Journal of Rehabilitation Medicine 51-1CompleteIssue | Page 9

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J. Oosterhaven et al.
Table III. Results for predictors for dropout in multiple logistic regression analysis
Domain
Predictors retained in any multiple logistic regression model
Sociodemographic
Predictors
Pre-treatment work status a (24)
Ethnicity (32)
Patient
Number of sick days (27)
Pre-contemplation, Action (28)
Opioid dependency, Any cluster B Dx (24)
Return to work expectation, Somatization (29)
Disease
Self-efficacy, Walk distance (30)
Ability to work (27)
Variability in pain (31)
Pain behaviour, Meds too long (33)
Length of disability (24)
Treatment
Potential predictors not retained in any model
Sociodemographic
Duration of work disability (29)
Type of institution, Phase of treatment (32)
Age a (27, 30, 32, 33)
Sex (24, 27, 29, 30, 32, 33)
Social status (27, 29, 33)
Job demand, Vibrations in job (27)
Original job available, Pre-treatment case settlement (24)
Patient
Pre-treatment work status a (33)
Depression (24, 28)
Anxiety disorder, Any cluster A Dx, Any cluster C Dx, Any cluster D Dx (24)
Disease
Pain distress, Catastrophizing (30)
Age first low back pain, smoking, ADL scores, Sport activities, Aerobic capacity, mobility,
isometric abdominal endurance, isometric back endurance (27)
Compensable body parts, Area of injury, Pretreatment surgery (24)
Pain site, chronicity (30)
Pain intensity a (27, 30, 31)
Severity of disability a (29)
Predictors retained in 2 multiple logistic regression models
Sociodemographic
Disease Pain intensity a (28, 29)
Predictors retained in 3 multiple logistic regression models
Disease Severity of disability a (24, 28, 33)
Age a (28, 29)
Number of multiple logistic regression models tested in
independent samples 0
Outcome variance explained 34% (Return to work expectation, Somatization, Age, Duration of work disability, mean
pain intensity) (29)
a
Conflicting results. ADL: activities of daily living. Any cluster A Dx: paranoid; schizoid; schizotypal; Any cluster B Dx; antisocial; borderline; histrionic; narcissistic;
Any cluster C Dx: avoidant; dependent; obsessive-compulsive; Any Cluster D Dx: otherwise.
with the reporting of the results of the multiple logistic
regression analyses (Table S2 1 predictors organized per
study and Table S3 1 : predictors grouped in domains).
Results for predictors for dropout in univariate
analyses
In total, 63 potential predictors were studied for an
association with dropout in univariate analyses in the
4 domains of the Meichenbaum & Turk: (i) sociode-
mographic domain (19); (ii) patient domain (21); (iii)
disease domain (21) and (iv) treatment domain (2)
(Table II). Most potential predictors were examined in
a single study. Only 10 out of 63 potential predictors
were investigated in more than one study.
Sociodemographic domain
Conflicting results were found for sex (24, 27, 30, 32,
33), ethnicity (24, 32), pre-treatment work-status (24,
33) and job demand (24, 27). Seven of the 8 studies
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included in this review investigated age as a potential
predictor for dropout in univariate analyses. Six out of
7 studies showed significant associations for younger
age as a predictor for dropout (27–30, 32, 33).
Patient domain
Conflicting results were found for depression as a
potential predictor for dropout. The results of the uni-
variate analyses revealed 2 studies with a significant
association of depression with dropout. One study
indicated that low depression scores were associated
with dropout (28) and another study showed that hig-
her scores on depression scales were associated with
dropout (24). Two studies found a non-significant as-
sociation with dropout (24, 33).
Disease domain
For 2 potential predictors in the disease domain con-
flicting results in the direction of the association with