Using smartphone-based personal sensing to understand and predict risk of psychotic relapse at the individual level.

尽管抗精神病药物在精神病急性期的疗效相对较高，但据估计，约84%的人将在最初36个月内至少经历一次新的发作(1)。精神病复发不仅对患者，而且对他们的家人都造成了相当大的痛苦。此外，精神病复发往往会导致住院治疗，因此与巨大的医疗费用有关。此外，每次复发似乎都会对疾病的潜在神经生物学产生负面影响，导致长期结果恶化(2)。因此，预防复发是精神病治疗中最关键的目标之一(3)。预测精神病复发的能力将为临床医生提供机会，为他们量身定做干预措施，并将其有效和高效地应用于处于最高风险的个人(4)。在个体层面上量化精神病复发和再次住院的概率的风险分数可以用来确定最需要干预的个人(5)。然而，目前我们对预测精神病复发的因素的了解非常有限，因此无法根据患者的风险水平对患者进行分层。使用机器学习和基于智能手机的数据来预测精神病复发机器学习(ML)方法的最新进展有可能彻底改变精神病预测(6)。当旨在预测个体而不是群体层面的复发时，ML特别有用，从而导致更大的潜力转化为临床实践，临床医生需要对个别患者做出决定。此外，ML模型通常是多变量的，因此可以捕捉不同预测变量之间的隐藏关系。有几种广泛使用的ML技术，分为三大类：监督、非监督和半监督(7)。最适合的技术视乎要执行的任务和数据的特征，例如样本大小和维度。由于智能手机日益普遍和灵活，特别适合作为数据收集的工具。此外，它们对参与者造成的负担最小，当参与者较少参与和更难到达时，即他们更需要支持和监测的时候，它们可能特别有用。导致本研究的工作城市思维应用程序(https://www.urbanmind.info/))是在过去5年中开发的，并已在普通人群(8)以及临床人群(首发精神病和超高精神病风险)中成功试用。它以生态瞬时评估(EMA)的形式收集主动数据和被动数据，如GPS位置和每天的步数。EMA方法涉及对参与者的实时和现实世界背景下的体验进行抽样(9)。参与者被问及他们此刻的感受，从而将回忆偏差的风险降至最低。在Social Mind研究中，将使用该应用程序的改编版本，特别关注社会环境和社会压力。

Despite the relatively high efficacy of antipsychotic medication in the acute stage of psychosis, it is estimated that around 84% of individuals will go on to experience at least one further episode within the first 36 months (1). Psychotic relapse causes considerable distress not only to the patients but also their families. Furthermore, psychotic relapse often results in hospitalisation and is therefore associated with significant health care costs. Additionally, each relapse appears to have a negative effect on the underlying neurobiology of the disorder, leading to a worsening of long-term outcomes (2). Relapse prevention is therefore one of the most critical targets in the treatment of psychotic disorders (3).The ability to predict psychotic relapse would provide the opportunity for clinicians to tailor interventions and to apply them effectively and efficiently to individuals who are at highest risk (4). A risk score which quantifies the probability of psychotic relapse and hospital readmission on an individual level could be used to identify individuals at most need of an intervention (5).However, we currently have a very limited understanding of factors that predict psychotic relapse and are therefore unable to stratify patients based on their levels of risk.Using machine learning and smartphone-based data to predictpsychotic relapseRecent advances in machine learning (ML) methods have the potential to revolutionise psychosis prediction (6). ML is particularly useful when aiming to predict relapse on an individual rather than group level, leading to greater potential of translation into clinical practice where clinicians need to make decisions about individual patients. In addition, ML models are typically multivariate andcan therefore capture the hidden relationships between different predictive variables.There are several widely-used ML techniques, divided into 3 broad categories: supervised, unsupervised and semi-supervised (7). The most suitable technique depends on the task to be performed and the characteristics of the data, such as sample size and dimensionality.Smartphones are particularly suitable as a tool for data collection given their increasing ubiquity and flexibility. In addition,they place minimal burden on participant and can be particularly useful when participants are less engaged and harder to reach which is when they are at greater need for support and monitoring.Work leading to the present investigationThe Urban Mind app (https://www.urbanmind.info/) has been developed over the past 5 years and has been successfully piloted in the general population (8)as well as in clinical populations (first-episode psychosis and ultra-high risk for psychosis). It collects active data in the form of Ecological Momentary Assessments (EMA) and passive data such as GPS location and number of steps per day. EMA methodology involves sampling participants experiences in real time and in real-world contexts (9). Participants are asked about how they feel in the moment, minimising the risk of recall bias. In the Social Mind study, an adapted version of the app will be used, with specific focus on social environment and social stress.