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Background Computerized cognitive behavioural therapy (cCBT) is proven to be an effective self-help tool for preventing depression and suicide. Patient adherence is one of the components that the effectiveness of cCBT self-help tools are dependent on and that needs to be researched for understanding and maximizing the effectiveness of computer-based interventions for depression and suicide.
Aims The aims of this study were to compare the adherence of iFightDepression (iFD) online tool user patients with and without an extra weekly phone call consultation and to test predictors of adherence.
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Depressive disorders are recognized as one of the most widespread lifetime disorders and unipolar depression is identified as a leading cause of disease burden . These findings legitimate the importance of reducing depressive symptoms by implementing cost-effective interventions. Despite the fact that cognitive behaviour therapy (CBT) is proved to be an efficient treatment for depression, mental health care resources are scarce and cannot fulfil the huge amount of need for face-to-face psychotherapeutic treatments. The aim of increasingly deploying computerized cognitive behavioural therapy (cCBT) is to improve access to psychological treatments, reduce waiting lists, speed up clinical recovery and to bridge the paucity of specialists in small towns. Moreover, guided web-based self-help interventions are proved to be cost-effective and time-saving for mental health care systems as well. Numerous studies have proved the cCBT to be effective in case of depression, however drop-out rates of online self-help interventions are usually high - between 5 and 38.7%. Systematic reviews showed that in web-based self-help treatments extra support obtains higher adherence than no guidance. Treatment-adherence, defined as the amount of a therapeutic intervention that an individual engages with or completes, has clear clinical implications: poor adherence limits the exposure to the full program and increases the required 'dosage' of treatment.
In the comparison of drop-out rates of different psychological treatments, no difference has been found between face-to-face and web-based interventions. Previous research has found that the drop-out risk from cCBT is significantly correlated with gender, educational level and age. Increased drop-out risk included male gender, younger age and lower educational level. This finding has been proved in another research as well, where females have been found to provide more effort to cope with depression compared to males.
With regard to adherence, a low educational level might negatively influence the adherence to cCBT as understanding the content of intervention can be troublesome. Results from previous studies suggested that younger individuals had higher adherence to web-based treatment, whilst some other studies showed that younger age was related to low treatment adherence. Several findings also suggested that additional forms of guidance or support via phone or email increase the level of adherence. In order to offer web-based treatment to patients with the most benefits, it would be useful to identify the predictors of treatment adherence and causes of the relatively high dropout rate.
Insomnia is one of the main symptoms of depression. Studies show insomnia to be one of the risk factors such for development of depression as for reducing response to depression treatment and increasing relapse of depression. Therefore, to quest a correlation between adherence in an online self-help program for depression and sleep disturbances would be subservient.
Our goal in this study was to identify predictors of adherence to a computerized CBT program and to investigate whether sociodemographic variables, hopelessness and sleep disturbances are connected to adherence. Another objective was to compare adherence in web-based self-help intervention with and without weekly follow-up phone-calls as extra support. Our hypothesis was that phone-support would increase adherence level of cCBT.
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73 participants in 3 patient groups
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Data sourced from clinicaltrials.gov
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