Smartphone language features may help identify adverse post-traumatic neuropsychiatric sequelae and their trajectories
Vizer, Lisa ; Pierce, Jennifer ; Ji, Yinyao ; Bucher, Meredith A ; Liu, Mochuan ; Ungar, Lyle ; Giorgi, Salvatore ; Xing, Zhaopeng ; House, Stacey L ; Beaudoin, Francesca L ... show 10 more
Authors
Pierce, Jennifer
Ji, Yinyao
Bucher, Meredith A
Liu, Mochuan
Ungar, Lyle
Giorgi, Salvatore
Xing, Zhaopeng
House, Stacey L
Beaudoin, Francesca L
Stevens, Jennifer S
Neylan, Thomas C
Clifford, Gari D
Jovanovic, Tanja
Linnstaedt, Sarah D
Zeng, Donglin
Germine, Laura T
Bollen, Kenneth A
Rauch, Scott L
Haran, John P
Storrow, Alan B
Lewandowski, Christopher
Musey, Paul I
Hendry, Phyllis L
Sheikh, Sophia
Jones, Christopher W
Punches, Brittany E
Hudak, Lauren A
Pascual, Jose L
Seamon, Mark J
Harris, Erica
Pearson, Claire
Peak, David A
Merchant, Roland C
Domeier, Robert M
O'Neil, Brian J
Sergot, Paulina
Sanchez, Leon D
Bruce, Steven E
Harte, Steven E
Kessler, Ronald C
Koenen, Karestan C
McLean, Samuel A
An, Xinming
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Abstract
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.
Lay Summary
Via usual smartphone use following trauma exposure, this study identified language markers associated with patient-reported severity and change in severity for multiple symptoms. Using language markers as a proxy for the status of and changes in specific symptoms supports efficient remote health status monitoring and can provide clinicians with valuable real-time insights into health, functioning, and recovery. These insights can be leveraged to guide targeted interventions tailored to individual trauma survivors.
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Vizer L, Pierce J, Ji Y, Bucher MA, Liu M, Ungar L, Giorgi S, Xing Z, House SL, Beaudoin FL, Stevens JS, Neylan TC, Clifford GD, Jovanovic T, Linnstaedt SD, Zeng D, Germine LT, Bollen KA, Rauch SL, Haran JP, Storrow AB, Lewandowski C, Musey PI Jr, Hendry PL, Sheikh S, Jones CW, Punches BE, Hudak LA, Pascual JL, Seamon MJ, Harris E, Pearson C, Peak DA, Merchant RC, Domeier RM, O'Neil BJ, Sergot P, Sanchez LD, Bruce SE, Harte SE, Kessler RC, Koenen KC, McLean SA, An X. Smartphone language features may help identify adverse post-traumatic neuropsychiatric sequelae and their trajectories. NPP Digit Psychiatry Neurosci. 2025;3(1):8. doi: 10.1038/s44277-025-00028-x. Epub 2025 May 20. PMID: 40406207; PMCID: PMC12092297.