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Shortened Depression Assessment Study

NCT ID: NCT05123794

Last Updated: 2023-12-08

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

39000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2019-09-01

Study Completion Date

2021-09-01

Brief Summary

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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.

Detailed Description

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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.

Conditions

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Depression

Keywords

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Depression Moderate Depression Severe Survey Methodology

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Eligibility Criteria

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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
Minimum Eligible Age

18 Years

Maximum Eligible Age

100 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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University of Toronto

OTHER

Sponsor Role lead

Responsible Party

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Kang Lee

Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Kang Lee

Role: STUDY_CHAIR

University of Toronto

Locations

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University of Toronto

Toronto, Ontario, Canada

Site Status

Countries

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Canada

Other Identifiers

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0032755

Identifier Type: -

Identifier Source: org_study_id