Identifying digital biomarkers of illness activity and treatment response in bipolar disorder with a novel wearable device (TIMEBASE): protocol for a pragmatic observational clinical study.
Gerard Anmella, Filippo Corponi, Bryan M Li, Ariadna Mas, Marina Garriga, Miriam Sanabra, Isabella Pacchiarotti, Marc Valentí, Iria Grande, Antoni Benabarre, Anna Giménez-Palomo, Isabel Agasi, Anna Bastidas, Myriam Cavero, Miquel Bioque, Clemente García-Rizo, Santiago Madero, Néstor Arbelo, Andrea Murru, Silvia Amoretti, Anabel Martínez-Aran, Victoria Ruiz, Yudit Rivas, Giovanna Fico, Michele De Prisco, Vincenzo Oliva, Aleix Solanes, Joaquim Radua, Ludovic Samalin, Allan H Young, Antonio Vergari, Eduard Vieta, Diego Hidalgo-Mazzei
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Abstract
Background: Bipolar disorder is highly prevalent and consists of biphasic recurrent mood episodes of mania and depression, which translate into altered mood, sleep and activity alongside their physiological expressions.
Aims: The IdenTifying dIgital bioMarkers of illnEss activity and treatment response in BipolAr diSordEr with a novel wearable device (TIMEBASE) project aims to identify digital biomarkers of illness activity and treatment response in bipolar disorder.
Method: We designed a longitudinal observational study including 84 individuals. Group A comprises people with acute episode of mania (n = 12), depression (n = 12 with bipolar disorder and n = 12 with major depressive disorder (MDD)) and bipolar disorder with mixed features (n = 12). Physiological data will be recorded during 48 h with a research-grade wearable (Empatica E4) across four consecutive time points (acute, response, remission and episode recovery). Group B comprises 12 people with euthymic bipolar disorder and 12 with MDD, and group C comprises 12 healthy controls who will be recorded cross-sectionally. Psychopathological symptoms, disease severity, functioning and physical activity will be assessed with standardised psychometric scales. Physiological data will include acceleration, temperature, blood volume pulse, heart rate and electrodermal activity. Machine learning models will be developed to link physiological data to illness activity and treatment response. Generalisation performance will be tested in data from unseen patients.
Results: Recruitment is ongoing.
Conclusions: This project should contribute to understanding the pathophysiology of affective disorders. The potential digital biomarkers of illness activity and treatment response in bipolar disorder could be implemented in a real-world clinical setting for clinical monitoring and identification of prodromal symptoms. This would allow early intervention and prevention of affective relapses, as well as personalisation of treatment.
期刊介绍：
Announcing the launch of BJPsych Open, an exciting new open access online journal for the publication of all methodologically sound research in all fields of psychiatry and disciplines related to mental health. BJPsych Open will maintain the highest scientific, peer review, and ethical standards of the BJPsych, ensure rapid publication for authors whilst sharing research with no cost to the reader in the spirit of maximising dissemination and public engagement. Cascade submission from BJPsych to BJPsych Open is a new option for authors whose first priority is rapid online publication with the prestigious BJPsych brand. Authors will also retain copyright to their works under a creative commons license.
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