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Background: Machine learning-based facial and auditory measurements have demonstrated relationships with schizophrenia diagnosis and severity. Here, we determine the robustness of these measurements through automated assessments conducted remotely through smartphones. Validity of remote and automated assessment conducted outside of controlled experimental settings can facilitate scaling such measurement tools to aid in risk assessment and tracking of treatment response in difficult to engage populations. Methods: Measurements of facial and vocal characteristics including facial expressivity, voice frequency, and speech prevalence were assessed in response to two classes of prompts previously utilized in experimental laboratory assessments including evoked prompts where subjects are guided to produce specific facial expressions and phonations as well as spontaneous prompts where subjects are presented stimuli in the form of emotionally evocative imagery and asked to freely respond. Vocal and facial measurements were assessed in relation to schizophrenia symptom severity and functional impairment as it relates to work status. Results: Vocal markers including speech prevalence, vocal jitter, fundamental frequency, and vocal intensity demonstrated specificity as markers of negative symptom severity while measurement of facial expressivity demonstrated itself as a robust marker of overall schizophrenia severity. Vocal markers also differentiated work status. Conclusion: Established facial and vocal measurements, collected remotely in response to automated task prompts, demonstrated validity as markers of schizophrenia severity. Vocal measurements demonstrated specificity as markers of negative symptoms and functional impairment. Clinical implications are discussed.
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10.1101/2020.12.02.20219741
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document_parses/pdf_json/067824506126568a46434f9ab743720a6e517538.json
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Facial and vocal markers of schizophrenia measured using remote smartphone assessments
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