Scaling Mental Healthcare in COVID-19 With Voice Biomarkers
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|ClinicalTrials.gov Identifier: NCT04510519|
Recruitment Status : Not yet recruiting
First Posted : August 12, 2020
Last Update Posted : August 13, 2020
|Condition or disease|
|Major Depressive Disorder Generalized Anxiety|
The anticipated impact of this study is defining voice biomarker features and reward functions for a deep reinforcement learning based system from group interactions that improve depression and anxiety outcomes. The investigators' ability to quantify the real-time impact of human-intervention in scaled group video sessions can be very meaningful for creating best practices in the area where measurement is infrequent.
The investigators' priority is to scale the optimal mix of individuals for group therapy sessions based on reward functions that maximize improvements in depression and anxiety scores. Current group therapy appointments may track little save few who use various group feedback questionnaires (e.g. OQ, GCQ, or GQ). Voice biomarkers can play a key role in the real-time measurement of mental health.
The proposed work is to conduct a feasibility study on creating reward functions that most effectively enable engagement for group sessions as measured by voice biomarkers before, during, and after group video meetings.
|Study Type :||Observational|
|Estimated Enrollment :||50 participants|
|Official Title:||Scaling Mental Healthcare in COVID-19 With Voice Biomarkers: An Observational Study|
|Estimated Study Start Date :||September 1, 2020|
|Estimated Primary Completion Date :||June 30, 2021|
|Estimated Study Completion Date :||June 30, 2021|
- Patient Health Questionnaire (PHQ-9) [ Time Frame: March 30, 2021 ]A repeated measures, within-subject design using exploratory techniques will identify the best group session features or combination of features that correlate with or predict change on the PHQ-9.
- Generalized Anxiety Disorder (GAD-7) [ Time Frame: June 20, 2021 ]A repeated measures, within-subject design using exploratory techniques will identify the best group session features or combination of features that correlate with or predict change on the GAD-7.