The goal of this observational study is to learn if computer analysis of voice recordings can detect Type 2 diabetes in adults. The main questions it aims to answer are: * Can advanced voice analysis accurately identify participants with Type 2 diabetes or pre-diabetes based on vocal biomarkers? * How do voice-based predictions compare to HbA1c blood test results for diabetes screening? * Can machine learning approaches effectively address the challenge of undiagnosed diabetes in population screening? Participants will: * Record themselves reading a short passage and answering brief questions out loud in a single online session. * Complete health questionnaires about diabetes risk factors, medications, and general health status. * A subset of participants (n=1,000) will provide a blood sample through an at-home HbA1c testing kit to validate voice-based predictions against laboratory results. * Use their own devices (computer, tablet, or smartphone) to complete all study activities online from home.
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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Accuracy of AI Model for Type 2 Diabetes Classification as Assessed by Voice Biomarker Analysis
Timeframe: Single assessment session at enrolment with HbA1c validation results obtained within 2 months of submission of voice measurement.