Maatoug Redwan, Oudin Antoine, Adrien Vladimir, Saudreau Bertrand, Bonnot Olivier, Millet Bruno, Ferreri Florian, Mouchabac Stephane, Bourla Alexis
Service de Psychiatrie Adulte de la Pitié-Salpêtrière, Institut du Cerveau (ICM), Sorbonne Université, Assistance Publique des Hôpitaux de Paris (AP-HP), Paris, France.
iCRIN (Infrastructure for Clinical Research in Neurosciences), Paris Brain Institute (ICM), Sorbonne Université, INSERM, CNRS, Paris, France.
Front Psychiatry. 2022 Jul 26;13:895860. doi: 10.3389/fpsyt.2022.895860. eCollection 2022.
Mood disorders are commonly diagnosed and staged using clinical features that rely merely on subjective data. The concept of digital phenotyping is based on the idea that collecting real-time markers of human behavior allows us to determine the digital signature of a pathology. This strategy assumes that behaviors are quantifiable from data extracted and analyzed through digital sensors, wearable devices, or smartphones. That concept could bring a shift in the diagnosis of mood disorders, introducing for the first time additional examinations on psychiatric routine care.
The main objective of this review was to propose a conceptual and critical review of the literature regarding the theoretical and technical principles of the digital phenotypes applied to mood disorders.
We conducted a review of the literature by updating a previous article and querying the PubMed database between February 2017 and November 2021 on titles with relevant keywords regarding digital phenotyping, mood disorders and artificial intelligence.
Out of 884 articles included for evaluation, 45 articles were taken into account and classified by data source (multimodal, actigraphy, ECG, smartphone use, voice analysis, or body temperature). For depressive episodes, the main finding is a decrease in terms of functional and biological parameters [decrease in activities and walking, decrease in the number of calls and SMS messages, decrease in temperature and heart rate variability (HRV)], while the manic phase produces the reverse phenomenon (increase in activities, number of calls and HRV).
The various studies presented support the potential interest in digital phenotyping to computerize the clinical characteristics of mood disorders.
情绪障碍通常使用仅依赖主观数据的临床特征进行诊断和分期。数字表型的概念基于这样一种理念，即收集人类行为的实时标记可以让我们确定一种病理学的数字特征。这种策略假定行为可以从通过数字传感器、可穿戴设备或智能手机提取和分析的数据中进行量化。这一概念可能会给情绪障碍的诊断带来转变，首次在精神科常规护理中引入额外的检查。
本综述的主要目的是对有关应用于情绪障碍的数字表型的理论和技术原理的文献进行概念性和批判性综述。
我们通过更新之前的一篇文章并在2017年2月至2021年11月期间在PubMed数据库中查询有关数字表型、情绪障碍和人工智能的相关关键词标题来进行文献综述。
在纳入评估的884篇文章中，45篇文章被考虑并按数据来源（多模态、活动记录仪、心电图、智能手机使用、语音分析或体温）进行分类。对于抑郁发作，主要发现是功能和生物学参数方面的下降[活动和步行减少、通话和短信数量减少、体温和心率变异性（HRV）降低]，而躁狂期则产生相反的现象（活动、通话数量和HRV增加）。
所呈现的各种研究支持数字表型在使情绪障碍的临床特征计算机化方面的潜在价值。
