Bipolar Disorder (BD)SPEECHBIPONCT07298278
Anticipating Depressive and Manic Episodes in Bipolar Disorders Using Vocal Biomarkers
A study testing Voice interviews and questionnaires carried out via the CALLYOPE application, Sleep measurements using an under-mattress sensor and Smartwatch for measuring activity, sleep, and skin temperature in people with Bipolar Disorder (BD), run by Centre Hospitalier St Anne.
Trial 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). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Bipolar Disorder (BD)
- What is being tested?
- Voice interviews and questionnaires carried out via the CALLYOPE application, Sleep measurements using an under-mattress sensor and Smartwatch for measuring activity, sleep, and skin temperature
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 170 participants
- Where
- No sites listed yet
- Study dates
- 20 Dec 2025to May 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Adult patient
- Patient capable of providing informed consent
- Patient suffering from bipolar disorder according to DSM-5-TR (2022) criteria
- Patient recently discharged from hospitalization or in remission after a mood episode within the last 12 months, with a MADRS score ≤10 and a YMRS score ≤8, or based on the psychiatrist's subjective evaluation
- Patient treated with lithium/antipsychotics/benzodiazepines (monotherapy or combination therapy)
- Patient capable of performing speech assessments and responding to questionnaires on a smartphone
- Patient able to speak, read, and understand French
- Patient enrolled in a social security system
Who cannot join
- Patient with a cognitive disorder
- Patient suffering from a known demential disorder
- Patient receiving treatment for a known addictive disorder
- Patient with a condition affecting speech production
- Patient with a neurological disorder (stroke or neurodegenerative diseases)
- Patient under legal protection, guardianship, or curatorship
- Subjects deprived of liberty by judicial or administrative decision
- Pregnant or breastfeeding women
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: ANTICIPATING DEPRESSIVE AND MANIC EPISODES IN BIPOLAR DISORDERS USING VOCAL BIOMARKERS
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.
Read more
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.
Read the 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)
- Being tested
- Voice interviews and questionnaires carried out via the CALLYOPE application, Sleep measurements using an under-mattress sensor, Smartwatch for measuring activity, sleep, and skin temperature
- Keywords
- Bipolar Disorders, Digital Biomarkers, Speech Analysis, Relapse prediction
- Medical subject headings
- Bipolar Disorder
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Voice interviews and questionnaires carried out via the CALLYOPE applicationOther
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).
Sleep measurements using an under-mattress sensorDevice
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.
Smartwatch for measuring activity, sleep, and skin temperatureDevice
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).
| Study group | Role | What they receive |
|---|---|---|
| Patients with Bipolar Disorder (6-month observational follow-up) | Experimental | Performing voice and language tests on the Callyope application: testing the voice and language analysis algorithm.
Daily recording (6 months) of all passive data via the connected watch and the under-mattress sensor and the number of steps via the Callyope application (remotely) Receives: Other: Voice interviews and questionnaires carried out via the CALLYOPE application; Device: Sleep measurements using an under-mattress sensor; Device: Smartwatch for measuring activity, sleep, and skin temperature |
What is being measured
The primary outcome is the main question the study is designed to answer.
Voice interviews recorded on Callyope application
The analysis of the interviews (acoustic and linguistic features) will be implemented in a voice model predicting a score.
Measured: Once a week, from Month 0 to Month 6 (end of the study visit)
Occurrence of relapses during the study period
Occurrence of relapse episodes during the 6-month follow-up period
Measured: 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
Depression severity will be assessed by the investigator with the MADRS (Montgomery-Asberg Depression Rating Scale) scale.
Score range (min - max): 0-60 higher score relates to worse depression severity
Measured: Month 0, Month 6
Changes from Baseline in the bipolar disorder severity assessed by the Clinical Global Impression scale
Bipolar Disorder severity will be assessed by the investigator with CGI (Clinical Global Impression) scale.
Score range (min - max): 0 - 7 for the severity subscale symptomatic remission correspond to a score ≤3 (CGI).
Measured: Month 0, Month 6
Changes from Baseline in the maniac symptoms severity at the Young Mania Rating Scale
Maniac symptoms severity will be assessed by the investigator with the YMRS (Young Mania Rating Scale).
Score range (min - max): 0-60 higher score relates to worse maniac symptoms severity.
Measured: Month 0, Month 6
Secondary outcomes (18)
Age
Age in years
Measured: Month 0
Sex
Sex (Male/Female)
Measured: Month 0
Weight
Weight in kilograms
Measured: Month 0
Plasma lithium concentration
Plasma lithium levels measured in mEq/L when available during the 6-month study period.
Measured: Up to 6 months (end of the study)
Plasma drug concentration
Plasma concentrations of ongoing psychotropic treatments measured when available during the 6-month study period
Measured: Up to 6 months (end of study)
Changes from Baseline in symptoms (depression, anxiety, functional autonomy, fatigue)
Symtpoms will be assessed by the score of the PHQ-9 (Patient Health Questionnaire - 9).
Score range (min - max): 0 - 27 Higher score relates to a worse outcome
Measured: Once a week, from Month 0 to Month 6 (end of the study visit)
Changes from Baseline in anxiety symptoms
Anxiety symptoms will be assessed by the score of the GAD-7 (Generalized Anxiety Disorder) questionnaire.
Score range (min - max): 0 - 21 Higher score relates to a worse outcome
Measured: Once a week, from Month 0 to Month 6 (end of the study visit)
Sleep disturbances severity (Athens Insomnia Scale total score)
Total score on the Athens Insomnia Scale (AIS) (range 0-24) ; Higher scores indicate more severe sleep disturbances
Measured: Once a week, from Month 0 to Month 6 (end of the study visit)
Medication adherence measured by Medication Adherence Report Scale total score
Adherence to medications will be assessed with the Medication Adherence Report Scale (MARS).
A higher score indicates better therapeutic adherence.
Measured: Once a week, from Month 0 to Month 6 (end of the study visit)
Fatigue measured by the Multidimensional Fatigue Inventory
Total fatigue score on the Multidimensional Fatigue Inventory (MFI).
Score range (min - max): 20 - 100; Higher total scores correspond with more acute levels of fatigue.
Measured: Month 0, Month 6
Quality of social relationships measured by the Social Network Index
Social Network Index (SNI). This questionnaire comprises a social diversity score (minimum = 0, maximum = 12), the total number of people in the social network, and the number of embedded social networks.
Measured: Month 0, Month 6
Assessment of Loneliness Using the UCLA 3-Item Loneliness Scale
Assessment of loneliness using the UCLA 3-Item Loneliness Scale. Scores range from 3 to 9, with higher scores indicating greater feelings of loneliness.
Measured: Month 0, Month 6
Mean number of steps
Collection of passive number of steps with the Callyope application and de wearable watch throughout the study
Measured: Continuous monitoring, from Month 0 to the end of study (Month 6)
Sleep duration
Measure of the total sleep time (hours) per night and the duration of sleep stages (hours) throughout the study thanks to the Withings Sleep Analyzer and wearable watch.
Measured: Continuous monitoring, from Month 0 to the end of study (Month 6)
Heart rate variability
Measure of the mean and continus heart rate variability (ms) thanks to the Withing watch.
Measured: Continuous monitoring, from Month 0 to the end of study (Month 6)
Blood oxygen saturation
Measure of the blood oxygen saturation (%) throughout the study thanks to th Withing watch
Measured: Continuous monitoring, from Month 0 to the end of study (Month 6)
Body temperature
Measure of the body temperature (°C) throughout the study thanks to the Withing watch.
Measured: Continuous monitoring, from Month 0 to the end of study (Month 6)
Sleep apnea
Measure of the apnea-hypopnea index during sleep throughout the study thanks to the Withings sleep analyzer.
Measured: Continuous monitoring, from Month 0 to the end of study (Month 6)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 Dec 2025
- Study start20 Dec 2025 (estimated)
- Primary completionMay 2027 (estimated)
- Study completionMay 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Bipolar Disorder (BD)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Basic science
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 170 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Centre Hospitalier St Anne
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Pierre-Alexis Geoffroy, Pr, Principal investigator, Paris Cité University; Centre ChronoS
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07298278
- Sponsor's protocol ID
- D24-P020
- Acronym
- SPEECHBIPO
- Status last verified
- Dec 2025
- First submitted
- 1 Dec 2025
- Last updated
- 23 Dec 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 23 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- ANTICIPATING DEPRESSIVE AND MANIC EPISODES IN BIPOLAR DISORDERS USING VOCAL BIOMARKERS
- ClinicalTrials.gov ID
- NCT07298278
- Acronym / sponsor ID
- SPEECHBIPO / D24-P020
- Conditions
- Bipolar Disorder (BD)
- Keywords
- Bipolar Disorders, Digital Biomarkers, Speech Analysis, Relapse prediction
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 170 participants (estimated)
- Lead sponsor
- Centre Hospitalier St AnneUniversity, hospital or other
- Study start
- 20 Dec 2025 (estimated)
- Primary completion
- May 2027 (estimated)
- Study completion
- May 2027 (estimated)
- First posted
- 23 Dec 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Basic science
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients with Bipolar Disorder (6-month observational follow-up) | Experimental | Other: Voice interviews and questionnaires carried out via the CALLYOPE application, Device: Sleep measurements using an under-mattress sensor, Device: Smartwatch for measuring activity, sleep, and skin temperaturePerforming voice and language tests on the Callyope application: testing the voice and language analysis algorithm. Daily recording (6 months) of all passive data via the connected watch and the under-mattress sensor and the number of steps via the Callyope application (remotely) |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (8 listed)
- Adult patient
- Patient capable of providing informed consent
- Patient suffering from bipolar disorder according to DSM-5-TR (2022) criteria
- Patient recently discharged from hospitalization or in remission after a mood episode within the last 12 months, with a MADRS score ≤10 and a YMRS score ≤8, or based on the psychiatrist's subjective evaluation
- Patient treated with lithium/antipsychotics/benzodiazepines (monotherapy or combination therapy)
Key exclusion criteria (8 listed)
- Patient with a cognitive disorder
- Patient suffering from a known demential disorder
- Patient receiving treatment for a known addictive disorder
- Patient with a condition affecting speech production
- Patient with a neurological disorder (stroke or neurodegenerative diseases)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Voice interviews and questionnaires carried out via the CALLYOPE applicationVoice 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). | Other | Patients with Bipolar Disorder (6-month observational follow-up) |
| Sleep measurements using an under-mattress sensorThe 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 | Patients with Bipolar Disorder (6-month observational follow-up) |
| Smartwatch for measuring activity, sleep, and skin temperatureThe 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). | Device | Patients with Bipolar Disorder (6-month observational follow-up) |
Primary outcome
Voice interviews recorded on Callyope application
Time frame: Once a week, from Month 0 to Month 6 (end of the study visit)
The analysis of the interviews (acoustic and linguistic features) will be implemented in a voice model predicting a score.
Occurrence of relapses during the study period
Time frame: From enrollment to the end of study at 6 months
Occurrence of relapse episodes during the 6-month follow-up period
Changes from Baseline in the depression score measured by the Montgomery-Asberg Depression Rating Scale
Time frame: Month 0, Month 6
Depression severity will be assessed by the investigator with the MADRS (Montgomery-Asberg Depression Rating Scale) scale. Score range (min - max): 0-60 higher score relates to worse depression severity
Changes from Baseline in the bipolar disorder severity assessed by the Clinical Global Impression scale
Time frame: Month 0, Month 6
Bipolar Disorder severity will be assessed by the investigator with CGI (Clinical Global Impression) scale. Score range (min - max): 0 - 7 for the severity subscale symptomatic remission correspond to a score ≤3 (CGI).
Changes from Baseline in the maniac symptoms severity at the Young Mania Rating Scale
Time frame: Month 0, Month 6
Maniac symptoms severity will be assessed by the investigator with the YMRS (Young Mania Rating Scale). Score range (min - max): 0-60 higher score relates to worse maniac symptoms severity.
18 secondary outcomes
Age
Time frame: Month 0
Sex
Time frame: Month 0
Weight
Time frame: Month 0
Plasma lithium concentration
Time frame: Up to 6 months (end of the study)
Plasma drug concentration
Time frame: Up to 6 months (end of study)
Changes from Baseline in symptoms (depression, anxiety, functional autonomy, fatigue)
Time frame: Once a week, from Month 0 to Month 6 (end of the study visit)
Changes from Baseline in anxiety symptoms
Time frame: Once a week, from Month 0 to Month 6 (end of the study visit)
Sleep disturbances severity (Athens Insomnia Scale total score)
Time frame: Once a week, from Month 0 to Month 6 (end of the study visit)
Medication adherence measured by Medication Adherence Report Scale total score
Time frame: Once a week, from Month 0 to Month 6 (end of the study visit)
Fatigue measured by the Multidimensional Fatigue Inventory
Time frame: Month 0, Month 6
Quality of social relationships measured by the Social Network Index
Time frame: Month 0, Month 6
Assessment of Loneliness Using the UCLA 3-Item Loneliness Scale
Time frame: Month 0, Month 6
Mean number of steps
Time frame: Continuous monitoring, from Month 0 to the end of study (Month 6)
Sleep duration
Time frame: Continuous monitoring, from Month 0 to the end of study (Month 6)
Heart rate variability
Time frame: Continuous monitoring, from Month 0 to the end of study (Month 6)
Blood oxygen saturation
Time frame: Continuous monitoring, from Month 0 to the end of study (Month 6)
Body temperature
Time frame: Continuous monitoring, from Month 0 to the end of study (Month 6)
Sleep apnea
Time frame: Continuous monitoring, from Month 0 to the end of study (Month 6)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Dec 2025.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Pierre-Alexis Geoffroy, Pr principal investigator
Paris Cité University; Centre ChronoS
Questions about this trial
What is this clinical trial studying?
A study testing Voice interviews and questionnaires carried out via the CALLYOPE application, Sleep measurements using an under-mattress sensor and Smartwatch for measuring activity, sleep, and skin temperature in people with Bipolar Disorder (BD), run by Centre Hospitalier St Anne.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 8 inclusion and 8 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 23 Dec 2025. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Pierre-Alexis Geoffroy, Pr (+33140258263, pa.geoffroy@ghu-paris.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Voice interviews recorded on Callyope application (time frame: Once a week, from Month 0 to Month 6 (end of the study visit)). The study lists 5 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 23 Dec 2025.