Anticipating Depressive and Manic Episodes in Bipolar Disorders Using Vocal Biomarkers

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.

Conditions

• Bipolar Disorder (BD)

Interventions

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).

Sponsors & Collaborators

• Centre Hospitalier St Anne

  lead OTHER

Principal Investigators

• Pierre-Alexis Geoffroy, Pr · Paris Cité University; Centre ChronoS

Study Design

Allocation

NA

Purpose

BASIC_SCIENCE

Masking

NONE

Model

SINGLE_GROUP

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2025-12-20

Primary Completion

2027-05-31

Completion

2027-05-31