Smartphone language features may help identify adverse post-traumatic neuropsychiatric sequelae and their trajectories.

NPP-digital psychiatry and neuroscience Pub Date : 2025-01-01 Epub Date: 2025-05-20 DOI:10.1038/s44277-025-00028-x
Lisa Vizer, Jennifer Pierce, Yinyao Ji, Meredith A Bucher, Mochuan Liu, Lyle Ungar, Salvatore Giorgi, Zhaopeng Xing, Stacey L House, Francesca L Beaudoin, Jennifer S Stevens, Thomas C Neylan, Gari D Clifford, Tanja Jovanovic, Sarah D Linnstaedt, Donglin Zeng, Laura T Germine, Kenneth A Bollen, Scott L Rauch, John P Haran, Alan B Storrow, Christopher Lewandowski, Paul I Musey, Phyllis L Hendry, Sophia Sheikh, Christopher W Jones, Brittany E Punches, Lauren A Hudak, Jose L Pascual, Mark J Seamon, Erica Harris, Claire Pearson, David A Peak, Roland C Merchant, Robert M Domeier, Brian J O'Neil, Paulina Sergot, Leon D Sanchez, Steven E Bruce, Steven E Harte, Ronald C Kessler, Karestan C Koenen, Samuel A McLean, Xinming An
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Abstract

Language features may reflect underlying cognitive and emotional processes following a traumatic event that portend clinical outcomes. The authors sought to determine whether language features from usual smartphone use were markers associated with concurrent posttraumatic symptoms and worsening or improving posttraumatic symptoms over time following a traumatic exposure. This investigation was a secondary analysis of the Advancing Understanding of RecOvery afteR traumA study, a longitudinal study of traumatic outcomes among survivors recruited from 33 emergency departments across the United States. Adverse posttraumatic sequelae were assessed over the six months following the initial traumatic exposure. Language features were extracted from usual smartphone use in a specialized app. Bivariate linear mixed models were used to identify and validate language features that are markers associated with posttraumatic symptoms. Participants were 1744 trauma survivors, with a mean age of 39 [SD = 13] years old, and 56% were female. Fourteen language features were associated with severity level of posttraumatic symptoms at specific timepoints (cross-sectional markers) and five features were associated with change in severity level of posttraumatic symptoms (longitudinal markers). References to the body and health or illness were predictive of worsening pain, somatic, and thinking/concentration/fatigue symptom severity over time. An increase in references to others was associated with improvement in somatic symptom severity over time and increases in expressions of causation or cognitive processes were associated with improvement in pain symptom severity over time. Language features derived from usual smartphone use can convey important information about health, functioning, and recovery following a traumatic event. Clinicians might utilize such information to determine who may experience a high symptom burden or risk of worsening posttraumatic symptoms.

智能手机语言功能可能有助于识别不良创伤后神经精神后遗症及其轨迹。
语言特征可能反映了创伤事件后潜在的认知和情感过程,预示着临床结果。作者试图确定通常使用智能手机的语言特征是否与并发创伤后症状以及创伤后症状随着时间的推移而恶化或改善相关。这项调查是对创伤后康复研究的二次分析,这是一项对来自美国33个急诊科的幸存者的创伤结果的纵向研究。在最初的创伤暴露后的六个月内评估不良创伤后后遗症。语言特征是在一个专门的应用程序中从通常的智能手机使用中提取出来的。双变量线性混合模型用于识别和验证与创伤后症状相关的语言特征。研究对象为1744名创伤幸存者,平均年龄39岁[SD = 13],其中56%为女性。14个语言特征与特定时间点创伤后症状严重程度相关(横断面标记),5个特征与创伤后症状严重程度变化相关(纵向标记)。对身体和健康或疾病的参考可以预测随着时间的推移疼痛、躯体和思维/集中/疲劳症状的恶化程度。随着时间的推移,提及他人的增加与躯体症状严重程度的改善有关,因果关系或认知过程的表达的增加与疼痛症状严重程度的改善有关。从智能手机的日常使用中获得的语言特征可以传达关于健康、功能和创伤事件后恢复的重要信息。临床医生可以利用这些信息来确定谁可能会经历高症状负担或创伤后症状恶化的风险。
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