Speech-Based Depression and Anxiety Screening for University Students (BUAP, Mexico)
Summary
The NIH has registered an observational clinical trial (NCT07553130) evaluating acceXible, a speech-based machine learning platform, for detecting depression and anxiety in university students at Benemerita Universidad Autonoma de Puebla in Mexico. The study uses spontaneous speech analysis through open-ended interview tasks against PHQ and GAD-7 reference scales, with secondary objectives examining speech-derived variables, participant engagement with digital mental health resources, and longitudinal score changes.
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What changed
The NIH has registered an observational study (NCT07553130) titled 'AcceXible Speech-Based Screening for Depression and Anxiety in University Students' at Benemerita Universidad Autonoma de Puebla in Mexico. The study will evaluate a speech-based machine learning platform that captures spontaneous speech through open-ended interview tasks and applies automated acoustic and linguistic analysis to screen for Major Depressive Disorder and anxiety.
For compliance and clinical operations teams, this registry entry signals emerging interest in digital biomarker technologies using speech analysis as an alternative or supplement to traditional psychometric instruments (PHQ-9, GAD-7). Organizations developing or deploying similar AI-driven mental health screening tools may benefit from monitoring this validation study's outcomes against established reference standards.
Archived snapshot
Apr 28, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
AcceXible Speech-Based Screening for Depression and Anxiety in University Students (BUAP, Mexico)
Observational NCT07553130 Kind: OBSERVATIONAL Apr 27, 2026
Abstract
Major depressive disorder (MDD) and anxiety are increasingly prevalent among university student populations, yet early detection remains reliant on psychometric instruments tied to diagnostic criteria (e.g., PHQ-9, GAD). Emerging evidence suggests that depression affects both the acoustic properties and content of speech, making speech analysis a promising candidate as a digital biomarker for early screening.
This study evaluates the validity of acceXible, a speech-based machine learning platform, for the detection and monitoring of depression and anxiety in the student population of the Benemérita Universidad Autónoma de Puebla (BUAP), Mexico. AcceXible captures spontaneous speech through open-ended interview tasks and applies automated acoustic and linguistic analysis.
The primary objective is to evaluate the validity of the acceXible spontaneous speech analysis system for depression and anxiety screening, assessed against the PHQ and GAD scales as reference standards. Secondary objectives include examining associations between speech-derived variables and other study measures, evaluating participant engagement with digital mental health resources, assessing user satisfaction with the platform, and analyzing longitudinal changes in scores across follow-up assessments.
Conditions: Depression - Major Depressive Disorder, Anxiety
Interventions: AcceXible
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