![Loading plot from the PCA of background variables, pain variables, psychological distress variables, and two life impact variables. To facilitate the graphic interpretation, a second nonsignificant component was calculated (ie, variations along the Y-axis are not significant). Hence, two components (p1 [horizontal] and p2 [vertical]) are shown.](https://www.researchgate.net/publication/331566824/figure/fig1/AS:11431281270835222@1723280455157/Loading-plot-from-the-PCA-of-background-variables-pain-variables-psychological-distress_Q320.jpg)
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Journal of Pain Research 2019:12 891–908
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ORIGINAL RESEARCH
open access to scientific and medical research
Open Access Full Text Article
http://dx.doi.org/10.2147/JPR.S190003
Who benets from multimodal rehabilitation –
an exploration of pain, psychological distress, and
life impacts in over 35,000 chronic pain patients
identied in the Swedish Quality Registry for
Pain Rehabilitation
Björn Gerdle1
Sophia Åkerblom2,3
Gunilla Brodda Jansen4
Paul Enthoven1
Malin Ernberg5,6
Huan-Ji Dong1
Britt-Marie Stålnacke7
Björn O Äng8–10
Katja Boersma11
1Pain and Rehabilitation Centre,
Department of Medical and Health
Sciences, Linköping University,
Linköping, Sweden; 2Department of Pain
Rehabilitation, Skåne University Hospital,
Lund, Sweden; 3Department of Psychology,
Lund University, Lund, Sweden; 4Division
of Rehabilitation Medicine, Department
of Clinical Sciences, Danderyd Hospital,
Stockholm, Sweden; 5Department of Dental
Medicine, Karolinska Institutet, Huddinge,
Sweden; 6Scandinavian Center for Orofacial
Neuroscience (SCON), Huddinge,
Sweden; 7Department of Community
Medicine and Rehabilitation, Rehabilitation
Medicine, Umeå University, Umeå,
Sweden; 8Department of Neurobiology,
Care Sciences and Society, Division of
Physiotherapy, Karolinska Institutet,
Huddinge, Sweden; 9Center for Clinical
Research Dalarna – Uppsala University,
Falun, Sweden; 10School of Education,
Health and Social Studies, Dalarna
University, Falun, Sweden; 11School of
Law, Psychology and Social Work, Örebro
University, Örebro, Sweden
Background: Chronic pain patients frequently suffer from psychological symptoms. There is
no consensus concerning the prevalence of severe anxiety and depressive symptoms and the
strength of the associations between pain intensity and psychological distress. Although an
important aspect of the clinical picture is understanding how the pain condition impacts life,
little is known about the relative importance of pain and psychological symptoms for individual’s
life impact. The aims of this study were to identify subgroups of pain patients; to analyze if
pain, psychological distress, and life impact variables influence subgrouping; and to investigate
how patients in the subgroups benefit from treatments.
Methods: Background variables, pain aspects (intensity/severity and spreading), psychological
distress (depressive and anxiety symptoms), and two life impact variables (pain interference and
perceived life control) were obtained from the Swedish Quality Registry for Pain Rehabilitation
for chronic pain patients and analyzed mainly using advanced multivariate methods.
Results: Based on >35,000 patients, 35%–40% had severe anxiety or depressive symptoms. Severe
psychological distress was associated with being born outside Europe (21%–24% vs 6%–8% in
the category without psychological distress) and low education level (20.7%–20.8% vs 26%–27%
in the category without psychological distress). Dose relationships existed between the two psy-
chological distress variables and pain aspects, but the explained variances were generally low.
Pain intensity/severity and the two psychological distress variables were significantly associated
(R2=0.40–0.48; P>0.001) with the two life impact variables (pain interference and life control).
Two subgroups of patients were identified at baseline (subgroup 1: n=15,901–16,119; subgroup
2: n=20,690–20,981) and the subgroup with the worst situation regarding all variables participated
less in an MMRP (51% vs 58%, P<0.001) but showed the largest improvements in outcomes.
Conclusion: The results emphasize the need to assess both pain and psychological distress
and not take for granted that pain involves high psychological stress in the individual case.
Not all patients benefit from MMRP. A better matching between common clinical pictures and
the content of MMRPs may help improve results. We only partly found support for treatment
resistance in patients with psychological distress burden.
Keywords: anxiety, chronic pain, control, depression, life impact, sociodemographic
Introduction
Chronic pain conditions are closely related to interactions between neurobiological,
psychological, and social factors.1 The International Association for the Study of Pain
definition of pain includes both sensory and emotional components. The prevalence of
Correspondence: Björn Gerdle
Pain and Rehabilitation Centre,
Department of Medical and Health
Sciences, Linköping University, SE-581 85
Linköping, Sweden
Tel +46 763 927 191
Email bjorn.gerdle@liu.se
Journal name: Journal of Pain Research
Article Designation: ORIGINAL RESEARCH
Year: 2019
Volume: 12
Running head verso: Gerdle et al
Running head recto: Gerdle et al
DOI: http://dx.doi.org/10.2147/JPR.S190003
This article was published in the following Dove Medical Press journal:
Journal of Pain Research
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Gerdle et al
chronic nonmalignant pain is high: about 19% of the adult
European population suffer from activity-limiting pain condi-
tions,2 with a higher prevalence and more health care seeking
in women than in men.3,4 However, prevalence figures vary
depending on study design.5
Compared with the general population, individuals with
chronic pain more frequently suffer from psychological
symptoms/conditions, especially anxiety and low mood.6–9
There are different theories about the complex bidirectional
relationship between pain and mood (ie, depressive and anxi-
ety symptoms).10–16 The presence of depressive and anxiety-
related symptoms/disorders in people with chronic pain is
associated with lower levels of functioning, poorer responses
to treatment, and greater health costs.10,17–26 Furthermore,
spreading of pain, long pain duration, and high pain severity
have been associated with worse course of depressive and
anxiety disorders.
These associations make it important to determine how
prevalent high levels of anxiety and depressive symptoms
are in chronic pain patients. Prevalence estimates in chronic
pain cohorts show varying figures, ranging from 2% to
80% in relation to depression and 1% to 65% in relation
to anxiety.15,27–30 These prominent variations may be due to
methodological issues, the pain conditions examined, health
care level provided (ie, primary care or specialized care),
sample size investigated, and whether questionnaires focusing
on symptoms or clinical assessments were used.15
A related question concerns the strength of the associa-
tions between pain aspects such as intensity and severity of
psychological distress. Relatively low correlations might
indicate that reducing psychological symptoms will not
reduce pain and vice versa. However, to some extent, changes
in depressive and anxiety symptoms are correlated with
changes in pain, but it has been shown that remission of
depression and/or anxiety does not eliminate pain.31 MMRP
distinguishes itself as a well-coordinated intervention lead-
ing to a complex intervention instead of a single treatment
and generally includes education, supervised physical
activity, training in simulated environments, and cognitive
behavioral therapy coordinated by an interdisciplinary team.
The presence of disturbing psychological symptoms might
indicate a need to consider the complex interaction of these
aspects to further optimize present MMRP to include rel-
evant psychological interventions. Investigating the strength
of multivariate correlation pattern between pain aspects and
symptoms of depression and/or anxiety may also shed light
on which of these variables vary the most and contribute the
most to variations in the clinical picture and the outcome
of rehabilitation efforts with respect to not only symptom
reduction but also return to work rate. This knowledge is
important for designing clinical assessments of chronic
pain patients.
An important aspect of the clinical assessment is how the
pain condition impacts life of the patient. The interference
with daily life is one of the reasons for seeking health care.32
Pain interference – an important disease-specific measure of
physical function33 – reflects how pain affects work, leisure,
and household activities as well as relationships with friends
and family. A sense of control may represent the perceived
ability to manage pain or pain-related matters,34 eg, the per-
ceived ability to control daily life and/or pain and the ability
to address problems and handle stressful situations.35,36 The
ability to control pain seems important for choice of coping
strategies.37 Perceived life control has been shown to correlate
relatively strongly both with pain and psychological distress
in chronic pain patients, but these relationships have not been
further explored.37 There is a lack of large studies of real-life
patients investigating if, to what extent, and how pain aspects
and psychological symptoms determine reported levels of
pain interference and life control.
In the context of improving outcomes of interven-
tions, there is a great interest in identifying subgroups
of chronic pain patients to investigate how these patients
benefit from pain rehabilitation.38 Most studies have been
hypothesis driven with respect to the input variables used
for the subgrouping. Although several studies have used
psychological characteristics as input variables to identify
the subgroups,39–44 few studies have used objective methods
to select input variables from a larger set of variables to
identify clusters.
The above identified knowledge gaps motivated this study
of chronic pain patients based on PROM data from SQRP.37
This study has the following aims:
1. To investigate the prevalence of severe depressive and
anxiety symptoms and to analyze to what extent such
symptoms intercorrelate with common pain characteris-
tics and sociodemographic variables.
2. To explore how pain aspects and psychological distress
symptoms intercorrelate with two life impact variables,
ie, pain interference and life control.
3. To identify clusters of patients based on the exploration of
baseline variables and to investigate which cluster benefits
most from MMRP in the longitudinal perspective.
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Subjects and methods
SQRP
The SQRP is recognized by the Swedish Association of Local
Authorities and Regions. All relevant clinical departments
within specialist care throughout Sweden deliver data to
SQRP.37 The SQRP is based on questionnaires (ie, PROM
data) that capture patients’ background, pain aspects, and
psychological distress symptoms (eg, depression and anxiety)
together with activity/participation aspects and health-related
quality of life variables. Patients complete the SQRP ques-
tionnaires on up to three occasions: 1) during assessment at
the first visit to the clinical department (baseline); 2) imme-
diately after MMRPs for those patients who participate in
MMRP; and 3) at a 12-month follow-up. In this study, cross-
sectional analyses were made from patients who answered
the SQRP at baseline. For the longitudinal analyses, the
subgroup of patients who participated in MMRP was used
(ie, also using the data registered immediately after MMRP
and at the 12-month follow-up).
Patients
All chronic pain patients (>3 months) and patients >18 years
of age (no upper age limit) were included in this study. These
patients were referred to different specialist clinics associ-
ated with SQRP between 2008 and 2016 from ~30 clinical
departments throughout Sweden.
Variables
Sociodemographic variables, pain aspects, psychological
distress variables, and life impact variables were selected
from the SQRP and used in the analyses.
Sociodemographic variables
Sociodemographic variables were age (years), gender (man
or woman), education level (dichotomized into university vs
other alternatives and denoted as University), and country
of birth (dichotomized into Europe vs outside Europe and
denoted as Outside Europe).
Pain aspects
Average pain intensity the previous week was captured using
an NRS. The endpoints of the NRS had verbal descriptions
(ie, 0 =no pain and 10 =worst possible pain). This variable
was denoted as NRS-7d.
Pain severity was registered using the Pain Severity sub-
scale of the MPI (pain severity; 0 =no pain to 6 =very intense
pain), which includes items concerning current pain intensity,
pain intensity the previous 7 days, and suffering due to pain.35,36
The spatial extent of pain on the body was measured using
36 predefined anatomical areas (18 on the front and 18 on
the back of the body). The patients marked the anatomical
areas where they experienced pain: 1) head/face, 2) neck,
3) shoulder, 4) upper arm, 5) elbow, 6) forearm, 7) hand, 8)
anterior aspect of chest, 9) lateral aspect of chest, 10) belly,
11) sexual organs, 12) upper back, 13) low back, 14) hip/
gluteal area, 15) thigh, 16) knee, 17) shank, and 18) foot.
The sum of painful areas was calculated (possible range: 0
and 36). This sum was labeled as the PRI.
Psychological distress variables
The HADS, which measures symptoms of anxiety and
depression, has good psychometric characteristics.45,46 The
validated Swedish translation of HADS was used and cho-
sen to reflect aspects of psychological distress.45,47 HADS
comprises seven items in each of the depression and anxiety
subscales (HAD-Depression and HAD-Anxiety). Both sub-
scale scores range between 0 and 21. A score of 7 or less on
each subscale indicates a noncase, a score of 8–10 indicates
a possible case, and a score of 11 or more indicates a definite
case.45 In this study, >11 was considered as severe anxiety
and depressive symptoms.
Life impact variables
The pain interference subscale of MPI (pain interference;
0 =no interference to 6 =extreme interference) reflects
interference of pain in everyday life (eg, work, housework,
and leisure activities and time with family, relatives, and
friends). The other MPI variable was the life control subscale
(life control; 0 =poor control to 6 =good control), which is
based on items concerning the ability to control daily life,
address one’s own problems, control pain, and handle stress-
ful situations.35,36
MMRPs
The SQRP does not provide detailed information about
the MMRPs at the individual centers. Generally, the pro-
grams – in agreement with international suggestions48 – are
delivered by a team of professionals (generally physician,
psychologist, occupational therapist, physiotherapist, and
social worker) and based on a biopsychosocial model of
chronic pain. MMRPs are mainly outpatient group-based
programs but with opportunities for individual interventions
if necessary based on the clinical picture and the aims of
the individual patient. Important components of the group-
based part are cognitive behavioral treatment, physiotherapy
including physical exercise, interventions targeting improved
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ergonomics, and occupational therapy. In addition, lectures
on basic pain physiology and pain management are offered
to patients and often to relatives, friends, and colleagues.
The programs generally have a duration of several weeks
(4–8 weeks) and with group-based activities 20–30 hours/
week. In addition, patients can have tasks to do at home
including encouraging them to do physical exercise on their
own. Recently published terminology for multicomponent
treatment approaches identifies MMRP as an example of
interdisciplinary treatment (https://www.iasp-pain.org/
PublicationsNews/NewsDetail.aspx?ItemNumber=6981).
Statistical analyses
All statistical analyses were performed using the statistical
package IBM SPSS Statistics (version 23.0) and SIMCA-P+
(version 13.0; Umetrics Inc., Umeå, Sweden). A probability
of <0.001 (two-tailed) was chosen as the criteria for signifi-
cance due to the large number of subjects. Most texts and
initial tables generally report the mean value ± 1 SD together
with median and range of the investigated variables for the
whole material. The subsequent analyses reported mean ± 1
SD in text and tables. SQRP uses predetermined rules when
handling single missing items of a scale or a subscale; details
have been reported elsewhere.49
ANOVA (including Bonferroni post hoc tests) was used
for group comparisons, Student’s t-test for paired observations,
and chi-squared test for differences in distribution. Pearson’s
test was used for bivariate correlation analysis, and this test
produced the correlation coefficient r and explained varia-
tion r2 (in %). Effect sizes (Cohen’s d) were computed for
between- and within-group situations using a web-based calcu-
lator (https://memory.psych.mun.ca/models/stats/effect_size.
shtml). The absolute effect size (| d |) was considered very large
for ≥1.3, large for 0.80–1.29, moderate for 0.50–0.79, small
for 0.20–0.49, and insignificant for <0.20.50 We used advanced
multivariate analyses; advanced PCA was used for the mul-
tivariate correlation analyses of all investigated variables and
OPLS for the multivariate regressions using SIMCA-P+. These
techniques do not require normal distributions.51
PCA was used to investigate the correlation patterns for
the investigated variables. A cross-validation technique was
used to identify nontrivial components (p). Variables loading
on the same component p are correlated, and variables with
high loadings but with opposing signs are negatively corre-
lated. Variables with high absolute loadings – ie, 95% jack-
knife uncertainty confidence interval nonequal to zero – were
considered significant. Note that the loadings obtained from
SIMCA-P+ are lower and not comparable with those from, eg,
SPSS. SIMCA-P+, in contrast to traditional statistical pack-
ages such as SPSS, uses the Non-linear Iterative Partial Least
Squares algorithm when compensating for missing data: for
variables/scales, maximum 50% missing data and for subjects,
maximum 50% missing data. The obtained components are
per definition not correlated and are arranged in decreasing
order with respect to explained variation. R2 describes the
goodness of fit – the fraction of sum of squares of all the vari-
ables explained by a principal component p.52 Q2 describes the
goodness of prediction – the fraction of the total variation of
the variables that can be predicted by a principal component
using cross-validation methods.52 Outliers were identified using
two methods: 1) score plots in combination with Hotelling’s
T2 and 2) distance to model in X-space. No extreme outliers
were detected. PCA was used to identify the most important
variables, and the most important variables were used as input
variables in a two-step cluster analysis to identify clusters (log-
likelihood measure distance, number of clusters determined
automatically, and Schwartz’s Bayesian cluster criterion as
options). To obtain reasonably large clusters, we required that
the ratio between clusters sizes be <3.0, per the convention
for this analysis. ANCOVA was used to manage regression
toward the mean bias for the changes in outcome variables
after MMRP using baseline values as covariates53 and with
the requirement that no interaction exists between independent
variable (cluster membership) and the covariate.
OPLS was used for the multivariate regression analyses
of pain interference and life control.52 The VIP indicates the
relevance of each X-variable pooled over all dimensions and
Y-variables, the group of variables that best explains Y. VIP
≥1.0 was considered significant if VIP had 95% jackknife
uncertainty confidence interval nonequal to zero. p(corr)
was used to note the direction of the relationship (positive
or negative). This is the loading of each variable scaled as
a correlation coefficient and thus standardizing the range
from –1 to +1.51 p(corr) is comparable between models. An
absolute p(corr) ≥0.5 is considered significant.51 For each
regression, R2, Q2, and the result (ie, P-value) of a cross-
validated analysis of variance (CV-ANOVA) are reported.
Results
Investigated variables
At the time of this investigation, the SQRP consisted of 39,916
chronic pain patients >18 years of age (72.0% women). Of
these, 14.1% were born outside Europe. Approximately one-
fourth (23.9%) of the patients had a university education. This
percentage is significantly lower compared with the population
proportion of 36.3% (χ2=2,639.103, df=1, P<0.001) ( Statistics
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Gerdle et al
Sweden, 2018; https://www.scb.se/en/). The investigated
continuous variables are displayed in Table 1. Previously, we
reported that women had significant worse situations with
respect to NRS-7d, pain severity, and PRI and men scored
significantly higher on HAD-Depression.54
Prevalence of severe levels of anxiety
and depression symptoms according to
HADS
The prevalence of severe anxiety symptoms (>11) according
to HAD-Anxiety was 39.5%. The corresponding figure for
Table 1 Age, pain variables, psychological distress variables, and
life impact aspects; mean, SD, median, and range together with
number of subjects (n) at baseline
Mean SD Median Range n
Age (years) 43.3 11.3 44.0 84 39,916
Pain variables
NRS-7d 7.0 1.8 7.0 10 38,404
Pain severity 4.5 1.0 4.7 6 38,643
PRI 13.9 8.9 13.0 36 39,916
Psychological distress
HAD-Anxiety 9.2 5.0 9.0 21 38,919
HAD-Depression 8.7 4.7 9.0 21 38,936
Life impact
Pain interference 4.4 1.1 4.5 6 38,263
Life control 2.7 1.2 2.8 6 38,478
Abbreviations: HAD-Anxiety, Anxiety subscale of Hospital Anxiety and
Depression Scale; HAD-Depression, Depression subscale of Hospital Anxiety
and Depression Scale; life control, MPI subscale concerning perceived life control;
MPI, Multidimensional Pain Inventory; NRS-7d, average pain intensity the last week
according to a numeric rating scale; Pain interference, MPI subscale concerning pain-
related interference in everyday life; Pain severity, MPI subscale concerning pain
severity; PRI, Pain Region Index.
HAD-Depression was 35.2%, and 24.8% had high values
(>11) on both HAD-Anxiety and HAD-Depression. The
proportion of patients with less severe psychological distress
according to these two variables (ie, <11) was 50.3%.
The continuous variables stratified for HAD-Anxiety and
HAD-Depression are shown in Tables 2 and 3. The three cat-
egories of the two subscales of HADS were generally associ-
ated with significant differences in the investigated variables.
Hence, the category with most severe depressive and anxiety
symptoms had the worst situation with respect to pain and life
impact. Relatively more men than women had severe depres-
sive symptoms (>11 for HAD-Depression) (men: 37.9% vs
women: 34.1%; χ2=54.6, P<0.001), but no gender differences
were found for severe anxiety symptoms (HAD-Anxiety)
(men: 39.3% vs women: 39.5%; χ2=0.104, P=0.950).
The proportion of patients born outside Europe differed
significantly between the HAD-Anxiety categories (0–7:
5.5%, 8–10: 10.3%, and 11–21: 24.1%; χ2=2,311.2, df=2,
P<0.001) and the HAD-Depression categories (0–7: 7.8%,
8–10: 13.9%, and 11–21: 21.0%; χ2=1,086.9, df=2, P<0.001).
Hence, patients born outside Europe were disproportionally
represented in the more severe categories of anxiety and
depressive symptoms.
The proportion with university education also differed
across the three HAD-Anxiety categories (0–7: 27.0%, 8–10:
24.4%, and 11–21: 20.8%; χ2=160.2, df=2, P<0.001) and
the HAD-Depression categories (0–7: 26.4%, 8–10: 24.6%,
and 11–21: 20.7%; χ2=133.6, df=2, P<0.001). Hence, the
categories with severe psychological distress had the lowest
proportion with university education.
Table 2 Investigated continuous variables (mean and SD) stratied for the three categories of HAD-Anxiety
HAD-Anxiety score 0–7 8–10 11–21 Statistics
Mean SD Mean SD Mean SD P-value Post hoc
Age (years) 44.6 11.3 42.6 11.2 42.2 11.1 <0.001 All different
Pain variables
NRS-7d 6.6 1.8 6.9 1.7 7.5 1.7 <0.001 All different
Pain severity 4.2 1.0 4.4 0.9 4.8 0.9 <0.001 All different
PRI 12.6 8.2 14.0 8.3 16.0 9.1 <0.001 All different
Psychological variables
HAD-Anxiety 4.2 2.1 9.0 0.8 14.4 2.7 <0.001 All different
HAD-Depression 5.6 3.7 8.6 3.6 11.9 4.0 <0.001 All different
Life impact
Pain interference 4.0 1.1 4.4 1.0 4.8 0.9 <0.001 All different
Life control 3.2 1.1 2.7 1.0 2.0 1.1 <0.001 All different
Note: Furthest to the right is the result of ANOVA (P-value) and post hoc tests.
Abbreviations: HAD-Anxiety, Anxiety subscale of Hospital Anxiety and Depression Scale; HAD-Depression, Depression subscale of Hospital Anxiety and Depression
Scale; Life control, MPI subscale
concerning perceived life control; MPI, Multidimensional Pain Inventory; NRS-7d, average pain intensity the last week according to a numeric
rating scale; Pain interference, MPI subscale concerning pain-related interference in everyday life; Pain severity, MPI subscale concerning pain severity; PRI, Pain Region Index.
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Intercorrelations
Bivariate intercorrelations
Highly significant bivariate intercorrelations according
to Pearson’s test exist between the pain variables and the
psychological distress variables (Table 4). As expected, the
bivariate correlations showed high intercorrelations between
the two psychological distress variables (r=0.67) and between
NRS-7d and pain severity (r=0.75) (Table 4). However, the
explained variances between pain intensity/severity variables
(NRS-7d and pain severity) and the psychological variables
(HAD-Anxiety and HAD-Depression) were low since r2 var-
ied between 3% and 11%. The corresponding figures for the
explained variations between pain variables and psychologi-
cal distress variables vs pain interference were 5%–35% and
4%–32% for life control. Age did not correlate significantly
with several of the other variables or had very low r2.
Table 3 Investigated continuous variables (mean and SD) stratied for the three categories of HAD-Depression
HAD-Depression 0–7 8–10 11–21 Statistics
Mean SD Mean SD Mean SD P-value Post hoc
Age (years) 43.2 11.9 43.1 11.1 43.5 10.6 0.015 0–7 NE 11–21, 8–10 NE 11–21
Pain variables
NRS-7d 6.6 1.8 7.0 1.7 7.6 1.6 <0.001 All different
Pain severity 4.2 1.0 4.5 0.9 4.8 0.9 <0.001 All different
PRI 12.8 8.3 14.4 8.5 15.9 9.0 <0.001 All different
Psychological variables
HAD-Anxiety 6.0 3.8 9.6 3.9 12.9 4.3 <0.001 All different
HAD-Depression 4.2 2.1 9.0 0.8 13.9 2.5 <0.001 All different
Life impact
Pain interference 3.9 1.1 4.5 0.9 5.0 0.8 <0.001 All different
Life control 3.3 1.0 2.6 1.0 1.9 1.0 <0.001 All different
Note: Furthest to the right is the result of ANOVA (P-value) and post hoc tests.
Abbreviations: HAD-Anxiety, Anxiety subscale of Hospital Anxiety and Depression Scale; HAD-Depression, Depression subscale of Hospital Anxiety and Depression
Scale; Life control, MPI subscale concerning perceived life control; MPI, Multidimensional Pain Inventory; NE, not equal; NRS-7d, average pain intensity the last week according
to a numeric rating scale; Pain interference, MPI subscale concerning pain-related interference in everyday life; Pain severity, MPI subscale concerning pain severity; PRI, Pain
Region Index.
Table 4 Bivariate correlations (Pearson’s correlation coefcients r) for the investigated continuous variables
Variables Age NRS-
7d
Pain
severity
PRI HAD-
Anxiety
HAD-
Depression
Pain
interference
Life
control
Age 10.031* –0.003 –0.001 –0.097* 0.017* 0.012 0.060*
NRS-7d 10.753* 0.234* 0.257* 0.266* 0.439* –0.352*
Pain severity 10.254* 0.306* 0.326* 0.588* –0.421*
PRI 1 0.201* 0.182* 0.224* –0.203*
HAD-Anxiety 10.666* 0.372* –0.514*
HAD-Depression 1 0.537* –0.562*
Pain interference 1–0.470*
Life control 1
Note: *P<0.001.
Abbreviations: HAD-Anxiety, Anxiety subscale of Hospital Anxiety and Depression Scale; HAD-Depression, Depression subscale of Hospital Anxiety and Depression
Scale; Life control, MPI subscale concerning perceived life control; MPI, Multidimensional Pain Inventory; NRS-7d, average pain intensity the last week according to a numeric
rating scale; Pain interference, MPI subscale concerning pain-related interference in everyday life; Pain severity, MPI subscale concerning pain severity; PRI, Pain Region Index.
Multivariate correlation patterns
The multivariate correlation pattern between the variables
was investigated using PCA. Variables located near each other
(eg, HAD-Depression vs HAD-Anxiety) are more strongly
correlated than more distant variables (eg, HAD-Depression
vs Pain severity) even though they may show relatively high
loadings on p1. The obtained significant model consisted of
one significant component (R2=0.32, Q2=0.19, n=38,934).
Figure 1 shows the loading plot, ie, the relationship between
the investigated variables.
The variables with the strongest absolute loadings on
the first component (p1) showed the largest variation across
subjects and were significantly correlated in the multivariate
context. Pain severity, pain interference, HAD-Depression,
life control, HAD-Anxiety, and NRS-7d had the highest
absolute loadings and were thus intercorrelated (Figure 1).
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Figure 1 Loading plot from the PCA of background variables, pain variables, psychological distress variables, and two life impact variables. To facilitate the graphic
interpretation, a second nonsignicant component was calculated (ie, variations along the Y-axis are not signicant). Hence, two components (p1 [horizontal] and p2
[vertical]) are shown.
Abbreviations: HAD-Anxiety, Anxiety subscale of Hospital Anxiety and Depression Scale; HAD-Depression, Depression subscale of Hospital Anxiety and Depression Scale;
life control, MPI subscale concerning perceived life control; MPI, Multidimensional Pain Inventory; MPI-Pain interference, MPI subscale concerning pain-related interference in
everyday life; NRS-7d, average pain intensity the last week according to a numeric rating scale; Outside Europe, born outside Europe; pain severity, MPI subscale concerning
pain severity; PCA, principal component analysis; PRI, Pain Region Index; University, University education.
HAD-Depression
HAD-Anxiety
Outside Europe
Pain interference
Pain severity
NRS-7d
PRI
Gender
Life control
Age
University
p1
–0.5
–0.6
–0.5
–0.4
–0.3
–0.2
–0.1
0
0.1
0.2
0.3
–0.4 –0.3 –0.2 –0.1 0 0.1 0.2 0.3 0.4
p2
in this multivariate context. Hence, pain interference was
determined both by pain and psychological variables. Posi-
tive correlations existed between these significant variables
and pain interference.
The two psychological distress variables HAD-Depres-
sion and HAD-Anxiety followed by pain severity and NRS-7d
showed the strongest and significant associations with life
control (Table 5, right part). The significant OPLS regres-
sion of life control consisted of one predictive component.
Spreading of pain (PRI) and sociodemographic variables had
no significant importance. Hence, life control was strongly
correlated with two psychological variables even though pain
intensity variables also contributed significantly (Supplemen-
tary S1). As expected, negative correlations existed between
these significant variables and life control.
The regressions displayed in Table 5 were recalculated
with the two psychological distress variables dichotomized
(severe symptoms vs less severe symptoms; cutoff >11) and
very similar results were obtained (Supplementary S2).
Life control had a negative correlation in relation to the other
important variables. Because age, gender, and university
education had low importance (ie, they were located near
zero according to p1), they were not significantly correlated
with the other variables in the multivariate context.
Regression of life impact variables: pain
interference and life control
Pain interference and life control were regressed using the
sociodemographic variables, pain variables, and psycho-
logical distress variables as regressors. For both regressions,
highly significant models were obtained (Table 5).
A mix of pain and psychological distress variables
(ie, pain severity, NRS-7d, HAD-Depression, and HAD-
Anxiety) was significantly associated with pain interfer-
ence (one predictive component) (Table 5, left part). Pain
severity was the variable with the strongest association
with pain interference. PRI (ie, spreading of pain) and
sociodemographic variables had no significant importance
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Identifying subgroups based on the most
important variables from the PCA
NRS-7d, pain severity, HAD-Anxiety, HAD-Depression,
pain interference, and life control were the most important
variables at baseline and showed the greatest variability
across patients according to the first component p1 of the
PCA (Figure 1). These variables were then used as input
variables in a two-step cluster analysis, which identified two
clusters. As intended, all input variables differed significantly
between the two clusters (Table 6); very large effect sizes
were noted. Cluster 2, which was somewhat larger than clus-
ter 1, reflected a significantly worse situation according to
all input variables. In addition, according to HADS (cutoff
>11), 59.4% of patients in cluster 2 and 12.8% of patients
in cluster 1 had severe symptoms of anxiety (χ2=8,287.9,
df=1, P<0.001); corresponding figures for HAD-Depression
(with the cutoff ≥11) were 56.1% and 9.6% (χ2=9,409.9,
df=1, P<0.001).
The two clusters were then compared for the variables
not included as input variables in the cluster analysis.
Spreading of pain on the body (PRI) was more pronounced
in cluster 2 (Table 6), which had a somewhat significantly
higher proportion of women. A nearly three times higher
prevalence of patients born outside Europe and a lower
proportion with university education were also evident in
cluster 2 (Table 6).
Table 5 OPLS regressions of pain interference (left part) and life control (right part)
Pain interference VIP p(corr)Life control VIP p(corr)
Regressors Regressors
Pain severity 1.73 0.83 HAD-Depression 1.73 –0.87
HAD-Depression 1.58 0.79 HAD-Anxiety 1.58 –0.84
NRS-7d 1.29 0.64 Pain severity 1.30 –0.63
HAD-Anxiety 1.09 0.54 NRS-7d 1.09 –0.55
PRI 0.66 0.33 PRI 0.63 –0.32
Outside Europe 0.36 0.16 Outside Europe 0.40 –0.22
University 0.27 –0.11 Age 0.19 0.10
Age 0.03 0.00 University 0.18 0.10
Gender 0.02 0.03 Gender 0.01 0.01
R20.48 R20.40
Q20.48 Q20.40
CV-ANOVA (P-value) <0.001 CV-ANOVA (P-value) <0.001
n38,241 n38,449
Notes: VIP (VIP >1.0 is signicant) and p(corr) are reported for each regressor, ie, the loading of each variable scaled as a correlation coefcient and thus standardizing
the range from –1 to +1. The sign of p(corr) indicates the direction of the correlation with the dependent variable (+, positive correlation; –, negative correlation). The
four bottom rows of each regression report R2, Q2, P-value of the CV-ANOVA, and number of patients included in the regression (n). Variables in bold type are signicant
regressors (VIP >1.0).
Abbreviations: CV-ANOVA, cross-validated analysis of variance; HAD-Anxiety, Anxiety subscale of Hospital Anxiety and Depression Scale; HAD-Depression, Depression
subscale of Hospital Anxiety and Depression Scale; Life control, MPI subscale concerning perceived life control; MPI, Multidimensional Pain Inventory; NRS-7d, average pain
intensity the last week according to a numeric rating scale; Pain interference, MPI subscale concerning pain-related interference in everyday life; Pain severity, MPI subscale
concerning pain severity; PRI, Pain Region Index; Outside Europe, born outside Europe; University, University education; OPLS, orthogonal partial least square regressions;
VIP, Variable Importance in Projection.
Outcomes of MMRP in the two clusters
More cluster 1 patients participated in MMRP than cluster
2 patients (57.5% vs 51.0%, χ2=144.5, df=2, P<0.001).
Significant differences existed for all variables post-MMRP
(all P<0.001) when comparing the two clusters; a similar pat-
tern was also found at the 12-month follow-up (all P<0.001)
(Table 7). Marked and significant differences also existed for
the dichotomized psychological distress (cutoff >11) between
the two clusters at post-MMRP (severe anxiety symptoms:
cluster 1 =11.2% and cluster 2 =39.8%, χ2=1,960.1, df=2,
P<0.001; severe depressive symptoms: cluster 1 =6.2% and
cluster 2 =29.7%, χ2=1,959.5, df=2, P<0.001) and at the
12-month follow-up (severe anxiety symptoms: cluster 1
=10.5% and cluster 2 =37.8%, χ2=1,156.5, df=2, P<0.001;
severe depressive symptoms: cluster 1 =8.1% and cluster 2
=32.9%; χ2=1,114.8, df=2, P<0.001).
In both clusters, significant improvements were found in
the six investigated variables both between baseline and post-
MMRP and between baseline and the 12-month follow-up
(Table 7). When comparing the absolute changes between
the two clusters post-MMRP and at the 12-month follow-up,
cluster 2 consistently showed the greatest improvements (all
P<0.001) (Table 7). To handle possible regression toward the
mean biases, we also checked these changes with ANCOVA
using the baseline value as a covariate; the cluster differences
remained clearly significant (P<0.001) for the changes related
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Table 6 Results of the two-step cluster analysis using NRS-7d, pain severity, HAD-Anxiety, HAD-Depression, pain interference, and
life control as input variables (above the horizontal line)
Variables Cluster 1 (n=15,901−16,119) Cluster 2 (n=20,690−20,981) Statistics
P-value
Cohen’s d
Mean SD Mean SD
NRS-7d 5.9 1.7 7.9 1.3 <0.001 1.32
Pain severity 3.8 0.9 5.0 0.6 <0.001 1.57
HAD-Anxiety 6.1 3.7 11.6 4.6 <0.001 1.32
HAD-Depression 5.5 3.4 11.2 4.1 <0.001 1.51
Pain interference 3.7 1.0 5.0 0.7 <0.001 1.51
Life control 3.4 0.9 2.1 1.0 <0.001 1.37
Age 43.4 11.6 42.9 10.9 <0.001 0.04
PRI 12.1 7.7 16.3 8.8 <0.001 0.51
Gender (% women) 71.4 73.2 <0.001 NA
Outside Europe (%) 6.2 18.9 <0.001 NA
University (%) 29.2 20.1 <0.001 NA
Notes: Continuous variables are shown as mean and SD, while the category variables are shown as proportions (%). The two clusters were also compared with respect to
PRI and sociodemographic variables. Furthest to the right is given effect sizes (Cohen’s d).
Abbreviations: HAD-Anxiety, Anxiety subscale of Hospital Anxiety and Depression Scale; HAD-Depression, Depression subscale of Hospital Anxiety and Depression
Scale; Life control, MPI subscale concerning perceived life control; MPI, Multidimensional Pain Inventory; NA, not applicable; NRS-7d, average pain intensity the last week
according to a numeric rating scale; Pain interference, MPI subscale concerning pain-related interference in everyday life; Pain severity, MPI subscale concerning pain severity;
PRI, Pain Region Index; Outside Europe, born outside Europe; University, University education.
Table 7 Within and between comparisons in the two clusters for BL vs immediately after MMRP (post) (upper part) and for BL vs
12-month FU (lower part)
Cluster 1 n=6,596–6,610 Cluster 2 n=7,473–7,514
BL Post Within
cluster
P-value
Cohen’s
d
BL Post Within
cluster
P-value
Cohen’s
d
Changes
between
clusters
P-value
Mean SD Mean SD Mean SD Mean SD
NRS-7d 5.9 1.7 5.2 2.1 <0.001 0.33 7.7 1.2 6.6 1.9 <0.001 0.60 <0.001
Pain severity 3.8 0.8 3.4 1.1 <0.001 0.42 4.9 0.6 4.3 1.0 <0.001 0.68 <0.001
HAD-Anxiety 6.3 3.6 5.8 3.7 <0.001 0.15 11.3 4.3 9.5 4.5 <0.001 0.44 <0.001
HAD-Depression 5.8 3.3 4.8 3.4 <0.001 0.31 10.9 3.8 8.3 4.3 <0.001 0.64 <0.001
Pain interference 3.7 1.0 3.4 1.1 <0.001 0.32 5.0 0.6 4.4 1.0 <0.001 0.74 <0.001
Life control 3.4 0.9 3.7 1.1 <0.001 –0.26 2.2 1.0 3.0 1.2 <0.001 –0.62 <0.001
Cluster 1 n=4,214–4,232 Cluster 2 n=4,349–4,375
BL FU Within
cluster
P-value
Cohen’s
d
BL FU Within
cluster
P-value
Cohen’s
d
Changes
between
clusters
P-value
Mean SD Mean SD Mean SD Mean SD
NRS-7d 5.9 1.6 5.0 2.3 <0.001 0.40 7.7 1.3 6.5 2.1 <0.001 0.59 <0.001
Pain severity 3.8 0.8 3.2 1.3 <0.001 0.52 4.9 0.6 4.2 1.2 <0.001 0.68 <0.001
HAD-Anxiety 6.2 3.5 5.5 3.8 <0.001 0.20 11.2 4.3 9.2 4.8 <0.001 0.45 <0.001
HAD-Depression 5.6 3.2 4.9 3.7 <0.001 0.20 10.7 3.8 8.6 4.7 <0.001 0.49 <0.001
Pain interference 3.7 0.9 3.2 1.3 <0.001 0.44 5.0 0.6 4.3 1.2 <0.001 0.71 <0.001
Life control 3.4 0.9 3.7 1.1 <0.001 –0.26 2.2 1.0 2.9 1.3 <0.001 –0.51 <0.001
Notes: Both within-cluster changes (P-value) and between-cluster changes (P-value) are shown. For the within comparisons are also reported Cohen’s d. Life control, MPI
subscale concerning perceived life control; pain interference, MPI subscale concerning pain-related interference in everyday life; pain severity, MPI subscale concerning pain
severity.
Abbreviations: HAD-Anxiety, Anxiety subscale of Hospital Anxiety and Depression Scale; HAD-Depression, Depression subscale of Hospital Anxiety and Depression
Scale; MPI, Multidimensional Pain Inventory; NRS-7d, average pain intensity the last week according to a numeric rating scale; PRI, Pain Region Index; MMRP, multimodal/
multidisciplinary rehabilitation program; BL, baseline; FU, follow-up.
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to both time points for NRS-7d, pain severity, pain interference,
and life control. The assumption of no interaction between
independent variable and covariate was violated for HAD-
Anxiety and HAD-Depression. The effect sizes for the within
clusters showed consistently higher absolute and generally
moderate effect sizes in cluster 2 (except for HAD-Anxiety for
both comparisons and HAD-Depression for one comparison),
whereas there were generally small effect sizes in cluster 1
(except for pain severity: baseline vs the 12-month follow-up).
Discussion
The following are the major results in this study of patients
in real-world practice settings from SQRP:
1. Thirty-five percent to 40% of patients had severe anxiety
or depressive symptoms with an overrepresentation in
patients born outside Europe, low education level, and
male gender (only depressive symptoms).
2. Dose relationships existed between the two psychological
distress variables and pain aspects (intensity, severity, and
spreading) and life impact variables.
3. Pain intensity/severity and psychological distress vari-
ables were significantly associated with the two life
impact variables at baseline.
4. Two clusters of patients were identified at baseline. A
lower percentage of patients in the cluster with worse
clinical situation participated in MMRP but showed larger
positive changes in outcomes. This cluster also had an
overrepresentation of patients born outside Europe and
patients with lower education level.
Prevalence of severe depressive and
anxiety symptoms and relations to
background variables
A recent meta-analysis concluded that chronic pain was more
strongly associated with anxiety than depression,55 a finding
that agrees with our finding of a somewhat higher prevalence
of severe anxiety symptoms than severe depressive symptoms
(39.5% vs 35.2%). In contrast to the meta-analysis, we found
that depressive symptoms were more strongly correlated
with pain intensity/severity and life impacts than anxiety
symptoms (Tables 4 and 5).
A substantial part of the present chronic pain patients
referred to specialist clinics perceived considerable psy-
chological distress and one-fourth of the patients report
severe psychological distress according to both facets of
HADS. Our nationwide results agree with an SQRP study
of chronic pain patients (n=4,665) referred to a university
hospital where 40% reported severe anxiety and/or depressive
symptoms according to HADS.37 In chronic low back pain
patients who were on sick leave, 18% were possible cases
of depression (cutoff >8) and 21% were possible cases of
anxiety (cutoff >8) according to HADS.30 Lower figures may
have been due to selection mechanisms since these patients
were recruited to take part in a randomized controlled study,
whereas patients in this study represented a clinical popula-
tion at the specialist level. As mentioned in the introduction,
prevalence estimates of anxiety and depressive comorbidities/
symptoms in chronic pain cohorts vary considerably,15,28,29,45
a situation that partly reflects methodological issues. It has
been stated that the highest prevalence of depression is found
among patients attending specialist pain departments.10,56 No
systematic clinical assessment of anxiety and/or depressive
conditions is available in SQRP, but at the higher end of the
recommended cutoffs (ie, >11) the specificity for identifying
cases of depression or anxiety disorders are good for both
HAD-Depression and HAD-Anxiety.46 Although most of the
patients did not report severe levels of psychological distress
using the cutoff >11 for either or both scales, these patients
may still suffer from psychological distress to some extent,
which contribute to negative life impacts (Table 5).
Dose relationships and strength of
correlations between pain aspects and
psychological distress
A dose relationship existed between pain aspects (inten-
sity, severity, and spreading) and anxiety and depressive
symptoms and vice versa (Tables 2–3). In addition, the two
life impact variables showed dose relationships vs the two
psychological distress variables. However, the bivariate
correlation coefficients were low between, eg, pain inten-
sity/severity and the two psychological distress variables
(r=0.26–0.33) (Table 4). Similar results have been reported
elsewhere.37,57 The multivariate correlation analysis (PCA;
Figure 1) confirmed the bivariate correlation pattern found
in Table 4. One clinical consequence of the low correlations
is that high pain intensity reported by a patient at the clinical
assessment will not necessarily mean severe psychological
distress even though this is the case at the group level.
The different categories of HAD-Anxiety and HAD-
Depression showed disproportional distributions of the
sociodemographic variables. Low education level and being
born outside Europe were somewhat, although significantly,
overrepresented in the patient group reporting the most severe
depressive and anxiety symptoms. Moreover, male gender was
overrepresented in the most severe group of depressive symp-
toms. In a Danish register linkage study of patients attending a
pain specialist clinic, men had higher prevalence of depression
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and anxiety disorders.58 In the SQRP, women were obviously
overrepresented at the specialist level, a finding also reported
in the Danish study.58 The higher proportion of women could
be due to factors such as women, compared with men, had a
higher prevalence of chronic pain,59,60 different severity (eg,
with respect to spreading of pain), and different societal selec-
tion mechanisms for treatment and rehabilitation.
The literature provides many explanations for the complex
bidirectional associations between chronic pain and anxiety
and depressive symptoms. Taken together, these explana-
tions point toward shared (transdiagnostic) mechanisms. On
a biological level, there is evidence of similar activated and
modulated brain regions including neuroplasticity altera-
tions,61–63 reduced levels of norepinephrine and serotonin,64
neuroinflammation, hyperactivity of hypothalamic–pituitary–
adrenal axis, and autonomic dysregulation,31,65 dysregulation
in the endocannabinoid system,66,67 sleeping disturbances,
and genetics.16,68,69 On a psychological and social level, there
is accumulating evidence for mutually shared vulnerability
and maintaining factors (including similar consequences, ie,
social isolation and reduced physical activity).16,70 Emotion
regulation may be seen as a transdiagnostic process tying
pain and depression/emotion.71,72 In this view, catastroph-
izing, behavioral avoidance, and thought suppression are
seen as adaptive psychological efforts to regain emotional
homeostasis in the face of aversive experiences such as pain
and low mood or anxiety. Depression and severe anxiety as
well as chronic pain may be a consequence of failed efforts
to regulate these negative experiences.
Regressors of life impact variables
A blend of pain intensity/severity and psychological distress
variables was important for the cross-sectional regressions of
the impact of life. The two negatively correlated life impact
variables life control and pain interference37,73 had the same
significant regressors at baseline (Table 5) even though their
relative importance differed somewhat between the two life
impact variables. In addition, Turk et al reported that pain
severity and depression correlated positively with pain inter-
ference and negatively with life control.74 Similar results are
found in other studies.75,76 In agreement with this study, pain
intensity was a significant regressor of pain interference in
rheumatoid arthritis patients with chronic pain, but in con-
trast to our results depression was a nonsignificant regres-
sor.77 Unlike our generalization, it might be argued that pain
interference is not used consistently and exclusively in the
literature78 and very broad definitions exist.79,80
Spreading of pain has been associated with worse out-
comes for pain intensity, chosen coping strategies and health
aspects in patient cohorts81 and in population studies.82–84 In
our nationwide study, PRI was not a significant regressor
of life impacts. Different results could have been due to the
fact that our patients represent the most severe cases and a
ceiling effect may be present for PRI.
Implications of the two clusters
The literature reflects great interest in identifying clusters
of patients and investigating how these clusters benefit from
treatment. For low back pain, such clusters have been based
on pathoanatomy, psychosocial variables, or patterns of signs
and symptoms.38 Psychological characteristics have been used
as input variables to identify subgroups/clusters in several
studies.39–44 Most of these studies have been hypothesis
driven with respect to the input variables used, whereas our
study from a larger set of variables used objective methods
to select input variables to identify clusters. It was obvious
that the variation in the clinical picture across patients not
only depended on the psychological distress variables but
also depended on pain intensity/severity and life impact
variables (Figure 1). Hence, the present cluster analysis using
the most important variables as input variables identified
two clusters with very prominent differences in the clinical
picture at baseline (Table 6). Cluster 2 had the worst situa-
tion with respect to all input variables. Our results agree with
other cluster analysis studies – including the famous clusters
of MPI – using psychological variables as input variables:
clusters with prominent psychological burden have higher
pain intensity/severity.39,40 Labels such as depression–pain
syndrome85 and depression–pain dyad86 have been coined,
and patients in cluster 2 appear to fulfill such labels but not
those in cluster 1. Thus, the need to include pain aspects,
psychological distress, and life impact aspects in the clinical
assessment is supported by this and other studies.39–41
In addition, variables that were not used for identifica-
tion showed significant cluster differences (Table 6). These
results, together with the results obtained at stratification of
the HADS variables, pinpoint that a generally severe situ-
ation has certain sociodemographic characteristics, which
reasonably must be considered when planning interventions
and treatments.
Surprisingly, patients with the most severe clinical picture
(ie, cluster 2) were less often selected to or participated in
MMRP, although this finding agrees with another study.39
Both clusters showed an agreement with earlier smaller
studies from SQRP, ie, there were significant improvements
in the input variables. The Swedish MMRPs are based on
the available evidence that this type of intervention is more
effective than usual care;87–93 the effect sizes are generally
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Gerdle et al
small to moderate. Smaller uncontrolled studies have
reported significant effects after MMRP on several of the
variables investigated here.94–97 This study was not primarily
performed to evaluate the effectiveness of MMRP, which
requires randomized controlled trials and systematic reviews/
meta-analysis. Instead, this study investigates chronic pain
patients in real-world practice settings within the concept of
practice-based evidence,98 and the longitudinal results (Table
7) agree with the systematic reviews of MMRP. If no effects
of MMRP were observed, MMRP in real-world practice set-
tings do not give the anticipated results.
Severe psychological distress has been associated with
worse treatment outcomes;18,25,26,99 however, cluster 2,
which had the highest pain intensity and the most severe
anxiety and depression symptoms, had significantly larger
improvements for all variables (Table 7) also when con-
trolling for possible regression toward the mean. Even if
greater improvement in cluster 2 is interpreted as a sign of
regression to the mean and that these patients did not benefit
from MMRP more than cluster 1, this cluster still improves
at least as well from MMRP as those without severe psy-
chological distress symptoms (cluster 1). This may seem
unexpected, but it is important to recognize that MMRPs
contain emotional and behavioral interventions stemming
from cognitive behavioral treatment clearly addressing
psychological symptoms.
Although treatment resistance after MMRP was not
found in cluster 2, the basic pattern with a worse situation
remained at the two time-points after MMRP (Table 7).
Indeed, a third of the patients in cluster 2 continued to have
clinically significant depressive and anxiety symptoms
post-treatment and at the 12-month follow-up. This number
suggests continued suffering for a significant proportion of
patients and that MMRP content may not fully address the
needs of these patients. Hence, because of their high entry
rates of problems, psychological comorbidity is still present
post-MMR, ie, patients with comorbidity have residual pain
and psychological problems. Such a pattern has recently been
found in another smaller SQRP study.39
The optimal composition, ie, included components and
their intensity, and duration of a complex intervention such
as MMRPs with respect to the clinical presentations of
patients are not well understood.87,91,100,101 Based on the dif-
ferences between clusters 1 and 2 both at baseline and after
MMRP, it is unclear whether MMRPs really should have the
same content for the two clusters. For cluster 2, it appears
that emotional and behavioral interventions are necessary
as generally applied in MMRPs in Sweden and this finding
agrees with the literature.102 Still, it could be questioned
whether these interventions or other components of MMRPs
were optimal (contents, intensity, and duration) since, eg, the
psychological distress levels after MMRP were still high in
cluster 2. However, the application of such methods may need
to be tailored to better target symptoms and characteristics
specific to patients belonging to cluster 1 with low/normal
levels of psychological distress if outcomes for this group
are going to improve.
The evaluation of complex interventions such as MMRP
is not clear-cut, and different definitions of a positive
outcome of an MMRP trial have been briefly presented
elsewhere.49 In clinical practice, there are several outcomes
and to make things even more complicated the important
goals of MMRP for the individual patient differs. Whether
pain intensity/severity belongs to the important outcomes
of pain treatments is a matter of debate among researchers,
clinicians, and patients.103–106 The concept of one or few
primary outcomes and few secondary outcomes applied in
pharmacological studies does not reflect the complexity of
MMRP. In cluster 2, the effect sizes were moderate for most
variables, while cluster 1 generally showed insignificant or
small effect sizes (Table 7). In addition, anchor-based meth-
ods for determining if the changes are clinically important
will give similar results. Hence, for the MPI variables and
especially the pain interference scale, a change of 0.6 has
been considered important107,108 and for NRS-7d a reduction
of at least two units or 30% has been considered important.109
Neither of the two clusters exhibited important changes in
NRS-7d immediately after MMRP and the 12-month follow-
up (Table 7), but pain severity showed important changes.
All MPI variables showed at least 0.6 changes in cluster 2 at
both time-points compared with baseline values (Table 7).
Cluster 1 only fulfilled this criterion for pain severity at
the 12-month follow-up. Unlike comparisons using such a
criterion (Cohen’s d or anchor-based), it can be argued that
smaller simultaneous changes for several outcomes may still
be clinically important.
In this sample, representative of the population of chronic
pain patients seeking specialist care in Sweden, 14% were
immigrants born outside Europe. This prevalence is a slight
significant overrepresentation (χ2=263.01, df=1, P<0.001)
compared with the proportion (11.5%) of immigrants born
outside Europe in the general population in Sweden (Sta-
tistics Sweden, 2018; https://www.scb.se/en/). Moreover,
these patients are disproportionally represented in cluster 2;
nearly three times higher prevalence of immigrant patients
compared with the other cluster. This means that chronic
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Gerdle et al
disabling pain (operationalized as seeking specialist care and
high distress and interference reporting) is more prevalent
among immigrants. This points toward the importance of not
overly biologizing pain but instead viewing and understand-
ing pain in a biopsychosocial context (the link with education
level also fits in this picture). Moreover, it also points toward
a pressing need to adapt health care and MMRP services
to fit the needs of these patients. The data also suggest that
this is not quite the case at this moment as these patients are
proportionally underrepresented in MMRP participation.
Strengths and limitations
The large number of patients with chronic pain conditions
with a nationwide representation is an obvious strength of
this study. It is likely that the findings are representative and
close to the true population values for chronic pain patients
referred to specialist care. However, patients referred to spe-
cialist clinics represent a selection of the most difficult cases,
so our results cannot be generalized to patients in primary
health care or to persons with chronic pain in the community.
We controlled for regression to the mean within the cohort
of patients, but the cohort is reasonably an extreme group
in relation to, eg, patients within primary health care, and it
was not possible to control for such regression to the mean.
Strength was the use of advanced multivariate data analysis.
Classical statistical methods such as multiple linear regres-
sion and logistic regression can quantify the level of relations
of individual factors but disregard interrelationships among
different factors and thereby ignore system-wide aspects (eg,
when a group of variables correlates with the investigated
dependent outcome).110 Classical methods assume variable
independence when interpreting results,111 and there are
several risks considering one variable at a time.112 In the
context of our aims, the problems handling missing data,
and the obvious risks for multicollinearity problems, we
have refrained from using multiple linear regression and
logistic regression. Instead, we used statistical methods tak-
ing advantage of correlated regressors. On the contrary, it is
not possible, as in multiple linear or logistic regression, to
isolate the effects for a certain variable upon the dependent
life impact variables regressed. Another limitation might be
that self-reports can be influenced by perceptions of social
desirability.77 Changes in the social context may have changed
and influenced the longitudinal analyses. However, we used
validated and well-known instruments even though they to
some extent represented different research epochs. The fact
that no control group or treatment as usual group was avail-
able, which ethically is complicated to arrange for a registry
of real-world practice patients, might have influenced our
interpretation of changes after MMRP.
Conclusion
Based on >35,000 patients in Sweden referred to specialist
care, our study found that 35%–40% of these patients had
severe anxiety or depressive symptoms. Severe psychological
distress was associated with some sociodemographic aspects.
Dose relationships existed between the two psychological
distress variables and pain aspects, but these correlations
were relatively weak. Pain intensity/severity and the two
psychological distress variables were significant regressors
of the two life impact variables. Two clusters of patients were
identified at baseline, and the patients in the cluster with the
worse situation participated less in MMRP but showed the
largest improvements in outcomes. The principle pattern of
absolute differences between the two clusters remained after
MMRP. The results emphasize the need to assess both pain
and psychological distress and not take for granted that pain
involves high psychological stress in the individual case.
Moreover, this study showed that not all patients benefit from
MMRP. A better matching between common clinical pictures
and the content of MMRPs may help improve results. We only
partly found support for treatment resistance in chronic pain
patients with psychological distress burden.
Abbreviations
ANCOVA, analysis of covariance; HADS, Hospital
Anxiety and Depression Scale; HAD-Anxiety, Anxiety
subscale of Hospital Anxiety and Depression Scale; HAD-
Depression, Depression subscale of Hospital Anxiety and
Depression Scale; life control, MPI subscale concerning
perceived life control; MMRP, multimodal/multidisci-
plinary rehabilitation program; MPI, Multidimensional
Pain Inventory; NRS, numeric rating scale; NRS-7d, aver-
age pain intensity the last week according to a numeric
rating scale; OPLS, orthogonal partial least square regres-
sions; Outside Europe, born outside Europe; pain interfer-
ence, MPI subscale concerning pain-related interference
in everyday life; pain severity, MPI subscale concerning
pain severity; PCA, principal component analysis; p(corr),
X loading p scaled as a correlation coefficient between X
and t, ie, this is the loading of each variable scaled as a
correlation coefficient and thus standardizing the range
from –1 to +1; PRI, Pain Region Index; PROM, Patient
Reported Outcome Measures; SQRP, The Swedish Quality
Registry for Pain Rehabilitation; University, University
education; VIP, Variable Importance in Projection.
Journal of Pain Research 2019:12
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Ethical approval and consent to
participate
The study was conducted in accordance with the Helsinki
Declaration and Good Clinical Practice and approved by the
Ethical Review Board in Linköping (Dnr: 2015/108-31). All
participants received written information about the study and
gave their written consent.
Data sharing statement
The datasets generated and/or analyzed in this study are
not publicly available as the Ethical Review Board has not
approved the public availability of these data.
Acknowledgments
This study was supported by grants from the Swed-
ish Research Council, County Council of Östergötland
(Research-ALF), and AFA Insurance. AFA Insurance, a com-
mercial founder, is owned by Sweden’s labor market parties:
The Confederation of Swedish Enterprise, the Swedish Trade
Union Confederation (LO), and The Council for Negotiation
and Co-operation (PTK). AFA Insurance insures employees
in the private sector, municipalities, and county councils. AFA
Insurance does not seek to generate a profit, which implies
that no dividends are paid to shareholders. The sponsors of
the study had no role in study design, data collection, data
analysis, data interpretation, writing of the report, or the deci-
sion to submit for publication. The authors had full access to
all the data in the study and had final responsibility for the
decision to submit for publication.
Author contributions
BG extracted the data from SQRP and analyzed the data
and drafted the manuscript. All authors contributed to the
conception of the study, data analysis, drafting and revising
the article, gave final approval of the version to be published,
and agree to be accountable for all aspects of the work.
Disclosure
The authors report no conflicts of interest in this work.
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Supplementary materials
Supplementary S1
When the two psychological distress variables were eliminated
from the regression models in Table 5, lower explained varia-
tions were obtained (MPI-Pain interference: R2=0.35, Q2=0.35,
CV-ANOVA P-value <0.001; MPI-Life control: R2=0.19,
Q2=0.19, CV-ANOVA P-value <0.001) and only the two pain
intensity/severity variables remained as significant regressors.
When the pain variables (including PRI) were excluded
from the regressions in Table 5 also, the explained varia-
tions decreased (MPI-Pain interference: R2=0.29, Q2=0.28,
CV-ANOVA P-value <0.001; MPI-Life control: R2=0.34,
Q2=0.34, CV-ANOVA P-value <0.001) and only the two
psychological distress variables remained as significant
regressors. Hence, the pain variables were somewhat more
important regressors of MPI-pain interference than the psy-
chological distress variables while vice versa was found for
the regressors of MPI-Life control.
When only the background variables were used as
regressors, significant regressions were obtained but that
explained very low part of the variation in MPI-Pain inter-
ference (R2=0.02, Q2=0.02, CV-ANOVA P-value <0.001)
and in MPI-Life control (R2=0.02, Q2=0.02, CV-ANOVA
P-value <0.001). Born outside Europe was significant in
both regressions and in the regression of MPI-Life control
also University has some importance.
Supplementary S2
For pain interference (R2=0.42, Q2=0.42, CV-ANOVA P<0.001),
the following significant regressors were found: pain severity
(VIP = 1.88, p(corr) = 0.89), NRS-7d (VIP = 1.40, p(corr) =
0.68) and HAD-Depression (Dichotom) (VIP = 1.34, p(corr) =
0.67). Hence, anxiety was not significant in this regression. For
MPI-Life control (R2=0.34, Q2=0.34, CV-ANOVA P<0.001), the
following significant regressors were found: HAD-Depression
(dichotomy) (VIP = 1.57, p(corr) = –0.78), pain severity (VIP =
1.47, p(corr) = –0.69), HAD-Anxiety (dichotomy) (VIP = 1.44,
p(corr) = –0.73) and NRS-7d (VIP = 1.23, p(corr) = –0.61).
Abbreviations
CV-ANOVA, cross-validated analysis of variance; HAD-Anxi-
ety, Anxiety subscale of Hospital Anxiety and Depression Scale;
HAD-Depression, Depression subscale of Hospital Anxiety
and Depression Scale; Life control, MPI subscale concerning
perceived life control; MPI, Multidimensional Pain Inven-
tory; Pain interference, MPI subscale concerning pain-related
interference in everyday life; NRS-7d, average pain intensity
the last week according to a numeric rating scale; University,
university education; VIP, Variable Importance in Projection.
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