Krishnamurti Tamar, Allen Kristen, Hayani Laila, Rodriguez Samantha, Rothenberger Scott, Moses-Kolko Eydie, Simhan Hyagriv
University of Pittsburgh School of Medicine.
Allegheny County Department Of Human Services.
Res Sq. 2023 Feb 21:rs.3.rs-2583296. doi: 10.21203/rs.3.rs-2583296/v1.
Depression is highly prevalent in pregnancy, yet it often goes undiagnosed and untreated. Language can be an indicator of psychological well-being. This longitudinal, observational cohort study of 1,274 pregnancies examined written language shared in a prenatal smartphone app. Natural language feature of text entered in the app (e.g. in a journaling feature) throughout the course of participants' pregnancies were used to model subsequent depression symptoms. Language features were predictive of incident depression symptoms in a 30-day window (AUROC = 0.72) and offer insights into topics most salient in the writing of individuals experiencing those symptoms. When natural language inputs were combined with self-reported current mood, a stronger predictive model was produced (AUROC = 0.84). Pregnancy apps are a promising way to illuminate experiences contributing to depression symptoms. Even sparse language and simple patient-reports collected directly from these tools may support earlier, more nuanced depression symptom identification.
抑郁症在孕期极为常见，但往往未被诊断和治疗。语言可以作为心理健康状况的一个指标。这项针对1274例妊娠的纵向观察性队列研究，对一款产前智能手机应用程序中分享的书面语言进行了研究。在参与者整个孕期输入该应用程序的文本（例如在日志功能中）的自然语言特征，被用于对随后的抑郁症状进行建模。语言特征在30天的时间窗口内可预测新发抑郁症状（曲线下面积=0.72），并能深入了解出现这些症状的个体在写作中最突出的主题。当自然语言输入与自我报告的当前情绪相结合时，可生成更强的预测模型（曲线下面积=0.84）。孕期应用程序是一种很有前景的方式，可阐明导致抑郁症状的经历。即使是从这些工具中直接收集到的少量语言和简单的患者报告，也可能有助于更早、更细致地识别抑郁症状。
