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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.
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10 participants in 1 patient group
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Arnaud Pouchon, MD; Mircea POLOSAN, MD-PhD
Data sourced from clinicaltrials.gov
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