Bipolar disorder (BD) is a chronic, cyclical mental illness affecting over 1% of the global population. It is characterized by alternating episodes of elevated mood and energy (mania or hypomania) and episodes of decreased mood and energy (depression). Manic episodes involve hyperactivity, decreased need for sleep, grandiosity, accelerated speech, and sometimes psychotic symptoms such as hallucinations or delusions. Depressive episodes, in contrast, are characterized by sadness, low energy, social withdrawal, sleep and appetite disturbances, and low self-esteem. Bipolar patients are at very high risk of suicide, with rates up to 20 times higher than in the general population; nearly half will attempt suicide during their lifetime, and 15-20% of these attempts are fatal. BD is associated with a substantial decrease in quality of life, often greater than that seen in other mood or anxiety disorders. This reduction is primarily driven by depressive symptoms, including residual ones that may persist during remission periods. The frequent comorbidity with anxiety disorders further exacerbates the burden of the illness. Recently, research has turned toward the concept of the digital phenotype to identify early markers of relapse using passive and continuous monitoring. Among potential digital biomarkers, voice has shown particular promise. Automated speech analysis, combined with machine learning algorithms, has demonstrated effectiveness in detecting psychiatric symptoms and differentiating mood states. In BD, vocal and linguistic patterns vary with mood fluctuations, suggesting that voice could serve as a sensitive indicator of relapse risk. The main hypothesis of the present study is that automated analysis of speech and lifestyle data can help develop a predictive model capable of identifying early signs of relapse, whether manic, depressive, or mixed, or transitions to high-risk states in individuals with bipolar disorder.
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Voice interviews recorded on Callyope application
Timeframe: Once a week, from Month 0 to Month 6 (end of the study visit)
Occurrence of relapses during the study period
Timeframe: From enrollment to the end of study at 6 months
Changes from Baseline in the depression score measured by the Montgomery-Asberg Depression Rating Scale
Timeframe: Month 0, Month 6
Changes from Baseline in the bipolar disorder severity assessed by the Clinical Global Impression scale
Timeframe: Month 0, Month 6
Changes from Baseline in the maniac symptoms severity at the Young Mania Rating Scale
Timeframe: Month 0, Month 6