Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, United States of America; Allegheny County Department of Human Services, Pittsburgh, PA, United States of America.
Division of General Internal Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America.
J Affect Disord. 2024 Nov 1;364:231-239. doi: 10.1016/j.jad.2024.08.029. Epub 2024 Aug 11.
Depression is a common pregnancy complication yet is often under-detected and, subsequently, undertreated. Data collected through mobile health tools may be used to support the identification of depression symptoms in pregnancy.
An observational cohort study of 2062 pregnancies collected self-reports of patient history, mood, pregnancy-specific symptoms, and written language using a prenatal support app. These app inputs were used to model depression risk in subsequent 30- and 60-day periods throughout pregnancy. A selective inference lasso modeling approach examined the individual and additive value of each type of patient-reported app input.
Depression models ranged in predictive power (AUC value of 0.64-0.83), depending on the type of inputs. The most predictive model included personal history, daily mood, and acute pregnancy-related symptoms (e.g., severe vomiting, cramping). Across models, daily mood was the strongest indicator of depression symptoms in the following month. Models that retained natural language inputs typically improved predictive accuracy and offered insight into the lived context associated with experiencing depression.
Our findings are not generalizable beyond a digitally literate patient population that is self-motivated to report data during pregnancy.
Simple patient reported data, including sparse language, shared directly via digital tools may support earlier depression symptom identification and a more nuanced understanding of depression context.
抑郁症是一种常见的妊娠并发症，但往往未被充分发现，因此也未得到充分治疗。通过移动健康工具收集的数据可用于支持妊娠期间识别抑郁症状。
本研究为一项前瞻性队列研究，共纳入了 2062 例妊娠，使用产前支持应用程序收集了患者病史、情绪、妊娠特异性症状和书面语言的自我报告。使用这些应用程序输入来模拟妊娠后 30 天和 60 天期间的抑郁风险。采用选择性推断套索模型分析方法来考察每种类型的患者报告应用程序输入的个体和附加价值。
抑郁模型的预测能力（AUC 值为 0.64-0.83）各不相同，取决于输入类型。预测能力最强的模型包括个人病史、日常情绪和急性妊娠相关症状（例如严重呕吐、痉挛）。在所有模型中，日常情绪是下一个月出现抑郁症状的最强指标。保留自然语言输入的模型通常会提高预测准确性，并深入了解与抑郁经历相关的生活背景。
我们的研究结果仅适用于具有数字化素养且有自我报告数据意愿的患者群体，不能推广到其他人群。
简单的患者报告数据，包括稀疏的语言，直接通过数字工具共享，可能有助于更早地识别抑郁症状，并更细致地了解抑郁的发生背景。
