Background: The proposed study stems from a CIHR-funded project, by which we developed and validated sex-specific prediction algorithms for major depressive episode (MDE). MDE is one of the most prevalent and disabling form of mental illness in the general population. Despite increased mental health services and antidepressants use in the past 10 years, there has been no measurable change in the prevalence of MDE in the Canadian general population, which motivates the search for additional strategies for reducing the burden of MDE. One strategy that has been successful in the fields of oncology, cardiology and diabetes is early identification and prevention – identifying people who are at high risk and taking preventive actions to lower the risk so as to prevent symptoms from progressing into an MDE. As multivariable risk prediction algorithms are used to estimate an individual’s risk (probability) of future disease, they can play an important role in the process of early identification.
The goal of the study is to develop an evidence base to guide depression risk disclosure. The Objectives are to conduct a randomized controlled trial (RCT) to answer the questions described above. We propose to recruit 350 high-risk men and 350 high-risk women across the country and randomized them into (1) the control group, and (2) the group receiving personalized depression risk information. The participants will be assessed at baseline, 6 and 12 months regarding accuracy of risk perception, use of self-help strategies and changes in psychological distress and functioning. Qualitative interviews will be conducted in sub-samples of the intervention group about how the personalized information affects risk perception, self-help behaviors and mental health.
Funding source: Canadian Institutes of Health Research