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

精神病，直接影响2 - 3%的人口，是一种严重的衰弱性精神障碍，其特征是一系列症状，包括错误的信念，错误的感知和混乱的思维。虽然抗精神病药物可以导致疾病急性期的快速改善，但大多数患者随后复发。这给患者及其家属造成了相当大的痛苦，并对NHS和社会产生了重大的经济影响。精神病临床管理的一个关键挑战是，我们对导致复发的因素了解有限;这意味着医生无法根据患者的风险水平优化个体患者的治疗。为了应对这一挑战，我们开发了一款智能手机应用程序，该应用程序允许在真实的时间内密切监控多个环境、时间点和位置的人（Urban Mind; www.example.com）。在本项目中，我们将使用Urban Mind应用程序的改编版本来测量225名首次发病患者的日常社会压力-这被认为是复发风险的强有力预测因素。我们将研究这种方法是否可以预测12个月内复发的风险。然后，我们将测试当我们在225名患者的不同组中使用该应用程序时是否会得到相同的结果。该项目的一个主要目的是研究通过对手机使用情况的持续后台监控收集的“被动”数据在多大程度上可以用作“主动”数据的代理，这些数据需要患者将他们的经历记录到他们的手机中。研究结果将用于开发一种预测模型，将日常社会压力的实时测量与未来精神病复发的风险联系起来。该模型可以整合到常规临床护理中，帮助医生根据患者的风险水平优化个体患者的治疗。这可以改善首次发作精神病患者的长期临床结果，并为社会带来重大经济效益。智能手机技术在精神卫生保健中的应用有可能从根本上改变评估、治疗和监测患者的方式。

Psychosis, which directly affects 2-3% of the population, is a severely debilitating psychiatric disorder characterised by a range of symptoms including false beliefs, false perceptions and disorganised thinking. Although antipsychotic medication can lead to rapid improvement in the acute-phase of the disorder, most patients subsequently relapse. This causes considerable distress to patients and their families, and has major economic implications both the NHS and society. A key challenge in the clinical management of psychosis is that we have limited understanding of the factors that lead to a relapse; this means that doctors are unable to optimise treatment of individual patients based on their level of risk. In other to address this challenge we have developed a smartphone app which allows the close monitoring of people across multiple contexts, time-points and locations in real time (Urban Mind; www.urbanmind.info). In the present project we will use an adapted version of the Urban Mind app to measure daily social stress - which is thought to be a strong predictor of the risk of relapse - in 225 patients with a first episode of the illness. We will examine whether this measure can predict the risk of relapse over a 12-month period. We will then test whether we get the same results when we use the app in a different group of 225 patients. A key aim of this project is to examine the extent to which "passive" data, which are collected via continuous background monitoring of phone usage and as such place minimal burden on patients, can be used as proxy for "active" data, which require patients to log their experiences into their phone. The results will be used to develop a predictive model linking real-time measures of daily social stress with risk of future psychotic relapse. This model could be integrated within routine clinical care, helping doctors optimise treatment of individual patients based on their level of risk. This could improve long-term clinical outcomes amongst people with first episode psychosis and have major economic benefits for society. The application of smartphone technologies in mental health care has the potential to fundamentally change the way in which patients are assessed, treated and monitored.