JMIR Publications

ABSTRACT

Background:

Depression is a common comorbid condition in individuals with chronic back pain (CBP), leading to poorer treatment outcomes and increased medical complications. Digital interventions have demonstrated efficacy in the prevention and treatment of depression, however high dropout rates are a major challenge, especially in clinical settings.

Objective:

The current study aimed to identify the predictors of dropout in a digital intervention for the treatment and prevention of depression in patients with comorbid CBP. We assessed which participant characteristics may be associated with a higher risk of dropout and whether intervention usage data could help improve the identification of individuals at-risk of dropout early on in treatment.

Methods:

Data were from two large-scale RCTs in which N=253 patients with a diagnosis of CBP and Major Depressive Disorder or subclinical depressive symptoms received a digital intervention for depression. In the first analysis, participant baseline characteristics were examined as potential predictors of dropout. In the second analysis, we assessed to what extent dropout could be predicted from a combination of participant baseline characteristics and intervention usage variables following completion of the first module. Dropout was defined as completing less than six modules. Analyses were conducted using logistic regression.

Results:

From participant baseline characteristics, lower level of education (OR=3.33) and both lower and higher age (a quadratic effect; age: OR=0.62, age^2: OR=1.55) were significantly associated with higher risk of dropout. In the analysis that aimed to predict dropout following completion of the first module, lower and higher age (age: OR=0.61, age^2: OR=1.58), medium versus high social support (OR=3.40) and a higher number of days to module completion (OR=1.05) predicted higher risk of dropout, whilst a self-reported negative event in the previous week was associated with lower risk of dropout (OR=0.22). We found no significant influence of pain disability or depression severity on dropout.

Conclusions:

Dropout can be predicted by participant baseline variables and the inclusion of intervention usage variables may improve the prediction of dropout early on in treatment. Being able to identify which individuals are at high risk of dropout from digital health interventions could provide intervention developers and supporting clinicians with the ability to intervene early and prevent dropout from occurring.