The Impact of Disclosing Personalized Depression Risk Information on High-risk Individuals' Outcomes

Summary

Major depressive episode (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 a 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 proposed study stems from a project funded by an operating grant from the Canadian Institutes of Health Research (CIHR). With the CIHR support, the team developed and validated sex-specific prediction algorithms for MDE. As risk prediction is at very early stage in psychiatry and MDE is still highly stigmatized, to facilitate the future implementation of the developed risk prediction algorithms, the proposed study seeks to answer the following research questions: (1) Does disclosure of personalized depression risk information promote high-risk individuals to take preventive actions? (2) Will disclosure of personalized depression risk information negatively affect high-risk people's mental health status in terms of increased psychological distress? To answer the questions, the investigators planned to conduct a randomized controlled trial (RCT) with an embedded qualitative component. The proposed study will develop an evidence base for guiding the disclosure of personalized risk information and understanding the process of risk communication and consumer empowerment, contributing to the advancement of early prevention of MDE in Canada.

Conditions

• Depression

Interventions

OTHER

personalized depression risk information

The personalized depression risk information is determined by sex-specific risk prediction algorithms based on one's family history of depression, current health status, childhood traumatic experience and ongoing life stressors. The information is in the form of probability of having a major depressive episode in the next 4 years.

Sponsors & Collaborators

• Canadian Institutes of Health Research (CIHR)

  collaborator OTHER_GOV

• University of Calgary

  lead OTHER

Principal Investigators

• JianLi Wang, PhD · University of Calgary

Study Design

Allocation

RANDOMIZED

Purpose

PREVENTION

Masking

TRIPLE

Model

PARALLEL

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2017-05-01

Primary Completion

2020-05-01

Completion

2020-12-31

Countries

• Canada

Study Locations