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Comparison of Vocal Biomarkers for Depression and Anxiety to Formal Clinical Assessments

NCT06464575 · Status: ENROLLING_BY_INVITATION · Type: OBSERVATIONAL · Enrollment: 540

Last updated 2024-06-18

No results posted yet for this study

Summary

Participants will be recruited to complete self reported surveys normally used as standards of care for screening and monitoring depression and anxiety symptom severity, provide a voice sample composed of an answer to open ended questions and then be assessed by a mental health professional using structured and clinically validated assessment tools for depression and anxiety. Their voice will be analyzed by machine learning models that predict the severity of depression and anxiety symptoms. The models' performance will be compared to the clinician assessments and how that correlation compares to a similar comparison between the clinician assessments with the self reported surveys. It is hypothesized that the performance of the machine learning models in assessing the severity of depression and anxiety symptoms is no worse than the self reported surveys when both are compared to clinician assessments. It is also hypothesized that presence or absence of the diagnoses of Major Depressive Disorder and Generalized Anxiety Disorder can be predicted better than chance by the analysis of the participant's voice sample using machine learning models.

Conditions

  • Depression/Anxiety

Sponsors & Collaborators

  • Ellipsis Health

    lead INDUSTRY

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2024-01-12
Primary Completion
2024-06-30
Completion
2024-06-30

Countries

  • United States

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06464575 on ClinicalTrials.gov