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Summary

A single video question can predict depression, anxiety, and trauma using text and voice analysis. This efficient screening method significantly reduces assessment time for mental health conditions.

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Area of Science:

  • Computational psychiatry
  • Digital mental health
  • Multimodal AI

Background:

  • Increasing demand for mental health screening.
  • Need for efficient, multi-condition assessment tools.
  • Current screening methods can be time-consuming and burdensome.

Purpose of the Study:

  • To evaluate the efficacy of a single video question for predicting depression, anxiety, and trauma.
  • To assess the feasibility of using text and voice analysis for mental health screening.
  • To reduce patient burden and assessment time in clinical settings.

Main Methods:

  • Developed a multimodal model integrating MPNet for text and HuBERT for voice prosody.
  • Trained the model on data from 2420 participants.
  • Utilized a single video question to capture responses for analysis.

Main Results:

  • The model accurately predicted self-reported depression (PHQ-9), anxiety (GAD-7), and trauma (PCL-5).
  • Achieved a 64.6% reduction in assessment time (78.4s vs 221.7s).
  • Demonstrated strong performance and demographic consistency across age, gender, and race/ethnicity.

Conclusions:

  • A single video question is a feasible and efficient method for multi-condition mental health screening.
  • This approach significantly reduces assessment time while maintaining accuracy.
  • Video-based screening shows high participant acceptance (98.6% willingness).