Posttraumatic stress disorder (PTSD) is often chronic and impairing. Mechanisms that maintain symptoms remain poorly understood because of heterogenous presentation. We parsed this heterogeneity by examining how individual differences in stress-symptom dynamics relate to the long-term maintenance of PTSD.

We studied 7,308 trauma-exposed World Trade Center responders who self-reported PTSD symptoms and stressful life events at annual monitoring visits for up to 20 years (average = 8.8 visits; [range = 4–16]). We used multilevel structural equation models to separate the stable and time-varying components of symptoms and stressors. At the within-person level, we modeled stress reactivity by cross-lagged associations between stress and future symptoms, stress generation by cross-lagged associations between symptoms and future stress, and autoregressive effects represented symptom persistence and stress persistence. The clinical utility of the stress-symptom dynamics was evaluated by associations with PTSD chronicity and mental health care use.

Stress reactivity, stress generation, and symptom persistence were significant on average (bs = 0.03–0.16). There were significant individual differences in the strength of each dynamic (interquartile ranges = 0.06–0.12). Correlations among within-person processes showed some dynamics are intertwined (e.g. more reactive people also generate stress in a vicious cycle) and others represent distinct phenotypes (e.g. people are reactive or have persistent symptoms). Initial trauma severity amplified some dynamics. People in the top deciles of most dynamics had clinically significant symptom levels across the monitoring period and their health care cost 6–17× more per year than people at median levels.

Individual differences in stress-symptom dynamics contribute to the chronicity and clinical burden of PTSD.

Adolescence is marked by a sharp increase in the incidence of depression, especially in females. Identification of risk for depressive disorders (DD) in this key developmental stage can help prevention efforts, mitigating the clinical and public burden of DD. While frequently used in diagnosis, nonverbal behaviors are relatively understudied as risk markers for DD. Digital technology, such as facial recognition, may provide objective, fast, efficient, and cost-effective means of measuring nonverbal behavior.

Here, we analyzed video-recorded clinical interviews of 359 never-depressed adolescents females via commercially available facial emotion recognition software.

We found that average head and facial movements forecast future first onset of depression (AUC = 0.70) beyond the effects of other established self-report and physiological markers of DD risk.

Overall, these findings suggest that digital assessment of nonverbal behaviors may provide a promising risk marker for DD, which could aid in early identification and intervention efforts.