Voice Technology to Identify Opioid Use

NCT07603778 | Completed

• 2 other identifiers: 06121R43DA060696-01
• Study Type: observational
• Target: 41
• Locations: 1 country
• Sites: 1

Brief Summary

This study explored whether changes in a person's voice could help identify opioid use in patients with opioid use disorder (OUD). Current methods for determining whether a patient is intoxicated or in withdrawal often rely on self-reporting and clinical judgment, which can be subjective and inconsistent. Drug tests are logistically challenging to administer and can be costly with repeated use. The project investigated whether physiological changes associated with opioid use could be detected through speech analysis technology. Researchers evaluated whether machine learning methods could identify voice patterns associated with opioid intoxication or withdrawal. The primary goal of the study was to assess the accuracy of voice-based biomarkers in identifying opioid use. The study also explored relationships between opioid use and specific speech characteristics.

Conditions

Opioid Use

Keywords

Voice Biomarkers; Opioids

Outcome Measures

Primary Outcomes (1)

• Sensitivity and Specificity

  Sensitivity and Specificity of the machine learning model when classifying voice samples into patient's state based on their speech.

  12 months after the enrollment

Secondary Outcomes (1)

• List of specific voice features/categories affected by substances

  12 months after enrollment

Study Arms (1)

Patients in treatment for opioid use disorder at the Volpicelli Center.

Prospective longitudinal observational cohort study with repeated measures where each participant completed two visits approximately 30 days apart with repeated speech and clinical measurements. This is a prospective observational study therefore no intervention will be applied.

Other: Not applicable- observational study

Interventions

Not applicable- observational study OTHER

Not Applicable - Observational Study

Patients in treatment for opioid use disorder at the Volpicelli Center.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

The study population is adult (18+) English-speaking men and women currently receiving OUD treatment at the Volpicelli Center, capable of consenting and completing the protocol, and free of severe psychiatric comorbidity or chronic speech-affecting conditions. Because participants are drawn from an active treatment population, they are by definition individuals already engaged in care for OUD rather than treatment-naïve or general-population samples - a relevant consideration when interpreting how the voice biomarker findings might generalize.

You may qualify if:

• Male or female
• At least 18 years old
• Be an active patient in treatment at the Volpicelli Center
• Have a diagnosis of Opioid Use Disorder (OUD)
• Ability to read English
• Able to comprehend and are willing to sign the informed consent form and are able to adhere to the protocol

You may not qualify if:

• Severe psychiatric comorbidity
• Non-fluency in the study language (English)

Sponsors & Collaborators

• Tenvos Inc.: lead
• Volpicelli Center: collaborator
• National Institute on Drug Abuse (NIDA): collaborator

Study Sites (1)

Loma Linda University Health, 24951 Circle Drive, Nichol Hall, Room #2042

Loma Linda, California, 92350, United States

Location

Related Links

• GitHub Repository with software code for extracting acoustic features, Montreal Forced Alignment and Audio Transcription (a.k.a. pre-processing pipeline).

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 10, 2026
• First Posted: May 22, 2026
• Study Start: December 10, 2024
• Primary Completion: June 30, 2025
• Study Completion: January 30, 2026
• Last Updated: May 22, 2026

Record last verified: 2026-05

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)