Department of Psychiatry and Emergency, Brest Medical University Hospital, Brest, France.
SPURBO EA 7479, Ubo, France.
BMC Psychiatry. 2019 Sep 7;19(1):277. doi: 10.1186/s12888-019-2260-y.
The screening of digital footprint for clinical purposes relies on the capacity of wearable technologies to collect data and extract relevant information's for patient management. Artificial intelligence (AI) techniques allow processing of real-time observational information and continuously learning from data to build understanding. We designed a system able to get clinical sense from digital footprints based on the smartphone's native sensors and advanced machine learning and signal processing techniques in order to identify suicide risk.
METHOD/DESIGN: The Smartcrisis study is a cross-national comparative study. The study goal is to determine the relationship between suicide risk and changes in sleep quality and disturbed appetite. Outpatients from the Hospital Fundación Jiménez Díaz Psychiatry Department (Madrid, Spain) and the University Hospital of Nimes (France) will be proposed to participate to the study. Two smartphone applications and a wearable armband will be used to capture the data. In the intervention group, a smartphone application (MEmind) will allow for the ecological momentary assessment (EMA) data capture related with sleep, appetite and suicide ideations.
Some concerns regarding data security might be raised. Our system complies with the highest level of security regarding patients' data. Several important ethical considerations related to EMA method must also be considered. EMA methods entails a non-negligible time commitment on behalf of the participants. EMA rely on daily, or sometimes more frequent, Smartphone notifications. Furthermore, recording participants' daily experiences in a continuous manner is an integral part of EMA. This approach may be significantly more than asking a participant to complete a retrospective questionnaire but also more accurate in terms of symptoms monitoring. Overall, we believe that Smartcrises could participate to a paradigm shift from the traditional identification of risks factors to personalized prevention strategies tailored to characteristics for each patient.
NCT03720730. Retrospectively registered.
出于临床目的而对数字足迹进行筛查，需要依赖可穿戴技术来收集数据并提取与患者管理相关的信息。人工智能 (AI) 技术可处理实时观测信息，并持续从数据中学习以构建认知。我们设计了一个系统，该系统能够基于智能手机的原生传感器和高级机器学习及信号处理技术，从数字足迹中获取临床意义，以识别自杀风险。
方法/设计：Smartcrisis 研究是一项跨国比较研究。该研究的目的是确定自杀风险与睡眠质量变化和食欲紊乱之间的关系。马德里 Jiménez Díaz 精神病学系医院（西班牙）和尼姆大学医院（法国）的门诊患者将被提议参加该研究。将使用两个智能手机应用程序和一个可穿戴臂带来捕获数据。在干预组中，一个智能手机应用程序（MEmind）将允许进行与睡眠、食欲和自杀意念相关的生态瞬时评估 (EMA) 数据捕获。
可能会对数据安全产生一些担忧。我们的系统符合患者数据的最高安全级别。还必须考虑与 EMA 方法相关的一些重要伦理考虑因素。EMA 方法需要参与者付出相当大的时间承诺。EMA 依赖于日常或更频繁的智能手机通知。此外，以连续的方式记录参与者的日常体验是 EMA 的一个组成部分。这种方法可能比要求参与者完成回顾性问卷更重要，但在症状监测方面也更准确。总的来说，我们相信 Smartcrises 可以参与从传统的风险因素识别到针对每个患者特征量身定制的个性化预防策略的范式转变。
NCT03720730。回顾性注册。
