NCT04703972

Identification of Predictive Biomarkers of Mood Relapses in Patients With Bipolar Disorder

• Status: Unknown
• Phase: N/A
• Study type: Interventional
• Enrollment: 10 (estimated)
• Sponsor: University Hospital, Grenoble · Academic / Other
• Sex: All
• Age: 18 Years – 75 Years
• Healthy volunteers: Accepted

Summary

Relapses in bipolar disorders are associated with a significant proportional functional impact, as well as worsening of the course of bipolar disorder, with impairment of the quality of functional remission, as well as the development of addictive, anxiety and suicidal comorbidities.The functional deficit and the instability of the mood disorder increase with thymic relapses. Currently, these relapses (transition from the state of remission, to a depressive or hyperthymic state) are difficult to predict and to treat because of the absence of correlation between the degree of severity of the stressful event (intensity associated stress) and the occurrence of relapse, taking into account the mediation of this relationship by the stress compensation / adaptation capacities, which are very individual. This project proposes to develop tools based on artificial intelligence technologies to monitor the level of stress and adaptation to life events as well as identifying relapse predictive factors of a patient by using portable and connected devices recording different physiological signals in order to alert him/her when there is a risk of relapse, thus anticipating therapeutic strategies.

Conditions

• Bipolar Disorder

Interventions

Type	Name	Description
BEHAVIORAL	clinical assessment (mood relapses identification)	clinical assessment via psychometric scales physiological data acquired automatically via the connected device (wristwatch, wristband)

Timeline

• Start date: 2021-01-13
• Primary completion: 2022-01-31
• Completion: 2022-06-30
• First posted: 2021-01-11
• Last updated: 2021-01-14