An evaluation of a public online intervention for depression
Creators: name: Bennett, Kylie
Publisher: name: The Australian National University
Year: 2019

description: Depression is a leading cause of disability worldwide, but only a minority of individuals receive evidence-based treatment for the condition. Moreover, there are barriers to accessing effective therapies such as cognitive behaviour therapy (CBT). Internet-delivered CBT (iCBT) is a scalable, accessible and potentially effective alternative to face-to-face CBT. This thesis is a detailed study of the unguided use among spontaneous and other community users of 'MoodGYM', a well-known, open-access, automated iCBT program for depression. The thesis comprises two systematic reviews of the effectiveness of iCBT and four empirical studies of MoodGYM, including two observational studies, a partially randomised controlled trial (PRPT) and a randomised controlled trial (RCT). The first review - a synthesis of previous systematic reviews - concluded that iCBT for depression is broadly effective for the treatment of depression. The second review focused specifically on automated interventions which were entirely self-directed and involved no direct human input in their delivery. It found some evidence for the effectiveness of automated iCBT, particularly for adults in community settings. However, findings from controlled efficacy trials may not translate into equivalent user outcomes in real-world settings, and there is a lack of systematically documented information about the user characteristics and outcomes of globally accessible online programs for mental health. Accordingly, the first observational study (N = 417,354) investigated the characteristics of spontaneous, community users of automated MoodGYM and found they were mostly female, highly educated and had high initial depression scores. Nearly half were referred by a health professional, with male users and those from the U.K. or rural areas more likely to be referred. The second observational study (N = 417,354) examined adherence and outcomes and their predictors among spontaneous users. Overall there was a small but significant decrease in depression symptoms among users (Cohen's d = 0.28). Completion of modules was best predicted by initial motivation, and improved depression by higher adherence and baseline depression and lower anxiety. The third empirical study (PRPT; N = 23,988) investigated differences in outcomes for participants who chose a program variant compared to those who agreed to randomisation, and which modules were associated with the best outcomes. Older, more educated or more anxious users were more likely to choose their program and choosing was a significant predictor of both adherence and depressive symptom improvement, although the latter was not significant after controlling for the program variant received. The introductory and cognitive restructuring modules (Modules 1 and 2) were the most critical program components. Finally, an RCT (N = 3,070) of unguided MoodGYM users examined the potential predictors and moderators of treatment effect. Although several variables predicted depression outcomes across all participants, the only outcome moderator (associated with the intervention but not the control) was self-reported ability to wash or dress oneself. Open-access MoodGYM has been accessed by a large number of public users, with small but significant reductions in depression symptoms, suggesting that automated iCBT has the potential for a significant public health impact. However, increasing user engagement may improve depression outcomes. The current studies suggest that motivating and educating users prior to intervention use and providing a choice of program components may increase adherence. There may also be value in emphasising the first two MoodGYM modules. This thesis provides some insight into for whom MoodGYM is most effective; however, further research is required, particularly into moderators of treatment effect and the attitudes and expectations of sub-groups of users to maximise the public health impact of automated iCBT.