Shortened Depression Assessment Study

University of Toronto (Other)
Overall Status
Completed ID
Actual Duration (Months)
Patients Per Site Per Month

Study Details

Study Description

Brief Summary

Participants will be asked to fill out an online questionnaire about their demographics information and all 42 items from the Depression Anxiety Stress Scale (DASS-42). A series of machine learning techniques will be applied to the dataset to develop a shortened assessment using the most important demographics and DASS-42 items from the original questionnaire, to predict depression levels indicated by DASS-42.

Condition or DiseaseIntervention/TreatmentPhase

    Detailed Description

    Clinical depression affects 5-10% of the world population each year and is a serious mental health issue globally. There are many traditional psychological scales that assess levels of depression in adults, where their items are often redundant in the information they carry, and their scoring is not necessarily linear to the item scores. Thus, machine learning techniques can help find the redundancy in the items, as well as the nonlinear relationship between the item scores and the final prediction. Using the Depression Anxiety Stress Scale 42 (DASS-42) as the basis, participants will be asked to fill out an online questionnaire about their demographics information (age, gender, country of residence, race, etc.) and all 42 items of DASS-42 to provide a dataset for this study. Feature selection techniques such as MRMR and Gini feature importance were applied to identify the most important features in the dataset. Then, using machine learning methods such as Logistic Regression, XGBoost, and Ensemble models, models will be fitted on the most important features to develop a shortened depression scale (7-9 items consisting of demographics items and DASS items) that accurately predicted the levels of depression (as measured by the AUC, ROC and F1 scores.

    Study Design

    Study Type:
    Actual Enrollment :
    39000 participants
    Observational Model:
    Time Perspective:
    Official Title:
    Using the Long to Short Approach to Develop Rapid Depressions Scales
    Actual Study Start Date :
    Sep 1, 2019
    Actual Primary Completion Date :
    Sep 1, 2021
    Actual Study Completion Date :
    Sep 1, 2021

    Outcome Measures

    Primary Outcome Measures

    1. Prediction of severity of depression symptoms [Less than 5 minutes]

      Prediction of severity level of depression based on survey items and demographics

    Eligibility Criteria


    Ages Eligible for Study:
    18 Years to 100 Years
    Sexes Eligible for Study:
    Accepts Healthy Volunteers:
    Inclusion Criteria:
    • Adults aged 18 and above

    • Must be able to read English

    • Must have access to the Internet worldwide

    Exclusion Criteria:
    • Children aged 17 and under

    • Persons who cannot read English

    • Persons that do not have access to Internet

    Contacts and Locations


    SiteCityStateCountryPostal Code
    1University of TorontoTorontoOntarioCanada

    Sponsors and Collaborators

    • University of Toronto


    • Study Chair: Kang Lee, University of Toronto

    Study Documents (Full-Text)

    None provided.

    More Information


    None provided.
    Responsible Party:
    Kang Lee, Professor, University of Toronto Identifier:
    Other Study ID Numbers:
    • 0032755
    First Posted:
    Nov 17, 2021
    Last Update Posted:
    Nov 17, 2021
    Last Verified:
    Nov 1, 2021
    Individual Participant Data (IPD) Sharing Statement:
    Plan to Share IPD:
    Studies a U.S. FDA-regulated Drug Product:
    Studies a U.S. FDA-regulated Device Product:
    Keywords provided by Kang Lee, Professor, University of Toronto
    Additional relevant MeSH terms:

    Study Results

    No Results Posted as of Nov 17, 2021