利用新型可穿戴设备(TIMEBASE)识别双相情感障碍疾病活动和治疗反应的数字生物标志物:实用观察临床研究方案。

IF 3.9 3区 医学 Q1 PSYCHIATRY BJPsych Open Pub Date : 2024-08-01 DOI:10.1192/bjo.2024.716
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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引用次数: 0

摘要

背景:双相情感障碍的发病率很高,由躁狂和抑郁这两种双相反复发作的情绪组成,会导致情绪、睡眠和活动的改变以及生理表现:我们设计了一项包括 84 人的纵向观察研究。A 组包括急性发作的躁狂症患者(12 人)、抑郁症患者(12 人患有双相情感障碍,12 人患有重度抑郁障碍(MDD))和具有混合特征的双相情感障碍患者(12 人)。研究级可穿戴设备(Empatica E4)将在 48 小时内记录四个连续时间点(急性期、反应期、缓解期和发作恢复期)的生理数据。B 组包括 12 名躁狂症患者和 12 名 MDD 患者,C 组包括 12 名健康对照者,他们将接受横截面记录。心理病理症状、疾病严重程度、功能和体力活动将通过标准化的心理测量量表进行评估。生理数据将包括加速度、体温、血容量脉搏、心率和皮肤电活动。将开发机器学习模型,将生理数据与疾病活动和治疗反应联系起来。将对来自未见过的患者的数据进行泛化性能测试:结果:正在进行招募:该项目将有助于了解情感障碍的病理生理学。双相情感障碍疾病活动和治疗反应的潜在数字生物标志物可在真实世界的临床环境中实施,用于临床监测和识别前驱症状。这将有助于早期干预和预防情感复发,以及个性化治疗。
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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.

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.

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来源期刊
BJPsych Open
BJPsych Open Medicine-Psychiatry and Mental Health
CiteScore
6.30
自引率
3.70%
发文量
610
审稿时长
16 weeks
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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