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Remote Assessment of Depression Using Digital Biomarkers From Cognitive Tasks
Regan L Mandryk1, Max V Birk2, Sarah Vedress1
1Interaction Lab, Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada.
Frontiers in Psychology
|January 3, 2022
Summary
A new digital tool uses cognitive tasks to detect sub-clinical depression. Behavioral metrics from attention and memory tests accurately predict depression scores, offering a scalable assessment method.
Area of Science:
- Digital health
- Psychiatry
- Cognitive neuroscience
Background:
- Depression assessment often relies on subjective self-report measures.
- Objective, scalable tools for detecting sub-clinical depression are needed.
- Cognitive impairments are recognized indicators in depression.
Purpose of the Study:
- To design and evaluate a digital assessment tool for sub-clinical depression.
- To integrate digital biomarkers from cognitive tasks into a user-friendly platform.
- To assess the tool's predictive validity for depression severity in home environments.
Main Methods:
- Development of a digital assessment tool based on the D2 Test of Attention, Delayed Matching to Sample Task, and Spatial Working Memory Task.
- Two online studies (n=269 and n=90) were conducted with participants using the tool in uncontrolled home settings.
- Validation against the Patient Health Questionnaire (PHQ-9) and demographic factors.
Main Results:
- Individual cognitive tasks significantly predicted PHQ-9 scores in the first study.
- Combined behavioral metrics from all three tasks replicated these findings in the second study.
- A multiple regression model explained 34.4% of PHQ-9 variance, with cognitive metrics offering unique predictive contributions.
Conclusions:
- The digital assessment tool effectively captures behavioral metrics indicative of depression.
- This tool shows promise for remote, objective, and scalable screening of sub-clinical depression.
- Integrating cognitive task data offers a novel approach to depression assessment.
