Using digital phenotyping measures to predict the symptoms and functional outcomes in first episode of psychosis

Psychotic disorders including schizophrenia are severe mental disorders affecting 2-3% of the population and
rank among the leading causes of disability worldwide. Although early intervention is effective in improving illness
outcome, a significant proportion of first-episode psychosis (FEP) patients experience persistent functional
impairment even after clinical remission. Accurate prediction of FEP patient trajectories will allow clinicians to
select better interventions at the beginning, leading to better patient outcomes and quality of life. However,
predicting FEP patient outcomes is challenging because assessments of the clinical and behavioral factors are
often based on patient self-report, which is vulnerable to recall and reporting biases. The biases can reduce the
accuracy of outcome prediction. Digital phenotyping, which refers to the use of mobile devices (e.g., smartphone,
wearable) to initiate data collection in everyday life, has great potential to address these issues. The goal of this
project is to develop and implement prediction models for symptom and functional outcomes in FEP, using both
self-reported and digital phenotyping data. If successful, the prediction models will greatly enhance clinicians’
capability of outcome prediction and decision making, leading to better patient outcome and quality of life.

Faculty Supervisor:

JianLi Wang

Student:

Partner:

Mental Health Research Canada;Nova Scotia Health

Discipline:

Life Sciences

Sector:

Health and Related Sciences & Technology; Professional, scientific and technical services; Public administration

University:

Dalhousie University

Program: