Advancing Real-Time Suicide Risk Detection Through the Digital Phenotyping Smartphone Application Screenomics

项目摘要/摘要 自杀是一个主要的公共卫生问题，仅2017年就造成超过4.5万人死亡1.自杀 自杀率继续上升2，自杀想法和行为(STB)的预测仍然停滞不前3 需要将重点从确定谁有自杀风险转移到个人何时有自杀风险。研究 利用生态瞬时评估每天以几个间隔收集数据已经证明 自杀意念和性传播疾病的风险因素在第四天的过程中迅速变化；然而，仍有必要改进 评估的粒度，以改进对实时风险提升的识别。以实现可靠的检测 在相对较短的时间窗口(例如，几分钟)内的STB将需要技术创新的方法 可以持续捕捉到自杀风险的动态本质。 我们建议使用一种新形式的数字表型，称为屏幕组学5-6，它可以捕获屏幕截图 每隔五秒从参与者的手机上。然后可以利用这些数据来间接识别实时的机顶盒 时间(通过生成和查看的文本)，以及通过个人参与预测STB 产生和使用社交互动(通过应用程序使用、文本消息和社交媒体文本)， 已知与性传播疾病的联系7.在过去一个月中80名患有性传播疾病的人中，将调查两个主要目标。 目标1是证明通过使用智能手机(即，网络浏览器、文本消息)收集的文本可以提供 作为直接评估STB的准确指标。目标2将确定预期的短期STB风险 与产生和消费的社交互动相关，而不是通过直接评估证明的。 研究团队(联合PI：Ammerman，Jacobucci；Co-I：酱；顾问：Kleiman，Ram，Robinson， 里夫斯、布尔乔亚、刘)拥有世界级的专业知识，在EMA数据收集方面拥有丰富的经验 在高危样本中，用于预测自杀的机器学习，收集和建模连续数据流， 包括截图数据，以及技术创新特有的道德和隐私做法。 为了有意义地降低自杀率，更细致入微地了解性传播疾病和相关的危险因素 实时是必需的。屏幕组学提供了近乎连续的监测，允许更接近 风险因素与性传播疾病之间的真实联系。事实上，有必要确定近期的风险因素。 在STB发生之前，成功实施干预并预防STB。这些发现将为 在STB检测和干预中使用被动数据所需的基础工作。考虑到严重的个人和 自杀的社会成本，这项工作具有重要的公共卫生影响。

PROJECT SUMMARY/ABSTRACT Suicide is a leading public health problem, accounting for over 45,000 deaths in 2017 alone 1. With suicide rates continuing to rise 2, and the prediction of suicidal thoughts and behaviors (STBs) remaining stagnant 3, there is a need to shift the focus from identifying who is at risk to when individuals are at risk for suicide. Studies utilizing ecological momentary assessment to collect data at several intervals per day have demonstrated that suicidal ideation and STB risk factors change rapidly across the course of the day 4; yet, there is a need to improve the granularity of assessment to improve identification of real-time risk elevation. To enable reliable detection of STBs within a relatively short window of time (e.g., minutes) will require technologically innovative methodologies that can continuously capture the dynamic nature of suicide risk. We propose the use of a novel form of digital phenotyping, termed Screenomics 5-6, that captures screenshots from participant’s phones every five seconds. These data can then be utilized to indirectly identify STBs in real- time (via generated and viewed text), as well as prospectively predict STBs via individual engagement in produced and consumed social interactions (via application usage, text messages, and social media text), which have knowns links to STBs 7. Among 80 individuals with past-month STBs, two primary aims will be investigated. Aim 1 is to demonstrate that text collected through smartphone use (i.e., web browser, text messages) can serve as an accurate proxy for the direct assessment of STBs. Aim 2 will identify prospective, short-term STB risk associated with produced and consumed social interactions not demonstrated via direct assessment. The research team (Co-PIs: Ammerman, Jacobucci; Co-I: Jiang; Consultants: Kleiman, Ram, Robinson, Reeves, Bourgeois, Liu) has access to world-class expertise, with extensive experience in EMA data collection in high-risk samples, machine learning for predicting suicide, collecting and modeling continuous data streams, including screenshot data, and ethical and privacy practices unique to technological innovations. To meaningfully reduce suicide rates, a more nuanced understanding of STBs and associated risk factors in real-time is required. Screenomics provides near continuous monitoring, allowing for a closer approximation of the true associations between risk factors and STBs. Indeed, there is a need to identify near-term risk factors prior to STB occurrences to successfully deliver an intervention and prevent STBs. These findings will lay the groundwork necessary for utilizing passive data in STB detection and intervention. Given the grave personal and societal cost of suicide, this work has important public health implications.