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 Universidad Autónoma de Querétaro, 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.
Study Type
OBSERVATIONAL
Enrollment
80
Speech analysis
Universidad Autónoma de Querétaro
Querétaro City, Querétaro, Mexico
Diagnostic accuracy of acceXible for depression and anxiety screening
Sensitivity, specificity, and positive predictive value of the acceXible speech analysis system relative to validated Spanish-language versions of the PHQ-9 and GAD-7 as reference standards. Sensitivity is defined as the probability of correctly classifying a participant with abnormal scores; specificity as the probability of correctly classifying a non-affected participant; and positive predictive value as the probability that a participant flagged by acceXible truly meets criteria for depression or anxiety.
Time frame: Baseline
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