Journal of Rehabilitation Medicine 51-1CompleteIssue | Page 10

Predictors of dropout: a systematic review
dropout were found in univariate analyses: for disabi-
lity (27, 31, 33) and pain intensity (27–31) (Table II).
Although significant results were identified for severity
of disability and mean pain intensity with dropout in
univariate analyses, the direction of the association
differed. Only 3 studies showed significant results for
pain intensity in association with dropout (28–30). The
direction of the association differed in these 3 studies,
in one study lower pain intensity (28) and in 2 studies
a higher pain intensity was found to be significantly
associated with dropout (29, 30).
Results for predictors for dropout in multiple logistic
regression analyses
In total 48 of 63 potential predictors were studied for an
association with dropout in multiple logistic regression
analyses. Of these 48 potential predictors, 26 were not
retained in any multiple logistic regression analyses,
for 4 predictors conflicting results were found and for
18 predictors significant results were identified in: (i)
the sociodemographic domain (2); (ii) patient domain
(8); (iii) disease domain (6); and (iv) treatment domain
(2). Table III presents an overview of the number of
predictors retained and not retained by the multiple
logistic regression models (35). Most predictors were
found in only a single study (24). Only one predictor,
severity of disability, was found in 3 studies (24, 28,
33). Only 2 studies reported results for the perfor-
mance of the multiple logistic regression models. The
Hosmer–Lemeshow test demonstrated in both studies
p-values above > 0.5 indicating a good fit (28, 30). No
multiple logistic regression models were externally
validated using independent samples.
Sociodemographic domain
In multiple logistic regression analyses conflicting
results were found for age and pre-treatment work
status as potential predictors for dropout. Younger
age was not retained in 4 models (27, 30, 32, 33) and
was retained in 2 multiple logistic regression models
as a predictor for dropout (28, 29). Not working pre-
treatment was not retained as a predictor for dropout in
one study (33) and was retained in another study (24).
Patient domain
In the patient domain 7 potential predictors that were
investigated in univariate analyses in association with
dropout were not retained in multiple logistic regres-
sion analyses (24, 28, 30). For 8 predictors of dropout
significant results were identified in multiple logistic
regression models: pre-contemplation, action (28),
opioid dependency, any cluster B Dx (24), return to
7
work expectation, somatization (29), self-efficacy and
walk distance (30).
Disease domain
For pain intensity and disability conflicting results
were demonstrated. In one study lower pain intensity
(28) was found to be significantly associated with
dropout. In 3 other studies higher pain intensity was
identified as a potential predictor for dropout (29–31).
Only one of these 3 studies showed significant results
in association with dropout in multiple logistic reg-
ression analyses (29). Two studies demonstrated that
more severe self-reported disability was a significant
predictor for dropout (24, 33). Another study found
that lower pain disability was significantly associated
with dropout (28).
DISCUSSION
The aim of this systematic review was to identify
predictors of dropout of patients with chronic musculo­
skeletal pain during interdisciplinary pain management
programmes. Eight studies with potential predictors
for dropout were determined. In total 63 potential
predictors were identified in univariate analyses in the
4 domains of retention, as described by Meichenbaum
& Turk: (i) sociodemographic domain (19); (ii) patient
domain (21); (iii) disease domain (21); and (iv) treat-
ment domain (2). Ten potential predictors (age, sex,
social status, education, ethnicity, job demand, depres-
sion, pre-treatment work status, pain intensity, and
severity of disability) were studied in more than one
study and multiple regression analyses revealed con-
flicting results for almost all these potential predictors.
These conflicting findings are in line with findings
known from the mental health literature for the follow­
ing predictors: younger age and being diagnosed with
a depression (10–12, 27–30, 32, 33). Similar reasons
were found in the literature for chronic musculoskele-
tal pain and mental health, for why younger age may
predict dropout from treatment: practical implica-
tions, such as having a day-time job or having young
children, which may be in conflict with an intensive
interdisciplinary treat­ment programme (10, 29, 33). It
is known that patients with severe depression, anxiety
and low motivation are often excluded from studies
about mental health. This may also be the case for
studies in this review (13).
Furthermore, this systematic review revealed
conflicting findings for pain intensity and disability
in association with dropout. An intriguing finding
was that one study showed the opposite results for
the direction of the association of pain intensity and
J Rehabil Med 51, 2019