ArticlePLOS digital health2026
Voice changes associated with stress and distress in people living with diabetes: Results from the Colive Voice study.
Article in PLOS digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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10 authors.
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Abstract
Stress and diabetes distress impact glycemic control, self-care, and health outcomes in people living with diabetes, but current diabetes technologies lack easy-to-use, non-invasive methods to detect these states. The objective was to identify vocal features associated with stress and diabetes distress in people living with diabetes. Thus, we analyzed data from 679 adults with diabetes (381 women, 298 men), recruited via the global vocal biomarker screening platform Colive Voice. Participants recorded a standardized 30-second text. Vocal features were extracted using DisVoice across phonation, prosody, articulation, and phonological domains. Associations between vocal features and stress (self-reported, scale: 0-4) and diabetes distress (Problem Areas in Diabetes (PAID) questionnaire; categories: PAID<20, 20 ≤ PAID<40, 40 ≤ PAID<60, PAID≥60) were analyzed separately for men and women. Multivariate ordinal logistic regression models were adjusted for age, language, diabetes type, and HbA1c, with False Discovery Rate correction.In women, results suggest that stress was associated with 25 voice features across all domains, while distress was linked to one articulatory feature, suggesting broader effects of stress. In men, both stress (25 features) and distress (18 features) affected multiple domains, but with distinct acoustic patterns. No voice features were shared between stress and distress. These patterns were consistent across age, diabetes type, and language.To conclude, voice changes reflect stress and diabetes distress in people with diabetes, each characterized by distinct acoustic features and additional sex-specific signatures. These findings highlight the potential of vocal biomarkers to differentiate and monitor these emotional states, supporting more timely and personalized interventions to improve diabetes outcomes.
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