Abstract
ABSTRACTThis study represents a practical advancement in the application of vocal biomarkers for mental health tracking in real-world settings. Through a prospective cohort study involving 104 participants from an outpatient psychiatric population, we introduced a novel “Mental Fitness Vocal Biomarker” (MFVB) score, derived from eight preselected vocal features supported by literature review. Our findings demonstrate the MFVB’s efficacy in objectively stratifying individuals based on risk for elevated mental health symptom severity using the M3 Checklist for transdiagnostic assessment (depression, anxiety, post-traumatic stress disorder, and bipolar) as reference standard. Continuous observation over time significantly improves efficacy, yielding a risk ratio of 1.53 (1.09-2.14, p=0.0138) for single 30-second voice samples to 2.00 (1.21-3.30, p=0.0068) for 2-week aggregations, depending on MFVB score. Notably, in the highly engaged subgroup (5-6 MFVB uses per week, 38% of participants), a risk ratio of 8.50 (2.31-31.25, p=0.0013) was observed, underscoring the utility of frequent and continuous observation. Participant feedback confirmed the user-friendliness of the application and perceived benefits, highlighting the MFVB’s potential as a cost-effective, scalable, and privacy-preserving adjunct to traditional psychiatric assessments. These results establish that vocal biomarkers are a promising tool for objective mental health tracking in real-world conditions, offering personalized insights into users’ mental well-being as they engage with clinical therapy or other beneficial activities that are associated with improved mental health risks and outcomes.
Publisher
Cold Spring Harbor Laboratory
Reference68 articles.
1. Sverdlov O , Curcic J , Hannesdottir K , Gou L , De Luca V , Ambrosetti F , et al. A Study of Novel Exploratory Tools, Digital Technologies, and Central Nervous System Biomarkers to Characterize Unipolar Depression. Front Psychiatry. 2021 May 6;12:640741.
2. Modern views of machine learning for precision psychiatry;Patterns,2022
3. Caldeira C , Chen Y , Chan L , Pham V , Chen Y , Zheng K. Mobile apps for mood tracking: an analysis of features and user reviews.
4. Understanding People’s Use of and Perspectives on Mood-Tracking Apps: Interview Study;JMIR Ment Health,2021
5. Smartphones in mental health: a critical review of background issues, current status and future concerns;Int J Bipolar Disord,2020