Digital biomarkers of mood states from speech in bipolar disorder

Regular monitoring is essential to effectively track mood fluctuations and assess ongoing treatment needs for mood disorders (e.g., identifying early signs of relapse, adjusting therapeutic interventions, and improving long-term outcomes). The current ongoing work aims at assessing the relationships between language and symptom severity in people with bipolar disorders, thus investigating potential mHealth mood detection mechanisms based on speech patterns. Acoustic features included conversational measures for nonverbal language and statistics for prosodic cues. Preliminary results, combining acoustic features and natural language processing (NLP) scores, were promising, somehow discriminating clinical conditions of people with BD when assessing their mood states. This approach may offer potential benefits for individualized mental health care and early intervention approaches in real-world scenarios.