背景:数字表型在临床研究中的应用已广泛增加;然而,很少有研究对自杀风险检测实施被动评估方法。一种新形式的数字表型有很大的潜力,称为屏幕组学,它通过屏幕截图捕获智能手机活动。
目的:本文集中于对2名过去1个月主动自杀意念的参与者进行全面的病例回顾,详细说明他们的被动(即,通过屏幕组学截图捕获获得)和主动(即,通过生态瞬时评估[EMA]获得)的风险概况,最终导致自杀危机和随后的精神病住院。通过这种分析,我们揭示了住院前风险过程的时间尺度,以及介绍了屏幕组学在自杀研究领域的新应用。
方法:为了强调屏幕组学在理解自杀风险方面的潜在益处,该分析集中于从住院前的屏幕截图-文本捕获中收集的特定类型的数据,以及自我报告的EMA反应。经过全面的基线评估,参与者完成了密集的时间采样期。在此期间,每5秒收集一次截图,而一个人的手机在使用35天,和EMA数据每天收集6次,共28天。在我们的分析中,我们专注于以下方面:与自杀有关的内容(通过屏幕截图和EMA获得),与自杀风险相关的风险因素在理论和实证上(通过截图和EMA获得),和社交内容(通过截图获得)。
结果:我们的分析揭示了几个关键发现。首先,自杀危机期间EMA依从性显著下降,两名参与者在住院前几天完成的EMA较少。这与导致住院的电话使用量总体增加形成鲜明对比,特别是社会使用的增加。Screenomics还在自杀危机的每个实例中捕获了突出的诱发因素,这些因素通过自我报告无法很好地发现,特别是身体上的痛苦和孤独。
结论:我们的初步发现强调了被动收集数据在理解和预测自杀危机方面的潜力。每个参与者的大量屏幕截图提供了他们日常数字互动的细粒度视图,揭示了不能单独通过自我报告捕捉到的新风险。当与EMA评估相结合时,屏幕组学提供了一个更全面的观点,一个人的心理过程在时间导致自杀危机。
BACKGROUND: Digital phenotyping has seen a broad increase in application across clinical research; however, little research has implemented passive assessment approaches for suicide risk detection. There is a significant potential for a novel form of digital phenotyping, termed screenomics, which captures smartphone activity via screenshots.
OBJECTIVE: This paper focuses on a comprehensive
case review of 2 participants who reported past 1-month active
suicidal ideation, detailing their passive (ie, obtained via screenomics screenshot capture) and active (ie, obtained via ecological momentary assessment [EMA]) risk profiles that culminated in
suicidal crises and subsequent psychiatric hospitalizations. Through this analysis, we shed light on the timescale of risk processes as they unfold before hospitalization, as well as introduce the novel application of screenomics within the field of suicide research.
METHODS: To underscore the potential benefits of screenomics in comprehending suicide risk, the analysis concentrates on a specific type of data gleaned from screenshots-text-captured prior to hospitalization, alongside self-reported EMA responses. Following a comprehensive baseline assessment, participants completed an intensive time sampling period. During this period, screenshots were collected every 5 seconds while one\'s phone was in use for 35 days, and EMA data were collected 6 times a day for 28 days. In our analysis, we focus on the following: suicide-related content (obtained via screenshots and EMA), risk factors theoretically and empirically relevant to suicide risk (obtained via screenshots and EMA), and social content (obtained via screenshots).
RESULTS: Our analysis revealed several key findings. First, there was a notable decrease in EMA compliance during
suicidal crises, with both participants completing fewer EMAs in the days prior to hospitalization. This contrasted with an overall increase in phone usage leading up to hospitalization, which was particularly marked by heightened social use. Screenomics also captured prominent precipitating factors in each instance of
suicidal crisis that were not well detected via self-report, specifically physical pain and loneliness.
CONCLUSIONS: Our preliminary findings underscore the potential of passively collected data in understanding and predicting suicidal crises. The vast number of screenshots from each participant offers a granular look into their daily digital interactions, shedding light on novel risks not captured via self-report alone. When combined with EMA assessments, screenomics provides a more comprehensive view of an individual\'s psychological processes in the time leading up to a suicidal crisis.