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Predictors of adherence among community users of a cognitive behavior therapy website

Authors Philip J Batterham, Alison L Neil, Kylie Bennett, Kathleen M Griffiths, Helen Christensen

Published Date March 2008 Volume 2008:2 Pages 97—105

DOI

Published 19 March 2008

Philip J Batterham, Alison L Neil, Kylie Bennett, Kathleen M Griffiths, Helen Christensen

Centre for Mental Health Research, The Australian National University, Canberra, ACT, Australia

Objective: To investigate the predictors of early and late dropout among community users of the MoodGYM website, a five module online intervention for reducing the symptoms of depression.

Method: Approximately 82,000 users accessed the site in 2006, of which 27% completed one module and 10% completed two or more modules. Adherence was modeled as a trichotomous variable representing non-starters (0 modules), early dropouts (1 module) and late dropouts (2–5 modules). Predictor variables included age, gender, education, location, referral source, depression severity, anxiety severity, dysfunctional thinking, and change in symptom count.

Results: Better adherence was predicted by higher depression severity, higher anxiety severity, a greater level of dysfunctional thinking, younger age, higher education, being female, and being referred to the site by a mental health professional. In addition, users whose depression severity had improved or remained stable after the first intervention module had higher odds of completing subsequent modules.

Conclusions: While the effect of age and the null effect of location were in accordance with prior adherence research, the significant effects of gender, education and depression severity were not, and may reflect user characteristics, the content of the intervention and unique aspects of online interventions. Further research directions are suggested to investigate the elements of open access online interventions that facilitate adherence.

Keywords: adherence, dropout, cognitive behavior therapy, depression, online interventions

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