Journal of Rehabilitation Medicine 51-10 | Page 43

Pain catastrophizing and dropout in chronic pain management
Baseline assessment measures
Sociodemographic data. To describe the research sample in this
study the following sociodemographic factors were collected at
baseline: age, sex (male, female), ethnicity (Dutch, non-Dutch),
marital status (single, living together), educational level: low
(level 0–2 early: primary education, lower secondary education);
intermediate (level 3–5: upper secondary, post-secondary, short
cycle tertiary), and high (level 6–8: bachelor, master, doctoral)));
work status (employed, unemployed, retired, school/study);
and ability to work (perceived ability to work 0–10 NRS) (16).
Pain intensity and pain duration. Other important pain-related
factors that were used to describe the research sample were pain
intensity (using a 0–10 numeric rating scale (NRS)) and pain
duration (pain duration 0–2 years, between 2–5 years, more than
5 years). These were derived from the intake questionnaire (16).
Potential predictors for dropout derived from E-CSM of Self-
Regulation. To develop a model to predict dropout in IPMPs
18 potential predictors were derived from 4 categories of the
E-CSM of Self-Regulation (8, 18): 1: Illness representations;
2: Treatment beliefs; 3: Emotional representations: pain self-
efficacy, pain catastrophizing, anxiety and depression; and 4:
Coping procedures for illness and emotion: pain catastrophizing
(see Fig. 1), as described below. All 18 potential predictors were
collected during baseline assessment.
Illness representations (category 1). The Brief Illness Percep-
tion Questionnaire Dutch language version measured illness
representations and emotional representations (Brief IPQ-DLV)
(19). The Brief IPQ covers 5 domains of illness representations,
as described in the E-CSM of Self-Regulation by Leventhal
(7, 18): consequences, timeline, control, identity, cause. The
Brief IPQ-DLV has 8 items rated using a 0–10 NRS for each
item. The Brief IPQ is a reliable instrument to measure illness
representations (19).
Treatment beliefs (category 2). Treatment beliefs were measu-
red with the Dutch translation and adaptation of the Treatment
Beliefs Questionnaire (TBQ), as designed by Cooper et al., (20,
21). The translated TBQ consists of 11 items on a 5-point Likert
scale (“totally disagree”, “disagree”, “neutral”, “agree” and
“totally agree”). Confirmatory factor analysis determined that
there were 3 domains: “necessity”, “concerns” and “practical
barriers”. Psychometric properties were investigated and consi-
dered to be fair to good. Internal consistency was fair to good,
with alpha values ranging from 0.66 to 0.87. Reproducibility
was high, with a small measurement error for both the “neces-
sity” and “concerns” subscales. Reliability for the “practical
barriers” subscale was fair.
Emotional representations (category 3):
Pain self-efficacy (category 3). Pain self- efficacy (emotional
representations) was measured with the Dutch Pain Self Efficacy
Questionnaire (PSEQ) (16, 22, 23). The PSEQ asks patients to
take the pain into account when rating their self-efficacy in cer-
tain activities and tasks grouped within 10 items. Each item was
scored on a 7-point scale, ranging from 0 (“not at all confident”)
to 6 (“completely confident”). Total scores ranged from 0 to 60,
with higher scores indicating stronger self-efficacy beliefs. This
instrument has been shown to be valid and reliable (22, 23).
Anxiety and depression (category 3). Anxiety and depression
(emotional representations) were measured with the Hospital
Anxiety and Depression scale (HADS) (24, 25). The HADS is
763
a valid and reliable 14-item short self-rating screening tool of
anxiety (7 items) and depression (7 items), scored on a 4-point
Likert scale (0–3) (24, 25). Higher scores on the 2 domains
indicate greater levels of depression or anxiety.
Pain catastrophizing (categories 3, 4). Pain catastrophizing
(emotional representations and coping procedures for illness
and emotion) was assessed with the Dutch version of the Pain
Catastrophizing Scale (PCS) (26, 27). Pain catastrophizing can
be defined as: “an exaggerated negative mental set brought to
bear during actual or anticipated painful experience”. One’s
imagination plays a role in anticipating negative outcomes,
which results in a cascade of negative cognitive and emotional
responses to pain: rumination (“I can’t stop thinking about how
much it hurts”), magnification (“I worry that something serious
may happen”), and helplessness (“It’s awful and I feel that it
overwhelms me”) (26, 27). Patients were asked to reflect on past
painful experiences and to indicate whether they experienced
one of the 13 thoughts or feelings during pain on a 5-point scale.
A PCS total score is calculated by summing the scores for all
13 items; thus, total scores range from 0 to 52. Higher scores
correspond to more catastrophizing thoughts. The PCS has been
shown to be valid and reliable (26, 27, 39).
Statistical analysis
All analyses were performed with the statistical software
package SPSS (version 23) for Windows (IBM SPSS Statistics
23). First, descriptive statistics (frequencies, QQ plots, means
and standard deviation (SD)) were computed for all potential
predictors to check the quality of the data. To check for multi-
collinearity, Spearman correlations were calculated between the
potential predictors of dropout, and a cut-off score of 0.70 was
used (28) (Table SI 1 ). Baseline differences were tested between
the dropout group (DG) and the completer group (CG) for de-
mographic and clinical variables derived from the E-CSM of
Self-Regulation, with Pearson’s χ 2 tests for categorical variables
and independent Welch t-tests for continuous variables.
Statistical analyses were carried out in 2 phases. First, explo-
ratory univariate logistic regression analyses were performed
on 18 potential predictors (Brief IPQ – 8 items), (TBQ – 3 do-
mains), (PSEQ – total score), (PCS total score and 3 domains)
and (HADS –2 domains) to identify predictors of dropout, which
could be considered for inclusion in multiple logistic regression
analyses. The most significant variables with p-values < 0.20 were
considered for inclusion in the multiple logistic regression model.
To determine the model that best predicted dropout, a forward
stepwise procedure was performed to select the variables that
were most significant and had clinical relevance. The number
of dropouts (n = 35) found in this study limited the possibilities
of including more than 4 variables, as the minimum number of
events per variable (EPV) required in multiple logistic regres-
sion analyses has been suggested to be at least 10 (29). First the
potential predictor with the strongest association was considered
for inclusion in multiple logistic regression analyses, followed
by the next best one, etc. Estimates of association were presented
as odds ratios (ORs), along with 95% confidence intervals (95%
CI). The Hosmer-Lemeshow test was performed to assess how
well the model fitted the data (28). To describe the discrimination
of the model, the area under the receiver operating characteristic
(ROC) curve (AUC) was calculated.
http://www.medicaljournals.se/jrm/content/?doi=10.2340/16501977-2609
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J Rehabil Med 51, 2019