2003), whereas wave 2 received
the Cognitive and Affective Mindfulness Scale- Revised (CAMS-R) (Feldman, Hayes,
Kumar, Greeson, & Laurenceau, 2007). Wave 1 was also given the Short Health Anxiety
Inventory (S-HAI; Salkovskis, Rimes, Warwick, & Clark, 2002), a measure of
hypochondriasis and health anxiety, whereas wave 2 was not. Wave 2 received the
McGill Pain Questionnaire (MPQ; Melzack, 1975); the Godin Leisure-Time Exercise
Questionnaire (Godin & Shepard, 1985); and was asked to specify the types of
medications taken for pain symptoms: all of these items were not present in wave 1 data
collection.
Measures
See Appendix C through F to view the following measures.
37


Demographics and Medical History. Participants responded to questions
regarding demographics and history of chronic pain such as the initial causes of pain,
location of pain, medication use, numbers of surgeries, and all types of treatments utilized.
Other health and lifestyle-related questions included substance use and pain-related legal
involvement. See Appendix C.
Chronic Pain Acceptance. The Chronic Pain Acceptance Questionnaire (CPAQ)
is a brief, self-report measure of acceptance of chronic pain that was originally derived
from the Acceptance and Action Scale (AAQ) (Geiser, 1992). Two subscales further
differentiate outcomes: Activity Engagement and Pain Willingness. Items include:
Despite the pain, I am now sticking to a certain course in my life and I would gladly
sacrifice important things in my life to control this pain better (McCracken & Eccleston,
2005, p. 165). The 20 items are rated on a 7-point Likert-type scale from 0 (never
true ) to 6 (always true ). This assessment has been found to be internally consistent
(a = .78-. 82) and has moderate to high correlations with measures of avoidance, patient
functioning and emotional distress (McCracken & Eccleston, 2005). See Appendix D.
Pain Disability Index. The Pain Disability Index (PDI) (Pollard, 1984) assesses
the degree to which individuals believe their pain interferes with various activities in their
daily lives. Specific areas include: occupation, family/home responsibilities, sexual
behavior, self-care, recreation, and social and life support activities. The PDI is a brief,
7-item, self-report measure and items are rated on a 0-10 scale ranging from no
disability to total disability. Internal consistency is reportedly high (Cronbach alpha
= .86); concurrent validity is strong (Tait, Chibnall, & Krause, 1990), and generally,
psychometric properties have been reported as adequate and support its utility (Turk &
38


Melzack, 2001). See Appendix E.
Positive and Negative Affect Scales. The Positive and Negative Affect Scales
(PANAS) is a brief 20-item self-report measure of Positive and Negative Affect (Watson,
Clark, & Tellegen, 1988). Each item is a mood state adjective (i.e. distressed) and is
rated on a scale of 1 (very slightly or not all) to 5 (extremely). The negative items
are summed to provide a NA score for negative affect, and the positive items are summed
to provide a PA score for positive affect. Reliability has been reported to be good,
ranging from .84 to .87 Cronbach alphas for the NA scale and .86 to .90 for the PA scale
(Watson, Clark, & Tellegen, 1988). See Appendix F.
Data Analysis
All analyses were conducted using PASW (Predictive Analytics Software;
formerly SPSS or Statistical Package for the Social Sciences) version 17 for Mac.
Analyses of demographic and pain characteristics of both sample waves were conducted
by wave and cluster group using descriptive, frequency, correlational and chi-square
analyses and are presented in the tables below.
Hypothesis one was analyzed using hierarchical cluster (Wards method)
procedures using both CPAQ Activities Engagement (AE) and Pain Willingness (PW)
variables to determine number of cluster groups. A hierarchical approach was chosen to
closely follow Vowles et al.s methodology but the type of hierarchical approach was not
specified: therefore, Wards method was chosen based on the following steps.
Hierarchical analyses were initially conducted using all method types for clustering:
between groups, within groups, centroid clustering, median linkage, nearest neighbor,
and farthest neighbor. Comparisons of all method types revealed a range of two to four
39


groups emerging. The identification of three groups was selected based on viewing
dendograms and fusion plots specifically for the two methods that most clearly
demarcated clusters, Wards method and nearest neighbor. Wards method was chosen
due to its most clearly marked cluster groups based on its dendogram and fusion plot.
Wards method was then conducted on two randomly selected subsamples from the
cohort to establish internal validity and cross-validation of the emerging number of three
clusters (Gore, 2000; Blashfield, 1977). The k-means method was utilized to determine
cluster group identification and was chosen based on precedent set by previous studies
examining CPAQ clusters (Costa & Pinto-Gouveia, 2010; Vowles et al., 2008).
Hypothesis two was tested by conducting a one-way multivariate analysis of
covariance (MANCOVA) to assess whether there were significant differences among the
three cluster groups of combined AE and PW on a linear combination of Perceived
Disability, Positive Affect, and Negative Affect. The three cluster groups, 1) Low AE
and Low PW (Low-Low); 2) High AE and High PW (or High-High); and 3) Medium AE
and Medium PW (Medium-Medium); served as the categorical independent variable.
Total scores for the Pain Disability Scale (PDI), or Perceived Disability; and Positive and
Negative Affect, respectively, from the Positive and Negative Affect Scale (PANAS),
were employed as the three continuous dependent variables. Age, years of education,
number of surgeries, and current level of pain were chosen as covariates based on prior
research that indicate these variables correlate highly with degree of perceived disability
and negative and positive affect. Univariate analyses (Analysis of Covariance;
ANCOVA) were subsequently conducted to ascertain specifically where differences
among dependent variables were observed between the three cluster groups.
40


Assumptions for multivariate analysis of covariance that are robust to violation
include independence, multivariate normality, and homogeneity of variance and
covariance. However, it is noteworthy that cluster sample sizes used to conduct the
MANCOVA were not of equal size. The resulting sample sizes likely contributed to
significant Boxs M values, thus indicating covariance matrices of the dependent
variables are not homogeneous across all three cluster groups. Similarly, Levenes Test
of Equality of Error Variances suggests heterogeneity among the error variance of the
dependent variables across the cluster groups. These limitations are discussed herein.
41


CHAPTER IV
RESULTS
Characteristics of the Participant Sample Compared to Population Means
Descriptive statistics comparing population means from non-cancer chronic pain
patient samples from clinical sites are reviewed here to more carefully examine the
differences between them and our online sample. Specifically, demographic and pain
characteristic means from two samples of CP patients recruited from subspecialty pain
clinics in the U.K. and two samples of CP patients from the U.S. (one primary care and
the other mixed outpatient and subspecialty) are compared to the current online study
sample in Table 1.
Table 1
Demographic and Pain Characteristics of Clinic Samples us Current Online Sample
Characteristic McCrackena
_______________________Mean (SD)
Age (Years) 42.3 (12.4)**
Education (Years) 13.1(3.5)**
Years in Pain 9.24(8.2)**
Pain Rating (11 point) -------
Vowlesa Chelminskyb Puderc
Mean (SD) Mean (SD) Mean (SD)
47.3 (11.4)** 51 (9.6)** 52.74 (14.4)**
12.5(3.0)** ------ 14.07(2.6)**
8.0** (mdn) ------ 10.06(11.0)**
7.0 (1.8) 6.8 -----
* p < .05 **p < .01
a U.K. Pain Subspecialty
b U.S. Primary Care
CU.S. Mixed outpatient sample: Pain clinic; CP support groups; media ads
Significant differences are found between these clinical samples and the current
online sample in both demographic and pain characteristics, with the exception of pain
ratings found on an 11-point Likert type scale. The two U.K. pain subspecialty clinical
42


samples described in McCracken, Vowles, and Zhao-O-Brien (2010) and Vowles and
McCracken (2008) were non-cancer CP outpatients that were seen at a multidisciplinary
pain treatment center in Southwest England (N= 205; N= 187, respectively).
Chelminsky et al.s 2005 CP outpatients (N= 85) were referred by their primary care
physicians at an academic primary care site at the University of North Carolina and
Puders 1988 sample included a mix of CP outpatients seen at University of Washington
pain clinics and patients recruited from local CP support groups, senior citizen groups,
and media advertisements in the Seattle, Washington area (N= 69).
As reported in Table 1, all clinical CP samples were primarily middle-aged and
mean age in years in the clinic samples ranged from 42.3 to 52.74. In comparison, our
online sample had a mean age of 44.73 (SD = 11.24) and significantly differed from each
clinical CP sample as indicated by independent t tests (all ps < .01). Our online sample
had a mean 14.81 years of education (SD = 2.42) and also significantly differed from
clinical means that presented with a range from 12.5 to 14.07 years as indicated by
independent t tests (allp's < .01). Although Chelminsky et al. (2005) did not report mean
years of education, authors report 34% had some college; 28% graduated from high
school; and 38% did not have a high school diploma in this primary care CP sample.
Duration of CP in years also differed from our online sample such that online participants
had 14.41 years (SD = 11.58) and clinical samples reported mean ranges of 8 (median) to
10.06 years. Online CP participants self-reported a mean current pain severity rating of
7.04 (SD = 1.99) on an 11-point Likert type scale, with 10 representing most severe pain.
The two clinical studies that reported current pain severity on similar 11-point Likert type
scales also indicated similar pain ratings (7.0 (SD = 1.8) in Vowles and McCracken,
43


2008; 6.8 in Chelminsky, et al., 2005) and were found to be non-significant when
compared to the online sample as reported by independent t-tests (both ps> .05).
Other important differences between sample types (online vs. clinical) that are not
reported in Table 1 are described herein. Our online sample was represented primarily by
women (n = 250; 83.3%). This was found to be the case in three out of four clinical
samples as well; however, the percentage of women was lower in all of these clinical
studies (65.7%, McCracken, Vowles, & Zhao-O-Brien, 2010; 64.2%, Vowles &
McCracken, 2008; 40%, Chelminsky, et al., 2005; and 71%, Puder, 1988). Similar
ethnicity ratios were represented across all studies, with Caucasians overrepresented:
82.7% of our online sample (n = 248); 98% (McCracken, Vowles, & Zhao-O-Brien,
2010); 98.4% (Vowles & McCracken, 2008); and 78% (Chelminsky, et al., 2005) in the
clinical studies. Our sample included 4.8% African Americans, 2.7% Hispanics, 1.3%
Asian or Pacific Islanders, and 8.7% other. Most studies did not fully describe ethnicity
representation and Puder (1988) did not address this demographic variable at all:
therefore, minority percentages were not available.
Marriage/partner status did not differ greatly across these five studies. The
majority of all participants across all studies were married: 49% in our online sample;
58.3% in McCracken, Vowles, and Zhao-O-Brien (2010); 68.4% in Vowles and
McCracken (2008); 49% in Chelminsky, et al. (2005); and 75.4% in Puder (1988). Other
marriage/partner categories contrasted slightly with our online sample, which included
15.7% single; 12.0% living with partner; 9.0% divorced; 4.0% separated; and 2.7%
widowed. McCracken, Vowles, and Zhao-O-Brien (2010) reported 31.2% single, 10.1%
divorced, and .5% widowed. Vowles and McCracken (2008) reported 13.4% single,
44


15.5% divorced, and 2.7% widowed. Both U.S. study authors combined these categories
to report 7% in stable relationships and 44% unmarried (Chelminsky et al., 2005);
whereas 24.6% were widowed, single or divorced in Puders 2008 study.
Notably, employment status differed across samples: 49.7% were unemployed in
our online group compared to 69.7% (McCracken, Vowles, & Zhao-O-Brien, 2010);
76.3% (Vowles & McCracken, 2008); and 58% (unemployed, retired, student or
homemaker) in Puder, 1988. Chelminsky, et al. (2005) did not report employment status.
Twenty-five percent of our online sample reported full (17.7%) or part-time (7.3%)
employment; however, only 15.2% of one of the U.K. pain subspecialty samples were
found to be working either full or part-time (McCracken, Vowles, & Zhao-O-Brien,
2010). In contrast, Puders 1988 mixed CP sample indicated 42% were employed full or
part-time.
Both online and clinical samples had non-cancer CP for at least three months in
duration, with the exception of Puders (1988) six-month criteria, and all five samples
represented similarly diverse variations of pain type and location. Due to differences in
reporting methods across studies, only general variations can be ascertained. Our sample
included the following types of pain and location in order of most prevalent: Lower
Limbs (47.2%); Lower Back (44.7%); Cervical Spine (39.7%); Upper Extremities
(37.3%); Full Body (32%); Head/Face (29.7%); Thoracic Spine (27.0%); Pain Location:
Other (21.0%); and Pelvic/Genital (16.0%). McCracken, Vowles, and Zhao-OBrien
(2010) reported pain diagnoses of CP syndrome or non-specific musculoskeletal pain
(50.4%); fibromyalgia (14.5%); post surgical (10.0%); complex regional pain syndromes
(CRPS; 6.5%); or other (e.g. arthritis, peripheral neuropathy, (19.0%)).
45


Vowles and McCracken (2008) reported 51.2% of their pain subspecialty sample
did not have established diagnoses (45.9% low back; 18.4% shoulder/arms; 16.8% full
body; 11.8% legs/pelvic region; 2.7% neck; 2.2% mid-back; and 2.2% other (head,
abdominal, others)). Fibromyalgia (35.7%) and smaller percentages of CRPS, arthritis,
and other comprised the rest of this sample. Chelminsky et al.s 2005 primary care
cohort included 49% spine (35% lumbar; 8% cervical; 6% thoracic); fibromyalgia (15%);
polyarthritis (9%); 16% other specific locations (knee, abdomen, elbow and hip) and
diffuse neuropathic pain (5%). Lastly, 70% of Puders 1988 mixed CP sample had
various musculoskeletal diagnoses including rheumatoid arthritis, osteoarthritis, sciatica,
temporomandibular joint syndrome, or fibromyalgia; followed by idiopathic (17%);
neurological (10%); and vascular (3%) pain disorders.
Demographic Characteristics of the Participant Sample
Descriptive statistics for both data waves (September 2008 to July 2009 and July
2010 to March 2011) are shown in Table 2. Three time periods (between September and
November 2008, from March to July 2009, and from July 2010 to March 2011) are
described in the Methods section; however, as previously described, the classification of
two data waves is used as it better defines these samples. Specifically, the 2008 and 2009
waves were collected using online support-only groups (versus intervention-seeking)
from Yahoo! Groups whereas the 2010-2011 wave includes online general public
group participants via Facebook.
Both data collection waves (wave 1, n = 148; wave 2,n = 152) were
predominately female (85.1% and 86.1% respectively), Caucasian (89.9%; 75.7%) and
were well-educated (wave 1, mean years = 15.08 (SD = 2.24); wave 2, mean years =
46


14.52 (SD = 2.58). No significant differences were found between the waves in terms of
gender, income, and years of education. Although participants in both waves were
primarily middle-aged (mean years = 47.14 and 42.25, respectively), an independent t-
test revealed there was a significant effect for age, t(290) = 3.81 ,P< .01. There were
also no significant differences found for income, with the majority of participants from
both waves earning between $5k and $14,999 (wave 1 = 19.6%; wave 2 = 22.6%) and
$30k and $49,000 (wave 1 = 19.6%; wave 2 = 22.6%), with even distribution across other
annual salary ranges.
Results from a chi-square test of independence also showed significant
differences between the two waves for ethnicity {% (6, N= 300) = 43.33,p < .01) with
more African American (4.1% vs .7%) and Hispanic participants (2% vs. .7%) in wave 1.
A chi-square test of independence was also performed to examine the differences in type
of employment. Overall, the majority of participants in this sample were unemployed
(wave 1 = 56.8%; wave 2 = 50%); however, a significant difference was found between
the two waves, {% (3, n = 278) = 9.96,p < .05). Wave 1 had more homemakers (23.6%)
than wave 2 (14.6%) whereas wave 2 was found to have significantly more participants
who were employed full-time (25.4%) compared to wave 1 (13.5%).
Table 3 displays descriptive statistics for each of the cluster groups, low AE and
low PW (or Low-Low), high AE and high PW (High-High), and medium AE and
medium PW (Med-Med) that were identified by k-means cluster analysis. Most
participants fell into the Low-Low group (n = 81), followed by the Med-Med (n = 71),
and the High-High groups (n = 50). No significant differences were found among these
three cluster groups in terms of demographic variables with the exception of type of
47


Table 2
Demographic Characteristics of Sample by Data Collection Wave
Characteristic Wave 1 (n = 148) n (%) Wave 2 (n = 152) n (%) 2 X P
Gender .06 .81
Male 22 (14.9) 20 (13.9)
Female 126 (85.1) 124 (86.1)
Income 16.77 .40
Below $5000 7(6.9) 8 (6.5)
$5000 $14,999 20 (19.6) 28 (22.6)
$15,000-$29,999 18 (17.7) 26 (21)
$30,000 $49,999 20 (19.6) 28 (22.6)
$50,000 $69,999 11 (10.8) 14(11.2)
$70,000 $89,999 13 (12.7) 9 (7.3)
$90,000 $109,999 9 (8.9) 5(4)
Over $110,000 4(3.8) 6 (4.8)
Employment 9.96 .02*
Full time 20(13.5) 33 (25.4)
Part time 9(6.1) 13 (10)
Homemaker 35 (23.6) 19 (14.6)
Unemployed 84 (56.8) 65 (50)
Ethnicity 43.33 <.01**
Caucasian 133 (89.9) 115 (75.7)
African American 6(4.1) 1(7)
Hispanic 3(2) 1(7)
Asian/Pacific Islander 0 (0) 2(1.3)
Other 6(4.1) 7(4.6)
Mean (SD) Mean (SD) t P
Age (years) 47.14 10.61 42.25 11.36 3.81 <01**
Range: 18-71 Range: 19-70
Education (years) 15.08 2.24 14.52 2.58 1.86 .06
Range: 10-22 Range: 6-24
* p < .05 **p < .01
employment. A chi-square test of independence reveals a significant difference in
employment type among these groups {% (6, n = 202) = 15.48,/) < .05). Participants
within the High-High group were most likely to be employed (32%) or a homemaker
48


Table 3
Demographic Characteristics of Sample by Cluster Group
Characteristic Cluster 1 Low-Low (n = 81) n (%) Cluster 2 High-High (n = n (%) Cluster 3 50) Med-Med (n = n (%) 71) 2 X P
Gender Male Female 13 (16) 68(84) 5(10) 45 (90) 11 (15.5) 60 (84.5) 1.04 .6
Income 39.01 .18
Below $5000 4(6) 3 (7.5) 3(5)
$5000 $14,999 20 (29.8) 6(15) 8(13.3)
$15,000-$29,999 12 (18) 8 (20) 9(15)
$30,000 $49,999 14 (20.9) 9 (22.5) 16 (26.7)
$50,000 $69,999 18(2) 2(5) 10 (16.6)
$70,000 $89,999 6(9) 6(15) 6(10)
$90,000 $109,999 1(1.5) 5 (12.5) 4 (6.6)
Over $110,000 2(3) 1 (2.5) 4 (6.7)
Employment 15.48 .02*
Full time 10(12.3) 16 (32) 14 (19.7)
Part time 2(2.5) 3(6) 7 (9.9)
Homemaker 14(17.3) 12 (24) 13 (18.3)
Unemployed 55 (67.9) 19(38) 37 (52.1)
Ethnicity 8.54 .74
Caucasian 74 (91.4) 44 (88) 68 (95.8)
African American 2(2.5) 1(2) 1 (1.4)
Hispanic 2(2.5) 1(2) 0(0)
Asian/Pacific Island 0(0) 0(0) 1 (1.4)
Other 3 (3.6) 4(8) 1 (1.4)
Mean (SD) Mean (SD) Mean (SD) F P
Age (years) 44.9 (9.83) Range: 20-70 44.78 (12.66) Range: 20-68 45.37(12.32) Range: 18-71 .05 .95
Education (years) 14.58 (2.29) Range: 10-22 15.18 (2.51) Range: 10-21 14.84 (2.08) Range: 11-20 .99 .37
* p < .05 **p < .01
49


(38%) and were the least likely to be unemployed (38%) when compared to the two other
groups. Conversely, participants in the Low-Low cluster were the most likely to be
unemployed (67.9%), and least likely to be a homemaker (17.3%) or employed either
part-time (2.5%) or full-time (12.3%). Those in the Med-Med cluster fell within all
middle ranges between the High-High and Low-Low group employment classifications.
Although there were no other significant differences among demographic
characteristics, variations among the three clusters show notable trends. The majority of
participants in the High-High and Med-Med groups self-reported higher earnings than the
Low-Low participants and the High-High participants also reported the highest levels of
education (M= 15.18, (SD = 2.51)), and Low-Low the lowest (M= 14.58, (SD = 2.29)).
Pain Characteristics of the Participant Sample
Descriptive statistics for all pain characteristics for both data collection waves
(September 2008 to July 2009 and July 2010 to March 2011) are shown in Table 4.
Independent t tests and chi-square tests of independence were again conducted to
determine if there were significant differences between the data waves. Bonferroni
adjusted criterion alpha levels were applied to pain locations (p = .005 (.05/9)) and the
remaining pain characteristics (p = .02 (.05/3)). Participants were asked to record all
areas they experienced pain; therefore, more than one area may have been indicated per
individual. Most results from independent t tests comparing the location of the
participants pain and the two waves were found to be significantly different, even when
accounting for adjusted criterion levels. These endorsed areas include pain located in the
lower back, lower limbs, upper extremities, cervical spine, pelvic/genital, and other. No
significant differences were found for head/face, thoracic spine, or fully body pain.
50


Table 4
Pain Characteristics of Sample by Data Collection Wave
Characteristic Wave 1 (n = 148) n (%) Wave 2 (n = 152) n (%) 2 X P
Pain Location
Lower Back 50 (33.8) 84 (55.3) 14 .00*
Lower Limbs 52 (35.1) 90 (59.2) 17.44 .00*
Upper Extremities 38 (25.7) 74 (48.7) 16.97 .00*
Head/Face 40 (27) 49 (32.2) .98 .33
Cervical Spine 42 (28.4) 77 (50.7) 15.55 .00*
Thoracic Spine 43 (29.1) 38 (25) .63 .43
Pelvic/Genital 9(6.1) 39 (25.7) 31.38 .00*
Full Body 49 (33.1) 47 (30.9) .17 .69
Pain Location: Other 63 (42.6) 0(0) 81.92 .00*
Mean (SD) Mean (SD) t P
Years in Pain 6.82 12.77 12.54 10.22 2.73 .00**
Range: 1-54 Range: 0-45
Current Pain Level: 0-10-point scale (10 = most)
7.13 1.93 6.92 2.06 .81 .42
Range: 0-10 Range: 0-10
Number of Surgeries 1.43 2.23 2.56 4.6 -2.28 .02
Range: 0-13 Range: 0-30
* p < .005 using Bonferroni correction for nine chi-square tests of independence.
** p < .02 using Bonferroni correction for three independent t tests.
Wave 1 endorsed having the most other locations of pain (42.6%), followed by lower
limb (35.1%), lower back (33.8%), full body (33.1%), and thoracic spine pain (29.1%).
Wave 2 was not offered the other pain location item: lower limbs (59.2%), lower back
(55.3%), cervical spine (50.7%), upper extremities (48.7%), and head/face pain (32.2%)
were the most frequently endorsed.
Additionally, an independent t test with a Bonferroni adjusted alpha level of .02
(.05/3) was conducted to examine the differences in participants number of years they
had experienced pain (Years in Pain.) Results showed a significant difference in these
51


number of years between the two data waves, 7(185) = 2.73,/) < .01. Wave 1 participants
self-reported they experienced significantly more years of pain (M = 16.82 (SD = 12.77))
than wave 2 (M = 12.54 (SD = 10.22)). Wave 2 participants reported having had a
greater number of surgeries (M = 2.56 (SD = 4.6)) compared to wave 1 participants (M =
1.43 (SD = 2.23)); however, with Bonferroni correction alpha levels applied per test (.02),
this was not significant, 7(132) = -2.28,/) = .02. The average current pain levels for the
entire sample, rated between zero and ten on an 11-point Likert scale, fell between 6.92
(SD = 2.06) for wave 2 and 7.13 (SD = 1.93) for wave 1. There were no significant
differences reported for current pain level between the two groups, 7(232) = .81,/) = .42.
Table 5 displays pain characteristics of the sample by the three cluster groups,
Low-Low (n = 81), High-High (n = 50), and Med-Med (n = 71). No significant
differences were found among the cluster groups, with the exception of Current Pain
Level. A one-way analysis of variance (ANOVA) was conducted to examine differences
among groups in Current Pain Level. These results show a significant difference among
the clusters, F(2, 191) = 3.76,/) < .05. The Low-Low group reported the highest pain
levels (M= 7.44 (SD = 1.98)); followed by the Med-Med group (M= 6.95 (SD = 1.9));
and lastly, the High-High cluster showed the lowest current pain ratings (M= 6.44 (SD =
2.22)).
Despite the absence of other significant differences among pain characteristics,
several trends are again worth noting. The top three endorsed pain locations among all
three groups were nearly identical: lower back, lower limbs, and cervical spine. Notable
differences among the groups were as follows: overall, Low-Low participants endorsed
more pain locations in general than both other cluster groups; High-High members
52


Table 5
Pain Characteristics of Sample by Cluster Group
Characteristic Cluster 1 Low-Low (n = 81) n (%) Cluster 2 High-High (n = 50) n (%) Cluster 3 Med-Med (n = n (%) ZD 2 X P
Pain Location
Lower Back 43 (53.1) 20 (40) 36 (50.7) 2.24 .33
Lower Limbs 45 (55.6) 20 (40) 37 (52.1) .21 3.11
Upper Extremities 36 (44.4) 13 (26) 30 (42.3) 4.87 .09
Head/Face 26 (32.1) 19 (38) 19 (26.8) 1.72 .42
Cervical Spine 41 (50.6) 21 (42) 27 (38) 2.55 .28
Thoracic Spine 24 (29.6) 18 (36) 23 (32.4) .58 .75
Pelvic/Genital 17 (21) 4(8) 11 (15.5) 3.92 .14
Full Body 32 (39.5) 11 (22) 23 (32.4) 4.31 .12
Pain: Other 18 (22.2) 18 (36) 19 (26.8) 2.97 .23
Mean (SD) Mean (SD) Mean (SD) F P
Years in Pain 14.39 11.67 16.71 11.16 13.83 11.92 .804 .45
Range: 0-54 Range: 1-48 Range: 1-41
Current Pain Level: 10-Point Scale (10 = Most)
7.44 1.98 6.44 2.22 6.95 1.9 3.76 .03*
Range: 0-10 Range: 0-10 Range: 0-10
# Surgery 1.43 2.23 1.64 4.1 1.95 4.17 .23 .80
Range: 0-13 Range: 0-25 Range: 0-30
* p < .05 **p < .01
reported the fewest pain locations; High-High experienced less upper extremity pain
(26% versus 44.4% and 42.3%); and pelvic/genital pain was the least frequently endorsed
among all cluster groups (21%, 8% and 15.5%). High-High members also reported
experiencing the longest periods of years in pain (M = 16.71 (SD = 11.16)) compared to
both the Low-Low group (M = 14.39 (SD = 11.67)) and Med-Med cluster (M = 13.83 (SD
= 11.92)).
Lastly, Table 6 displays comparisons of pain location by gender. Chi-square tests
of independence and independent t tests were conducted to determine differences
53


between genders among types of pain location. Again, Bonferroni adjusted criterion
alpha levels were applied to pain locations (p = .005 (.05/9)) and the remaining pain
characteristics (p = .02 (.05/3)). Chi-square test results suggest that when compared to
men (n = 42), women (n = 250) experienced upper extremity pain significantly more, (%
(1, n = 292) = 12.02,p< .005), as well as head/face (% (1, n = 292) = 10.17,p< .005);
and cervical spine pain, (% (1, n = 292) = 11.79,/) < .005). Further examination of Years
in Pain, Current Pain Level and Number of Surgeries suggest that there are no significant
differences among these variables by gender. In the total sample, women reported higher
spine pain, (% (1, n = 292) = 11.79,/) < .005). Further examination of Years in Pain,
Current Pain Level and Number of Surgeries suggest that there are no significant
differences among these variables by gender. In the total sample, women reported higher
pain ratings (M = 7.1 (SD = 1.96)) when compared to men (M = 6.72 (SD = 2.14));
women reported slightly more Years in Pain (M = 14.44 (SD = 11.78)) than men (M =
14.25 (SD = 10.4)); and men have had more pain-related surgeries (M= 2.22 (SD =
3.16)) than women (M= 1.84 (SD = 3.5)).
Hypothesis One
Hypothesis one states that there will be no differences in the cluster groups
identified in this online CP support group sample compared to the cluster groups
identified by Vowles et al. (2008) using the same methodology outlined in that study.
Specifically, the following group clusters were predicted:
1) Low Activity Engagement Low Pain Willingness
2) High Activity Engagement High Pain Willingness
3) High Activity Engagement Low Pain Willingness
54


Table 6
Pain Characteristics by Gender
Characteristic Men (n = 42) n (%) Women (n = 250) n (%) 2 X P
Pain Location
Lower Back 14 (33.3) 120 (48) 3.12 .08
Lower Limbs 16 (38.1) 126 (50.4) 2.18 .14
Upper Extremities 6 (14.3) 106 (42.5) 12.02 .00*
Head/Face 4 (9.5) 85 (34) 10.17 .00*
Cervical Spine 7(16.7) 112 (44.8) 11.79 .00*
Thoracic Spine 7 (16.7) 74 (29.6) 3 .08
Pelvic/Genital 7 (16.7) 41 (16.4) .00 .97
Full Body 7(16.7) 89 (35.6) 5.84 .02
Pain Location: Other 6 (14.3) 57 (22.8) 1.54 .22
Mean (SD) Mean (SD) t P
Years in Pain 14.25 10.4 14.44 11.78 .08 .94
Range: 1-38 Range: 0-54
Current Pain Level: 0-10-point scale (10 = most) 6.72 2.14 Range: 0-10 7.1 1.96 Range: 0-10 1.05 .30
Number of Surgeries 2.22 3.16 Range: 0-13 1.84 3.5 Range: 0-30 -.62 .54
* p < .005 using Bonferroni correction for nine chi-square tests of independence.
** p < .02 using Bonferroni correction for three independent t tests.
Hierarchical cluster analysis using Wards method, followed by k-means cluster
analysis were conducted using Activity Engagement (AE) and Pain Willingness (PW)
totals from the Chronic Pain Acceptance Questionnaire (CPAQ). This method was
chosen to replicate Vowles et al.s 2008 methodology as closely as possible. The AE/PW
k-means cluster analysis with a maximum of 10 iterations specified three clusters: Low
AE and Low PW Low-Low (n = 81), High AE and High PW High-High (n = 50) and
Medium AE and Medium PW Med-Med (n = 71) (see Table 7). These findings
indicate that hypothesis one was largely supported: our predicted Low-Low and High-
55


High groups emerged but the third group varied slightly such that a Medium AE/Medium
PW group appeared. AE and PW means for each cluster are consistent with their
designated labels and are reported in Table 7. Average scores for the Low-Low group are
the lowest of the three clusters: AE (M = 15.75 (SD = 6.26)) and PW (M = 15.75 (SD =
7.1)). High-High members self-reported the highest ratings for both AE (M = 48.4 (SD =
16.2)) and for PW (M= 30 (SD = 7)). The Med-Med groups scores fell between the two
other clusters: AE (M= 33.68 (SD = 5.52)) and PW (M= 21.4 (SD = 6.7)).
Table 7
CPAQ Score Means of the Patient Clusters
Cluster 1 Cluster 2 Cluster 3
Low-Low (n = 81) High-High (n = 50) Med-Med (n = 71)
Mean (SD) Mean (SD) Mean (SD)
Activity Engagement 15.75 (6.26) 48.4 (6.2) 33.68 (5.52)
Pain Willingness 15.75(7.1) 30(7) 21.4 (6.7)
Hypothesis Two
The second hypothesis stated that in this online support group sample, self-
reported scores of Positive Affect, Negative Affect and Perceived Disability would differ
overall by cluster group, when controlling for demographic and pain characteristics as
indicated. Groups were to be identified via cluster analysis as described in the hypothesis
one. In the case that identical cluster groups were identified, it was hypothesized that
cluster group 1 (Low-Low) was expected to show higher levels of both Negative Affect
and Perceived Disability and lower levels of Positive Affect. Conversely, cluster group 2
(High-High) was expected to show lower levels of Negative Affect and Perceived
Disability and higher levels of Positive Affect. Group 3 (High AE/Low PW) was
56


predicted to show moderate levels of Positive Affect, Negative Affect and Perceived
Disability. Results from testing hypothesis one suggest that these groups were largely
similar; therefore, we proceeded to conduct the following statistical analyses using the
current three clusters.
In order to test hypothesis two, a one-way multivariate analysis of covariance
(MANCOVA) was conducted to examine differences in Perceived Disability, Negative
Affect and Positive Affect within each cluster group (Low-Low, High-High, and Med-
Med). The categorical cluster group type (Low-Low, High-High, and Med-Med) served
as the independent (i.e., group) variable and Perceived Disability, Negative Affect and
Positive Affect comprised the three continuous dependent variables. Age, number of
surgeries, years of education, and current level of pain were used as covariates based on
conducted correlational analyses that revealed significant relationships between these
survey items and the three dependent variables. The a priori criterion significance level
was set at .05.
As previously mentioned, homogeneity of variance and covariance were not
obtained as indicated by a significant Boxs M value (M= 38.25,p < .01). This finding
may have been partially due to the unequal sample sizes that resulted from conducting the
MANCOVA (n = 30, 52 and 60). However, assumptions for multivariate analysis of
covariance that are robust to violation include homogeneity of variance and covariance as
well as independence, and multivariate normality (Tabachnick & Fidell, 2006).
Significant results were obtained from the MANCOVA according to Wilks k,
(.55), F(6, 266) = 15.39,/) < .01, thus suggesting the combined DVs were significantly
affected by the type of cluster group (Table 8). These findings also showed a moderate
57


effect size (tjp2 = .26) between the three cluster groups (Low-Low High-High, and Med-
Med) and the combined DVs (Tabachnick & Fidell, 2006).
Table 8
MANCOVA Results by Perceived Disability, Positive Affect and Negative Affect
Variable Wilks X df F >1r2 P
Cluster Group .55 6 15.39 .26 .00**
a Age, number of surgeries, years of education, and current level of pain were used as
covariates.
* p < .05 **p < .01
Prior to conducting follow-up univariate tests, Levenes Tests of Equality of Error
Variances were conducted. These results showed heterogeneity among the error variance
of both the Perceived Disability (F(2, 139) = 7.58,p< .01) and Negative Affect (F( 2,
139) = 6.99, p < .01) dependent variables across the three cluster groups. Again,
assumptions for multivariate analysis of covariance that are robust to violation include
homogeneity of variance and covariance (Tabachnick & Fidell, 2006).
A series of one-way analysis of covariance analyses (ANCOVAs) were then
conducted to examine individual mean differences between each of the DVs and the
independent variable (cluster type). The same covariates that were described in the
MANCOVA analysis were used here as well. As indicated in Table 9, all ANCOVAs
were significant for each of the DVs (all ps < .01) with effect sizes (rjp2) in the moderate
ranges from .22 (Negative Affect and Perceived Disability) to .29 (Positive Affect).
Findings suggest that hypothesis two correctly predicted Perceived Disability,
Negative Affect, and Positive Affect differences by cluster groups. Specifically, the
58


Low-Low cluster displayed both the most Negative Affect (M= 28.05 (SD = 9.33)) and
Perceived Disability (M= 49.57 (SD = 9.46)) and the least Positive Affect (M= 20.28
(SD = 7.86)). Conversely, the High-High group (n = 30) showed the least Negative
Affect (M= 17.57 (SD = 5.81)); the least Perceived Disability (M= 32.28 (SD = 15.64));
and the most Positive Affect (M= 32.03 (SD = 6.49)). Lastly, the Med-Med group
revealed predicted averages in the moderate ranges: Negative Affect (M= 21.98 (SD =
6.88)); Perceived Disability (M= 38.8 (SD = 12.71)); and Positive Affect (M= 26.85 (SD
= 6.54)).
59


Table 9
ANCOVA Results, Means and Standard Deviations for Positive Affect, Negative Affect, and Perceived Disability by Cluster Groupa
Characteristic Cluster 1: Low-Low (n = 60) Mean (SD) Cluster 2: High-High (n = 30) Mean (SD) Cluster 3: Med-Med (n = 52) Mean (SD) df F ni P
Positive Affect 20.28 (7.86) 32.03 (6.49) 26.85 (6.54) 2 27.17 .29 <.01**
Negative Affect 28.05 (9.33) 17.57 (5.81) 21.98 (6.88) 2 19.03 .22 <.01**
Perceived Disability 49.57 (9.46) 32.28(15.64) 38.8(12.71) 2 19.64 .22 <.01**
a Age, number of surgeries, years of education, and current level of pain were used as covariates.
*p < .05 **p< .01
ON
o


CHAPTER IV
DISCUSSION
Chronic pain is a debilitating and costly health concern for over 116 million
Americans and their families with an approximate cost of $635 billion in litigation,
compensation, healthcare, and lost productivity (IOM, 2011). New CP treatments are
needed to foster more positive medical and functional outcomes and lower cost burden
for patients. Findings from previous studies suggest that profiling CP patients by levels
of Acceptance, a core concept in Acceptance and Commitment Therapy (ACT), may
have clinical utility in designing alternative and more effective CP interventions.
The overall purpose of this study was to examine how CP Acceptance
(specifically, Activity Engagement and Pain Willingness) may be utilized to group a CP
online sample using a cluster analysis design. Given these predicted clusters, we sought
to determine what degree of Perceived Disability, Positive Affect and Negative Affect
might differentially exist among these cluster groups. Costa and Pinto-Gouveias (2010)
and Vowles et al. (2008) studies found three cluster groups emerged in a clinic sample: a
Low-Low AE and PW group; a High-High AE and PW group; and a mixed group. These
studies findings suggest fewer negative traits and behaviors (pain-related anxiety,
depressive symptoms, and physical disability) were present in the Low-Low group vs. the
High-High group (Costa & Pinto-Gouveia, 2010; Vowles et al., 2008).
Sample characteristics
The current online sample (N= 300) was primarily women (83.3%; n = 250);
Caucasian (82.7%; n = 248) middle-aged (44.73 (SD = 11.24)); married (49%); and of
higher education (14.81 (SD = 2.42)). The largest group of participants was unemployed
61


(49.7%); followed by homemakers (18%); and full-time (17.7%) and part-time (7.3%)
workers. Mean years with chronic pain was 14.41 (SD = 11.58) and average current pain
rating was 7.04 (SD = 1.99) on an 11-point Likert scale with 11 as most severe. The most
prevalent pain locations were Lower Limbs (47.2%); Lower Back (44.7%); Cervical
Spine (39.7%); Upper Extremities (37.3%); Full Body (32%); Head/Face (29.7%);
Thoracic Spine (27.0%); Pain Location: Other (21.0%); and Pelvic/Genital (16.0%).
Although variations among demographic and pain characteristics were found
when comparisons were made to other non-cancer CP clinical samples in early published
work; by gender; by cluster groups; and by both data collection waves, these were not
substantial differences.
Because two waves of data collection were conducted at two time periods
(September 2008 to July 2009 in wave 1 and July 2010 to March 2011 in wave 2), and
were recruited from two different support group Internet sites (Yahoo! Groups and
Facebook, respectively), comparisons were made to determine whether significant
differences existed between them. No significant differences were found between the
waves in terms of gender, income, and years of education. The waves did differ
significantly in terms of age (wave 1 participants were an average of 4.89 years older);
ethnicity (wave 1 included more African American (4.1% vs .7%) and Hispanic
participants (2% vs. .7%)); and employment (wave 1 had more homemakers (23.6% vs
14.6%) and fewer full-time workers (13.5% vs 25.4%)).
The waves also differed significantly by pain characteristics with variations in
pain locations (lower back, lower limbs, upper extremities, cervical spine, pelvic/genital,
and other). No significant differences were found for head/face, thoracic spine, or full
62


body pain. One possible explanation for these differences may be due to study design
changes made between data collection waves 1 and 2. The pain location: other item
was not offered at wave 2: therefore, wave 1 participants may have felt their pain
condition was best described by this catch-all category whereas wave 2 was directed to
characterize their pain in the more diverse specific pain location items. However, these
differences may also be simply due to wide variations in pain location sites as reported by
CP populations.
Lastly, years in pain differed between the waves, with wave 1 participants self-
reporting a mean of nearly half as many years (6.82 (SD = 12.77)) as wave 2 participants
(12.54 (SD = 10.22)). Given these findings, distinguishing characteristics of the earlier
Yahoo! Groups (wave 1) compared to the wave 2 Facebook cohort include: older, more
ethnically diverse, more homemakers and fewer full-time workers, more other pain
locations, and fewer years with CP.
These differences may reflect variations in the type of online participant that is
drawn to frequent either Facebook or Yahoo! Groups. Although no studies of Facebook
or Yahoo! Groups online CP support groups exist to date; several studies estimate more
women than men utilize Facebook (57% women in 2012, AnsonAlex.com, 2012; and
61% in 2011, Kiser, 2011) and users tend to be younger, in general: 29% range from age
18 to 25; 23% are aged 26 to 34; and 18% are aged 35 to 44 (Communityl02.com, 2011).
More research should be done in this area to illuminate the differences among online
support group users.
Comparisons between gender within the total sample, as well as by cluster group,
showed fewer significant differences. Only differences in pain location were found
63


between men and women, with women reporting significantly more upper extremity
(42.5% vs 14.3%), head/face (34% vs. 9.5%), and cervical spine pain (44.8% vs 16.7%).
It is a possibility that unequal sample sizes (women n = 250; men n = 42) may have
contributed to these significant differences; however, the PASW software is designed to
account for this inequality in sample sizes. Lastly, only two differences were found
between the three cluster groups (Low-Low, High-High, and Med-Med): employment
type, with Low-Low participants reporting the most unemployment (67.9% vs 38% in the
High-High and 52.1% in the Med-Med group); and current pain level, with the Low-Low
cluster reporting the highest ratings (7.44 (SD = 1.98)) compared to the lowest ratings in
the High-High group (6.44 (SD = 2.22)).
A primary question in conducting research utilizing online chronic pain support
group members is whether these findings may generalize to clinical chronic pain
populations. In attempts to answer this question, comparisons were made to both clinical
samples in the United States and the United Kingdom. Compared to four other CP
clinical samples (two U.K. pain subspecialty samples; one U.S. primary care; and one
U.S. mixed outpatient, support group and media respondents), single sample t tests
revealed significant differences suggesting that our online sample was slightly younger,
more educated, and had experienced more years in pain. When other demographic and
pain characteristics were qualitatively compared due to studies differences in reporting,
our sample appeared to have more female than male participants (83.3% women); and
more were employed full or part-time or were homemakers (43%); however, our sample
had fairly comparable diverse representations of CP locations.
64


Overall, these findings suggest that participants in our online sample are
simultaneously different and similar to general population means, as represented by these
four clinical samples from the U.S. and U.K. When compared to other online CP
samples, however, our cohorts characteristics are more similar. Gender trends in online
intervention studies that recruit chronic illness or CP patients exclusively from Web sites
report greater numbers of women. For example, 62.5% women were recruited in
Burhman et al.s 2004 study of an Internet therapy with telephone support and 88%
women chronic headache patients were enrolled in Devineni and Blanchards study
(2005). Likewise, 71.6% and 71.2% women chronic illness patients were enrolled in
usual care and intervention groups, respectively, in Lorig et al.s 2006 study (total N =
780); and 90.5% and 89.8% usual care and treatment group women were enrolled in
Lorig et al.s 2008 study of arthritis and fibromyalgia therapy (N= 866). Likewise, these
studies reflect comparable age means to our online sample (44.73 (SD = 11.24)): 44.6
(.SD = 10.4); 43.6 (SD = 12.0); and 52.2 (SD = 10.9) (Buhrman et al., 2004; Devineni &
Blanchard, 2005; Lorig et al., 2008, respectively). These findings suggest that online CP
patients differ from clinical survey respondents due to sampling bias. As described in
other online studies, Internet recruitment readily excludes individuals who lack computer
skills, the means to own equipment and afford related service costs, and those who may
not have time to engage with CP support groups (Macea et al., 2010).
Hypothesis One
Hypothesis one states that there will be no differences in the cluster groups
identified in this online CP support group sample compared to the cluster groups
identified by Vowles et al. (2008) using the same methodology outlined in that study.
65


Specifically, we predicted three clusters would emerge: 1) Low Activity Engagement -
Low Pain Willingness; 2) High Activity Engagement High Pain Willingness; and 3)
High Activity Engagement Low Pain Willingness. This hypothesis was largely
supported such that the following three cluster groups emerged in our online sample: 1)
Low Activity Engagement Low Pain Willingness; 2) High Activity Engagement High
Pain Willingness; and 3) Medium Activity Engagement Medium Pain Willingness.
This third group also varied in Costa and Pinto-Gouveias 2010 replication and expansion
of Vowles et al.s 2008 study such that this third group represented those with High
Activity Engagement and Low Pain Willingness.
It is interesting that the characteristics of the middle group vary between the
current and previous studies and are, in general, less distinct than the other two clusters.
Similarly, the consistency of replicating cluster groups using the MPI measure has also
been shown to vary between studies and CP samples. For example, six clusters are
known to emerge from the MPI profiling procedure, (Unanalyzable, Anomalous, and
Hybrid in addition to Dysfunctional, Adaptive Copers and Interpersonally Distressed).
Although results vary across studies, studies have indicated that 3-30% of CP patients fall
into these unusable categories (Okifuji, 1999; Ravani, 2005; Zaza, Reyno, & Moulin,
2000). Other studies suggest that characteristics are so similar between the groups (e.g.
Interpersonally Distressed and Dysfunctional) that they should be combined (Epker &
Gatchel, 2000). Therefore, it appears that although both MPI and Acceptance profiles
predict better and worse functioning depending on profile type, Acceptance has thus far
shown more reliability in replicating similar cluster groups with no outlier groups. It may
be that variability in replicating the exact profiles across samples is a challenge within the
66


CP population, due to diversity of pain type and location. Future research limited to
specific pain location and type may reveal key constructs among these varied CP groups.
Hypothesis Two
Hypothesis two states that self-reported scores of Positive Affect, Negative Affect
and Perceived Disability will differ overall by group, when controlling for demographic
and pain characteristics as indicated. Specifically, cluster group 1 will be expected to
show lower levels of Negative Affect and Perceived Disability and higher levels of
Positive Affect. Conversely, cluster group 3 will be expected to show higher levels of
Negative Affect and Perceived Disability and lower levels of Positive Affect. Group 2
will show moderate levels of Positive Affect, Negative Affect and Perceived Disability.
Although results of hypothesis one were dependent on hypothesis two and the
third cluster group differed from the predicted group (Medium AE Medium PW was
our current finding), results support this hypothesis, such that we predicted moderate
levels of Positive Affect, Negative Affect and Perceived Disability. Specifically,
Perceived Disability, Positive Affect, and Negative Affect all significantly differed by
group in the predicted directions. Overall, the Low-Low group revealed the least
favorable of these characteristics among the three variables: the lowest mean Positive
Affect (M = 20.28 (SD = 7.86)); the highest mean Negative Affect (M = 28.05 (SD =
9.33)); and the highest mean Perceived Disability (M = 49.57 (SD = 9.46)). Similarly,
the High-High cluster showed the most favorable degree of these traits: specifically, this
group reflected the highest mean Positive Affect (M= 32.03 (SD = 6.49)); the lowest
mean Negative Affect (M= 17.57 (SD = 5.81)); and the lowest Perceived Disability (M=
32.28 (SD = 15.64)). Also, the Med-Med participants had levels between these two
67


groups: Positive Affect (M= 26.85 (SD = 6.54)); Negative Affect (M= 21.98 (SD =
6.88)); and Perceived Disability (M= 38.8 (SD = 12.71)).
Significantly less physical and psychosocial disability was also found in the High-
High group in Vowles et al.s 2008 study as measured by the Sickness Impact Profile
(SIP; Bergner, Bobbitt, Carter, & Gilson, 1981). Similarly, Vowles et al.s Low-Low
group also showed significantly higher ratings in physical and psychosocial disability
when compared to the two other cluster groups. Although Positive and Negative Affect
were not specifically measured in these two prior studies by Vowles et al. (2008) and
Costa and Pinto-Gouveia (2010), depressive symptoms (sadness, guilt, lethargy) are
reflected in low Positive Affect and high Negative Affect in the PANAS measure
(Watson, 1988). Both Vowles et al. (2008) and Costa and Pinto-Gouveia (2010) found
the most depression in the Low-Low group and conversely, the least, in the High-High
group as measured by the Beck Depression Inventory (BDI; Beck, Ward, Mendelson,
Mock, & Erbaugh, 1961) and the Self-Compassion Scale (Neff, 2003), respectively.
Given the significant parallels in all three studies, our replicated findings strongly suggest
those with higher Pain Willingness and Activities Engagement also experience less pain-
related disability, and more positive and fewer negative emotions, which may be
manifested in depression.
Limitations
The primary limitations in this study include data collected from a retrospective
self-report sample. Retrospective data is subject to inaccuracies in participants self-
reporting: therefore, prospective data collection, in which participants are followed and
assessed over time, is preferable (Kazdin, 2003). Providing objective behavioral
68


measures would also strengthen this study design, as well as collateral assessments such
as semi structured questionnaires administered to participants health providers or
significant others. These measures could provide convergent validity and verify
participants subjective ratings. Additionally, online recruitment, despite increased
feasibility over in-person data collection, did reveal bias in our sample of CP patients,
suggesting these results may not generalize to clinical primary care or subspecialty CP
patients in the general population. Notably, however, given the proliferation of online
venues for support, information and intervention, online samples may prove to be a
clinical population upon which specific norms may be based in the future. Lastly,
previous studies using cluster analysis have reported difficulty consistently reproducing
the same clusters, therefore demonstrating some degree of limitation of this type of
methodology (Gore, 2000).
Future Directions
Future research should further illuminate the precise mechanisms that contribute
to our studys findings in efforts to design and implement more effective CP
interventions. Specifically, our results matched previous studies such that those with low
Acceptance experience more negative outcomes (low positive affect and both high
negative affect and perceived disability) whereas significantly more positive outcomes
are found for those with high Acceptance; however, future studies might explore specific
mechanisms regarding how this occurs in CP populations. This research is important for
improving Acceptance-based interventions targeting increased Activity Engagement and
Pain Willingness.
69


Given the rapidly increasing numbers of healthcare consumers turning to the
Internet for health information, future Acceptance-based studies should also provide
assessment and profiling interventions online to CP patients as a viable alternative to in-
person treatments (Baker et al., 2003; Nguyen, et al., 2003; Taylor, 2011). As reported
previously, current online interventions, primarily based in CBT, have known efficacy in
targeting and improving CP symptom management and affective and behavioral
outcomes (Macea et al., 2010). Specifically, these therapies improved health outcomes
(lower pain intensity) in several studies as well as contributed to significant behavioral
changes such as reduced medical utilization and medication use; improved functional
outcomes including less work disability, and increased self-efficacy to pursue life
activities despite the presence of pain and symptoms, in general (Berman et al., 2009;
Burhman et al., 2004; Devineni & Blanchard, 2005; Guttberg, 2007; Guttberg, 2006;
Lorig, et al., 2008; Lorig, et al., 2006; Lorig, et al., 2002; Macea et al., 2010; Schulz et al.,
2007; Strom, Pettersson, & Andersson, 2000).
Results from previous studies suggest that future studies may benefit from
increased staff contact, whether via telephone, email, or in-person, that expresses
encouragement and support to maintain adherence in order to lower attrition rates
(Buhrman et al., 2004; Guttberg, 2007). Increased privacy and access to services for
patients and reduced cost for both the healthcare consumer and provider are also
significant advantages of these interventions, given rising healthcare costs (Lorig et al.,
2002; Lorig et al., 2008; Macea et al., 2010). Future studies should also include random
assignment to the treatment and control group, as well as 12-month follow-up to monitor
intervention efficacy longitudinally. Special recruitment efforts and appropriate
70


incentives should be offered to reach participants who may not be able to afford computer
access and services as well as online male CP patients, due to their less active
engagement in Internet CP support group studies (Buhrman et al., 2004; Devineni &
Blanchard, 2005; Lorig et al., 2006; Lorig et al., 2008).
According to Vlaeyen and Morley, Acceptance-based profiling may help fill the
gap in which there is ample room for improvement: a change the field has been waiting
for (Vlaeyen & Morley, 2005, p. 4). Subsequent development of these online tailored
approaches via Acceptance-based profiling have significant implications for not only
changing the type of delivery of therapy services, but also for improving patients
symptoms and affective and behavioral outcomes.
Summary
We conducted a cluster analysis on a sample of online CP support group members
based on levels of Acceptance (Activity Engagement and Pain Willingness). Three
clusters emerged suggesting our first hypothesis was largely supported: 1) Low Activity
Engagement Low Pain Willingness; 2) High Activity Engagement High Pain
Willingness; and 3) Medium Activity Engagement Medium Pain Willingness. Results
from the MANCOVA then confirmed our second hypothesis: those in the Low-Low
group showed the most Negative Affect and Perceived Disability and the least Positive
Affect. Conversely, the High-High group showed the least amount of Negative Affect
and Perceived Disability and the highest scores on Positive Affect. The middle group
revealed moderate scores across these same measures of mood and functional outcome.
Implications from these results include 1) Acceptance is a viable construct that predicts
mood and functional outcome in an online CP sample; 2) Acceptance-based profiling is a
71


promising new method of characterizing those with CP; 3) Acceptance-based profiling
interventions, whether conducted in vivo or online, provides the field with an alternative
treatment that may improve the efficacy and effectiveness of existing CP therapies.
72


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APPENDIX A
Hello!
You are being invited to participate in a project designed to increase understanding of
chronic pain. Specifically, we are conducting a study on persons with chronic pain who
are involved in online support groups. We are inviting participants (age 18 and older)
who have been and are currently experiencing a recurrent or nonremitting chronic pain
condition for at least 3 months to complete an online survey. It is anticipated that this
survey will take 45-60 minutes of your time to complete.
In addition, we request that you forward/share the survey link to other individuals with
chronic pain who may wish to participate in this study so that they may have the
opportunity to assist us in gathering information about chronic pain and online support
groups.
Participants who complete the survey may elect to be entered into a drawing to win one
of twelve $50 VISA gift cards. We expect to have approximately 600 people complete
the survey. If everyone enters the drawing your odds of winning one of the gift cards
would be 1 in 50.
To participate in this study, please click on the following link:
http: //www.questionpro.com/akira/T akeSurvey?id= 1179976
This study has been approved by Spalding Universitys Research Ethics Committee in
support of dissertation requirements for the completion of a doctorate of psychology
(Psy.D.) degree. If you have any questions regarding this study, please feel free to
contact Kristen Crafton, M.A. at chronicpaincvberstudv@hotmail.com or Dr. Steve
Katsikas at skatsikas@spalding.edu.
Sincerely,
Kristen Crafton, MA
Steve Katsikas, PhD
School of Professional Psychology
Spalding University
91


APPENDIX B
HSRC # 10-0538
An Exploratory Correlational Study of Persons with Chronic Pain Involved in Internet-Based
Chronic Pain Groups
Abbie Beacham, Ph.D.
Facebook advertisement:
You are invited to take part in a research study: An Exploratory Correlational Study of
Persons with Chronic Pain Involved in Internet-Based Chronic Pain Groups (HSRC# 10-
0538], This study is being led Abbie Beacham, Ph.D., at the University of Colorado Denver.
The study involves answering a series of online questionnaires. Upon completion of the
study questionnaires, you will have to opportunity to enter a drawing for a gift card. Please
click one of the following links if you are interested in learning more about the research
study.
92


Full Text

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IMPROVING CHRONIC PAIN PATIENT PROFILING: AN ACCEPTANCE BASED APPROACH USING AN ONLINE SAMPLE by Jessica C. Payne Murphy B.A., Smith College, 1999 A thesis submitted to the Faculty of the Graduate School of the University of Colorado in parti al fulfillment of the requirements for the degree of Masters of Arts Clinical Health Psychology Program 2012

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ii This thesis for the Master of Arts degree by Jessica C. Payne Murphy has been approved for the Clinical Health Psychology Program by Abbie O. Beacham, PhD, Chair and Advisor David Albeck, PhD Josette G. Harris, PhD Date: 10 /2 6 /12

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iii Payne Murphy, Jessica C. (M.A., Clinical Health Psychology Program) Improving Chronic Pain Patient Profiling: An Acceptance Based Approach Using an Online Sa mple Thesis directed by Visiting Associate Professor Abbie O. Beacham, Ph.D. ABSTRACT Over 116 million Americans experience chronic pain (CP) incurring an annual cost of $635 billion in healthcare and lost work. Acceptance based therapies have gained in creasing recognition for improving functional outcomes. In our online CP patient support group sample, we predicted 1) CP patients would cluster into low, medium and high groups of CP Acceptance and 2) self reported scores of Positive Affect (PA), Negativ e Affect (NA) and Perceived Disability (PD) would differ overall by cluster, with the most positive outcomes found in the high cluster and the least found in the low. The total sample ( N = 300; Mean age = 44.7 years, SD = 11.2) was primarily female (85.6% ), Caucasian (82%), married/partnered (53.1%), and well educated ( M = 14.8 years, SD = 2.4). Years with chronic pain was 14.4 ( SD = 11.6) and average pain intensity was 7.4/10. Participants completed demographic, medical history, the Chronic Pain Accepta nce Questionnaire (CPAQ), Positive and Negative Affect Scales (PANAS), and the Pain Disability Index (PDI). A k means cluster analysis was conducted using Activity Engagement (AE) and Pain Willingness (PW) totals from the Chronic Pain Acceptance Questionn aire (CPAQ). As predicted, cluster analysis with a maximum of 10 iterations specified three clusters: Low AE/Low PW (Low Low; n = 81); High AE/High PW (High High; n = 50); and Medium AE/Medium PW (Med Med; n = 71). A MANCOVA was then conducted to examine differences in PA, NA and PD within each cluster group, covarying for age, number of su rgeries, years of education, and current pain level.

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iv Significant MANCOVA results were obtained according to Wilks' (.55), F (6, 266) = 15.39 p < .01 Follow up ANCOVAs revealed mean differences in the predicted directions: the High High group showed t he most PA ( M = 32.03 ( SD = 6.49)); and the least NA ( M = 17.57 ( SD = 5.81)) and PD ( M = 32.28 ( SD = 15.64)). Conversely, the Low Low group displayed the least PA ( M = 20.28 ( SD = 7.86)); and the most NA ( M = 28.05 ( SD = 9.33)) and PD ( M = 49.57 ( SD = 9.4 6)). Findings support utility of online tailored interventions targeting CP Acceptance subgroups to improve functional status. The form and content of this abstract are approved. I recommend its publication. Approved: Abbie O. Beacham, Ph.D.

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v TABLE OF CONTENTS CHAPTER I. INTRODUCTION..................................................................................................... 1 II. REVIEW OF THE LITERATURE........................................................................... 3 Medical Manifestations and Functional Disability in Chronic Pain ...................... 3 Demographic Factors Prevalent in Chronic Pain ................................................... 4 Psychological Manifestati ons of Chronic Pain ...................................................... 5 Positive and negative affectivity and pain... .............................................. 5 Mood disorders, functional disabilit y and pain. ......................................... 9 Psychosocial Treatment Approaches ...................................... ............................... 9 Operant model of pa in............. 10 Cognitive and cognitive beh avioral therapy.... 10 Third wave behavioral treatment approaches: Acceptance and Commitment Therapy. ................................... ........................ 15 Profiling Chronic Pain Patients to Improve Treatment Efficacy ......................... 18 Profiling Chronic Pain Patients: Acceptance Based Approaches ........................ 21 Improvin g Access to and Dissemination for Chronic Pain Populations: Online Interventions ...................................................................................... 25 Purpose of the Present Study........................ ....................................................... 33 Study Hypotheses................................................................................................ 34 III. METHOD................................................................. ............................................... 35 Participants........................................................................................................... 35 Materials and Procedures ................................. .................................................... 36

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vi Measures...................................................................................................... 37 Data Analysis................................................................... .................................... 39 IV. RESULTS 42 Characteristics of the Participant Sample Compared to Population Means.....42 Demographic Characteristics of the Participant Sample..4 6 Pain Characteristics of the Participant Sample.50 Hypothesis One ................................................................................ .................... 54 Hypothesis Two .................... ............................................................................... 56 VI. DISCUSSION.. 61 Sample characteristics ......................................................................................... 61 Hypothesis One .................................................................................................... 65 Hypothesis Two ............................................................................................. ...... 67 Limitations ........................................................................................................... 68 Future Directions ................................................................................. ................ 69 Summary.............................................................................................................. 71 REFERENCES............................................................................................ ..................... 73 APPENDIX..................................................................................................................... .. 91 A. Invitation to Participants: Wave 1 ................................................. .................. 91 B. Invitation to Participants: Wave 2 ................................................................... 92 C. Participant Questionnaire Battery.. ......................................................... 93 D. Chronic Pain Acce ptance Questionnaire ....................................................... 131 E. Pain Disability Index ..................................................................................... 132 F. Positive and Negative Affect Scale ..................... .......................................... 134

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vii LIST OF TABLES TABLE 1. Demographic and Pain Characteristics of Clinic Samples vs. Current Online Sample ......................................................................................... 42 2. Demog raphic Characteristics of Sample by Data Collection Wave.. ........................... 48 3. Demographic Characteristics of Sample by Cluster Group.. ........................................ 49 4. Pain Characteristics of Sample by Data Collection Wave.. ..... ..................................... 51 5. Pain Characteristics of Sample by Cluster Group.. ....................................................... 53 6. Pain Characteristics by Gender.. ..................................................................... .............. 55 7. CPAQ Score Means of the Patient Clusters .. ................................... ............................. 56 8. MANCOVA Results by Perceived Disability, Positive Affect and Negative Affect.. ............................... ............................................................. 58 9. ANCOVA Results, Means and Standard Deviations for Positive Affect, Negative Affect, and Perceived Disability by Cluster Group. 60

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viii LIST OF FIGURES FIGURE 1. CP Activity Engagement High, Medium and Low score groups and MPI Subscale score plots24 2. CP Willingness High, Medium and Low score groups and MPI Subscale score plots24

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1 CHAPTER I I NTRODUCTION Chronic pain (CP) is a debilitating and prevalent health concern that is a tremendous financial and emotional burden for over 1 16 million Americans and their families ( Institute of Medicine of the American Academies, 2011 ) CP is defined by three months or more of an unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in terms of such damage ( I nternational Association for the Study of Pain, 2011 ). Disability compensation, healthcare costs and lost work productivity are significant problems in CP populations. The prevalence of CP in most industrialized countries is 10 1 3 % of the population (Croft, 1993; Harstall & Ospina 2003 ; Wolfe, 1995). The expense of CP to the consumer and healthcare system is considerable with an approximate cost of $635 billion in litigation, compensation, health care and lost productivity ( IOM, 2011 ) L oss of daily activity and work productivity due to pain are significant: a 2001 study indicated that 13% of nearly 30,000 randomly sampled working Americans missed productive work time due to pain conditions in a proscribed two week period (Stewart, Ricci, Chee, Morganstein, & Lipton, 2003) The d ynamics among the initial cause of pain, the onset of CP and resulting functional disability are complex Malingering and workers compensation studies specific to c hronic pain have shown mixed results; however, Waddell et al. (1993) suggest that fear avo idance of work tasks and activities that may cause pain are more predictive of disability and work loss than physiological factors. In general, evidence suggests that c hronic pain patients receiving disability compensa tion have higher levels

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2 of pain and depression, and exhibit decreased productivity and poorer prognosis overall (Gatchel & Gardea, 1999). C urrent ly, comprehensive pain programs have n ot proven to be cost effective or therapeutically effective (Gatchel & Okifuji, 2006) New CP treatments are needed to foster more positive medical and functional o utcomes and lower cost burden on both patients and consumers.

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3 CHAPTER II REVIEW OF THE LITERATURE Medical Manifestations and Functional Disability in Chronic Pain It was once believed that the cause and experience of pain were almost exclusively based in physiology. However, empirical findings have now shifted this thinking in th e field to expand this etiology to a combination of physiological, psychological, social, cultural and behavioral factors (Turk & Okifuji, 2002). The experience and report of pain is a subjective and private one comprised of one's perception of not only the pain severity, but also the perception of his/her suffering related to the pain (Gatchel, 2004). Higher pain ratings and perceptions of suffering have been found to impact medical and functional outcomes in numerous studies (Arnstein, Caudill, Mandle, Norris & Beasley, 1999; Stroud, Thorn, Jensen, & Boothby, 2000; Turner, Holtzman, & Mancl, 2007; Turner, Jensen, & Romano, 2000). Cross sectional studies suggest that pain ratings and percep tions of suffering also impact perceived d isability, or one's perception of his or her ability to perform tasks, in those with either acute or chronic pain ( Feuerstein & Thebarge, 1991; Koho, Aho, Watson & Hurri 2001; Roth & Geisser, 2002; Swinkels Meewisse, Roelofs, Oostendorp, Verbeek, & Vlaeyen, 2006; Turner, Jensen, & Romano, 2000). Across multiple studies, pain intensity ratings are found to correlate significantly with Perceived Disability, but not object ively rated disabilit y (Alschuler, Theisen Goodvich, Haig, & Geisser, 2008; Geisser, Haig, & Theisen, 2000; Millard, Wells, & Thebarge, 1991). Cross sectional studies examining the effects of pain related beliefs on Perceived Disability suggest that fear of pain is a stronger predictor of higher Perceived Disability rather than actual

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4 functional ability (Crombez, Vlaeyen, Heuts, & Lysens, 1999; Waddell, Newton, Henderson, Somerville, & Main, 1993). More recently, studies suggest that fear avoidance, lower self efficacy and depression significantly contribute to both higher perceived and actual disability (Alschuler et al., 2008; Denison, Asenlof, & Lindberg, 2004; Geisser et al., 2000; Geisser, Robinson, Miller, and Bad e, 2003). These studies suggest that Perceived Disability is a significant factor in one's personal expression of pain, regardless of the primary cause or nociceptive origin, duration, and severity of pain. Demographic Factors Prevalent in Chronic Pain A review of 15 prevalence studies of non malignant (non cancer) pain suggests that female gender, lower socioeconomic status (SES) and middle age were more common in both primary care and in the general population (Verhaak, Kerssens, Dekker, Sorb i, & Bensin g, 1998). Peak prevalence was found to range between ages 45 and 65 and CP prevalence increasingly rose by age. In a 2000 epidemiological study of over 17,000 surveyed, CP patients were more often women (20% vs. 17.1% in men), older (55 to 69 years of ag e), and of lower SES and education level as well (Blyth et al., 2000). Large epidemiological studies suggest the most common pain regions within CP populations include osteoarthritis/arthritis, rheumatoid arthritis, back pain, headache, limb pain, and g astrointestinal pain ( Breivik Collett Ventafridda Cohen, & Gallacher 2006; Elliott, Smith, Penny, Smith, & Chambers, 1999; Ohayon & Schatzberg, 2003). Pain intensity ratings in the general population generally fall above 5 on a 10 point Likert scale (10 = worst pain) and the most frequently reported pain dura tions include: two to five years (22%); f ive to 10 years (20%); and 20 years or more (21%) (Breivik et al., 2006).

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5 Psychological Manifestations of Chronic Pain The influence of social, psychological and behavioral factors which exacerbate or minimize one's pain experience, has received considerable attention in the literature. Turk and Nash (1996) report that pain is rated more strongly when pe ople are feeling depressed and hopeless. Other findings indicate that depression is 3 to 4 times more prevalent in lower back pain patients than in the general population (Sullivan, Reesor, Mikail, & Fisher, 1992) Several other studies have suggested that those with chronic lower back pain have higher rates of depr ession and diagnosed personality disorders and are more likely to abuse drugs and alcohol in comparison to those with acute pain (Brewer & Karoly, 1992; Kinney, Gatchel, Polatin, Fogarty, & Mayer, 1993; Sullivan, et al., 1992) Positive and n egative affectivity and pain Both positive and negative affect have also been exam ined and are found to alternately improve and exacerbate CP symptoms ( Affleck, Tennen, Urrow, & Higgins, 1992; Litt, Shaffer, & Napolitano, 2004; Smith & Zautra, 2002; Zautra, Smith, Affleck & Tennen, 2001 ). Watson and colleagues' Positive and Negativ e Affect Scales (PANAS) (1988) have been utilized to measure positive and negative affect in many studies, including those examining chronic pain Watson et al.'s underlying theory suggests that positive and negative affect are orthogonal, and are measure d as such: bo th affect types lie on two different spectra, varying from high t o low levels on each scale (Watson, Clark & Tellegen, 1988 ). N egative affect (NA) includes "a variety of aversive mood states including anger, contempt, disgust, guilt, fear, and nervousness" whereas p ositive affect (PA) has been defined as "the extent to which a person feels enthusiastic, active and alert" (Watson,

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6 Clark & Tellegen, 1988 p. 1063.) H igh PA a s characterized by "high energy, full concentration, and pleasurable engagement" and low PA is defined as "sadness and lethargy" (Watson, Clark & Tellegen, 1988, p. 1063). Several correlational studies suggest that both high NA and low PA are found to best characterize depressive symptomatology, as they correlate significa ntly with Beck Depression Inventory self ratings ( Jolly, Dyck, Kramer, & Wherry, 1994; Watson, Clark & Carey, 1988; Watson, Clark & Tellegen, 1988 ). Although the PANAS is not employed as a diagnostic measure those who score both high on the NA scale and low on the PA scale are likely to exhibit symptoms of if not diagnoses of depression. Few studies exist examining affective symtomatology and CP; however, several studies suggest higher ratings of NA are f ound in CP samples and that both NA and PA significantly contribute to pain levels and mood in those with CP. Specifically, current findings suggest that PA may significantly dampen the eff ect of NA and lower pain intensity ratings as these results were consistently found over a several week to six month time span (Strand et al., 2006; Litt, Shafer and Napolitano, 2004; Zautra et al., 2005) High NA appears to be a significant mood indicator among those with CP. Smith and Zautra (2002) compared persona lity factors disease activity and NA among those with osteoarthritis and rheumatoid arthritis ( n = 175) and normal healthy controls ( n = 80) over a 12 week period. Findings suggest the arthritis groups had significantly higher NA compared to healthy controls H igh NA in both disease groups was correlated with higher interpersonal stress, interpersonal sensitivity, and measured neuroticism using the NEO Personality Inventory ( Costa & McCrae, 1992). Furthermore, those with higher

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7 neuroticism and inter personal stress in the arthritis groups showed significant increases in self reported disease activity, as indicated by higher pain levels, joint tender ness, overall arthritis symptoms and activity limitations ( Smith & Zautra, 2002). Zautra et al.'s 2005 study highlights the effects of PA and NA on stress and pain over a 10 12 week period in 124 women with osteoarthritis or fibromyalgia. Weekly measures included: the PANAS, an average level of pain self report, a questionnaire rating perceived interperso nal stress across multiple domains, and an eight item neuroticism measure derived from the Big Five Inventory (John, Donahue, & Kentle, 1991). The Big Five Inventory is a 44 item questionnaire, similar to Costa and McCrae's (1992) 240 item NEO Personality Inventory, Revised (NEO PI R), such that both measure personality traits among five categories: extroverted, agreeable, conscientious, emotionally stable, and open ness to new experiences (Gosling, Rentfrow, & Swann, 2003). As hypothesized, mu ltilevel modeling results revealed that higher levels of we ekly stress and pain were associated with weekly increases in NA. Weekly increases in NA also correlated with higher levels of pain in future weeks. Interestingly, weekly PA was associated with decreased NA, stress and pain and increased PA appeared to contribute to significantly lower pain over subsequent weeks. One limitation of this study is that participants were only asked to retrospectively recall their average pain level over the past week: curren tly experienced NA or PA could significantly bias this rating. Re gardless, results highlight the complex dynamics among PA NA, stress and pain levels in CP patients. Litt, Sh af er and Napolitano (2004) examined momentary PA, NA, pain levels, and coping st yles and their results give further credence to Zautra et al.'s (2005) findings

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8 Multiple daily self ratings were taken over a six month period, thus improving the internal validity of Zautra et al. 's design. Participants ( N = 30) with temporomandibular dysfunction pain (TMD) were asked to give ratings on a hand held computer four to seven times per day for six months. Multiple measures assessed PA and NA (i.e. mood states such as depressive and anxious mood), self efficacy, pa in ratings, physical and emotional sensitivity, and appraisal and coping styles. H igh NA (particularly anger, anxiety, boredom and sadness) was found to be the most significant predictor of higher current and subsequent pain levels, followed by depressive mood. Conversely, coping self efficacy and PA (happy and peppy) were correlated with lowe r momentary pain ratings. Similar to Zautra et. al's 2005 findings, Litt et al.'s result s indicate that current high NA correlated with higher pain ratings at the v ery next time point and current high PA was related to lower pain at the next time point. From these two studies, it appears that higher PA leads to current and subsequent lower pain ratings. F indings from Strand et al.'s 2006 study further suggest these relationships exist in CP patients and imply that PA directly moderates the effect of NA on pain. Strand and colleagues replicated Zautra et al.'s 2005 study d esign and included a sample of 43 rheumatoid arthritis patients living in Norway. Results again suggest higher NA correlated with more wee kly pain and stress, but not PA; and those with higher NA experienced higher inter personal stress and depression, but not PA Also, those with higher levels of depression had significantly higher NA. When partic ipants experience d increased pain, NA was more prevalent ; however, weekly PA moderated the effect of NA on pain. Authors also compared weekly versus overall PA and found that weekly or

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9 "state" levels only significantly contributed whereas "trait" PA, or o verall PA, did not (Strand et al., 2006) High NA and low PA are also found to be correlated with increased functional disability in those with rheumatoid arthritis (Zautra et al., 1995). In this study examining three indep endent samples ( n = 179; 177 and 134) participants with more functional disability had both higher NA and convers ely, lower PA Confirmatory factor and mediation analyses revealed the same significant relationships among the three groups: coping mediates the relationship between aff ect (both negative and positive) and disease (pain and activity limitation). Most notably, findings indicated that those with higher PA used more adaptive and less maladaptive coping (catastrophizing), while those with more NA used more maladaptive coping and had more pain and activity limitations. Furthermore, h igh NA again correlated with higher pain levels as well as more maladaptive coping styles (Zautra et al., 1995). Mood disorders, f unctional d isability and pain Many additional studies also suggest psychological factors contribute to functional disability and may predispose individuals to the laying of the foundation for long term chronic pain. In a 2001 World Health Organization study of nearly 5,500 CP primary care patients, 555 were found to have a persistent pain d isorder and a pre existing depressive and anxiety disorder significantly predicted chronic pain onset (Gureje, Simon, & Von Korff, 2001). Additionally, depression and anxiety contribute to increased functional disability and sel f reported higher levels of pain at 6 and 12 months post injury in those with lower extremity trauma ( N = 601) (Wegener et al., 2011). Psychosocial Treatment Approaches

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10 Due to these complex contributing factors, it is the dominant belief that CP is a biopsychosocial condition that responds most favorably to behavioral and multidimensional treatment approaches. Operant model of pain. Fordyce's Operant Model of Pain was a prevailing theory of chronic pain maintenance in the 1970's, following Melzack and Wall's 1965 Gate Control Theory (Melzack & Wall, 1965). This behavioral approach involves the concept of pain behaviors or "things that people say or do when they are suffering or in pain, such as avoiding activities or exercise for fear of reinjury" (Ga tchel, Peng, Peters, Fuchs & Turk, 2007, p. 582). Turk and Rudy (1986) explain that Fordyce's model is primarily based on these pain behaviors or "behavioral manifestations" (p. 761). These are communication s from CP patients to providers, family member s and others, which are influenced by consequences, and are therefore "subject to reinforcement" in this model ( Turk & Rudy, 1986, p. 761). Behavioral t reatment therefore, specifically targets pain behaviors and works to change the antecedents and the co nsequences to the behavior to modify or shape the se behaviors Fordyce asserts there are three components necessary to change behavior through operant conditioning: 1) identify the behavior to be changed; 2) decide on which types of reinforcers would be most effective; and 3) establish enough control over one's enviro nment in order to influence behavior via consequences and schedules of reinforcement (Fordyce, 1968, p. 181). Although there have been many treatment advances in CP since this time t hese operant conditioning principles continue to be regarded as effective in the treatment of CP Cognitive and cognitive behavioral therapy. Cognitive and cognitive behavioral

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11 therapy (CBT) app roaches to CP management began to gain considerable attention in the 1990's and have become the primary non medical intervention for CP Negative cognitions specific to pain are correlated with higher pain, disability and general distress and increased healthcare and pain medic ation use: therefore they are considered key intervention target s (Gil, Abrams, Phillips, & Keefe, 1989; Jen sen, Turner, Romano, & Karoly, 1991; Stroud, Thorn, Jensen, & Boothby, 2000). The largest area in cognitive based approaches focus es primarily on changing maladaptive pain beliefs or negative cognitions by using either cognitive restructuring, active or p roblem solving coping, or emotion focused coping strategies (Geisser, Robinson, Riley, 1999 ; Thorn, Cross & Walker, 2007 ). Problem solving coping is defined as changing one's physical environment to remove or alter the adverse effects of the stressor whereas emotion focused coping is characterized by changing the way one "attends to or interpret[s]" the stressor (Lazarus, 1993, p. 8). Cross sectional CP studies suggest active or problem solving coping is more effective than emotion focused coping stra tegies in maintaining positive mood, activity level and generally positive pain adjustment (Geisser, Robinson, & Riley, 1999; Snow Turek, Norris, & Tan, 1996). CP researchers have focused specifically on catastrophizing, and those who "expect the worst from their pain problem, ruminate about pain sensations, and feel helpless about controlling their pain are said to catastrophize as a result of their pain experience (Thorn Boothby, & Sullivan, 2002, p. 128 ). Catastrophizing has also been correlated with increased disability, pain intensity, and depression in those with CP ( Sullivan et al., 2001 ) Multiple intervention studies that focus on decreasing catastrophizing and negative cognitions utilize both cognitive restructuring and

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12 behavioral techniqu es. Multip le cognitive behavioral intervention studies that target catastrophizing and negative cognitions suggest improvements in these areas as well as decreases in pain intensity, psychosocial symptoms, and physical disability (Jensen, Turner, & Romano 1994; Keefe, et al., 1991; Parker et al., 1989; Sullivan et al., 2001; Thorn, Boothby, & Sullivan, 2002; Turner & Clancy, 1986). More recently, researchers suggest that catastrophizing is really a more complex combination of emotion and cognitive appraisal than once thought and although this remains an important treatment target, this concept requires further investigation ( Jones, Rollman, White, Hill, & Brooke, 2003). The efficacy of cognitive behavioral therapy is undisputed for many psychological disorders and t here have been over 325 intervention studies published on CBT since 2006 CBT for chronic pain is based on Melzack and Wall's Gate Control Theory and on the belief that behav ioral improvements can be made by modifying dysfunctional thinking and beliefs ( Dozois & Dobso n 2001; Melzack & Wall, 1965). Although the exact mechanisms of CBT are unknown, the structure of CBT interventions for chronic pain is designed to educate and train patients to identify and reconceptualize both maladaptive cognitions and behaviors that serve to maintain their pain experience. Teaching the patient how to conceptualize both his or her pain and their situation in addition to educating them how to more effectively manage their problems helps to improve their perception and provides hope. Encouraging the patient to take an active role in their pain management; teaching them how to self m onitor thoughts, feelings, and behaviors; and providing training on how to give self praise for achievements boosts self confidence and fosters a sense of control over the pain experience. Lastly, creative

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13 problem solving strategies are introduced to anti cipate obstacles and to maintain treatment gains (Turk & Melzack, 2001 ). Meta analyses of CBT for chronic pain demonstrate its efficacy to reduce distress and pain experience in general; increase positive/active coping; and improve daily functioning and ov erall quality of life (Morley, et al., 1999; Hoffman et al., 2007) Hoffman et al.'s 2007 review of psychological interventions for chronic low back pain found consistent significant support for CBT for chronic pain among 22 research studies. Again, rando mized controlled trials of CBT and other interventions were found to show significant improvements over attention control and wait list groups, with CBT touted as the most effective. Significant and moderate to large effects were found on pain intensity, health related quality of life, and depression for CBT. In addition, multidisciplinary programs, including those with CBT, were suggested to be superior to other active treatments in producing improved work related outcomes at both short and long term fol low up (Hoffman et al., 2007). Although empirical evidence is robust for CBT for CP, systemic weaknesses in research design and generalizability to all CP patients question the true efficacy of these treatments for all individuals More importantly, th e exact mechanism of CBT for CP is still unknown and the majority of effect sizes are moderate, thus suggesting that more research is needed to further sift out the core ingredients' mediating and sustaining the more positive outcomes. As mentioned previously, t he empirical efficacy of CBT is undisputed for many psychological disorders. Meta analyses conducted for a range of conditions demonstrate that the largest mean effect sizes are found for unipolar depression, generalized anxiety

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14 disorder, pan ic disorder with or without agoraphobia, social phobia, posttraumatic stress disorder, and childhood depressive and anxiety disorders (comparison weighted grand mean effect size = .95, ( SD = .08)) (Butler, Chapman, Forman, & Beck, 2006). Chronic pain, as well as marital distress, anger, and childhood somatic disorders, were found to have only moderate effect sizes ( ES = .62 ( SD = .11) ) with CP outcomes showing a range of modest effect sizes of .14 to .40 when compared to active treatment controls (Butler et al., 2006; Morley et al., 1999). Additionally, evidence of long term effects is limited for CBT for CP, thus suggesting that more research is needed ( Morley et al., 1999). Morley et al.'s 1999 review similarly notes weaknesses in the current resear ch, drawing attention to the underpowered analyses and a wide range in the quality of treatment and attention control group protocols across these 25 randomized controlled studies (RCT's) The majority of these RCT's depend primarily on self report questi onnaires and lack multimethod assessment approaches (Morley et al., 1999) These studies also omit intervention provider training details and competency measures: in those that did discuss trainer competency, only 60% were found to be qualified, and treatment fidelity was not accounted for or discussed (Morley et al., 1999). Internal validity is also questioned due to lack of consideration of patient expectations of the treatment (Morley et al., 1999). The variable of patient expectation has been fo und to be a significant predictor of improved outcomes for several CBT for CP interventions and is generally not accounted for in most of these studies : for example, s tudy participants who believed their treatment to be effective were fives times more like ly to improve regardless of which treatment arm they were assigned (Kole Snijders, Vlaeyen, Goossens,

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15 Rutten van Mšlken, Heuts, van Breukelen, & von Eek, 1999; Kalauokalani, Cherkin, Sherman, Koepsell, & Deyo, 2001). CBT for CP studies additionally demonst rate that a large proportion of individuals suffering from CP do not benefit from this treatment, nor any other therapies irrespective of modality type. Attrition rates average five to 26% for these CBT intervention studies, with higher dropouts in depres sed individuals, a common comorbidity in CP samples. An additional confounding factor is comparison across studies with samples that differ in pain location (e.g. lower back vs. temporomandibular pain) and etiology (Turk & Rudy, 1990). It has been sugges ted that better categorization of participants by pain location may improve definitive findings respective to pain type and location, thus improving treatment efficacy (Turk, 2005). Continued efforts to improve CP outcomes are focused primarily in varying CBT protocols; however, leaders in this field suspect that there is "ample room for improvement in the efficacy of CBT in CP" (Vlaeyen & Morley, 2005, p. 4). Third wave behavioral treatment approaches: Acceptance and Commitment Therapy. A recent persp ective in behaviorally based interventions has received increasing attention in the literature since 1994 (Hayes, 1994): Acceptance and Commitment Therapy (ACT). This "third wave" behavioral ther apy stems from Relational Frame Theory (RFT) (Hayes, Barnes Holmes, & Roche, 2001). RFT is an extension of behavioral theory involving both language and cognition. This theory essentially states that individuals develop operantly conditioned responses to stimuli that are not directly conditioned to those r esponses, but are somehow similar and are therefore generalized. This process, in behavioral learning theory has been described as

PAGE 24

16 "abstraction"; however, Hayes et al. expand on this theory by stating that this process may be "arbitrarily appli[ed]" based on the individual and "contextually dependent" in which they are exposed to the stimulus (McIlvane, 2003, p. 29 30). For example, those with CP may develop generalized conditioned responses to particular stimuli, thus altering their behavior, limiting their experiences, and invariably, leading to lower levels of function. A common cycle found in CP is avoiding tasks that may invoke more pain; however, as the negative experience of pain continues, the range of activity becomes more and more limited for fear of exacerbating their pain condition. To illustrate a person may avoid lifting heavy books after re injuring their back at the library but he or she may also avoid lifting any books or even more simply avoid the library due to the similarities to the conditioned stimuli. Additionally, social factors such as enabling or alternately, criticizing behaviors from family or friends further shape s behavior such that one's world becomes more and more restrict ed. Negative interpretation of the pain experi ence and themselves often leads to justification for disengaging from adaptive and daily life activity in an effort to "control or avoid painful experiences" and thus "mov[ es ] them away from healthy life functioning" (McCracken & Eccleston, 2005, p. 164.) Acceptance of chronic pain has been defined as "living with pain without reaction, disapproval or attempts to avoid it" (McCracken & Eccleston, 2003, p. 198.) Specifically, CP Acceptance requires a "disengagement from struggling with pain, a realistic approach to pain and pain related circumstances, and an engagement in positive everyday activities" (McCracken & Eccleston, 2003, p. 198.) This approach differs from more traditional CBT approaches because "there is little emphasison changing the content of thoughts;

PAGE 25

17 rather, the emphasis is on changing awareness of and relationship to thoughts" (Segal, 2001) The definition of Acceptance is two fold: Pain W illingness and Activity Engagement. The Chronic Pain Acceptance Questionnaire, originally developed by Geiser (1992), has been found to yield this two factor structure: Pain Willingness (PW) and Activity Engagement (AE). Of the PW items, several include "I would gladly sacrifice important things in my life to control this pain better" and "I need to concentrate on getting rid of my pain" (McCracken Vowles, & Eccleston, 2004, p. 165) PW has been defined as essentially one's degree of willingness to expe rience pain as well as related thoughts and feelings (McCracken & Eccleston, 2005). The AE items include "Despite the pain, I am now sticking to a certain course in my life" and "It's not necessary for me to control my pain in order to handle my life well (McCracken, Vowles, & Eccleston, 2004, p. 165) AE is one's degree of willingness to engage in life's activities, despite the existence of pain (McCracken, Vowles, & Eccleston, 2004). Both cross sectional and prospective studies examining Acceptance and ACT show significant positive chronic pain outcomes. Higher levels of AE and PW in CP samples are associated with significantly lower levels of pain related anxiety, depression related interference with functioning, and physical and psychosocial disability. Higher AE and PW were also positively correlated with work status (McCracken & Eccleston, 2003) PW also negatively predicted number of pain medications whereas AE was positively correlated with uptime and work status (McCracken & Eccleston, 2005) Intervention studies comparing ACT and treatment as usual groups with CP outpatients also suggest promising findings, with treatment efficacy sustained over three

PAGE 26

18 month follow up. Significant improvements were seen in physical and psychosocial disability outcomes, depression, pain intensity, pain related anxiety and number of medical visits, school and work absence s (McCracken, MacKichan, & Eccleston 2007; McCracken, et al., 2005; Vowles & McCracken, 2008; Dahl, Wilson, & Nilsson 2004) Taken together, these findings suggest that Acceptance based approaches m a y offer considerable clinical utility in treatment of CP Behavioral, cognitive, cogn itiv e behavioral and a cceptance based treatment approaches have contributed substantially to our understanding of the maintenance of CP and provide moderately effective treatment options. H owever, studies continue to suggest there is heterogeneity within CP samples such that different treatments work more effectively for different individuals (Turk, 2005). Researchers have suggested that patients may find more effective therapeutic responses if treatment is tailored to particular characteristics they may hold (Costa & Pinto Gouveia, 2010; Vowles, McCracken, McLeod, & Eccleston 2008). Profiling CP patients by specific characteristics name ly those that are influential in promoting more positive outcomes, may improve the efficacy and effectiveness of behav ioral, cognitive, cognitive behavioral and acceptance based treatment approaches. Profiling Chronic Pain Patients to Improve Treatment Efficacy Characterizing CP patients based on single physiological or psychological factors have been the primary means of grouping patients to create more effective CP treatments; however it may be useful to group patients based on dimensions of patient characteristics (Turk, 2005). Due to the complexity of CP experience and varied treatment response, a more multidimensional approach has been suggested (Dworkin & LeResche, 1992; Turk

PAGE 27

19 & Rudy, 1988). To date, only one grouping method based on cognitive, affective and beh avioral variables has been examined. The most widely known application is the West Haven Yale Multidimensional Pain Inventory (MPI; Kerns, Turk, & Rudy, 1985) This measure has known statistical and clinical utility ( Hunsley & Mash, 2008 ). The MPI measures severity and impact of pain on work, social and other life activities in this population across twelve domains of functioning (Wehmer, 1990) and subjective feelings of self control, problem solving abilities and patients' perceptions of themselves (Kerns, Turk, & Rudy, 1985) Currently, it is the most commonly used method of profiling chronic pain patients by their varying beliefs and behavio rs. The MPI includes several subscales and cluster analysis further differentiates patients (Turk & Rudy, 1988). Among the multidimensional scales are "1) pain severity and suffering; 2) perceptions of how pain interferes with their lives, including in terference with family and marital/couple functioning, work and social and recreational activities; 3) dissatisfaction with present levels of functioning in family relationships, marital/couple relationship, work and social life; 4) appraisals of support r eceived from significant others; 5) perceived life control, incorporating perceived ability to solve problems and feelings of personal mastery and competence; 6) affective distress including depressed mood, irritability, and tension; and 7) activity levels (Tur k & Melzack, 2001) Cluster analysis produces more concise conceptualizations of patients and groups them into three distinct profiles: "Dysfunctional", "Adaptive Copers" and "Interpersonally Distressed" (Turk & Rudy, 1988). Patients displaying the profile of the Dysfunctional group are characterized by high pain severity, marked interference with

PAGE 28

20 everyday life due to pain, high affective distress, low perception of life control, and a low activity level. Patients in the Adaptive Coper group report lower pain severity, lower interference, lower levels of affective distress, higher degree of life control and a higher activity level. Patients in the Interpersonally Distressed group display lower reported levels of social support, lower scores on solic itous and distracting responses from significant others, and higher scores on punishing responses (Rudy, Turk, Zaki, & Curtin, 1989) Empirical support has emerged to suggest that these profiles may predict outcomes for chronic pain patients. Differences between the Dysfunctional and Adaptive Coper groups in particular have been found to show discrepancies in the utilization of pain medication, work status, time spent in bed, and scores indicating pain behaviors such as affective distress and seeking help, with Adaptive Copers using fewer pain medications and negative coping behaviors and demonstrating more active involvement in work and life activities (Turk & Melzack, 2001). Also, some profiles are more likely to benefit from treatment and predict treatment completion (Rudy, Turk, Kubinski, & Zaki, 1995). Following a six week intervention including biofeedback and cognitive behavioral therapy for CP Dysfunctional groups significantly improved and had larger reductions in pain intensity, perceived interference of pain symptoms in their lives, depression, an d negative cognitions in comparison to Adaptive Copers and Interpersonally Distressed Groups at six month follow up (Rudy et al., 1995). Three additional groups of patient "profiles" have been identified; however, research findings suggest that these gro ups have not been found to be prevalent and in general, have not show n high clinical utility for this population ( Ravani, 2005 ). Despite

PAGE 29

21 the theoretical advantages to grouping CP patients based on psychosocial and behavioral characteristics, the empirical support and clinical application of profiles for use in clinical conceptualization and treatment have been sparse. However, g iven the existing positive results and the complexity of CP conditions, finding new ways to characterize patients for purposes of tailoring effective treatment interventions is a worthwhile pursuit. As stated previously leading researchers in the CP fiel d have suggested that there is "ample room for improvement in the efficacy of CBT in chronic pain"; therefore, opening the door for additional approaches and interventions (Vlaeyen & Morley, 2005, p. 4). New methods of profiling CP patients based on key d efining characteristics may inform the development of more specific, effective and tailored interventions for CP. Profiling Chronic Pain Patients: Acceptance Based Approaches Recent studies have examined ways of grouping CP patients using the Acceptance construct and specific clusters and characteristics that differ within samples. Vowles, McCracken, McLeod, & Eccleston's 2008 study examined cluster analysis results within a sample of CP outpatients seen at a specialty treatment clinic ( N = 641) and foun d three distinct clusters: 1) both low AE and PW 2) both high AE and PW, and 3) high AE and low PW. Subsequent comparisons demonstrated that the high AE and PW group reported significantly lower "pain, depression, pain related anxiety, physical and psych osocial disability, medical visits, medications, daily restand daily activity" than the low AE and PW group (Vowles, McCracken, McLeod, & Eccleston, 2008, p. 288). T he t hird group (high AE and low PW) was reported to significantly differ from both other groups on depression, pain related anxiety, daily rest, and psychosocial disability. This third

PAGE 30

22 group also differed significantly from either the Low or High group in the five remaining dependent variables: pain, physical disability, medical visits, daily activity, and classes of medicine (Vowles, et al 2008). A 2010 study conducted by Costa and Pinto Gouveia further suggests the validity and reliability of these three clus ters. The study findings show that three similar clusters emerged in a mixed CP outpatient primary care and tertiary care sample ( N = 103): 1) both low AE and PW, 2) both high AE and PW, and a slightly varied third group, 3) medium AE and low PW The authors labeled this third group "High AE and Low PW"; however, they also state that AE scores fell closer to the mean and therefore "Medium AE" is a more accurate title. Post hoc analyses demonstrated that among the subgro ups, those in the H igh clust er showed lower levels of anxiety, depression, stress and self compassion in comparison to the Low Acc eptance group. In addition, this medium Acceptance group showed significantly higher levels of depression, stress, self judgment, and over identification (versus mindfulness) in comparison to the high Acceptance group It is interesting to note that t his medium group (with relatively similar mean scores on Pain Willingness but scores that are closer to the mean for Activity Engagement when compared to the low AE of the Low Acceptance group) also showed significantly lower levels of depression and stress, in comparison to the low Acceptance group Both author groups suggest this medium group displays "good enough" functioning but self report poorer emotional and social well being and perhaps have more attachment to finding pain relief (low PW) than the High Acceptance group. T herefore, Costa and Pinto Gouveia (2010) add that AE may be a "buffer" characteristic that contributes to fewer advers e affects of stress, such as not amplifying negative emotions

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23 and sensations that contribute to depression Likewise, PW may be an important characteristic to supplement AE such that individuals are not simply grinning and bearing' pain through increased AE but are truly willing to accept their pain as well (Costa & Pinto Gouveia, 2010 ; Vowles et al., 2008 ). Regardless, future studies would benefit from further investigating this medium group to better understand the associations among AE, PW and importa nt psychosocial characteristics. Subsequent studies examining 300 s tudy participants in online CP support groups similarly suggest the emergence of these Acceptance groups. One study examining CP Acceptance found similar CP Acceptance subgroups using a di fferent methodology: tertile cutoffs based on levels of CP Acceptance. More specifically, subgroups were formed by creating tertiles of simply high, medium and low scores on both of the CPAQ subsc ales (AE and PW). The resulting two sets of three subgroup s were: High AE, Medium AE, Low AE, High PW, Medium PW, and Low PW. Despite this difference in grouping method from Vowles et al., 2008's clusters ((1) both low AE and PW 2) both high AE and PW, and 3) high AE and low PW) results were remarkably similar Payne Murphy et al.'s results suggest that both the High AE and PW tertile group s reflected scores that were very similar to that of the MPI "Adaptive Coper" profile Conversely, both the Low AE and PW tertile group s reflected scores that were very sim ilar to that of the MPI "Dysfunctional" profile (Payne Murphy, Lillis, Brown, Herbst & Beacham, 2010; Payne Murphy, Beacham, & Brown, 2011). These results demonstrate a consistent pattern that can be seen in Figures 1 and 2

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24 Figure 1. CP Activity E ngagement High, Medium and Low s core grou ps and MPI Subscale score plots Figure 2. CP Wi llingness High, Medium and Low s core groups and MPI Subscale score plots !" !! !# $% $& &" '()*(+,-* ./0/1,23 '()*4,5/* )-2/15/1/-6/ '()*7/8+2,0/* '99: '()*4,5/* ;9-219< '()*=92+<* >62,0,23 49?*;(*>62,0,23*@-8+8/A/-2 '/:*;(*>62,0,23*@-8+8/A/-2 B,8C*;(*>62,0,23*@-8+8/A/-2 NOTE: All subscale scores converted to T scores !" !! !# $% $& &" '()*(+,-* ./0/1,23 '()*4,5/* )-2/15/1/-6/ '()*7/8+2,0/* '99: '()*4,5/* ;9-219< '()*=92+<* >62,0,23 49?*;(*(+,-*@,<<,-8-/AA '/:*;(*(+,-*@,<<,-8-/AA B,8C*;(*(+,-*@,<<,-8-/AA NOTE: All subscale scores converted to T scores

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25 Another study conducted with preliminary data analyses in the same online CP sample ( N = 255), demonstrated that High, Medium and Low Acceptance subgroups differed on ratings of perceived disability of functioning. Both High AE and High PW subgroups scored significantly lower on the following P ain Disability Index ( Pollard, 1984 ) scales in comparison to the Low AE and Low PW subgroups: family/home responsibilities, recreation, social activities, occupation, sexual behavior, self care, and life support (Kinman, Beacham, Payne Murphy, Lillis, & Brown, 2010). Findings from these studies provid e further support for Acceptance as a key factor in CP patients' functional and affective outcomes. Improving Access to and Dissemination for Chronic Pain Populations: Online Interventions Healthcare consumers, particularly those with CP and chronic illne sses are looking to online resources with increasing frequency for social support, psychoeducation, and treatment options. The most recent Harris Poll (2011) found that 74% of American adults, or 173 million people, look to the Inter net for health care i nformation; 60% have searched within the past month; and 39% search "often" for this information (Taylor, 2011). The numbers of individuals who have ever searched for health information has risen 47% in the past 13 years and continues to climb (Taylor, 20 11). With the popularity of such sites as WebMD (http://www.webmd.com/) and the Mayo C linic website (http://www.mayoclinic.com/), more patients are seeking drug interaction, symptom, and diagnoses information, as well as peer support group forums and pati ent doctor communication sites and email portals ( Nguyen, Carrieri Kohlman, Rankin, Slaughter, & Stulbarg, 2003). In a study of over 4700 consumers, almost 50% reported that online

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26 health information improved their understanding of their condition and corresponding treatments, and 30% stated this information "improved [their] ability to manage their health care needs without visiting a doctor or healthcare provider" (Baker, Wagner, S inger, & Bundorf, 2003, p. 2404). Some assert that online support groups also have an advantage over in person resources such that they reach those with restricted mobil ity, geographic limitations, and/or who desire anonymity (Nguyen et al., 2003). Due t o this increasing popularity and the ease with which researchers can contact online participants, Internet administered psychosocial interventions for both psychological and health conditions are becoming more common as well A pproximately 10 randomized c ontrol trials of Internet self management interventions for adults (18 years and older) with chronic pain, including headache, have been published since 2000 and one systematic review and meta analysis All randomized control studies required participants to login and submit responses at least once weekly via home based computers. Over half of these studies ( n = 6 ) followed CBT based protocols and almost all studies included some form of relaxation training, stress management, problem solving, and /or psychoeducation on chronic pain and/or exercise with an emphasis on improved pain self management The length of intervention for the majority of the studies was six weeks with one to three month follow up; however, four studies e xtended follow up to six or 12 months Intervention designs and f indings from these studies are reviewed below. Lorig et al.'s group has contributed significantly to this literature with three randomized control studies examining CBT based Internet interventions for CP and disease The first, published in 2002, examined a six week closed medical provider

PAGE 35

27 moderated email discussion group that included a copy of a CP treatment book and a videotape modeling how to maintain an active life with pain for participants ( n = 202 vs. n = 252 waiting list control ) with chronic low back pain. Despite only a mean of eight emails submitted per participant, one year follow up findings suggest significant improvements in pain intensity, disability, role functioning, and "health distress" (time spent worrying about one's health) as measured by the R oland Morris Scale, Illness Intrusiveness Scale and two other self developed scales. Significant decreases in physician visits for the treatment group were also reported : 1.5 visits less in the past six months vs. .65 visits for the control group (Lorig, et al., 2002). Lorig et al. expanded their intervention approach to more comprehensive CBT based protocols in 2006 and 2008 to examine changes in health behaviors and outcomes in those with rheumatoid arthritis, osteoarthritis fibromyalgia, type 2 diab etes or heart or lung disease The authors' 2006 study examined the effects of a six week intervention in 457 treatment participants (compared to 501 usual care control patients) with arthritis, type 2 diabetes, or heart or lung disease. Key web based in tervention features included: interactive didactics and psychoeducation on participant specific chronic diseases, medication and exercises; cognitive symptom management such as negative emotions, self talk and relaxation ; physician patient communi cation ; healthy eating; action planning ; solving problem training ; a discussion board; and a hardcopy book similar in content Moderators led participants ( n = 25 per group) each day to support utilization and offer encouragement and could be contacted by email f or questions Pertinent findings at one year post intervention suggest intervention participants logged in an average of 26.5 times over the course of the study and significant changes were found in

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28 1) pain, fatigue, health distress, shortness of breath; 2) stretching and strength exercise; and most notably, 3) self efficacy ( Lorig, Ritter, Laurent, & Plant, 2006). Similar findings were reported in Lorig, Ritter, Laurent and Plant's 2008 study investigating a six week online CBT based protocol in patien ts with rheumatoid arthritis, osteoarthritis, and fibromyalgia ( n = 433 versus n = 422 usual care control) thu s garnering further support for online CBT interventions for chronic pain Online intervention features were almost identical to the 2006 study, but 2008 participants received tailored exercise activities based on self report questionnaire responses S tudy findings showed participants averaged higher weekly log ins (31.6) than 2006 respondents and significant improvements using an intent to treat analysis were also found at one yea r follow up in 1) self reported pain, health distress, activity limitation, and global health ; and 2) self efficacy. Notably, no significant changes in healthcare utilization were found and fibro myalgia patients showed fewer improvements over time compared to those with rheumatoid arthritis and osteoarthritis ( Lorig Ritter, Laurent, & Plant, 2008 ). It is also worthwhile to note significant attrition rates were found in all three of Lorig et al., 's studies: the 2008 study reports a 40% dropout rate (204 out of 855); 22% in the first six months and 7% in the second six months of the 2002 study (159 of 580); and 19% (178 of 958) in the 2006 study (Macea, Gajos, Calil, & Fregni, 2010). The remaining CBT based online treatments for CP are similar in design and also report favorable and equivocal findings; however, treatment length and type and duration of research staff interaction varied Burhman, Falthenhag, Str š m, and Andersson (2004) examined the effect of an online six week CBT therapy in a randomized sample ( n = 22 treatment; n = 29 control) of adults with chronic back pai n. Web based intervention

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29 features mirrored previous studies (applied relaxation, tailored exercise inst ruction, cognitive reconstruction, problem solving, adaptive coping, daily diary submissions, and psychoeducation on CP and exercise) but followed a CP specific CBT model published by Vlaeyen and Linton (2000). In addition to email contact by a CBT traine d therapist, participants also received one structured telephone call from their therapist weekly to promote motivation. Telephone contact may ha ve improved attrition rates in comparison to other non telephone support studies: only five participants (9%) were lost to follow u p. Clinically significant improvements at three month follow up include: 1) control of pain, ability to decrease pain, catastrophizing, and praying an d hoping as self reported in the Coping Strategies Questionnaire (CSQ); 2) increased life control and a decrea se in punishing responses as measured by the MPI; 3) improved beliefs and attitudes towards one's pain and ability to function per the Pain and Impairment Relationship Scale (PAIRS) and 4) fewer depressive symptoms per the Hospita l Anxiety and Depression Scale (HADS) (Burhman, Falthenhag, Str š m, & Andersson, 2004) Expanding on Buhrman et al.'s design, Guttberg ( 2006 ) examined a 20 week web based CBT based therapy in which chronic pain and "burnout" (on sick leave for six months or more) participants ( n = 30 versus n = 30 wait list controls) watched 19 videos and read handouts; responded to related questions; and participated in weekly online Socratic discussion moderated by a CBT trained therapist and a staff member who had a good quality of life despite a fibromyalgia diagnosis The videos focused on teaching "cognitive self treatment of chronic pain and burnout" and included such topics as "Down in the Dumps or Depressed," Obstacles to Change," and S etting Limits" (p. 222 223). This treatment approach also differed from others of this type such that

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30 participants first met study staff in person for one half day prior to intervention related online exchanges Follow up findings at 23 weeks post baseli ne showed significant favorable changes in 1) depress ion as measured by the HADS; 2) vitality, social functioning, role physical, and bodily pain according to the Medical Outcomes 36 item Short Form Health Survey (SF 36) (Sullivan et al., 1995); and 3 ) 13 of 23 participants who were not on disability pensions increased their work capacity. Only the significantly improved work capacity of 12 of 23 non disability receiving participants was maintained however, at o ne year follow up ( Guttberg, 2007 ). Last ly only 13% ( n = 5) were lost to attrition over the course of the study (Guttberg, 2007). N on CBT web based randomized control trial interventions for CP have shown promising yet less conclusive findings. Berman, Iris, Bode, and Drengenberg's 2009 online mind body intervention compared changes in pain intensity, self efficacy, depression, anxiety, and responses to pain in treatment ( n = 41 ) and control ( n = 37) participants aged 55 and older with CP. Over a six week period, participants were required to log in at least once each week to the study site to access 1) the initial module, which educated users on problem solving around change according to Prochaska, No rcross and DiCle mente's Six Stage Model Program (1994), and 2) six remaining self care tutorials that could be completed in any order: relaxation, breathing, positive thinking, writing about positive experiences, writing about negative experiences, and creative visual expression. Participants also received additional information on CP and how to co mmunicate with others about pain, and daily email support and encouragement from study staff. Although attrition was lower than other studies described herein (12%), only improvement in participants' awareness of their responses to their pain was found

PAGE 39

31 significant, and pain intensity between login and logoff were notable but non significant (Berman, Iris, Bode, & Drenge nberg, 2009). Lastly, results from a five month online pilot intervention examining CP self management for chronic low back pain ( n = 20; n = 15 control) suggests improved pain intensity and physical activity and decreased medical visits and opioid use at post treatment. Participants received access to an online portal containing psychoeducation on chronic low back pain; videos and descriptions of specific exercises; videos and other materials written by health professionals ; a forum and chat room; and a "Tell a Story" page in which to discuss personal histories with other patients. The authors state participants also completed the treatment with improved procedural and declarative knowledge of course material. More generally reported findings lacked significance levels, attrition, pain intensity ratings and other important measure s which unfortunately do not lend to the validity and reliability of results (Schulz, Rubinell, & Hartung, 2007). Web b ased treatment approaches differ for headache treatment : a combination of relaxation and autogenic training ( passive concentration of bodily perceptions (e.g., heaviness and warmth of arms ) that are facilitated by self suggestions ) has shown efficacy in two randomized control trials for adults with headache (Stetter & Kupper, 2002, p. 45). Str š m Pettersson and Andersson 's team (2000) was on e of the first groups to conduct a randomized control trial of an Internet based self help treatment for a CP condition (headache) Over a six w eek period, adult participants (post treatment n = 20) with recurrent non cluster headache s received an Internet delivered relaxation program based on … st's (1987) applied relaxation and autogenic training (DeGood, 1996). Participants had minimal contact with experimenters; received training weekly; and

PAGE 40

32 submitted weekly self report measures online regarding problem solving and relaxation training practice H eadache diarie s, including pain ratings, were submitted daily. Findings suggest exactly h alf of those in the treatment group ( 10 of 20) showed significant improvement over waiting group controls ( n = 25) in the areas of headache frequency and pain intensity ; however, no differences were found in depression symptoms from pre to post treatment Attrition was a significant challenge with 56% leaving the study ( Str š m Pettersson, & Andersson, 2000) Devineni and Blanchard (2005) then streamlined Str š m et al.'s design in to a four week online intervention but expanded treatment to include cognitive stress coping therapy for those with tension type headache ( n = 12 ; n = 20 symptom monitoring control ), whereas those with migraine and mixed headache ( n = 27; n = 35 symptom monitoring control) received autogenic training Both treatment groups also received progressive muscle relaxation training using downloadable audio with guided instruction; practice d a limited biofeedback exercise; completed baseline and two week post treatment self report measures; and maintain ed a dai ly headache diary for eight weeks Participants had minimal email contact from study staff. Results were generally positive with 38.5% of the treatment group having a 50% reduction in disability scores due to headache (and without increased medication us e); however, 11.6% of this group also experienced worse headache symptoms at post treatment. Attrition was high as well, with 38.5% lost to follow up from baseline to post treatment, a span of eight weeks (Devineni & Blanchard, 2005). Given these studies' findings, randomized control trials for Internet interventions for chr onic pain and chronic disease offer a promising and more cost effective alternative

PAGE 41

33 to in person therapies. Significant weaknesses in these studies include high attrition, with an estimated average rate of 26.6%; and exclusion of adults with chronic pain who do not have home based computers or who cannot afford Internet services (Macea, et al., 2010). Macea et al.'s (2010 ) meta analysis and systematic review reports a .285 pool ed effect size using nine of the described studies herein and two additional onl ine therapies for adolescents: t herefore, these therapies had a n approximately small to medium effect (.29) when intervention groups were compared to controls. More s pecific ally, these therapies improved health outcomes (lower pain intensity) in several studies as well as contributed to significant behavioral changes such as reduced medical utilization and medication use ; improved functional outcomes including less work disab ility, and increased self efficacy to pursue life activities despite the presence of pain and symptoms, in general. Additionally, only two studies reported approximate intervention cost ($115 (Lorig, et al., 2002) and $243 per patient (Lorig, et al., 2008 )) but both are less costly than in person care (Macea, et al., 2010). Despite current weaknesses, t he efficacy and effectiveness of these online therapies and the increased frequency of Internet education resources make online health care consumers an important target population for clinical research and, ultimately, behavioral and psychosocial intervention. Purpose of t he Present Study The purpose of the present study is to replicate Vowles et al.'s (2008) group cluster methodology using an internet ba sed support group sample of self identified chronic pain patients. Replicating this methodology in this online sample may further our understanding of the role of Acceptance in those with CP and may provide credence to a

PAGE 42

34 more parsimonious (as compared to MPI profiles) approach of grouping CP patients based on these characteristics. Furthermore, simplified methods may lead to the development of tailored interventions based on these groupings to maximize positive outcomes in chronic pain patients. Findings from this study and subsequent development of tailored approaches to intervention with CP subgroups may likewise inform new modalities of intervention delivery (i.e., electronic, telehealth, internet based). Study Hypotheses Hypothesis 1: It is hypothesized that the re will be no differences in the cluster groups identified in this online CP support group sample compared to the cluster groups identified in Vowles et al 's (2008) clinical sample using the same methodology outlined in that study. S pecifically, the following group clusters are predicted: 1) Low Activity Engagement Low Pain Willingness 2) High Activity Engagement High Pain Willingness 3 ) High Activity Engagement Low Pain Willingness Hypothesis 2: It i s hypothesized that in the online support group sample, self reported scores of Positive Affect, Negative Affect and Perceived Disability will differ overall by group when controlling for demographic and pain characteristics as indicated. Specifically, cluster group 1 wil l be expected to show lower levels of Negative Affect and Perceived Disability and higher levels of Positive Affect. Conversely, cluster group 3 will be expected to show higher levels of Negative Affect and Perceived Disability and lower levels of Positiv e Affect. Group 2 will show moderate levels of Positive Affect, Negative Affect and Perceived Disability. Groups will be identified via cl uster analysis as described in h ypothesis o ne

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35 CHAPTER III METHOD The proposed study is an analysis of archival data from a previous data collection conducted at Spalding University in Louisville, Kentucky and a subsequent variation of that study conducted at the University of Colorado Denver in Denver, Colorado. The study procedure and data collection was reviewed and approved by the both the Spalding University Research Ethics Boards and University of Colorado Denver Colorado Multipl e Institutional Review Board for their respective data collection waves. Participants Self identified chronic pain patients were recruited via online chronic pain support groups. These groups were inclusive of individuals with non malignant (i.e., not re lated to cancer) chronic pain (pain lasting 3 months), were 18 years of age or older, and were able to read English. The following groups were excluded from the studies: a) 12 step; b) biofeedback; c) intervention based; d) prayer/religious; e) m edicati on focused (e.g., Opioid, Oxy Contin); f) malignant pain (cancer); and g) litigious groups. Three waves of data collection were conducted between September and November 2008, from March to July 2009 and from July 2010 to March 2011. Data from two sample g roups of participants were collected: targeted adult members that sought support only (versus intervention seeking) and general public group participants that did not subscribe to group membership. For specific details of collection procedure and wave var iations refer to Materials and Procedure. The total sample ( N = 300; Mean age = 44.7 years, SD = 11.2) was primarily female (85.6%), Caucasian (82%), married/partnered (53.1%), and well educated ( M = 14.8 years, SD = 2.4), with an

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36 average income between $30k and $40k. Most common pain locations were lower limbs and back ( n = 132 and 134, respectively). Mean y ears with chronic pain was 14.4 ( SD = 11.6) and average pain intensity was 7.4/10. Materials and Procedure Chronic pain group selection for the fi rst two initial data waves was derived from the "Yahoo! Groups" search engine by searching under "chronic pain support group ." Due to the identical procedure and materials followed in these first two data waves that were completed by the same research group, these two waves are henceforth referred to as "Wave 1" in this document. Appropriate group facilitators/moderators were the n contacted individually to propose research involvement. Following approval from group moderators to approach members, an announcement was posted to invite participation in a brief onli ne study on the group website ( A ppendix A ) Responding members were then directed to a link that produced instructions for survey completion, informed consent, a page requesting demographic information to become eligible for the gift card incenti ve, and the multi page survey ( Appendix C ). Moderators were then contacted tw o weeks and one month following to repost the study recruitment announcement. The second data collection wave was recruited via Facebook, a po pular social networking site, on chronic pain related group web pages and included posting invitations to parti cipate in an online research survey. Searches for "chronic pain" produced 147 groups, of which several closed groups required permission from st udy staff to post study survey invitations Once permission from th ese groups was granted, three waves of posting to both open and closed groups began over the course of five weeks (Appendix B) Responding group members were directed to the Survey Monkey

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37 survey site which was nearly identical in structure and material in both the new wave one and wave two of data collection This entailed an introductory page, informed consent, demographic related questions, the multi page self report questionnaires, a page seeking demographic information to enroll them in the gift card incentive lottery, and a concluding page. For both waves of data collection, participants were encouraged to contact study staff by electronic mail if they had any questions or concerns. The re wa s only one difference between th e two collection wave s that is pertinent to the present study: W ave 1 was given the option to select "pain location: other" for the question, "Please indicate the location of your pain: (Check all that apply ) ," whereas w ave 2 was not. Other changes made from wave 1 to wave 2 that do not apply to the pr esent study include the following. W ave 1 participants completed the M indfulness Attention A wareness S cale (MAAS) ( Brown & Ryan, 2003) where as wave 2 received the C ognitive and A ffective M indfulness S cale Revised (CAMS R) (Feldman, Hayes, Kumar, Greeson & Laurenceau, 2007). Wave 1 was also given the Short Health Anxiety Inventory (S HAI; Salkovskis, Rimes, Warwick, & Clark, 2002) a measure of hypochondriasis and health anxiety whereas wave 2 was not. Wave 2 received the McGill Pain Questionnaire (MPQ; Melzack, 1975 ); the Godin Leisure Time Exercise Questionnaire (Godin & Shepard, 1985); and was asked to specify the types of medications taken for pain symptoms : all of these items were not present in wave 1 dat a collection Measures See A ppendix C through F to view the following measures.

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38 Demographics and Medical History Participants responded to questions regarding demographics and history of chronic pain such as the initial causes of pain, location of pain, medication use, numbers of surgeries, and all types of treatments utilized. Other health and lifestyle related questions included substance use and pain related legal involvement. See Appendix C Chronic Pain Acceptance The Chronic Pain Acceptanc e Questionnaire (CPAQ) is a brief, self report measure of acceptance of chronic pain that was originally derived from the Acceptance and Action Scale (AAQ) (Geiser, 1992). Two subscales further differentiate outcomes: Activity Engagement and Pain Willingn ess. Items include: "Despite the pain, I am now sticking to a certain course in my life" and "I would gladly sacrifice important things in my life to control this pain better" (McCracken & Eccleston, 2005, p. 165) The 20 items are rated on a 7 point Likert type scale from 0 ("never true") to 6 ("always true"). This assessment has been found to be internally consistent ( = .78 .82) and has moderate to high correlations with measures of avoidance, patient functioning and emotional distress (McCracken & Eccleston, 2005) See A ppendix D Pain Disability Index The Pain Disability Index (PDI) (Pollard, 1984) assesses the degree to which individuals believe their pain interferes with various activities in their daily lives. Specific areas include: occupation, family/home responsibilities, sexual behavior, self care, recreation, and socia l and life support activities. The PDI is a brief, 7 item, self report measure and items are rated on a 0 10 scale ranging from "no disability" to "total disability." Internal consist ency is reported ly high (Cronbach alpha = .86); concurrent validity is strong ( Tait, Chibnall, & Krause, 1990) and generally, p sychometric properties have been reported as adequate and support its utility ( Turk &

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39 Melzack, 2001). See A ppendix E Positive and Negative Affect Sc ales The Positive and Negative Affect Scales (PANAS) is a brief 20 item self report measure of Positive and Negative Affect (Watson, Clark, & Tellegen, 1988). Each item is a mood state adjective (i.e. "distressed") and is rated on a scale of 1 ("very slightly or not all") to 5 ("extremely"). The n egative items a re summed to provide a NA score for negative affect, and the positive items a re summed to provide a PA score for positive affect. Reliability has been reported to be good, ranging from .84 to .87 Cronbach alphas for the NA scale and .86 to .90 for the PA scale (Watson, Clark, & Tellegen, 1988). See A ppendix F Data Analysis All analyses were conducted using PASW (Predictive Analytics Software; formerly SPSS or Statistical Package for the Social Sciences) version 17 for Mac. Analyses of d e mographic and pain characteristics of both sample waves were conducted by wave and cluster group using des criptive, frequency, correlational and chi square analyses and are presented in the tables below Hypothesis one was analyzed using hierarchical cluster (Ward's method) procedures using both CPAQ Activities Engagement (AE) and Pain Willingness (PW) variables to determine number of cluster groups. A hierarchical approach was chosen to closely follow Vowles et al.'s methodology but the type of hiera rchical approach was not specified: therefore, Ward's method was chosen based on the following steps. Hierarchical analyses were initially conducted using all method types for clustering : between groups, within groups, centroid clustering, median linkage, nearest neighbor, and farthest neighbor. C omparison s of all m ethod types revealed a range of two to four

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40 groups emerging. The i dentification of three groups was selected based on viewing dendograms and fusion plots specifically for the two methods that most clearly demarcated clusters Ward's method and nearest neighbor Ward's method was chosen due to its most clearly marked cluster groups based on its dendogram and fusion plot. Ward's method was then conducted on two randomly selected subsamples from the cohort to establish internal validity and cross validation of the emerging number of three clusters (Gore, 2000; Blashfield, 1977). The k means method was utilized to determine cluster group identification and was chosen based on precedent set by previous studies examining CPAQ clusters ( Costa & Pinto Gouveia, 2010; Vowles et al., 2008). Hypothesis two was tested by conducting a one way multivariate analysis of c ovariance (MANCOVA) to assess whether there were significant differ ences among the three cluster groups of combined AE and PW on a linear combination of Perceived Disability, Positive Affect, and Negative Affect. The three cluster groups, 1) Low AE and Low PW (Low Low); 2) High A E and High PW (or High High); and 3) Mediu m AE and Medium PW (Medium Medium); served as the categorical independent variable. Total scores for the Pain Disability Scale (PDI) or Perceived Disability; and Positive and Negative Affect, respectively, from the P ositive and N egative A ffect S cale (PAN AS) were employed as the three continuous dependent variables. Age, years of education, number of surgeries and current level of pain were chosen as covariates based on prior research that indicate these variables correlate highly with degree of perceiv ed disability and negative and positive affect Univariate analyses ( Analysis of Covariance; ANCOVA ) were subsequently conducted to ascertain specifically where differences among dependent variables were observed between the three cluster groups.

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41 A ssumpt ions for multivariate analysis of covariance that are robust to violation include independence, multivariate normality, and homogeneity of variance and covariance. However, it is noteworthy that cluster sample sizes used to conduct the MANCOVA were not of equal size The resulting sample sizes likely contributed to significant Box's M values, thus indicating covariance matrices of the dependent variables are not homogeneous across all three cluster groups. Similarly, Levene's Test of Equality of Error Va riances suggests heterogeneity among the error vari ance of the dependent variables across the cluster groups. These limitations are discussed herein.

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42 CHAPTER IV RESULTS Characteristics of the Participant Sample Compared to Population Means Descriptive statistics comparing population means from non cancer chronic pain patient samples from clinical sites are reviewed here to more carefully examine the differences between them and our online sample. Specifically, demographic and pain characteristic means from t wo samples of CP patients recruited from subspecialty pain clinics in the U.K. and two samples of CP patients from the U.S. (one primary care and the other mixed outpatient and subspecialty) are compared to the current online study sample in Table 1. Table 1 Demographic and Pain Characteristics of Clinic Samples vs. Current Online Sample Characteristic McCracken a Vowles a Chelminsky b Puder c Mean (SD) Mean (SD) Mean (SD) Mean (SD) Age (Years) 42.3 (12.4) ** 47.3 (11.4) ** 51 (9.6 ) ** 52.74 (14.4)** Education (Years) 13.1 (3.5) ** 12.5 (3.0) ** ------14.07 (2.6 )** Years in Pain 9.24 (8.2)** 8 .0 ** (mdn) ------10.06 (11.0 )** Pain Rating (1 1 point) -----7.0 (1.8) 6.8 -----* p < .05 ** p < .01 a U.K. Pain Subspecialty b U.S. Primary Care c U.S. Mixed outpatient sample: Pain clinic; CP support groups; media ads Significant differences are found between these clinical samples and the current online sample in both demographic and pain characteristics, with the exception of pain ratings found on an 11 point Likert type scale. The two U.K. pain subspecialty clinical

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43 samples described in McCracken, Vowles, and Zhao O Brien (2010) and Vowles and McCracken (2008) were non cancer CP outpatients that were seen at a multidisciplinary pain treatment center in Southwest England ( N = 205; N = 187, respectively). Chelminsky et al.'s 2005 CP out patients ( N = 85) were referred by their primary care physicians at an academic primary care site at the University of North Carolina and Puder's 1988 sample included a mix of CP outpatients seen at Univers ity of Washington p ain clinics and patients recruited from local CP support groups, senior citizen groups, and media advertisements in the Seattle, Washington area ( N = 69). As reported in Table 1, a ll clinical CP samples were primarily middle aged and mean age in years in the clinic samples ranged from 42.3 to 52.74. In comparison, our online sample had a mean age of 44.73 ( SD = 11.24) and significantly differed from each clinical CP sample as indicated by inde pendent t tests (all p 's < .01). Our online sample had a mean 14.81 years of education ( SD = 2.42) and also significantly differed from clinical means that presented with a range from 12.5 to 14.07 years as indicated by independent t tests (all p 's < .01) Although Chelminsky et al. ( 2005 ) did not report mean years of education, authors report 34% had some college; 28% graduated from high school; and 38% did not have a high school diploma in this primary care CP sample. Duration of CP in years also differed from our online sample such that online participants had 14.41 years ( SD = 11.58) and clinical samples reported mean ranges of 8 (median) to 10.06 years. Online CP participants self reported a mean current pain seve rity rating of 7.04 ( SD = 1.99) on an 1 1 point Likert type scale with 1 0 representing most severe pain. The two clinical studies that reported current pain severity on similar 11 point Likert type scales also indicated similar pain ratings ( 7.0 ( SD = 1.8) in Vowles and McCracken,

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44 2008; 6.8 in Chelminsky, et al., 2005) and were found to be non significant when compared to the online sample as reported by independent t tests (both p 's > .05). Other important differences between sample t ype s (online vs. clinical) that are not reported in Table 1 are described here in Our online sample was represented primarily by women ( n = 250; 83.3%). This was found to be the case in three out of four clinical samples as well ; however, the percentage of women was lower in all of these clinical studies ( 65.7%, McCracken, Vowles, & Zhao O Brien, 2010; 64.2%, Vowles & McCracken, 2008; 40%, Chelminsky, et al., 2005; and 71%, Puder, 1988). Similar ethnicity ratios were represented across all studies, with Caucasians overrepresented: 82.7% of our online sample ( n = 248) ; 98% (McCracken, Vowles, & Zhao O Brien, 2010); 98.4% (Vowles & McCracken, 2008); and 78% ( Chelminsky, et al., 2005 ) in the clinical studies Our sample included 4. 8 % African American s, 2.7 % Hispanics, 1.3 % Asian or Pacific Islanders, and 8.7% other. M ost studies did not fully describe ethnicity representation and Puder (1988) did not address this demographic variable at all : therefore, minority percentages were not available. Marriage/partner status did not differ greatly across these five studies The majority of all participants across all studies were married : 49% in our online sample; 58.3% in McCracken, Vowles, and Zhao O Brien ( 2010 ) ; 68.4% in V owles and McCracken ( 2008 ); 49% in Chelminsky, et al. ( 2005 ) ; and 75.4% in Puder ( 1988 ) Other marriage/partner categories contrasted slightly with our online sample which included 15.7% single; 12.0% living with partner; 9 .0 % divorced; 4.0% separated; and 2.7% widowed. McCracken, Vowles, and Zhao O Brien (2010) reported 31.2% single, 10.1% divorced, and .5% widowed. Vowles and McCracken (2008) reported 13.4% single,

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45 15.5% divorced, and 2.7% widowed. Both U.S. study autho rs combined these categories to report 7% in stable relationships and 44% unmarried (Chelminsky et al., 2005); whereas 24.6% were widowed, single or divorced in Puder's 2008 study. Notably, e mployment status differ ed across samples: 49.7% were unemployed in our online group compared to 69.7% ( McCracken, Vowles, & Zhao O Brien, 2010 ); 76.3% ( Vowles & McCracken, 2008 ) ; and 58% (unemployed, retired, student or homemaker ) in Puder 1988. Chelminsky, et al. (2005) did not report employment status. T wenty five percent of our online sample reported full (17.7%) or part time (7.3%) employment; however, only 15.2% of one of the U K pain subspecialty samples were found to be working either full or part time (McCracken, Vowles, & Zhao O Brien, 2010) In contrast, Puder's 1988 mixed CP sample indicated 42% were employed full or part tim e. Both online and clinical samples had non cancer CP for at le ast three months in duration, with the exception of Puder's (1988) six month criteria, and all five samples represented similar ly diverse variations of pain type and location Due to differences in reporting methods across studies, only general variation s can be ascertain ed Our sample included the following types of pain and location in order of most prevale nt : Lower Limbs (47.2%); Lower Back (44.7%); Cervical Spine (39.7%); Upper Extremities (37.3%); Full Body (32%); Head/Face (29.7%); Thoracic Spine (27.0%); Pain Location: Other (21.0%); and Pelvic/Genital (16.0%). McCracken, Vowles, and Zhao O'Brien (2010 ) reported pain diagnoses of CP syndrome or non specific musculoskeletal pain (50.4%); fibromyalgia (14.5%); post surgical (10.0%) ; complex regional pain syndromes ( CRPS; 6.5%); or oth er (e.g. arthritis, peripheral neuropathy, (19.0 % ) )

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46 Vowles and McCracken (2008) reported 51.2% of their pain subspecialty sample did not have established diagnoses ( 45.9% low back; 18.4% shoulder/arms; 16.8% full body ; 11.8% legs/pelvic region; 2.7% neck ; 2.2% mid back; and 2.2% other (head, abdominal, others)) F ibr omyalgia (35.7%) and smaller percentages of CRPS, arthritis, and o ther comprised the rest of this sample. Chelminsky e t al.'s 2005 primary care cohort included 49% spine (35% lumbar; 8% cervical; 6% thoracic); fibromyalgia (15%); polyarthritis (9%); 16% other specific locations (knee, abdomen, elbow and hip) and diffuse neuropathic pain (5%). Lastly, 70% of Puder's 1988 mixed CP sample had various musc uloskeletal diagnoses including rheumatoid arthritis osteoarthritis, sciatica, temporomandibular joint syndrome, or fibromyalgia ; followed by idiopathic (17%); neurological (10%); and vascular (3%) pain disorders. Demographic Characteristics of the Partic ipant Sample Descriptive statistics for both data waves ( September 2008 to July 2009 and July 20 10 to March 201 1 ) are shown in Table 2 Three time periods (between September and November 2008, from March to July 2009 and from July 2010 to March 2011) are describ ed in the Methods section ; however, as previously described, the classification of two data waves is used as it better defines these samples. Specifically, the 2008 and 2009 waves were collected using online s upport only groups (versus intervention seeking) from "Yahoo! Groups" whe reas the 2010 2011 wave includes online general public group participants via Facebook. Both data collection waves (wave 1 n = 148; wave 2 n = 152) were predominately female (85.1% and 86.1% respectively), Cauc asian (89.9%; 75.7%) and were well educated (wave 1 mean years = 15.08 ( SD = 2.24); wave 2 mean years =

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47 14.52 ( SD = 2.58). No significant differences were found between the waves in terms of gender, income, and years of education. Although participants in both waves were pr imarily middle aged (mean years = 47.14 and 42.25, respectively), an independent t test revealed the re was a significant effect for age t ( 290) = 3.81, p < .01. There were also no significant differences found for income, with the majority of participants from both waves earning between $5k and $14,999 (wave 1 = 19.6%; wave 2 = 22.6%) and $30k and $49,000 (wave 1 = 19.6%; wave 2 = 22.6%), with even distribution across ot her annua l salary ranges. Results from a chi square test of independence also showed significant differences between the two waves for ethnicity ( 2 ( 6, N = 300 ) = 43.33, p < .01) with more African American (4.1% vs .7%) and Hispanic participants (2% vs. .7%) in wave 1 A chi square test of independence was also performed to examine the differences in type of employment Overall, the majority of participants in this sample were unemployed (wave 1 = 56.8%; wave 2 = 50%); however, a significant difference was fo und between the two waves, ( 2 ( 3, n = 278 ) = 9.96, p < .05). Wave 1 had more homemakers (23.6%) than wave 2 (14.6%) whereas wave 2 was found to have significantly more participants who were employed full time (25.4%) compared to wave 1 (13.5%). Table 3 displays d escriptive statistics for each of the cluster groups, low AE and low PW (or Low Low) high AE and high PW (High High), and medium AE and medium PW (Med Med) that were identified by k means cluster analysis. M ost participants f ell into the Low Low group ( n = 81), followed by the Med Med ( n = 71), and the High High groups ( n = 50). No significant differences were found amo ng these three cluster groups in terms of demographic variables with the exception of type of

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48 Table 2 Demographic Characteristics of Sample by Data Collection Wave ______________________________________________ __________________________ Characteristic Wave 1 ( n = 148) Wave 2 ( n = 152) n (%) n (%) 2 p Gender .06 .81 Male 22 (14.9) 20 (13.9) Fema le 126 (85.1) 124 (86.1) Income 16.77 .40 Bel ow $500 0 7 (6.9) 8 (6.5) $5 000 $14,999 20 (19.6) 28 (22.6) $15,000 $29,999 18 (17.7) 26 (21) $ 30,000 $49,999 20 (19.6) 28 (22.6) $50,000 $69,999 11 (10.8) 14 (11.2) $70,000 $89,999 13 (12.7) 9 (7.3) $90,000 $109,999 9 (8.9) 5 (4) Ove r $110,000 4 (3.8) 6 (4.8) Employment 9.96 .02* Full time 20 (13.5) 33 (25.4) Part time 9 (6.1) 13 (10) Hom emaker 35 (23.6) 19 (14.6) Unemploye d 84 (56.8) 65 (50) Ethnicity 43.33 <.01** Caucasi an 133 (89.9) 115 (75.7) Af rican American 6 (4.1) 1 (.7) Hispanic 3 (2) 1 (.7) A sian/Pacific Islander 0 (0) 2 (1.3) Other 6 (4.1) 7 (4.6) Mean (SD) Mean (SD) t p Age (yea rs) 47.14 10.61 42.25 11.3 6 3.81 <.01** Range: 18 71 Range: 19 70 Ed ucation (years) 15.08 2.24 14.52 2.58 1.86 .06 Range: 10 22 Range: 6 24 p < .05 ** p < .01 employment. A ch i square test of independence reveal s a significant difference in employment type among these groups ( 2 ( 6 n = 202 ) = 15.48, p < .05). Participants within the High High group were most likely to be employed (32%) or a homemaker

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49 Table 3 Demographic Characteristics of Sample by Cluster Group Chara cteristic Cluster 1 Cluster 2 Cluster 3 Low Low ( n = 81) Hig h High ( n = 50) Med Med ( n = 71) n (%) n (%) n (%) 2 p Gender 1.04 .6 Male 13 (16) 5 (10) 11 (15.5) Female 68 (84) 45 (90) 60 (84.5) Income 39.01 .18 Below $5000 4 (6) 3 (7.5) 3 (5) $5000 $14,999 20 (29.8) 6 (15) 8 (13.3) $15,000 $29,999 12 (18) 8 (20) 9 (15) $30,000 $49,999 14 (20.9) 9 (22.5) 16 (26.7) $50,000 $69,999 18 (2) 2 (5) 10 (16.6) $ 70,000 $89,999 6 (9) 6 (15) 6 (10) $9 0,000 $109,999 1 (1.5) 5 (12.5) 4 (6.6) Over $110,000 2 (3) 1 (2.5) 4 (6.7) Employment 15.48 .02* Full time 10 (12.3) 16 (32) 14 (19.7) Part t ime 2 (2.5) 3 (6) 7 (9.9) Homemaker 14 (17.3) 12 (24) 13 (18.3) Unemployed 55 (67.9) 19 (38) 37 (52.1) Ethnicity 8.54 .74 Caucasian 74 (91.4) 44 (88) 68 (95.8) African American 2 (2.5) 1 (2) 1 (1.4) Hispanic 2 (2.5) 1 (2) 0 ( 0) Asian/Pacific Island 0 (0) 0 (0) 1 (1.4) Other 3 (3.6) 4 (8) 1 (1.4) Mean (SD) Mean (SD) Mean (SD) F p Age (years) 44.9 (9.83) 44.78 (12.66) 45.37 (12.32) .05 .95 Range: 20 70 Range: 20 68 Range: 18 71 Education (yea rs) 14.58 (2.29) 15.18 (2.51) 14.84 (2.08) .99 .37 Range: 10 22 Range: 10 21 Range: 11 20 p < .05 ** p < .01

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50 (38%) and were the least likely to be unemployed (38%) when compared to the two other groups. Conversely, participants in the Low Low cluster were the most likely to be unemployed (67.9%), and least likely to be a homemaker (17.3%) or employed either part time ( 2.5%) or full time (12.3%) Those in the Med Med cluster fell within all middle ranges between the High High and Low Low group employment classifications. Although there were no other significant differences among demographic characteristics, variations among the three clusters show notable trends. The majority of participants in the High High and Med Med groups self reported higher earnings than the Low Low participants and the High High participants also reported the highest levels of education ( M = 15.18, ( SD = 2.51)), and Low Low the lowest ( M = 14.58, ( SD = 2.29) ). Pain Characteristics of the Participant Sample Descriptive statistics for all pain characteristics for both data collection waves ( September 2008 to July 2009 and July 20 10 to March 201 1 ) are shown in Table 4 Independent t tests and chi square tests of independence were again conducted to determine if there were significant differences between the data waves. Bonferroni adjusted criterion alpha levels were applied to pain locations ( p = .005 (.05/9)) and the remaining pain characteristics ( p = .02 (.05/3)). Participants were asked to record all areas they experienced pain; th erefore, more than one area may have been indicated per individual. Mo st results from independent t tests comparing the location of the participants' pain and the two waves were found to be significantly different even when acco unting for adjusted criterion levels These endorsed areas include pain located in the lower back, lower limbs, upper extremities, cervical spine, pelvic/genital, and other.' No significant differences were found for head/face, thoracic spine, or fully body pain.

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51 Table 4 Pain Characteristics of Sample by Data Collection Wave ______________________________________________ __________________________ Wave 1 ( n = 148) Wave 2 ( n = 152) Character istic n (%) n (%) 2 p Pain Location Lower Back 50 (33.8) 84 (55.3 ) 14 .00* Lower Limbs 52 (35.1) 90 (59.2 ) 17.44 .00* Upper Extremitie s 38 (25.7) 74 (48.7 ) 16.97 .00* Head/Face 40 (27) 49 (32.2 ) .9 8 .33 Cervical Spine 42 (28.4) 77 (50.7) 15 55 .00* Thoracic Spine 43 (29.1) 38 (25 ) .63 .43 Pelvic/Genital 9 (6.1) 39 (25.7 ) 31.38 .00* Full Body 49 (33.1) 47 (30.9) .17 .69 Pain Location: Other 63 (42.6) 0 (0 ) 81.92 .00* Mean (SD) Mean (SD) t p Years in P ain 6.82 12.77 12.54 10.22 2.73 .00** R ange: 1 54 Range: 0 45 Current Pain Level: 0 10 point scale (10 = most) 7. 13 1.93 6.92 2.06 .81 .42 Range: 0 10 Range: 0 10 Nu mber of Surgeries 1.4 3 2.23 2.56 4.6 2.28 .02 R ange: 0 13 Range: 0 30 p < .0 05 using Bonferroni correction for nine chi square tests of independence. ** p < .0 2 using Bonferroni correction for three independent t tests. Wave 1 endorsed having the most other' locations of pain (42.6%), followed by lower limb (35.1%), lower back (33.8%), full body (33.1%), and thoracic spine pain (29.1%). Wave 2 was not offered the other' pain location item: lower limbs (59.2 %), lower back (55.3%), cervical spine (50.7%), upper extremities (48.7%), and head/face pain (32.2%) were the most frequently endorsed. Additionally, an independent t test with a Bonferroni adjusted alpha level of .02 (.05/3) was conducted to examine the differences in participants' number of years they had experienced pain (Years in Pain.) Res ults showed a significant difference in the se

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52 number of years between the two data wa ves, t (185 ) = 2.73 p < .01. Wave 1 participants self reported th ey experienced significantly more years of pain ( M = 16.82 ( SD = 12.77)) than wave 2 ( M = 12.54 ( SD = 10.22)). W ave 2 participants reported having had a greater number of surgeries ( M = 2.56 ( SD = 4.6) ) compared to wave 1 participants ( M = 1.43 ( SD = 2.23 ) ) ; however, with Bonferroni correction alpha levels applied per test (.02), this was not significant, t (132 ) = 2 .28 p = .0 2 The average current pain levels for the entire sample, rated between zero and ten on a n 1 1 point Likert scale, fell between 6.9 2 ( SD = 2.06) for wave 2 and 7.13 ( SD = 1.93) for wave 1. There were no significant differences reported for current pain level between the two groups, t (232) = .81 p = .42. Table 5 displays pain characteristics of the sample by the three cluster groups, Low Low ( n = 81) High High ( n = 50) and Med Med ( n = 71) N o significant differences were found among the cluster groups, with the exception of Current Pain Level. A one way ana lysis of variance (ANOVA) was conducted to examine differences among groups in Current Pain Level. These results show a significant difference among the clusters, F (2, 191 ) = 3.76 p < .05 The Low Low group reported the highest pain levels ( M = 7.44 ( SD = 1.98)); followed by the Med Med group ( M = 6.95 ( SD = 1.9)); and lastly, the High High cluster showed the lowest current pain ratings ( M = 6.44 ( SD = 2.22)). Despite the absence of other significant differences among pain characteristics, several trends are again worth noting. The top three endorsed pain locations among all three groups were nearly identical: lower back, lower limbs, and cervical spine. Notable differences among the groups were as follows: ove rall Low Low participants endorsed more pain locations in general than both other cluster groups; High High members

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53 Table 5 Pain Characteristics of Sample by Cluster Group Characteristic Cluster 1 Cluster 2 Cluster 3 Low Low ( n = 81) High High ( n = 50) Med Med ( n = 71) n (%) n (%) n (%) 2 p Pain Location Lower Back 43 (53.1) 20 (40) 36 (50.7) 2.24 .33 Lower Limbs 45 (55.6) 20 (40) 37 (52.1) .21 3.11 Upper Extremities 36 (44.4) 13 (26) 30 (42.3) 4.87 .09 Head/Face 26 (32.1) 19 (38) 1 9 ( 26.8) 1.72 .42 Cervical Spine 41 (50.6) 21 (42) 27 (38) 2.55 .28 Thoracic Spine 24 (29.6) 18 (36) 23 (32.4) .58 .75 Pelvic/Genital 17 (21) 4 (8) 11 (15.5) 3.9 2 .14 Full Body 32 (39.5) 11 (22) 2 3 (32.4) 4.31 .12 Pain: Other 18 (22.2) 18 (36) 19 (26.8) 2.97 .23 Mean (SD) Mean (SD) Mean (SD) F p Years in Pain 14.39 11.67 16. 71 11.16 13.83 11.92 .804 .45 Range : 0 54 Rang e: 1 48 Range: 1 41 Current Pain Level: 10 Point Scale (10 = Most) 7.44 1.98 6.44 2.22 6.95 1. 9 3.76 .03* Range: 0 10 Range : 0 10 Range: 0 10 # S urgery 1.43 2.23 1.64 4.1 1.95 4.17 .23 .80 Range: 0 13 Range: 0 25 Range: 0 30 p < .05 ** p < .01 reported the fewest pain locations; High High experienced less upper extremity pain (26% versus 44.4% and 42.3%); and pelvic/genital pain was the least frequently endorsed among all cluster groups (21%, 8% and 15.5%). High High members also reported experiencing the longest periods of years in pain ( M = 16.71 ( SD = 11.16)) compared to both the Low Low group ( M = 14.39 ( SD = 11.67)) and Med Med cluster ( M = 13.83 ( SD = 11.92)). Lastly, Table 6 displays comparisons of pain location by gender. Chi square tests of independence and i ndependent t tests were conducted to determine differences

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54 between genders among types of pain location. Again, Bonferroni adjusted criterion alpha levels were applied to pain locations ( p = .005 (.05/9)) and the remaining pain characteristics ( p = .02 (.05/3)). Chi square test results suggest that when compared to men ( n = 42), women ( n = 250) experienced u pper extremity pain significantly more, ( 2 ( 1, n = 292 ) = 12.02, p < .005), as well as head/face ( 2 ( 1, n = 292 ) = 10.17, p < .005); and cervical spine pain ( 2 ( 1, n = 292 ) = 11.79, p < .005). Further examination of Years in Pain, Current Pain Level and Number of Surgeries suggest that there are no significant differences among these variables by gender. In the total sample, women reported higher spine pain ( 2 ( 1, n = 292 ) = 11.79, p < .005). Further examination of Years in Pain, Current Pain Level and Number of Surgeries suggest that there are no significant differences among these variables by gender. In the total sample, women reported higher pain ratings ( M = 7.1 ( SD = 1.96)) when compared to men ( M = 6.72 ( SD = 2.14)); women reported slightly more Years in Pain ( M = 14.44 ( SD = 11.78)) than men ( M = 14.25 ( SD = 10.4)); and men have had more pain related surgeries ( M = 2.22 ( SD = 3.16)) than women ( M = 1.84 ( SD = 3.5)). Hypothesis One Hypothesis one states that there will be no differences in the cluster groups identified in this online CP support group sample compared to the cluster groups identified by Vowles et al. (2008) using the same methodology outlined in that study. Specifically, t he following group clusters were predic ted: 1) Low Activity Engagement Low Pain Willingness 2) High Activity Engagement High Pain Willingness 3) High Activity Engagement Low Pain Willingness

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55 Table 6 Pain Characteristics by Gender ______________________________________________ _________________________ M en Women ( n = 42) ( n = 250) Character istic n (%) n (%) 2 p Pain Location Lower Back 14 (33.3) 120 (48 ) 3.12 .0 8 Lower Limbs 16 (38.1) 126 (50.4 ) 2.18 .14 Upper Extremities 6 (14.3) 106 (42.5) 12.02 .00* Head/Face 4 (9.5) 85 (34) 10.17 .00* Cervical Spine 7 (16.7) 112 (44.8) 11.79 .00* Thoracic Spine 7 (16.7) 74 (29.6 ) 3 .0 8 Pelvic/Genital 7 (16.7 ) 41 (16.4 ) .0 0 .97 Full Body 7 (16.7) 89 (35.6) 5 .8 4 .0 2 Pain Location: Other 6 (14.3) 57 ( 22.8) 1.54 .22 Mean (SD) Mean (SD) t p Years in Pain 14.25 10.4 14.44 11.78 .08 .94 Range: 1 38 Range: 0 54 Current Pain Level: 0 10 point scale (10 = most) 6.72 2.14 7.1 1.96 1.05 .30 Range: 0 10 Range: 0 10 Number of S urgeries 2.22 3.16 1.84 3.5 .62 .54 Range: 0 13 Range: 0 30 p < .0 05 using Bonferroni correction for nine chi square tests of independence. ** p < .0 2 using Bonferroni correction for three independent t tests. Hierarchical cluster analysis using Ward's method, followed by k means cluster analysis were conduc ted using Activity Engagement (AE) and Pain Willingness (PW) totals from the Chronic Pain Acceptance Questionnaire (CPAQ). This method was chosen to replicate Vowles et al.'s 2008 methodology as closely as possible. The AE/PW k means cluster analysis with a maximum of 10 iteratio n s specified three clusters: Low AE and Low PW "Low Low" ( n = 81), High AE and High PW "High High" ( n = 50 ) and Medium AE and Medium PW Med Med ( n = 71 ) (see Table 7 ) These findings indicate that h ypothes is o ne was largely supported: our predicted Low Low and High

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56 High groups emerged but the third group varied slightly such that a Medium AE/Medium PW group appeared AE and PW means for each cluster are consistent with their designated labels and are reported in Table 7 A verage scores for the Low Low group are the lowest of the three clusters: AE ( M = 15.75 ( SD = 6.26)) and PW ( M = 15.75 ( SD = 7.1)). High High members self reported the highest ratings for both AE ( M = 48.4 ( SD = 16.2)) and for PW ( M = 30 ( SD = 7)). The M ed Med group's scores fell between the two other clusters: AE ( M = 33.68 ( SD = 5.52)) and PW ( M = 21.4 ( SD = 6.7)). Table 7 CPAQ Score Means of the Patient C lusters Cluster 1 Cluster 2 Cluster 3 Low Low ( n = 81) High High ( n = 50) Med Med ( n = 71) Mean (SD) Mean (SD) Mean (SD) Activity En gagement 15.75 (6.26) 48.4 (6.2) 33.68 (5.52) Pain Willingness 15.75 (7.1) 30 (7) 21.4 (6.7) Hypothesis Two The second hypothesis stated that in this online support group samp le, self reported scores of Positive Affect, Negative Affect and Perceived Disability would differ overall by cluster group, when controlling for demog raphic and pain characteristics as indicated. Groups were to be identified via cluster analysis as described in the hypothesis one In the case that identical cluster groups were identified it was hypothesized that cluster group 1 (Low Low) was expected to show higher levels of both Negative Affect and Perceived Disability and lower levels of Positive Affect. Conversely, cluster group 2 (High High) was expected to show lower levels of Negative Affect and Perceived Disability and higher levels of Positive Affect. Group 3 (High AE/Low PW) was

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57 predicted to show moderate levels of Positive Affect, Negati ve A ffect and Perceived Disability Results from testing hypothesis one suggest that these groups were largely similar; therefore, we proceeded to conduct the following statistical analyses using the current three clusters. In order to test hypothesis two a one way multivariate analysis of covariance (MANCOVA) was conducted to examine differences in Perceived Disability, Negative Affect and Positive Affect within each cluster group ( Low Low, High High, and Med Med ). The categorical cluster group type ( Low Low, High High, and Med Med ) served as the independent (i.e., group) variable and Perceived Disability, Negative Affect and Positive Affect comprised the three continuous dependent variables. Age, number of surgeries, years of education, and current level of pain were used as covariates based on conducted correlational analyses that revealed significant relationships between these survey items and the three dependent variables The a priori criterion significance level was set at .05. As previously mentioned, homogeneity of variance and covariance were not obtained as indicated by a significant Box's M value ( M = 38.25, p < .01) This finding may have b een partially due to the unequal sample sizes that resulted from conducting the MANCOVA ( n = 30, 52 and 60). However, a ssumpt ions for multivariate analysis of covariance that are robust to violation include homogeneity of variance and covariance as well as independence, and multivariate normality (Tabachnick & Fidell, 2006 ). Significant results were obtained from the MANCOVA according to Wilks' (.55) F (6, 266 ) = 15.39 p < .01 thus suggesting the combined DVs were significantly affected by the type of cluster group ( Table 8 ). These findings also showed a moderate

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58 effect size ( p 2 = .26) b etween the three cluster groups ( Low Low High High, and Med Med ) and the combined DVs (Tabachnick & Fidell, 2006) Table 8 MANCOVA Results by Perceived Disability, Positive Affect and Negative Affect a Wilks' Variable df F p 2 p Cluster Group .5 5 6 15.39 26 00** a Age, number of surgeries, years of education, and current level of pain were used as covariates. p < .05 ** p < .01 Prior to conducting follow up univariate tests, Levene's T ests of Equality of Error Variances were conducted. These results showed heterogeneity among the error variance of both the Perceived Disability ( F (2, 139 ) = 7.58 p < .01) and Negative Affect ( F (2, 139 ) = 6.99 p < .01) dependent variables across the three cluster groups. Again, a ssumpt ions for multivariate analysis of covariance that are robust to violation include homogeneity of variance and covariance (Tabachnick & Fidell, 2006). A series of one way analysis of covariance analyses (ANCOVAs) were then con ducted to examine individual mean differences between each of the DVs and the independent variable (cluster type). The same covariates that were described in the MANCOVA analysis were used here as well As indicated in Table 9 all ANCOVAs were significant for each of the DVs (all p 's < .01) with effect sizes ( p 2 ) in the moderate r anges from .22 (Negative Affect and Perceived Disability) to .29 (Positive Affect) Findings suggest that hypothesis two correctly predicted Perce ived Disability, Negative Affect, and Positive Affect differences by cluster groups. Specifically, the

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59 Low Low cluster displayed both the most Negative Affect ( M = 28.05 ( SD = 9.33)) and Perceived Disability ( M = 49.57 ( SD = 9.46)) and the least Positive Affect ( M = 20.28 ( SD = 7.86)). Conversely, the High High group ( n = 30) showed the least Negative Affect ( M = 17.57 ( SD = 5.81)) ; the least Perceived Disability ( M = 32.28 ( SD = 15.64)) ; and the most Positive Affect ( M = 32.03 ( SD = 6.49)). Lastly, the Med Med group revealed predicted averages in the moderate ranges: Negative Affect ( M = 21.98 ( SD = 6.88)); Perceived Disability ( M = 38.8 ( SD = 12.71)); and Positive Affect ( M = 26.85 ( SD = 6.54)).

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60

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61 CHAPTER IV DISCUSSION Chronic pain is a debilitating and costly health concern for over 116 million Americans and their families with an approximate cost of $635 billion in litigation, compensation, healthcare, and lost productivity (IOM, 2011). New CP treatments are needed to foster more positive medical and functional outcome s and lower cost burden for patients Findings from previous studies suggest that profiling CP patients by levels of Acceptance a core concept in Acceptance and Commitment Therapy (ACT), may have clinical utility i n designing alternative and more effective CP interventions. The overall purpose of this study was to examine how CP Acceptance (specifically, Activity Engagement and Pain Willingness) may be utilized to group a CP online sample using a cluster analysis design. Given these predicted clusters, we sought to determine what degree of Perceived Disability, Positive Affect and Negative Affect might differ entially exist among these cluster groups. Costa and Pinto Gouveia's (2010) and Vowles et al. (2008) studi es found three cluster groups emerge d in a clinic sample : a Low Low AE and PW group ; a High High AE and PW group; and a mixed group. These studies' findings suggest fewer negative traits and behaviors (pain related anxiety, depressive symptoms, and physic al disability) were present in the Low Low group vs. the High High group (Costa & Pinto Gouveia, 2010; Vowles et al., 2008). Sample characteristics The current online sample ( N = 300) was primarily women (83.3%; n = 250); Caucasian (82.7%; n = 248) middle aged (44.73 ( SD = 11.24)); married (49%) ; and of higher education (14.81 ( SD = 2.42)). The largest group of participants was unemployed

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62 (49.7%); followed by homemakers (18%); and full time (17.7%) and part time (7.3%) workers. Mean years with chro nic pain was 14.41 ( SD = 11.58) and average current pain rating was 7.04 ( SD = 1.99) on an 11 point Likert scale with 11 as most severe. The most prevalent pain locations were Lower Limbs (47.2%); Lower Back (44.7%); Cervical Spine (39.7%); Upper Extremit ies (37.3%); Full Body (32%); Head/Face (29.7%); Thoracic Spine (27.0%); Pain Location: Other (21.0%); and Pelvic/Genital (16.0%). Although variations among demographic and pain characteristics were found when comparisons were made to other non cancer CP clinical samples in early published work; by gender; by cluster groups; and by both data collection wa ves, these were not substantial differences Because two waves of data collection were conducted at two time periods (September 2008 to July 2009 in wave 1 and July 2010 to March 2011 in wave 2), and were recruited from two different support group Internet sites ("Yahoo! Groups" and Facebook, respectively), compa risons were made to determine whether sig nificant differences existed between them. No significant differences were found between the waves in terms of gender, income, and years of education. The waves did differ significantly in terms of age (wave 1 participants were an average of 4.89 years older); ethnicity (wave 1 incl uded more African American (4.1% vs .7%) and Hispanic participants (2% vs. .7%)); and employment (wave 1 had more homemakers (23.6% vs 14.6%) and fewer full time workers (13.5% vs 25.4%)). The waves also differed significantly by pain characteristics wit h variations in pain locations (lower back, lower limbs, upper extremities, cervical spine, pelvic/genital, and other'). No significant differences were found for head/face, thoracic spine, or fu ll

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63 body pain. One possible explanation for these differenc es may be due to study design changes made between data collection waves 1 and 2. The "pain location: other" item was not offered at wave 2: therefore, wave 1 participants may have felt their pain condition was best described by this "catch all" category whereas wave 2 was directed to characterize their pain in the more diverse specific pain location items. However, these differences may also be simply due to wide variations in pain location sites as reported by CP populations. Lastly, years in pain diff ered between the waves, with wave 1 participants' self reporting a mean of nearly half as many years (6.82 ( SD = 12.77)) as wave 2 participants (12.54 ( SD = 10.22)). Given these findings, distinguishing characteristics of the earlier "Yahoo! Groups" (wave 1) compared to the wave 2 Facebook cohort include: older, more ethnically diverse, more homemakers and fewer full time workers, more "other" pain locations, and fewer years with CP. These differences may reflect variations in the type of online particip ant that is drawn to frequent either Facebook or Yahoo! Groups. Although no studies of Facebook or Yahoo! Groups online CP support groups exist to date; several studies estimate more women than men utilize Facebook (57% women in 2012, AnsonAlex.com, 2012; and 61% in 2011, Kiser, 2011) and users tend to be younger, in general: 29% range from age 18 to 25 ; 23% are aged 26 to 34 ; and 18% are aged 35 to 44 (Community102.com, 2011). More research should be done in this area to illuminate the differences among online support group users. Comparisons between gender within the total sample, as well as by cluster group, showed fewer significant differences. Only differences in pain location were found

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64 between men and women, with women reporting significantly mor e upper extremity (42.5% vs 14.3%), head/face (34% vs. 9.5%), and cervical spine pain (44.8% vs 16.7%). It is a possibility that unequal sample sizes (women n = 250; men n = 42) may have contributed to these significant differences; however, the PASW soft ware is designed to account for this inequality in sample sizes. Lastly, only two differences were found between the three cluster groups (Low Low, High High and Med Med ): employment type, with Low Low participants reporting the most unemployment (67.9% vs 38% in the High High and 52.1% in the Med Med group); and current pain level, with the Low Low cluster reporting the highest ratings (7.44 ( SD = 1.98)) compared to the lowest ratings in the High High group (6.44 ( SD = 2.22)). A primary question in conducting research utilizing online chronic pain support group members is whether these findings may generalize to clinical chronic pain populations. In attempts to answer this question, comparisons were made to both clinical sample s in the United States and the United Kingdom. Compared to four other CP clinical samples (two U.K. pain subspecialty samples; one U.S. primary care; and one U.S. mixed outpatient, support group and media respondents), single sample t tests revealed signi ficant differences suggesting that our online sample was slightly younger, more educated, and had experienced more years in pain. When other demographic and pain characteristics were qualitatively compared due to studies' differences in reporting, our sa m ple appeared to have more female than male participants (83.3% women); and more were employed full or part time or were homemakers (43%); however, our sample had fai rly comparable diverse representations of CP locations.

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65 Overall, these findings suggest that participants in our online sample are simultaneously differ ent and similar to general population means, as represented by these four clinical samples from the U.S. and U.K. When compared to other online CP samples, however, our cohort's characteristi cs are more similar. Gender trends in online intervention studies that recruit chronic illness or CP patients exclusively from Web sites report greater numbers of women. For example, 62.5% women were recruited in Burhman et al.'s 2004 study of an I nterne t therapy with telephone support and 88% women chronic headache patients were enrolled in Devineni and Blanchard's study (2005). Likewise, 71.6% and 71.2% women chronic illness patients were enrolled in usual care and intervention groups respectively, in Lorig et al.'s 2006 study (total N = 780); and 90.5% and 89.8% usual care and treatment group women were enrolled in Lorig et al.'s 2008 study of arthritis and fibromyalgia therapy ( N = 866). Likewise, these studies reflect comparable age means to our on line sampl e (44.73 ( SD = 11.24)): 44.6 ( SD = 10.4); 43.6 ( SD = 12.0); and 52.2 ( SD = 10.9) (Buhrman et al., 2004; Devineni & Blanchard, 2005; Lorig et al., 2008, respectively). These findings suggest that online CP patie nts differ from clinical survey respondents due to sampling bias. As described in other online studies, Internet recruitment readily excludes individuals who lack computer skills, the means to own equipment and afford related service costs, and those who may not have time to engage with CP support groups (Macea et al., 2010). Hypothesis One Hypothesis one states that there will be no differences in the cluster groups identified in this online CP support group sample compared to the cluster groups identified by Vowles et al. (2008) usin g the same methodology outlined in that study.

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66 Specifically, we predicted three clusters would emerge: 1) Low Activity Engagement Low Pain Willingness ; 2) High Activity Engagement High Pain Willingness; and 3 ) High Activity Engagemen t Low Pain Willi ngness This hypothesis was largely supported such that the followin g three cluster groups emerged in our online sample: 1) Low Activity En gagement Low Pain Willingness; 2) High Activity Engag ement High Pain Willingness; and 3 ) Medium Activity Engagem ent Medium Pain Willingness. This third group also varied in Costa and Pinto Gouveia's 2010 replication and expansion of Vowles et al.'s 2008 study such that this third group represented those with High Activity Engagement and Low Pain Willingness. It is interesting that the characteristics of the middle group vary between the current and previous studies and are, in general, less distinct than the other two clusters. Similarly, the consistency of replicating cluster groups using the MPI measure has also been shown to vary between studies and CP samples. For example, six clusters are known to emerge from the MPI profiling procedure, (Unanalyzable, Anomalous, and Hybrid in addition to Dysfunctional, Adaptive Copers and Interpersonally Distressed). Alt hough results vary across studies, studies have indicated that 3 30% of CP patients fall into these unusable categories (Okifuji, 1999; Ravani, 2005 ; Zaza, Reyno, & Moulin, 2000). Other studies suggest that characteristics are so similar between the group s (e.g. Interpersonally Distressed and Dysfunctional) that they should be combined (Epker & Gatchel 2000). Therefore, it appears that although both MPI and Acceptance profiles predict better and worse functioning depending on profile type, Acceptance has thus far shown more reliability in replicating similar cluster groups with no outlier groups It may be that v ariability in replicating the exact profiles across samples is a challenge within the

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67 CP population, due to diversity of pain type and location. Future research limited to specific pain location and type may reveal key constructs among these varied CP groups. Hypothesis Two Hypothesis t wo states that self reported scores of Positive Affect, Negative Affect and Perceived Disability will differ overall by group, when co ntrolling for demographic and pain characteristics as indicated. Specifically, cluster group 1 will be expected to show lower levels of Negative Affect and Perceived Disability and higher levels of Positive Affect. Conversely, cl uster group 3 will be expected to show higher levels of Negative Affect and Perceived Disability and lower levels of Positive Affect. Group 2 will show moderate levels of Positive Affect, Negative Affect and Perceived Disability. Although results of hy pothesis one were dependent on hypothesis two and the third cluster group di ffered from the predicted group (Medium AE Medium PW was our current finding), r esults support this hypothesis such that we predicted moderate levels of Positive Affect, Negativ e Affect and Perce ived Disability. Specifically Perceived Disability, Positive Affect, and Negative Affect all significantly differed by group in the predicted directions. Overall, the Low Low group revealed the least favorable of these characteristics among the three variables: the lowest mean Positive Affect ( M = 20.28 ( SD = 7.86)); the highest mean Negative Affect ( M = 28.05 ( SD = 9.33)); and the highest mean Perceived Disability ( M = 49.57 ( SD = 9.46)). Similarly, the High High cluster showed the mo st favorable degree of these traits: specifically, this group reflected the highest mean Positive Affect ( M = 32.03 ( SD = 6.49)); the lowest mean Negative Affect ( M = 17.57 ( SD = 5.81)); and the lowest Perceived Disability ( M = 32.28 ( SD = 15.64)). Also, the Med Med participants had levels between these two

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68 groups: Positive Affect ( M = 26.85 ( SD = 6.54)); Negative Affect ( M = 21.98 ( SD = 6.88)); and Perceived Disability ( M = 38.8 ( SD = 12.71)). Significantly less physical and psychosocial disability was also found in the High High group in Vowles et al.'s 2008 study as measured by the Sickness Impact Profile (SIP; Bergner, Bobbitt, Carter, & Gilson, 1981). Similarly, Vowles et al.'s Low Low grou p also showed significantly higher ratings in physical and psychosocial disability when compared to the two other cluster groups. Although Positive and Negative Affect were not specifically measured in these two prior studies by Vowles et al. (2008) and C osta and Pinto Gouveia ( 2010 ) depressive symptoms (sadness, guilt, lethargy) are reflected in low Positive Affect and high Negative Affect in the PANAS measure (Watson, 1988). Both Vowles et al. (2008) and Costa and Pinto Gouveia (2010) found the most de pression in the Low Low group and conversely, the least, in the High High group as measured by the Beck Depression Inventory (BDI; Beck, Ward, Mendelson, Mock, & Erbaugh, 1961) and the Self Compassion Scale (Neff, 2003), respectively. Given the significan t parallels in all three studies, our replicated findings strongly suggest those with higher Pain Willingness and Activities Engagement also experience less pain related disability, and more positive and fewer negative emotions, which may be manifested in depression. Limitations The primary limitations in this study include data collected from a retrospective self report sample. Retrospective data is subject to inaccuracies in participants' self reporting: therefore, prospective data collection, in which participants are followed and assessed over time, is preferable (Kazdin, 2003) Providing objective behavioral

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69 measures would also strengthen this study design, as well as collateral assessments such as semistructured questionnaires administered to participants' health providers or significant others. These measures c ould provide convergent validity and verify participants' subjective ratings Additionally, online recruitment, despite increased feasibility over in person data co llection, did reveal bias in our sample of CP patients suggesting these results may not generalize to c linical primary care or subspecialty CP patients in the general population. Notably, however, given the proliferation of online venues for support, information and intervention, online samples may prove to be a "clinical" population upon which specific no rms may be based in the future. Lastly previous studies using cluster analysis have reported difficulty consistently reproducing the same clusters, therefore demonstrating some degree of limitation of this type of methodology (Gore, 2000). Future Direc tions Future research should further illuminate the precise mechanisms that contribute to our study's findings in efforts to design and implement more effective CP interventions. Specifically, our results matched previous studies such that those with low Acceptance experience more negative outcomes (low positive affect and both high negative affect and perceived disability) whereas significantly more positive outcomes are found for those with high Acceptance; however, future studies might explore specific mechanisms regarding how this occurs in CP populations. This research is important for improving Acceptance based interventions targeting increased Activity Engagement and Pain Willingness.

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70 Given the rapidly increasing numbers of healthcare consumers turning to the Internet for health information, future Acceptance based studies should also provid e assessment and profiling interventions online to CP patients as a viable alternative to in pers on treatments (Baker et al., 2003; Nguyen, et al., 2003; Taylor, 2011). As reported previously, current online interventions primarily based in CBT, have known efficacy in target ing and improving CP symptom management and affective and behavioral outcome s (Macea et al., 2010). Specifically, these therapies improved health outcomes (lower pain intensity) in several studies as well as contributed to significant behavioral changes such as reduced medical utilization and medication use; improved functional o utcomes including less work disability, and increased self efficacy to pursue life activities despite the presence of pain and symptoms, in general (Berman et al., 2009; Burhman et al., 2004; Devineni & Blanchard, 2005; Guttberg, 2007; Guttberg, 2006; Lori g, et al., 2008; Lorig, et al., 2006; Lorig, et al., 2002; Macea et al., 2010; Schulz et al., 2007; Str š m, Pettersson, & Andersson, 2000). Results from previous studies suggest that future studies may benefit from increased staff contact, whether via te lephone, email, or in person, that expresses encouragement and support to maintain adherence in order to lower attrition rates (Buhrman et al., 2004; Guttberg, 2007). Increased privacy and access to services for patients and reduced cost for both the heal thcare consumer and provider are also significant advantages of these interventions, given rising healthcare costs (Lorig et al., 2002; Lorig et al., 2008; Macea et al., 2010). Future studies should also include random assignment to the treatment and cont rol group, as well as 12 month follow up to monitor intervention efficacy longitudinally. Special recruitment efforts and appropriate

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71 incentives should be offered to reach participants who may not be able to afford computer access and services as well as online male CP patients, due to their less active engagement in Internet CP support group studies (Buhrman et al., 2004; Devineni & Blanchard, 2005; Lori g et al., 2006; Lorig et al., 2008). According to Vlaeyen and Morley, Acceptance based profiling may help fill the gap in which there is "ample room for improvement": a change the field has been waiting for (Vlaeyen & Morley, 2005, p. 4). Subsequent development of these online tailored appro aches via Acceptance based profiling have significant implications for not only changing the type of delivery of therapy services, but also for improving patients' symptoms and affective and behavioral outcomes. Summary We conducted a cluster analysis o n a sample of online CP support group members based on levels of Acceptance ( Activity Engagement and Pain Willingness ). Three clusters emerged suggesting our first hypothesis was largely supported : 1 ) Low Activity Engagement Low Pain Willingness ; 2 ) Hig h Activity Engag ement High Pain Willingness; and 3) Medium Activity Engage ment Medium Pain Willingness. Results from the MANCOVA then confirmed our second hypothesis: those in the Low Low group showed the most Negative Affect and Perceived Disability and the least Positive Affect Conversely, the High High group showed the least amount of Negative Affect and Perceived Disability and the highest scores on Positive Affect The middle group revealed moderate scores across these same measures of mood and functional outcome. Implications from these results include 1) Acceptance is a viable construct that predicts mood and functional outcome in an online CP sample; 2) Acceptance based profiling is a

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72 promising new method of characterizing those with CP; 3) Acceptance based profiling interventions, whether conducted in vivo or online, provides the field with an alternative treatment that may improve the efficacy and effectiveness of existing CP therapies.

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91 APPENDIX A Hello! You are being invited to participate in a project designed to increase understanding of chronic pain. Specifically, we are conducting a study on persons with chronic pain who are involved in online support groups. We are inviting participants (age 18 and older) who have been and are currently experiencing a recurrent or "nonremitting" chronic pain condition for at least 3 months to complete an online survey. It is anticipated that this survey will take 45 60 minutes of your time to complete. In addition, we request that you forw ard/share the survey link to other individuals with chronic pain who may wish to participate in this study so that they may have the opportunity to assist us in gathering information about chronic pain and online support groups. Participants who complete the survey may elect to be entered into a drawing to win one of twelve $50 VISA gift cards. We expect to have approximately 600 people complete the survey. If everyone enters the drawing your odds of winning one of the gift cards would be 1 in 50. To p articipate in this study, please click on the following link: "##$%&&'''()*+,#-./$0.(1.2&34-03&534+6*07+89-:;<<=>>=? This study has been approved by Spalding University's Research Ethics Committee in support of dissertation requirements for the completion of a doctorate of psychology (Psy.D.) degree. If you have any questions regarding this study, please feel free to contact Kristen Crafton, M.A. at 1"0./-1$3-/18@+0,#*:8A".#23-B(1.2 or Dr. Steve Katsikas at ,43#,-43,A,$ 3B:-/C(+:* ( Sincerely, Kristen Crafton, MA Steve Katsikas, PhD School of Professional Psychology Spalding University

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92 APPENDIX B D6EF!G!
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93 APPENDIX C Please begin by providing the following general information: Gender ! !! Male ! !! Female ! What is your current age? __________ years What is your current height? __________ feet __________ inches What is your current weight? __________ lbs. What is your current cigarette smoking status? Currently smoke Previously smoked, but quit Never smoked The following questions are about cigarette smoking. If you are not currently a smoker, please skip ahead to page 3 How much do you smoke? Number of cigarettes per day that you smoke: __________ Number of days per week that you smoke: __________ How long have you been a smoker? __________ years

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94 Please rate your agreement with the following statements: I intend to QUIT smoking sometime. !! V-,3C0++! 1.2$B+#+B8 ! V-,3C0++ ! V-,3C0++! ,.2+'"3# !!! [/,*0+ !!!!! MC0++! ,.2+'"3# !!!! MC0++ !!!!!! MC0++! 1.2$B+#+B8 ! For me, to QUIT smoking is: !! I K! \.0#"B+,, ! I ] ! I < ! H! ^+*#03B! ! < ! ] ! K! _3B*3@B+ ! Most people who are important to me think that I: !! I K! 6".*B:! /.#!`[R5! ,2.4-/C ! I ] ! I < ! H!! ^+*#03B! ! < ! ] ! K! 6".*B:! `[R5! ,2.4-/C ! Many people like me who smoke, or have smoked cigarettes, have either QUIT smoking or are working on quitting smoking. !! I K! F.2$B+#+B8! P3B,+ ! I ] ! I < ! H! ^+*#03B ! < ! ] ! K! F.2$B+#+B8! #0*+ ! For me, to QUIT smoking cigarettes is: !! I K! [/$B+3,3/# ! I ] ! I < ! H! ^+*#03B ! < ! ] ! K! QB+3,3/# ! For me, to QUIT smoking cigarettes is: I K!!! S3: ! I ] ! I < ! H! ^+*#03B! ! < ! ] ! K!! T..: ! It's only up to me whether or not I QUIT smoking cigarettes. !! I K! 6#0./CB8! :-,3C0++ ! I ] ! I < ! H! ^+*#03B! ! < ! ] ! K! 6#0./CB8! 3C0++ ! For me, to QUIT smoking would be: !! I K! R2$.,,-@B+ ! I ] ! I < ! H! ^+*#03B ! < ! ] ! K! Q.,,-@B+ Total years of education (EXAMPLE: high school + 1 year of college = 13 years of education): __________ years

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95 Are you currently employed? ! !! Full time ! !! Part time ! !! Homemaker ! !! Unemployed if so, how long? aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa Occupation (if employed): _____________________________________ ______________________ Household annual income (per year; check one): Below $5,000 $5,000 $9,999 $10,000 $14,999 $15,000 $19,999 $20,000 $29,999 $30,000 $39,999 $40,000 $49,999 $50,000 $59,999 $60,000 $69,999 $70,000 $79,999 $80,000 $89,999 $90,000 $99,999 $100,000 $109,999 $110,000 $119,999 $120,000 $129,999 $130,000 $139,999 $140,000 $149,999 Over $150,000 Please indicate your primary race: American Indian/Alaska Native Asian Hispanic or Latino Native Hawaiian or other Pacific Islander Black or African American White (not of Hispanic origin) Other please specify: _____________________________________ If applicable, please indicate your secondary race: Not applicable American Indian/Alaska Native Hispanic or Latino Native Hawaiian or other Pacific Islander Black or African American White (not of Hispanic origin) Other please specify: _________________________________ ____

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96 Marital/Partner status: Single Married Living with partner Divorced Widowed Separated Do you have children (under 18 living) at home? Yes No If yes, how many? __________ Insurance type: No insurance Medicaid/Passport Medicare 3 rd Party payer Private carrier Do you receive Social Security or Worker's Compensation benefits? Yes No Have you had or are you involved in legal action regarding your pain? Yes No How long hav e you had your pain problem? (Answer in years and months.) ________________________________________________________ Please indicate the location of your pain: (Check all that apply.) Head/face Upper extremities (arms, wrists, hands, fingers, etc.)

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97 Thoracic spine Cervical spine (upper back, neck) Lower back Pelvic/genital Lower limbs (legs, knees, feet, toes, etc.) Full body Other please specify: _________________ ____________________ Circumstance under which your pain began: Accident at work Accident at home Motor vehicle accident Other accident Following illness Following surgery Following childbirth At work (no accident) At home (no accident) No known cause Other please specify: _____________________________________ Please check the healthcare givers you have seen since your pain condition began: (Check all that apply.) Acupuncturist Anesthesiologist Cardiologist Chiropractor Clergyman Dentist Dermatologist (skin) Faith healer Ear, Nose & Throat (ENT) General/Family Practice Hypnotist Internal Medicine (Internist) Neurologist Neurosurgeon Ophthalmologist (eyes) Orthopedist (bones and joints) Pediatrician Plastic Surgeon Psychiatrist Psychologist Radiologist Surgeon (general) Urologist En docrinologist Physical Therapist Osteopath Other please specify: _____________________________________ Please check the treatments that you have received for your pain (other than medications): (Check all that apply.) Surgery how many? __________ Exercise Physical therapy Acupuncture

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98 Occupational therapy Massage Other please specify: _____________________________________ Please list all the prescription medications/drugs you have taken in the last 6 months: Please list all the over the counter medications/drugs you have taken in the last 6 months: Please list all the natural/herbal medications/drugs you have taken in the last 6 months: Do you think you have used too much of any drug (listed above) in the past year because of your pain? Yes No Uncertain Do you feel you need to take more of the pain medication than your doctor has prescribed? Yes No Uncertain Are yo u concerned that you use too much pain medication? Yes No Uncertain Overall, how much pain relief have pain treatments or medications provided? (Please mark the one percentage that most shows how much relief you have received.)

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99 0% No relief 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Complete relief How many days per week do you experience pain? (Check one.) 1 2 3 4 5 6 7 How many days per week do you use the following: 0 1 2 3 4 5 6 7 Coffee Cola Wine Beer Liquor Marijuana When you use the following, how much do you take in daily? Coffee (cups/day): __________ Cola (glasses/day): __________ Wine (glasses/day): __________ Beer (12oz. cans/day): __________ Liquor (ounces/day): __________ Marijuana (cigarettes/day): __________ How many times have you been intoxicated with alcohol in the past year? __________ times Do you feel you drank more beer, wine, or liquor in the last year because of your pain?

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100 Yes No For the following, please mark the answer that best matches your level of pain: (0 = no pain at all; 10 = worst pain imaginable.) Average level of pain over the past week ? 0 No pain at all 1 2 3 4 5 6 7 8 9 10 Worst pain imaginable Highest or peak pain over the past week ? 0 No pain at all 1 2 3 4 5 6 7 8 9 10 Worst pain imaginable Lowest level of pain over the past week ? 0 No pain at all 1 2 3 4 5 6 7 8 9 10 Worst pain imaginable Level of pain right now ? 0 No pain at all 1 2 3 4 5 6 7 8 9 10 Worst pain imaginable Have you ever been told you have a problem with alcohol? Yes No Have you ever been told you have a problem with drugs? Yes No

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101 If pain management sessions were available, would you be interested in participating in them? Yes No If you attended these sessions, where would you most like to attend them? At my doctor's office At a psychology clinic On the Internet Other please specify: _____________________________________ Please feel free to take a break for a few minutes before continuing. 1. In the past month, how many times did you visit a primary/family healthcare provider? Do not include visits while in the hospital, a hospital emergency room, or visits to specialty pain management provider. Fill in with "0" or another number. _____ times 2. In the past month, how many times did you visit a specialty pain management physician? Do not include visits while in the hospital, a hospital emergency room, or visits to a primary/family provider. Fill in with "0" or another number. _____ times 3. In the past month, how many times did you go to a hospital emergency room? Fill in with "0" or another number. _____ times 4. In the past month, how many different times did you stay in a hospital overnight or longer? Fill in with "0" or another number. _____ times 5. In the past month, how may total nights did you spend in the hospital? Fill in with "0" or another number. _____ times 6. In the past month, how many times have you been to a pharmacist to have prescription medications filled? _____ times 7. In the past month, how many different pharmacists did you visit to have prescription medications filled? _____ times

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102 Below you will find a list of statements. Please rate the truth of each statement as it applies to you. Use the follo wing scale to make your choice. For instance, if you believe a statement is Always True,' you would write a 6 in the blank next to that statement 0 never true 1 very seldom true 2 seldom true 3 sometimes true 4 frequently true 5 almost always true 6 always true __________ 1. I am able to take action on a problem even of I am uncertain what is the right thing to do. __________ 2. A person who is really "together" should not struggle with things the way I do. __________ 3. When I feel depressed or anxious, I am unable to take care of my responsibilities. __________ 4. I try to suppress thoughts and feelings that I don't like by just not thinking about them. __________ 5. There are not many activities that I stop doing when I am feeling depressed or anxious. __________ 6. It's OK to feel depressed or anxious. __________ 7. It is unnecessary for me to learn to control my feelings in order to handle my life well. __________ 8. I rarely worry about getting my anxieties, worries, and feelings under control. __________ 9. In order for me to do something important, I have to have all my doubts worked out. __________ 10. I'm not afraid of my feelings. __________ 11. When I compare myself to other people, it seems that most of them are handling their lives better than I do. __________ 12. I try hard to avoid feeling depressed or anxious. __________ 13. Anxiety is bad. __________ 14. Despite doubts, I feel as though I can set a course in my life and then stick to it.

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103 __________ 15. If I could magically remove all the painful experiences I've had in my life, I would do so. __________ 16. I am in control of my life. Below you will find a list of statements. Please rate the truth of each statement as it applies to you. Use the following rating scale to make your choices. For instance, if you believe a statement is Always True,' you would write a 6 in the blank next to that statement 0 never true 1 very rarely true 2 seldom true 3 sometimes true 4 often true 5 almost always true 6 always true __________ 1. I am getting on with the business of living no matter what my level of pain is __________ 2. My life is going well, even though I have chronic pain __________ 3. It's OK to experience pain __________ 4. I would gladly sacrifice important things in my life to control this pain better __________ 5. It's not necessary for me to control my pain in order to handle my life well __________ 6. Although things have changed, I am living a normal life despite my chronic pain __________ 7. I need to concentr ate on getting rid of my pain __________ 8. There are many activities I do when I feel pain __________ 9. I lead a full life even though I have chronic pain __________ 10. Controlling pain is less important than any other goals in my life __________ 11. My thoughts and feelings about pain must change before I can take important steps in my life __________ 12. Despite the pain, I am now sticking to a certain course in my life

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104 __________ 13. Keeping my pain level under control takes first priority whenever I'm doing something __________ 14. Before I can make any serious plans, I have to get some control over my pain __________ 15. When my pain increases, I can still take care of my responsibilities __________ 16. I will have better c ontrol over my life if I can control my negative thoughts about pain __________ 17. I avoid putting myself in situations where my pain might increase __________ 18. My worries and fears about what pain will do to me are true __________ 19. It's a relief to realize that I don't have to change my pain to get on with my life __________ 20. I have to struggle to do things when I have pain Below are areas of life that some people value. I am interested in your quality of life in each of these areas. One aspect of quality of life involves the importance you place on each area of living. Not everyone will value these areas, nor will people value these areas in the same way. Rate each area according to your own personal sense of importance. Pleas e rate the importance of each area by circling a number 0 through 10 Please select N/A if a category does not apply to you. Importance = How important is this area to you? Satisfaction = Overall, how satisfied are you with the quality and depth of you r experience in this aspect of your life? Actions (last week) = How often have you done something to move you forward in this area during the last week? N/A = Not applicable 0 = Not at all 5 = Moderately 10 = Very important Family (other than marriage or parenting): Importance: N/A 0 1 2 3 4 5 6 7 8 9 10 Satisfaction: N/A 0 1 2 3 4 5 6 7 8 9 10

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105 Actions (last week): N/A 0 1 2 3 4 5 6 7 8 9 10 Intimate Relationships (e.g. marriage, couples): Importance: N/A 0 1 2 3 4 5 6 7 8 9 10 Satisfaction: N/A 0 1 2 3 4 5 6 7 8 9 10 Actions (last week): N/A 0 1 2 3 4 5 6 7 8 9 10 Parenting: Importance: N/A 0 1 2 3 4 5 6 7 8 9 10 Satisfaction: N/A 0 1 2 3 4 5 6 7 8 9 10 Actions (last week): N/A 0 1 2 3 4 5 6 7 8 9 10 Friends/Social Life: Importance: N/A 0 1 2 3 4 5 6 7 8 9 10 Satisfaction: N/A 0 1 2 3 4 5 6 7 8 9 10 Actions (last week): N/A 0 1 2 3 4 5 6 7 8 9 10 Work/Career: Importance: N/A 0 1 2 3 4 5 6 7 8 9 10 Satisfaction: N/A 0 1 2 3 4 5 6 7 8 9 10 Actions (last week): N/A 0 1 2 3 4 5 6 7 8 9 10

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106 Education/Training: Importance: N/A 0 1 2 3 4 5 6 7 8 9 10 Satisfaction: N/A 0 1 2 3 4 5 6 7 8 9 10 Actions (last week): N/A 0 1 2 3 4 5 6 7 8 9 10 Recreation/Fun: Importance: N/A 0 1 2 3 4 5 6 7 8 9 10 Satisfaction: N/A 0 1 2 3 4 5 6 7 8 9 10 Actions (last week): N/A 0 1 2 3 4 5 6 7 8 9 10 Spirituality: Importance: N/A 0 1 2 3 4 5 6 7 8 9 10 Satisfaction: N/A 0 1 2 3 4 5 6 7 8 9 10 Actions (last week): N/A 0 1 2 3 4 5 6 7 8 9 10 Citizenship/Community Life: Importance: N/A 0 1 2 3 4 5 6 7 8 9 10 Satisfaction: N/A 0 1 2 3 4 5 6 7 8 9 10 Actions (last week): N/A 0 1 2 3 4 5 6 7 8 9 10 Health/Physical Self Care:

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107 Importance: N/A 0 1 2 3 4 5 6 7 8 9 10 Satisfaction: N/A 0 1 2 3 4 5 6 7 8 9 10 Actions (last week): N/A 0 1 2 3 4 5 6 7 8 9 10 For each of the 7 categories of life activities listed, please circle the number on the scale, which describes the level of disability you typically experience. A score of "0" means no disability at all, and a score of "10" signifies that all of the activities in which you would normally be involved have been totally disrupted or prevented by your pain. 1. Family/Home Responsibilities: This category r efers to activities related to the home or family. It includes chores or duties performed around the house (e.g. yard work) and errands or favors for other family members (e.g. driving the children to school). 1 No Disability 2 3 4 5 6 7 8 9 10 Total Disability 2. Recreation: This category includes hobbies, sports, and other similar leisure time activities. 1 No Disability 2 3 4 5 6 7 8 9 10 Total Disability 3. Social Activity: This category refers to activities, which involve participation with friends and acquaintances other than family members. It includes parties, theater, concerts, dining out, and other social activities. 1 No Disability 2 3 4 5 6 7 8 9 10 Total Disabili ty 4. Occupation: This category refers to activities that are part of or directly related to one's job. This includes nonpaying jobs as well, such as housewife or volunteer work. 1 No Disability 2 3 4 5 6 7 8 9 10 Total Disability 5. Sexual behavior: This category refers to the frequency and quality of one's sex life. 1 No Disability 2 3 4 5 6 7 8 9 10 Total Disability

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108 6. Self care: This category includes activities which involve personal maintenance and independent daily living (e.g., taking a shower, driving, getting dressed, etc.). 1 No Disability 2 3 4 5 6 7 8 9 10 Total Disability 7. Life support activity: This category refers to basic life supporting behaviors, such as eating, sleeping, and breathing. 1 No Disability 2 3 4 5 6 7 8 9 10 Total Disability This scale consists of a number of words that describe different feelings and emotions. Read each item and then circle the appropriate answer next to that word. Indicate to what extent you have felt this way during the past week. Use the following sca l e to record your answers: (1) = Very slightly or not at all (2) = A little (3) = Moderately (4) = Quite a bit (5) = Extremely Very slightly or not at all A little Moderately Quite a bit Extremely 1. Interested 1 2 3 4 5 2. Distressed 1 2 3 4 5 3. Excited 1 2 3 4 5 4. Upset 1 2 3 4 5 5. Strong 1 2 3 4 5 6. Guilty 1 2 3 4 5 7. Scared 1 2 3 4 5 8. Hostile 1 2 3 4 5 9. Enthusiastic 1 2 3 4 5 10. Proud 1 2 3 4 5 11. Irritable 1 2 3 4 5 12. Alert 1 2 3 4 5 13. Ashamed 1 2 3 4 5 14. Inspired 1 2 3 4 5 15. Nervous 1 2 3 4 5 16. Determined 1 2 3 4 5 17. Attentive 1 2 3 4 5 18. Jittery 1 2 3 4 5 19. Active 1 2 3 4 5 20. Afraid 1 2 3 4 5 Sometimes things happen to people that are stressful or disturbing events that involve experiencing or witnessing actual or threatened death or serious injury of oneself or others. Have any of the following events happened to you? Please select all that apply.

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109 Military combat Natural or man made disaster Violent attack (robbery, mugging, physical, sexual assault) Severe auto accident Being kidnapped Being diagnosed with w life threatening illness Taken hostage Sudden injury/serious accident Terrorist attack Observed someone hurt or killed Torture Learned about a family member or close friend that was hurt or killed Incarceration (POW, Concentration camp) Learned that your child has a life threatening illness Check here if none of these events have happened to you. Below is a list of problems and complaints that people sometimes have in response to stressful experiences. Please read each one carefully, put an X in the box to indicate how much you have been bothered by that problem in the past month Not at all A little Bit Moderately Quite a bit Extremely 1. Repeated, disturbing memories thoughts or images of a stressful experience? 2. Repeated, disturbing dreams of a stressful experience? 3. Suddenly acting or feeling as if a stressful experience were happening again (as if you were reliving it)? 4. Feeling very upset when something reminded you of a stressful experience? 5. Having physical reactions (e.g., heart pounding, trouble breathing, sweating) when something reminded you of a stressful experience? 6. Avoiding thinking about or talking about a stressful or avoiding having feelings related to it? 7. Avoiding activities of situations because they reminded you of stressful experience? 8. Trouble remembering important parts of a stressful experience? 9. Loss of interest in activities that you used to enjoy?

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110 10. Feeling distant or cutoff from other people? 11. Feeling emotionally numb or being unable to have loving feelings for those close to you? 12. Feeling as if your future will somehow be cut short? 13. Trouble falling or staying asleep? 14. Feeling irritable or having angry outbursts? 15. Having difficulty concentrating? 16. Being "super alert" or watchful or on guard? 17. Feeling jumpy or easily startled? Hang in there! You are halfway through! You may want to stand up and take a break for a few minutes. Please circle the number that best corresponds to how much you agree with each item. If any of the items concern something that is not part of your experience (for example, "It scares me when I feel shaky" for someone who never trembled or felt shaky) answ er on the basis of what you expect you think you might feel if you had such an experience. Otherwise, answer all items on the basis of your own experience. Be careful to circle only one number for each item and please answer all items. Very little A little Some Much Very much 1. It is important for me not to appear nervous. 1 2 3 4 5 2. When I cannot keep my mind on a task, I worry that I might be going crazy. 1 2 3 4 5 3. It scares me when my heart beats rapidly. 1 2 3 4 5 4. When my stomach is upset, I worry that I might be seriously ill. 1 2 3 4 5 5. It scares me when I am unable to keep my mind on a task. 1 2 3 4 5 6. When I tremble in the presence of others, I fear what people might think of me. 1 2 3 4 5 7. When my chest gets tight, I get scared that I won't be able to breathe properly. 1 2 3 4 5 8. When I feel pain in my chest, I worry that I'm going to have a heart attack. 1 2 3 4 5 9. I worry that other people will notice 1 2 3 4 5

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111 my anxiety. 10. When I feel "spacey" or spaced out I worry that I may be mentally ill. 1 2 3 4 5 11. It scares me when I blush in front of people. 1 2 3 4 5 12. When I notice me heart skipping a beat, I worry that there is something seriously wrong with me. 1 2 3 4 5 13. When I begin to sweat in a social situation, I fear people will think negatively of me. 1 2 3 4 5 14. When my thoughts seem to speed up, I worry that I might be going crazy. 1 2 3 4 5 15. When my throat feels tight, I worry that I could choke to death. 1 2 3 4 5 16. When I have trouble thinking clearly, I worry that there is something wrong with me. 1 2 3 4 5 17. I think it would be horrible to faint in public. 1 2 3 4 5 18. When my mind goes blank, I worry that there is something terribly wrong with me. 1 2 3 4 5 The following relate to your usual sleep habits during the past month only Your answers should indicate the most accurate reply for the majority of days and nights in the past month. Please answer all questions. 1. During the past month, what time have yo u usually gone to bed at night? USUAL BED TIME __________ 2. During the past month, how long (in minutes) has it usually taken you to fall asleep each night? NUMBER OF MINUTES __________ 3. During the past month, when have you usually gotten up in the morning? USUAL GETTING UP TIME __________ 4. During the past month, how many hours of actual sleep did you get at night? (This may be different than the number of hours you spend in bed.) HOURS OF SLEEP PER NIGHT __________

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112 For each of the remaining ques tions, choose the one best response. Please answer all questions. 5. During the past month, how often have you had trouble sleeping because you a. Cannot get to sleep within 30 minutes Not during the past month Less than once a week Once or twice a week Three or more times a week b. Wake up in the middle of the night or early in the morning Not during the past month Less than once a week Once or twice a week Three or more times a week c. Have to get up to use the bathroom Not during the past month Less than once a week Once or twice a week Three or more times a week d. Cannot breathe comfortably Not during the past month Less than once a week Once or twice a week Three or more time s a wee k e. Cough or snore loudly Not during the past month Less than once a week Once or twice a week Three or more times a week f. Feel too cold Not during the past month Less than once a week Once or twice a week Three or more times a week g. Feel too hot Not during the past month Less than once a week Once or twice a week Three or more times a week h. Had bad dreams Not during the past month Less than once a week Once or twice a week Three or more times a week i. Have pain Not during the past month Less than once a week Once or twice a week Three or more times a week j. Other reason(s), please describe: How often during the past month have you had trouble falling asleep because of this? Not during the past month Less than once a week Once or twice a week Three or more times a week

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113 6. During the past month, how would you rate your sleep quality overall? Very good Fairly good Fairly bad Very bad 7. During the past month, how often have you taken medicine (prescribed or "over the counter") to help you sleep? Not during the past month Less than once a week Once or twice a week Three or more times a week 8. During the past month, how often have you had trouble staying awake while driving, eating meals, or engaging in social activity? Not during the past month Less than once a week Once or twice a week Three or more times a week 9. During the past month how much of a problem has it been for you to keep up enthusiasm to get things done? No problem at all Only a very slight problem Somewhat of a problem A very big problem 10. Do you have a bed partner or roommate? No bed partner or roommate Partner/roommate in other room Partner in same room, but not same bed Partner in same bed Please rate the truth of each statement as it applies to you, using the following scale: 1 Not at all characteristic of me 2 3 4 5 Entirely characteristic of me __________ 1. Unforeseen events upset me greatly.

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114 __________ 2. It frustrates me not having all the information I need. __________ 3. One should always look ahead so as to avoid surprises. __________ 4. A small, unforeseen event can spoil everything, even with the best of planning. __________ 5. I always want to know what the future has in store for me. __________ 6. I can't stand being taken by surprise. __________ 7. I should be able to organize everything in advance. __________ 8. Uncertainty keeps me from living a full life. __________ 9. When it's time to act, uncertainty paralyzes me. __________ 10. When I am uncertain, I can't function very well. __________ 11. The smallest doubt can stop me from acting. __________ 12. I must get away from all uncertain situations. For each of the following, choose only the best word that describes your feelings and sensations at this moment (right now). Please answer all questions. 1. Choose one of the following: Flickering quivering pulsing throbbing beating pounding 2. Choose one of the following: Jumping flashing shooting 3. Choose one of the following: Pricking boring drilling stabbing lancinating 4. Choose one of the following: Sharp

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115 cutting lacerating 5. Choose one of the following: Pinching pressing gnawing cramping crushing 6. Choose one of the following: Tugging pulling wrenchin g 7. Choose one of the following: Hot burning scalding searing 8. Choose one of the following: Tingling itchy smarting stinging 9. Choose one of the following: Dull sore hurting aching heavy 10. Choose one of the following: Tender taut rasping splitting 11. Choose one of the following: t iring e xhausting

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116 12. Choose one of the following: s ickening s uffocating 13. Choose one of the following: f earful frightful terrifyin g 14. Choose one of the following: p unishing grueling cruel vicious killing 15. Choose one of the following: wretched blinding 16. Choose one of the following: a nnoying troublesome miserable intense unbearable 17. Choose one of the following: s preading radiating penetrating piercing 18. Choose one of the following: t ight numb drawing squeezing tearing 19. Choose one of the following: c ool

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117 cold freezing 20. Choose one of the following: n agging nauseating agonizing dreadful torturin g

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! 118 People have a variety of ways of relating to their thoughts and feelings. For each of the items below, rate how much each of these ways applies to you. Rarely/ not at all Sometimes Often Almost always 1. It is easy for me to concentrate on what I am doing. 2. I am preoccupied by the future. 3. I can tolerate emotional pain. 4. I can accept things that I cannot change. 5. I can usually describe how I feel at that moment in considerable detail. 6. I am easily distracted. 7. I am preoccupied by the past. 8. It's easy for me to keep track of my thoughts and feelings. 9. I try to notice my thoughts without judging them. 10. I am able to accept the thoughts and feelings I have. 11. I am able to focus on the present moment. 12. I am able to pay close attention to one thing for a long period of time. These next two pages are for males only. If you are female, please skip ahead two pages and answer the questions for females. Thank you. Below are images of male bodies. They are all slightly different, even though some of them might look the same. Please select the image that most closely resembles your body and circle the letter under it

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! 119 Now select the image that most closely resembles the way you would like your body to look ideally and circle the letter under it. 3 @ 1 : + P C ! b 4 B 2 / ! $ )

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! 120 ! !"#$#%&#'(%()*%+,-#$%,.#%/*.%/#0,1#$%*&123%%4/%2*5%,.#%0,1#6%+1#,$#%$78+%,"#,9%()*% +,-#$3%%!",&7%2*53 % % % Below are images of female bodies. They are all slightly different, even though some of them might look the same. Please select the image that most cl osely resembles your body and circle the letter under it. 3 @ 1 : + P C ! b 4 B 2 / ! $ )

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! 121 Now select the image that most closely resembles the way you would like your body to look ideally and circle the letter under it. 3 @ 1 : + P C ! b 4 B 2 / ! $ )

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! 122 You are almost done! Please feel free to get up and take a break for a few minutes. Please report the frequency and average duration of any exercise over the past week in the spaces below. As an example, if you exercised four times last week at a moderate intensity, you would write "4" in the frequency space next to moderate exercise. We would like you to also give an average of the time you spent exercising. In our example, if two of those "4" exercise sessions were 30 minutes and the other two were 20 minutes, you would put 25 minutes in the duration space following moderate exercise When answering these questions, please remember to: a. Only count the exercise that was done in your free time (i.e., not occupational or housework). b. Note that the difference between the three categories is the intensity of the exercise. c. If you did not participate in a type of exercise, put "0" in the frequency space. 3 @ 1 : + P C ! b 4 B 2 / ! $ )

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! 123 STRENUOUS EXERCISE (Heart beats rapidly, sweating) Examples: running, jogging, vigorous swimming, vigorous long distance bicycling, vigorous aerobic dance classes. Frequency: _______ ___ times Duration: __________ minutes MODERATE EXERCISE (Not exhausting, light perspiration) Examples: fast walking, tennis, easy bicycling, easy swimming, popular and folk dance. Frequency: __________ times Duration: __________ times MILD EXERCISE (Minimal effort, no perspiration) Examples: easy walking, yoga, bowling, shuffleboard, horseshoes, golf. Frequency: __________ times Duration: __________ minutes An important part of our evaluation includes examination of pain from YOUR pe rspective. You know your pain better than anyone, so the information you give is very helpful in planning a treatment program for you. Please read each question carefully and then do your best to answer each one. Do not skip any questions. After you h ave completed the questionnaire, check your responses to make sure that you have answered each question. Please use the last page to add any additional information or comments that you think would be of help to us in better understanding your pain problem A. Some of the questions in this questionnaire refer to your "significant other." A significant other is a person with whom you feel closest. This includes anyone that you relate to on a regular or infrequent basis. It is very important that you id entify someone as your "significant other." Please indicate below who your significant other is (check one): Spouse Partner/Companion Housemate/ Roommate Friend Neighbor Parent/Child/Other relative Other (please describe): __________________________________________

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! 124 B. Do you currently live with this person? Yes No When you answer questions in the following pages about "your significant other", always respond in reference to the speci fic person you just indicated. Please continue on the next page. SECTION 1 This part asks questions to help us learn more about your pain and how it affects your life. Under each question is a scale to mark your answer. Read each question carefully and then circle a number on the scale under that question to indicate how that s pecific question applies to you. An example may help you to better understand how you should answer these questions. EXAMPLE How nervous are you when you ride in a car when the traffic is heavy? 0 Not at all nervous 1 2 3 4 5 6 Extremely nervous If you are not at all nervous when riding in a c ar in heavy traffic, you would want to circle the number 0. If you are very nervous when riding in a car in heavy traffic, you would then circle the number 6. Lower numbers would be used for less nervousnes s, and higher numbers for more nervousness. PLEASE ANSWER THE FOLLOWING QUESTIONS 1. Rate the level of your pain at the present moment. 0 No pain 1 2 3 4 5 6 Very intense pain 2. In general, how much does your pain interfere with your day to day activities? 0 No 1 2 3 4 5 6 Extreme

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! 125 interference interference 3. Since the time your pain began, how much has your pain changed your ability to work? (____ Check here if you are not working for reasons other than your pain). 0 No change 1 2 3 4 5 6 Extreme change 4. How much has your pain changed the amount of satisfaction or enjoyment you get from taking part in social and recreational activities? 0 No change 1 2 3 4 5 6 Extreme change 5. How supportive or helpful is your significant other ( this refers to the person you indicated above ) to you in relation to your pain? 0 Not at all supportive 1 2 3 4 5 6 Extremely supportive 6. Rate your overall mood during the past week 0 Extremely low 1 2 3 4 5 6 Extremely high 7. On the average, how severe has your pain been during the last week ? 0 Not at all severe 1 2 3 4 5 6 Extremely severe 8. How much has your pain changed your ability to take part in recreational and other social activities? 0 No change 1 2 3 4 5 6 Extreme change 9. How much do you limit your activities in order to keep your pain from getting worse? 0 1 2 3 4 5 6

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! 126 Not at all Very much 10. How much has your pain changed the amount of satisfaction or enjoyment you get from family related activities ? 0 No change 1 2 3 4 5 6 Extreme change 11. How worried is your spouse (significant other) about you because of your pain? 0 Not at all worried 1 2 3 4 5 6 Extremely worried 12. During the past week how much control do you feel you have had over your life? 0 No control 1 2 3 4 5 6 Extreme control 13. How much suffering do you experience because of your pain? 0 No suffering 1 2 3 4 5 6 Extreme suffering 14. How much has your pain changed the amount of satisfaction or enjoymen t you get from work? (_____ Check here if you are not presently working.) 0 No change 1 2 3 4 5 6 Extreme change 15. How attentive is your spouse (significant other) to you because of your pain? 0 Not at all attentive 1 2 3 4 5 6 Extremely attentive 16. During the past week how well do you feel you've been able to deal with your problems? 0 Not at all 1 2 3 4 5 6 Extremely well

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! 127 17. How much has your pain changed your ability to do household chores? 0 No change 1 2 3 4 5 6 Extreme change 18. During the past week how successful were you in coping with stressful situations in your life? 0 Not at all successful 1 2 3 4 5 6 Extremely successful 19. How much has your pain interfered with your ability to plan activities? 0 No interference 1 2 3 4 5 6 Extreme interference 20. During the past week how irritable have you been? 0 Not at all irritable 1 2 3 4 5 6 Extremely irritable 21. During the past week how tense or anxious have you been? 0 Not at all tense or anxious 1 2 3 4 5 6 Extremely tense & anxious SECTION 2 In this section, we are interested in knowing how your spouse (or significant other) responds to you when he or she knows you are in pain. On the scale listed below each question, check one of the responses to indicate how often your spouse (or significant other) responds to you in that particular way when you are in pain. PLEASE ANSWER ALL OF THE 12 QUESTIONS 1. Asks me what he or she can do to help. _____Never _____Seldom _____Sometimes _____Often

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! 128 2. Gets irritated with me. _____Never _____Seldom _____Sometimes _____Often 3. Takes over my jobs or duties. _____Never _____Seldom _____Sometimes _____Often 4. Talks to me about something else to take my mind off the pain. _____Never _____Seldom _____Sometimes _____Often 5. Gets frustrated with me. _____Never _____Seldom _____Sometimes _____Often 6. Tries to get me to rest. _____Never _____Seldom _____Sometimes _____Often 7. Tries to involve me in some activity. _____Never _____Seldom _____Sometimes _____Often 8. Gets angry with me. _____Never _____Seldom _____Sometimes _____Often 9. Gets me pain medication. _____Never _____Seldom _____Sometimes _____Often 10. Encourages me to work on a hobby. _____Never _____Seldom _____Sometimes _____Often 11. Gets me something to eat or drink. _____Never _____Seldom _____Sometimes _____Often 12. Turns on the T.V. to take my mind off my pain. _____Never _____Seldom _____Sometimes _____Often SECTION 3 Listed below are 15 daily activities. Please indicate how often you do each of these checking one of the responses on the scale listed below each activity. Please complete all 15 questions. 1. Wash dishes. _____Never _____Seldom _____Sometimes _____Often 2. Go out to eat. _____Never _____Seldom _____Sometimes _____Often

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! 129 3. Go grocery shopping. _____Never _____Seldom _____Sometimes _____Often 4. Go to a movie. _____Never _____Seldom _____Sometimes _____Often 5. Visit friends. _____Never _____Seldom _____Sometimes _____Often 6. Help with the house cleaning. _____Never _____Seldom _____Sometimes _____Often 7. Take a ride in a car or bus. _____Never _____Seldom _____Sometimes _____Often 8. Visit relatives. (_____Check here if you do not have relatives within 100 miles.) _____Never _____Seldom _____Sometimes _____Often 9. Prepare a meal. _____Never _____Seldom _____Sometimes _____Often 10. Wash the car. _____Never _____Seldom _____Sometimes _____Often 11. Take a trip. _____Never _____Seldom _____Sometimes _____Often 12. Go to a park or beach. _____Never _____Seldom _____Sometimes _____Often 13. Do the laundry. _____Never _____Seldom _____Sometimes _____Often 14. Work on a needed household repair. _____Never _____Seldom _____Sometimes _____Often 15. Engage in sexual activities. _____Never _____Seldom _____Sometimes _____Often Congratulations! You have completed the survey! As a thank you for your time and participation, we would like to offer you the opportunity to be entered into a drawing for 1 of 12 $50.00 Visa gift cards.

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! 130 Please note that the information you provide us will not be used for any other purpose than to se nd you the gift card, should you be selected in the drawing. If you do not wish to provide us with this information, that is perfectly okay. Name: Street address: Street address: City, State, and Zip Code: Phone number (with area code): Email address:

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! 131 APPENDIX D Chronic Pain Acceptance Questionnaire (CPAQ) Directions: below you will find a list of statements. Please rate the truth of each statement as it applies to you. Use the following rating scale to make your choices. For instance, if you believe a statement is Always True,' you would write a 6 in the blank next to that statement ______________________________________________________________________ 0 1 2 3 4 5 6 Never Very Seldom Sometimes Often Almost Always true rarely true true true always true true true 1. I am getting on with the business of living no matter what my level of pain is 2. My life is going well, even though I have c hronic pain !!! 3. It's OK to experience pain !!! 4. I would gladly sacrifice important things in my life to control this pain better 5. It's not necessary for me to control my pain in order to handle my life well !!! 6. Although things have changed, I am living a normal life despite my chronic pain 7. I need to concentrate on getting rid of my pain !!! 8. There are many activities I do when I feel pain !!! 9. I lead a full life even though I have chronic pain !!! 10. Controlling pain is less important than any other goals in my life !!! 11. My thoughts and feelings about pain must change before I can take important steps in my life !!! 12. Despite the pain, I am now sticking to a certain course in my life !!! 13. Keeping my pain level under contro l takes first priority whenever I'm doing something !!! 14. Before I can make any serious plans, I have to get some control over my pain 15. When my pain increases, I can still take care of my responsibilities !!! 16. I will have better control over my life if I can control my negative thoughts about pain !!! 17. I avoid putting myself in situations where my pain might increase !!! 18. My worries and fears about what pain will do to me are true !!! 19. It's a relief to realize that I don't have to change my pain to get on with my life 20. I have to struggle to do things when I have pain !!!

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! 132 APPENDIX E Pain Disability Index (PDI) The rating scales below are designed to measure the degree to which several aspects of your life are presently disrupted by chronic pain. In other words, we would like to know how much your pain is preventing you from doing what you would normally do, or from doing it as well as you normally would. Respond to each category by indicating the overall impact of pain in your life, not just when the pain is at its worst. For each of the 7 categories of life activity l isted, please circle the number on the scale which describes the level of disability you typically experience. A score of 0 means no disability at all, and a score of 10 signifies that all of the activ ities in which you would normally be involved have been totally disrupted or prevented by your pain. (1) Family/Home Responsibilities: This category refers to activities related to the home or family. It includes chores or duties performed around the house (e.g. yard work) and errands or favors for other family members (e.g. driving the children to school). 0 1 2 3 4 5 6 7 8 9 10 No Total Disability Disability (2) Recreation: This category includes hobbies, sports, and other similar leisure time activities. 0 1 2 3 4 5 6 7 8 9 10 No Total Disability Disability (3) Social Activity: This category refers to activities, which involve participation with friends and acquaintances other than family members. It includes parties, theater, concerts, dining out, and other social functi ons. 0 1 2 3 4 5 6 7 8 9 10 No Total Disability Disability (4) Occupation: This category refers to activities that are a part of or directly related to one's job. This includes non paying jobs as well, such as that of a housewife or volunteer worker. 0 1 2 3 4 5 6 7 8 9 10 No Total Disability Disability (5) Sexual Behavior: This category refers to the frequency and quality of one's sex life. 0 1 2 3 4 5 6 7 8 9 10 No Total Disability Disability

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! 133 (6) Self Care: This category includes activities which involve personal maintenance and independent daily living (e.g. taking a shower, driving, getting dressed, etc.). 0 1 2 3 4 5 6 7 8 9 10 No Total Disability Disability (7) Life Support Activity: This category refers to basic life supporting behaviors such as eating, sleeping, and breathing. 0 1 2 3 4 5 6 7 8 9 10 No Total Disability Disability

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! 134 APPENDIX F Positive and Negative Affect Scale (PANAS) This scale consists of a number of words that describe different feelings and emotions. Read each item and then circle the appropriate answer next to that word. Indicate to what extent you have felt this way during the past week. Use the following sca le to record your answers. (1) = Very slightly or not at all (2) = A little (3) = Moderately (4) = Quite a bit (5) = Extremely Very Slightly or Not at All A Little Moderately Quite a Bit Extremely 1. Interested 1 2 3 4 5 2. Distressed 1 2 3 4 5 3. Excited 1 2 3 4 5 4. Upset 1 2 3 4 5 5. Strong 1 2 3 4 5 6. Guilty 1 2 3 4 5 7. Scared 1 2 3 4 5 8. Hostile 1 2 3 4 5 9. Enthusiastic 1 2 3 4 5 10. Proud 1 2 3 4 5 11. Irritable 1 2 3 4 5 12. Alert 1 2 3 4 5 13. Ashamed 1 2 3 4 5 14. Inspired 1 2 3 4 5 15. Nervous 1 2 3 4 5 16. Determined 1 2 3 4 5 17. Attentive 1 2 3 4 5 18. Jittery 1 2 3 4 5 19. Active 1 2 3 4 5 20. Afraid 1 2 3 4 5