NCT07298278

Anticipating Depressive and Manic Episodes in Bipolar Disorders Using Vocal Biomarkers

ANTICIPATING DEPRESSIVE AND MANIC EPISODES IN BIPOLAR DISORDERS USING VOCAL BIOMARKERS

• Status: Not Yet Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 170 (estimated)
• Sponsor: Centre Hospitalier St Anne · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

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.

Detailed description

Bipolar disorder (BD) is a chronic and cyclical illness that affects a significant portion of the population, representing more than 1% worldwide. It is characterized by alternating episodes of elevated mood and energy (mania or hypomania) and episodes of decreased mood and energy (depression). These mood episodes manifest as substantial variations in energy levels and behavior, which recur over time and have a major social and occupational impact. According to the World Health Organization, BD rank as the fourth leading cause of morbidity and mortality. Manic episodes are marked by hyperactivity, exalted mood, insomnia, inflated self-esteem, expansive speech and behavior, and sometimes psychotic symptoms (such as delusions of persecution or hallucinations). In contrast, depressive episodes are characterized by low energy, sadness, social withdrawal, hypersomnia or insomnia, and low self-esteem, often accompanied by weight loss or gain and decreased or increased appetite. The risks associated with manic, depressive, or mixed episodes are numerous; notably, individuals with BD have a suicide rate up to 20 times higher than that of the general population. Nearly half of patients with BD will attempt suicide at least once in their lifetime, and 15-20% of these attempts are fatal. BD are associated with a marked reduction in quality of life, often greater than that observed in other mood or anxiety disorders. This decrease in quality of life is more strongly correlated with depressive symptoms than with manic or hypomanic symptoms. Furthermore, poor quality of life is related to residual depressive symptoms that may persist during remission periods, as well as to the high comorbidity of bipolar disorder with anxiety disorders. The annual relapse rate ranges between 40% and 61% during the first two years following the initiation of treatment. This high incidence of relapse makes stabilization particularly difficult for patients with BD, with a period of significant vulnerability following each episode. The average duration of hospitalization is 58 days, at an approximate cost of €850 per day, resulting in direct hospitalization costs related to mood disorder relapses of about €3 billion per year. According to the French Court of Auditors, for every euro of direct cost, there are two euros of indirect costs related to social benefits and the negative impact on employment. Extrapolating these figures, the cost of hospitalizations due to relapses in BD is estimated at €45 billion across Europe. Moreover, each relapse or rehospitalization irreversibly affects the individual's cognitive functioning and contributes to social and occupational disintegration. Staging models of the illness based on neuroprogression have been developed, taking into account the number of relapses and the degree of functional impairment. However, these models are not yet implemented in clinical practice. Preventing (hypo)manic and depressive episodes through early intervention is therefore a key priority both at the individual level and as a major public health issue. A new line of research has emerged in mood disorders, focusing on the digital phenotype. Among new digital biomarkers of relapse, voice appears to be a promising parameter. Several studies have demonstrated the efficacy of automated speech analysis, using machine learning models, to aid in the diagnosis of psychiatric disorders. In bipolar disorder, the illness has been shown to influence patients' vocal and linguistic features. Thus, the main hypothesis of the study is that automated speech analysis and lifestyle data can be used to develop a model capable of predicting either relapse (manic, depressive, or mixed episode) or the transition to a high-risk state in patients with bipolar disorder.

Conditions

• Bipolar Disorder (BD)

Interventions

Type	Name	Description
OTHER	Voice interviews and questionnaires carried out via the CALLYOPE application	Voice interviews carried out via the Callyope application: they consist of a series of tests, divided into two parts: Structured tasks (same content for each participant) and Semi-structured tasks (content varies for each participant). The simultaneous analysis of several speech tasks allows us to break down the different stages of speech production and the important factors that influence its achievement. In addition, patients will complete self-questionnaires via the application. Finally, lifestyle habits (number of steps) will be recorded via the application. These different tests will be carried out on the application at the inclusion visit (M0), then every week (+/- 3 days) until the end of study visit at 6 months (M6).
DEVICE	Sleep measurements using an under-mattress sensor	The under-mattress sensor will allow continuous sleep recording (sleep duration, sleep onset and wake times, sleep apnea, sleep cycles, etc.) for patients over a 6-month period, from M0 to M6.
DEVICE	Smartwatch for measuring activity, sleep, and skin temperature	The smartwatch will allow continuous recording of the patient's activity patterns, sleep, and skin temperature. It will be worn continuously from inclusion (M0) until the end of the study at 6 months (M6).

Timeline

• Start date: 2025-12-20
• Primary completion: 2027-05-01
• Completion: 2027-05-01
• First posted: 2025-12-23
• Last updated: 2025-12-23