This study aims to advance research on group sessions for mental health. The first-of-its-kind study measuring various features in a group setting, combining rich metadata in creating state-of-the-art machine learning models, and developing workflows for mental health that are both scalable and personalized.
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.
Inclusion Criteria:
- Ownership of a personal iPhone (version iOS 12.0 or later) and willingness to install
and maintain the Kintsugi app for remote participation throughout the study duration
- Able to understand and comply with instructions in English
Exclusion Criteria:
- Has any other clinically significant medical condition or circumstance that, in the
opinion of the Investigator, could affect patient safety, preclude evaluation of
response, interfere with the ability to comply with study procedures, or prohibit
completion of the study
- Has a visual or physical motor impairment that could interfere with study tasks
- Is site personnel directly affiliated with this study
Kintsugi Mindful Wellness, Inc.
Berkeley, California, United States