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2009 Development and Validation of the PEG- Assessing Pain Intensity and Interference

2009 Development and Validation of the PEG- Assessing Pain Intensity and Interference - Clinical Hub, UW Health Clinical Tool Search, UW Health Clinical Tool Search, Questionnaires, Related


BRIEF REPORT
Development and Initial Validation of the PEG, a Three-item Scale
Assessing Pain Intensity and Interference
Erin E. Krebs, MD, MPH
1,2,3
, Karl A. Lorenz, MD, MSHS
5,6,7
, Matthew J. Bair, MD, MS
1,2,3
,
Teresa M. Damush, PhD
1,2,3
, Jingwei Wu, MS
3
, Jason M. Sutherland, PhD
4
,
Steven M. Asch, MD, MPH
5,6,7
, and Kurt Kroenke, MD
1,2,3
1
Center on Implementing Evidence-Based Practice( Roudebush VAMedical Center, Indianapolis, IN, USA;
2
Regenstrief Institute, Inc., Indianapolis,
IN, USA;
3
Department of Medicine( Indiana University School of Medicine, Indianapolis, IN, USA;
4
The Dartmouth Institute for Health Policy and
Clinical Practice( Dartmouth Medical School, Hanover, NH, USA;
5
VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA;
6
RAND
Corporation, Santa Monica, CA, USA;
7
Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
BACKGROUND: Inadequate pain assessment is a barrier
to appropriate pain management, but single-item “pain
screening” provides limited information about chronic
pain. Multidimensional pain measures such as the Brief
Pain Inventory (BPI) are widely used in pain specialty and
research settings, but are impractical for primary care. A
brief and straightforwardmultidimensional painmeasure
could potentially improve initial assessment and follow-
up of chronic pain in primary care.
OBJECTIVES: To develop an ultra-brief pain measure
derived from the BPI.
DESIGN: Development of a shortened three-item pain
measure and initial assessment of its reliability, validity,
and responsiveness.
PARTICIPANTS: We used data from 1) a longitudinal
study of 500 primary care patients with chronic pain
and 2) a cross-sectional study of 646 veterans recruited
from ambulatory care.
RESULTS: Selected items assess average pain intensity
(P), interference with enjoyment of life (E), and interfer-
ence with general activity (G). Reliability of the three-
item scale (PEG) was α=0.73 and 0.89 in the two study
samples. Overall, construct validity of the PEG was
good for various pain-specific measures (r=0.60–0.89 in
Study 1 and r=0.77–0.95 in Study 2), and comparable
to that of the BPI. The PEG was sensitive to change and
differentiated between patients with and without pain
improvement at 6 months.
DISCUSSION: We provide strong initial evidence for
reliability, construct validity, and responsiveness of the
PEG among primary care and other ambulatory clinic
patients. The PEG may be a practical and useful tool to
improve assessment and monitoring of chronic pain in
primary care.
KEY WORDS: pain; measurement; primary care.
J Gen Intern Med 24(6):733–8
DOI: 10.1007/s11606-009-0981-1
? Society of General Internal Medicine 2009
BACKGROUND
Inadequate pain assessment has been identified as a key
barrier to appropriate pain management.
1–3
Recently, impor-
tant initiatives have aimed to increase awareness of pain as a
clinical problem by promoting better pain assessment.
4–6
These initiatives have led to widespread adoption of pain
screening through measurement of current pain intensity.
In chronic pain, the most common type of pain seen in
primary care, assessment of pain intensity alone is inade-
quate. Guidelines encourage comprehensive assessment that
includes measurement of pain-related functioning, which may
be even more relevant to patients’ overall quality of life than
intensity.
7,8
To facilitate chronic pain assessment, numerous
multidimensional patient-reported measures have been devel-
oped;
9–12
however, none of these have been widely adopted in
the general medical settings where most chronic pain treat-
ment is delivered.
In primary care, use of multidimensional pain measures is
limited by factors such as instrument length and scoring
complexity; however, a brief and straightforward multidimen-
sional measure could potentially improve assessment of
chronic pain. We sought to develop a very brief measure that
would be feasible, valid, and sensitive to change in primary
care. We started with the Brief Pain Inventory (BPI) because it
is relatively easy to administer, score, and interpret; includes
items assessing pain intensity and functional interference; and
has been validated in many pain conditions.
10,13–17
As its
name implies, the BPI is shorter than other multidimensional
pain measures, but it is still too lengthy for implementation in
primary care practice. We hypothesized that a shortened scale
based on the BPI could be developed that would be more
feasible, but just as useful, for assessing chronic pain in
primary care. Our objectives were to develop an ultra-brief
scale derived from the BPI and to initially assess its reliability,
validity, and responsiveness.
Received September 10, 2008
Revised March 17, 2009
Accepted March 26, 2009
Published online May 6, 2009
JGIM
733

METHODS
Participants
We used data from two sources: 1) Stepped Care for Affective
Disorders and Musculoskeletal Pain (SCAMP), a longitudinal
study that enrolled a total of 500 patients with chronic musculo-
skeletal pain, and 2) Helping Veterans Experience Less Pain
(HELP-vets), a cross-sectional study of 646 veterans receiving care
at VA clinics. We used data from Study 1 to develop and initially
validate the ultra-brief measure and data from Study 2 to confirm
reliability and validity in an independent patient population.
Study 1 (SCAMP) enrolled 500 primary care patients with
persistent back, hip, or knee pain of at least moderate severity,
250 of whom had concurrent depression.
18
Participants were
recruited from university (n=300) and VA-affiliated (n=200)
internal medicine clinics in Indianapolis. Patients with concur-
rent depression were enrolled in a trial of depression and pain
treatment vs. usual care (n=250). Thosewithout depressionwere
followed in a parallel observational study (n=250). The mean age
of SCAMPparticipantswas 59 years; 52%werewomen, 58%were
white, and 38% were black. The mean numeric rating of current
pain (on a 0–10 scale) was 6.1 (SD 1.9) at baseline.
Study 2 (HELP-vets) enrolled a random visit-based sample of
646 veterans from ambulatory care clinics at two VA hospitals
and six affiliated community sites in three urban California
counties. Patients with chronic illness were over-sampled by
design. The mean age was 63 years and 95% were male. Self-
reported race/ethnicity was 54% white, 30% black, and 10%
Latino. Sixty-one percent of participants reported pain at the time
of enrollment and63%hadone ormore pain diagnoses (33%back
pain, 45% other musculoskeletal pain, 12% neuropathic pain,
5% headache). The mean rating of current pain (on a 0–10 scale)
was 3.1 (SD 3.2) overall and 5.1 (SD 2.6) among those with pain.
Measures
Study 1 participants completed the BPI, Chronic Pain Grade
questionnaire (CPG), Roland disability scale, and SF-36 bodily
pain scale at baseline; they completed the BPI, CPG, and pain
global rating of change at 6 months. Study 2 was cross-
sectional; participants completed the BPI, Functional Morbidity
Index, and a single-item rating of overall pain-related distress.
&
The Brief Pain Inventory (BPI) includes two scales that
assesspain intensity and pain-related functional impairment
(physical and emotional).
13,15
The four items of the BPI
severity scale assess the intensity of current pain and pain
at its least, worst, and average during the pastweek on scales
from 0 (“no pain”)to10(“pain as bad as you can imagine”).
The BPI interference scale assesses pain-related functional
interference with seven items assessing different domains
(general activity, mood, walking ability, normal work, rela-
tions with other people, sleep, and enjoyment of life) rated
from 0 (“does not interfere”)to10(“interferes completely”).
&
The Chronic Pain Grade questionnaire (CPG) includes two
three-item scales (intensity and disability) that are trans-
formed into 0–100 scores.
19
An algorithm classifies pain
into four graded categories: 1) low disability-low intensity,
2) low disability-high intensity, 3) high disability-moderately
limiting, and 4) high disability-severely limiting. The CPGhas
been validated in primary care, chronic pain, and general
populations.
20–22
&
The Roland Disability questionnaire is a pain-specific
measure of physical disability validated in patients with
back pain and other chronic pain conditions.
23,24
It
includes a checklist of 24 statements about pain effects
on function; the score is the number of items endorsed.
&
The Short-Form 36-item questionnaire (SF-36) Bodily Pain Scale
is a two item scale assessing pain severity and interfer-
ence.
25,26
Responses are transformed into a 0–100 score.
&
The Pain Global Rating of Change is a single item assessing
patients’ overall impression of change in their pain. Study
1 participants were asked whether their pain was worse,
about the same, or better since the start of the study.
Those who reported that pain was better were asked to rate
the magnitude of improvement (a little, somewhat, moder-
ately, a lot, or completely better). Global ratings of change
may be more sensitive to improvement and better correlat-
ed with patient satisfaction than serial measures.
27
&
The Functional Morbidity Index was developed to assess
general functional status in older adults.
28
Patients indi-
cate whether they are able perform four different activities
independently, and if not, whether the impairment is due
to a health problem.
Table 1. Reliability and Item-total Correlations for PEG and
Alternate Scales in Study 1 Sample (n=500)
Items Alpha Item-total
correlation
Alpha (deleted
variable)*
Preferred scale (PEG) 0.73
Average intensity 0.49 0.74
General activity 0.65 0.52
Enjoyment of life 0.59 0.62
Alternate scale 1 0.70
Average intensity 0.48 0.68
General activity 0.60 0.49
Mood 0.53 0.62
Alternate scale 2 0.66
Average intensity 0.50 0.59
General activity 0.53 0.49
Sleep 0.48 0.62
Alternate scale 3 0.77
Average intensity 0.50 0.76
General activity 0.63 0.68
Sleep 0.56 0.73
Enjoyment of life 0.65 0.66
*Cronbach coefficient alpha for the scale with that item deleted.
Figure 1. The PEG three-item scale. *Items from the Brief Pain
Inventory reproduced with permission from Dr. Charles Cleeland.
734 Krebs et al: PEG Scale Development and Validation JGIM

&
Overall Pain Distress is a single item: “How much did
overall pain distress or bother you during the past week?”
Response options are not at all, a little bit, somewhat, quite
a bit, and very much.
Item Selection
We used a consensus-based process, drawing on a literature
review, expert opinion, and statistical data, to develop a
shortened scale.
29
Pre-specified criteria guided initial item
selection. First, we decided to include at least one item
representing each of three domains included in the BPI: pain
intensity, physical functioning, and emotional functioning. We
then selected items with the following characteristics: 1) easy
to understand and applicable to patients with all types of pain;
2) good statistical characteristics (e.g., high response variabil-
ity, high item-remainder correlation); 3) similar performance in
depressed and non-depressed patients.
We chose “pain average” for the intensity item because it
had a good distribution of responses, lacking the ceiling and
floor effects seen with “pain worst” and “pain least,” respec-
tively. We did not select “pain now” because we wanted to avoid
duplicating information provided by the “fifth vital sign,” and
capture intermittent pain. Although the ideal reporting period
for pain assessment is debated, recalled average pain over one
week is a valid measure of pain intensity.
30,31
BPI interference items include those assessing physical
status (general activity, walking, normal work), emotional
status (mood, relations with others, enjoyment of life), and
sleep. For physical interference, we chose “interference with
general activity” because it applies equally to all patients, as
opposed to “interference with work” (which may be affected by
occupation, employment status, etc.) and “interference with
walking” (which may not apply to non-ambulatory patients or
those with upper body pain).
For emotional interference, we considered both “interference
with mood” and “interference with enjoyment of life.” In our
experience, “interference with relations with other people” is
more difficult than other items for patients to answer. We
wanted a scale that would discriminate between chronic pain
and depression, which commonly co-occur.
32
In our sample of
patients with and without comorbid depression, we found that
“interference with enjoyment of life” was more independent of
depression than “interference with mood.” We also considered
“interference with sleep” in place of the emotional interference
items.
We reached consensus on a preferred three-item scale (“pain
average,”“interference with enjoyment of life,” and “interfer-
ence with general activity”) and alternative three-item and
four-item scales, which we then evaluated statistically.
Reliability and Validity
We assessed reliability (internal consistency) by calculating
Cronbach’s coefficient alpha. To assess construct validity, we
compared the PEG with measures of pain and function using
Pearson correlation coefficients. We used multiple measures
for construct validity assessment, including the BPI, because
no criterion standard exists for pain. We hypothesized that
coefficients would be slightly higher for comparisons with
pain-specific functional measures than for those with pain
severity measures (because two of the three PEG items assess
function). We also expected that coefficients would be larger for
comparisons with pain-specific functional measures than for
comparisons with generic functional measures.
Responsiveness
Assessment of responsiveness, or sensitivity to change,
requires an independent standard to define change.
33
We used
two different measurements to define the presence or absence
of patient improvement: 1) global rating of change and 2) serial
CPG grade. We categorized patients according to their pain
trajectory as assessed by each of the two measures. Global
rating of change categories were defined by the patient’s
retrospective assessment at 6 months of the change in their
Table 2. PEG and Individual Item Statistics at Baseline in Study 1
and Study 2
Study 1
(n=500)
Study 2
(n=638)
Mean (SD) Mean (SD)
PEG scale 6.1 (2.2) 4.1 (3.1)
Item 1: average pain intensity 6.1 (1.9) 3.9 (2.8)
Item 2: interference with general activity 6.4 (2.7) 4.4 (3.6)
Item 3: interference with enjoyment of life 5.9 (3.3) 4.0 (3.9)
Study 1 included primary care patients with chronic musculoskeletal
pain; Study 2 included ambulatory VA patients, 61% of whom had
current pain. For each item, the response range is 0–10; the PEG scale
score is the mean of the three individual item scores. Higher scores
represent worse pain.
Table 3. Correlation between the PEG, BPI Scales, and other Measures at Baseline
Measure
Correlation coefficient
Study 1 (n=500)
BPI severity BPI interference CPG intensity CPG disability Roland disability SF-36 bodily pain*
PEG 0.69 0.89 0.64 0.67 0.60 −0.61
BPI severity – 0.58 0.82 0.47 0.41 −0.46
BPI interference –– 0.62 0.71 0.70 −0.65
Study 2 (n=638)
BPI severity BPI interference Functional morbidity Overall pain distress
PEG 0.84 0.95 0.54 0.77
BPI severity – 0.75 0.47 0.78
BPI interference –– 0.55 0.72
* For SF-36, low score represents worse pain. For all other measures, a high score is worse.
735Krebs et al: PEG Scale Development and ValidationJGIM

pain since the trial began: 1) improved (“better”), 2) unchanged
(“about the same”), and 3) worse (“worse”). CPG categories were
defined by the change in CPG grade from baseline to 6 months:
1) improved (pain grade decreased by ≥1 level), 2) unchanged
(pain grade at baseline = pain grade at follow-up), and 3) worse
(pain grade increased by ≥1 level).
Using data from Study 1, we assessed responsiveness by
calculating the following three metrics: 1) change score
(difference between mean score at baseline and follow-up), 2)
effect size (ES; change score divided by the standard deviation
of the baseline score), and 3) standardized response mean
(SRM; change score divided by the standard deviation of the
change score). These calculations were performed for patients
in the improved, unchanged, and worse categories. Confidence
intervals for SRM were calculated as + /- 1.96 divided by the
square root of the sample size.
34
We assessed responsiveness
using all three methods because they can produce differing
results and because agreement is lacking on the preferred
method.
34,35
We compared responsiveness of the PEG, BPI
severity and BPI interference scales by comparing ES and
SRM for each measure among patients in the improved
category. Finally, we assessed responsiveness to varying
degrees of improvement by comparing change scores for PEG
and BPI scales to degree of improvement by global rating of
change.
RESULTS
Item Selection
Using baseline data from the Study 1 sample, we assessed the
preferred and alternate scales. Results were similar for all
scales evaluated (Table 1). Our preferred scale demonstrated
initial characteristics similar to or better than the alternatives,
so we chose it as our final scale. From here on, we refer to this
final abbreviated scale as the PEG, an acronym representing
the three items: “Pain average,”“interference with Enjoyment
of life,” and “interference with General activity.” Principal
components analysis of the PEG in both samples demonstrat-
ed a single factor, accounting for 66% of the variance in Study
1 and 81% in Study 2.
The PEG comprises 1 intensity item and 2 interference
items (Fig. 1). Consistent with BPI scoring, we calculated the
average of individual item scores to get an overall PEG score
(potential range 0–10). Table 2 shows means and standard
deviations (SD) for each item and the full three-item scale in
both populations.
Reliability and Validity
Reliability of the PEG was 0.73 in Study 1 and 0.89 in Study 2.
Table 3 shows correlation matrices for the PEG, BPI scales, and
other pain and function measures in both study populations.
Overall, construct validity of the PEG was good (r=0.60–0.89 in
Study 1 and r=0.77–0.95 for pain-specific measures in Study
2), with correlations comparable to those of the BPI scales. As
expected, PEG correlations were slightly higher for BPI interfer-
ence (r=0.89 and 0.95) than for BPI severity (r=0.69 and 0.84)
and for CPG disability (r=0.67) than for CPG intensity (r=0.64).
Correlations were higher for the pain-specific function mea-
sures than for the Functional Morbidity Index (r=0.54), a
generic measure of function.
Responsiveness
Six-month follow-up data was available for 210 Study 1 clinical
trial participants. The proportion with pain improvement was
approximately the same according to global rating of change
(31.4%) and serial CPG pain grade (29.5%); however, more
patients were classified as worse by global rating (29.0%) than
by CPG grade (15.0%). Table 4 shows PEG scores and measures
of responsiveness for patients classified as improved, unchanged,
and worse at 6 months. Confidence intervals for the improved
and unchanged groups did not overlap, but the unchanged and
worse groups were not statistically different from each other. The
improved group according to global rating of change had a mean
improvement of 3.0 points (SD 2.5) on the PEG. Similarly, the
Table 4. Responsiveness of PEG among Patients Classified by Pain Global Rating of Change and Serial CPG Grade at 6 Months (n=210)
PEG baseline PEG 6 months PEG change* ES† SRM‡
Global rating of change
Improved (n=66) 6.35 (2.32) 3.37 (2.33) 2.99 (2.49) 1.29 1.20 (0.96, 1.44)
Unchanged (n=83) 6.95 (1.75) 6.49 (1.91) 0.46 (1.60) 0.26 0.29 (0.07, 0.51)
Worse (n=61) 6.99 (1.73) 7.10 (1.75) −0.10 (1.69) −0.06 −0.06 (-0.31, 0.19)
CPG grade
Improved (n=62) 6.83 (1.73) 4.20 (2.37) 2.62 (2.66) 1.51 0.99 (0.74, 1.24)
Unchanged (n=115) 6.92 (2.11) 6.40 (2.35) 0.53 (1.80) 0.25 0.29 (0.11, 0.47)
Worse (n=32) 6.16 (1.66) 6.07 (2.43) 0.08 (1.92) 0.05 0.04 (-0.31, 0.39)
*Change score = baseline score – 6 month score
†ES = change score/SD of baseline score
‡SRM = change score/SD of change score
Figure 2. Mean change in PEG and BPI scales compared with
global rating of change at 6 months (n=210).
736 Krebs et al: PEG Scale Development and Validation JGIM

improved group according to serial CPG grade had a mean
change of 2.6 points (SD 2.7).
The SRM among participants who improved at 6 months
according to global rating of change were similar for the PEG
(1.20, 95%CI 0.96, 1.44), BPI severity (1.04, 95%CI 0.80, 1.28),
and BPI interference (1.13, 95% CI 0.89, 1.37). Results were
similar for improvement according to serial CPG grade (data not
shown). For all measures of improvement, ES and SRM were
consistent with a large effect. Figure 2 shows 6-month change
scores for the PEG, BPI severity scale, and BPI interference scale
plotted against global rating of change.
DISCUSSION
We demonstrated that the PEG, an ultra-brief three-item scale
derived from the BPI, was a reliable and valid measure of pain
among primary care patients with chronic musculoskeletal
pain and diverse VA ambulatory patients. The PEG appears
comparable to the BPI in terms of responsiveness to change.
These findings support our hypothesis that an abbreviated
scale derived from the BPI may be both useful and practical for
chronic pain assessment in primary care and other ambulato-
ry care settings, such as medical and surgical specialty clinics.
Strengths of this study include the confirmation of reliability
and validity in an independent patient population, the diversity of
the study populations, and the availability of multiple pain and
functional measures with which to assess construct validity. Our
choice of the BPI as the basis for our abbreviated scale
development is another strength. The BPI is a widely used
instrument that has been validated in numerous patient popula-
tions, clinical settings, and languages. BPI items are rated from
0–10, a format that hasbecome familiar to patients and clinicians
since assessment of pain with numeric scales has been broadly
implemented inUShealth care settings.We tookadvantage of our
collective experience with the BPI in observational and interven-
tional research by employing a consensus-based process for
scale shortening, consistent with recommendations to avoid
over-reliance on statistical techniques.
29
We believe our use of two different ambulatory study
populations is a strength; however, each study has its own
limitations. Study 1 was a sample of patients with chronic
back and lower extremity musculoskeletal pain and included
an over-representation of patients with depression (50% by
design). The more clinically diverse patient population of Study
2, including ambulatory VA patients with and without chronic
pain, enhances the generalizability of our findings. However,
Study 2 included fewer pain measures with which to assess
construct validity and was cross-sectional; therefore, we were
able to assess responsiveness only in the first sample. Forty
percent of Study 1 patients and 100% of Study 2 patients were
recruited from VA clinics, so our findings may be less
generalizable to non-VA settings.
We found that the PEG differentiated well between patients
who improved and those who did not. According to responsive-
ness metrics, patients in the improved category had a large
improvement in PEG score, whereas those in the unchanged
category had a minimal change. Proper interpretation of the
magnitude of change according to SRM and one group pre-post
ES is not entirely clear, although authors have suggested that
Cohen’s definition of small (0.2), moderate (0.5), and large (0.8)
effects can be applied to interpretation of both responsiveness
measures.
36,37
We did not find evidence of PEG responsiveness in
the worse direction (i.e., change scores between those who were
unchanged and those whowereworse did not significantly differ).
We are limited in our ability to adequately assess sensitivity to
worsening becausewe evaluated responsiveness in a single study
population that likely had a ceiling effect for worsening due to
high baseline pain severity.
The competing demands of primary care, in which visits are
short and pain is only one of several problems warranting
attention, make efficiency of assessment a paramount con-
cern.
38,39
A balance must be found between feasibility and key
characteristics such as reliability, validity, and responsiveness.
For example, ultra-brief depression measures containing two
to three items perform better than single item depression
measures.
40
We also evaluated a four-item abbreviated scale,
but found that adding an item contributed little. An abbrevi-
ated version of the BPI that eliminated a few items has been
previously published,
41
but the PEG is the first ultra-brief
scale based on the BPI.
New assessment strategies are needed to support improved
chronic pain management in primary care. We believe the PEG,
which includes items assessing pain intensity, emotional func-
tion, and physical function, is an important step forward.
However, further studies are needed to confirm our findings and
validate the PEG in additional patient populations. Prospective
research should determine whether serial pain measurement
can improve the quality of clinical decision-making and pain
outcomes in primary care. Given the huge clinical and societal
burden associated with pain, developing efficient and effective
strategies to enhance care is an important priority.
Acknowledgements: WethankCharlesCleeland,PhD,TitoMendoza,
PhD, Lisa Shugarman, PhD, and Cathy Sherbourne, PhD for their
insightful contributions to this manuscript and Andy Lanto, MS, for
assistance with data analysis.
We presented a preliminary abstract of this work as a poster at
the 31st annual meeting of the Society of General Internal Medicine,
April 2008.
SCAMP was supported by a grant from the National Institute of
Mental Health to Dr. Kroenke (MH-071268). HELP-Vets was sup-
ported by the Health Services Research & Development (HSR&D)
service of the US Department of Veterans Affairs (IIR-03–150). Drs.
Krebs, Lorenz, and Bair were supported by VA HSR&D Research
Career Development Awards.
Conflict of Interest Statement: We disclose the following financial
relationships: 1) Drs. Lorenz and Asch have received research
funding from the Amgen Corporation; 2) Dr. Bair has received
research funding and honoraria from Eli Lilly and served on an
advisory board for Abbott.
Corresponding Author: Erin E. Krebs, MD, MPH, Roudebush
VAMC, 11H, 1481 W. 10th Street, Indianapolis, IN 46202, USA
(e-mail: krebse@iupui.edu).
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