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

通过数字表型智能手机应用程序推进实时自杀风险检测 Screenomics

• 批准号: 10428874
• 负责人: Brooke A Ammerman
• 金额: $ 23.48万
• 依托单位: UNIVERSITY OF NOTRE DAME
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-04 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10428874
• 关键词: Accounting; Address; Cellular Phone; Cessation of life; Clinical; Communication; Consumption; Data; Data Collection; Detection; Dictionary; Dimensions; Ecological momentary assessment; Ethics; Exploratory/Developmental Grant for Diagnostic Cancer Imaging; Feeling suicidal; Generations; Goals; Health; Heterogeneity; Individual; Internet; Intervention; Knowledge; Link; Loneliness; Machine Learning; Measurement; Methodology; Methods; Mission; Modality; Modeling; Monitor; National Institute of Mental Health; Nature; Negative Valence; Outcome; Participant; Persons; Phenotype; Positive Valence; Privacy; Proxy; Psychophysiology; Public Health; Research; Resolution; Risk; Risk Behaviors; Risk Factors; Sampling; Sampling Studies; Social Interaction; Source; Suicide; Suicide prevention; Techniques; Text; Text Messaging; Time; United States National Institutes of Health; Work; actigraphy; base; behavior prediction; data streams; deep learning; digital; experience; high risk; improved; innovation; mortality; novel; prevent; prospective; reducing suicide; smartphone Application; social media; social relationships; societal costs; suicidal behavior; suicidal risk; suicide rate; technological innovation; text searching; therapy development

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.

项目摘要/摘要 自杀是一个领先的公共卫生问题，仅在2017年就会占45,000多人1。 速度持续上升2，自杀思想和行为的预测（STB）仍然停滞3， 需要将重点从确定谁处于有自杀风险的危险中的危险中。研究 利用生态瞬时评估每天几个间隔收集数据，这表明 自杀构想和STB风险因素在第4天的过程中迅速变化；但是，有必要改进 评估的粒度以改善实时风险升高的识别。为了可靠的检测 STB在相对较短的时间窗口内（例如，会议分钟）将需要技术创新的方法 这可以不断捕获自杀风险的动态性质。 我们建议使用一种新型的数字表型形式，称为屏幕截图 每五秒钟从参与者的手机开始。然后可以将这些数据用于间接识别Real-中的STB 时间（通过生成和查看的文本），以及预期通过个人参与预测STB 产生和消费社交互动（通过应用程序用法，文本消息和社交媒体文本），这是 已知与STBS 7的链接。在80个过去的STBS中，将研究两个主要目标。 AIM 1是证明通过智能手机使用（即Web浏览器，短信）收集的文本可以使用 作为直接评估STB的准确代理。 AIM 2将确定潜在的短期STB风险 与产生和消费的社交互动相关，无法通过直接评估证明。 研究团队（Co-Pis：Ammerman，Jacobucci; Co-I：Jiang;顾问：Kleiman，Ram，Robinson， 里夫斯，资产阶级，刘）可以使用世界一流的专业知识，并具有丰富的EMA数据收集经验 在高风险样本中，用于预测自杀，收集和建模连续数据流的机器学习， 包括屏幕截图数据以及技术创新所特有的道德和隐私实践。 为了有意义地降低自杀率，对STB和相关风险因素有更细微的了解 需要实时。屏幕名单可提供几乎连续的监视，从而更接近 风险因素与STB之间的真正关联。确实，有必要确定近期风险因素 在STB之前，要成功提供干预措施并防止STB。这些发现将使 在STB检测和干预中使用被动数据所需的基础。鉴于坟墓的个人和 自杀的社会成本，这项工作具有重要的公共卫生影响。