A Digital Assessment Paradigm for PTSD as a Foundation for Tailored Early Intervention at Scale

Background: Post-Traumatic Stress Disorder (PTSD) manifests heterogeneously across cognitive, affective, physiological, and behavioral domains. Current gold-standard assessment methods face scalability limitations, creating barriers to timely diagnosis and intervention. Digital assessment paradigms offer potential solutions, but their diagnostic accuracy and feasibility need validation, particularly for at-risk populations such as military and police personnel. Aims & Objectives: This study aimed to develop and validate a digital assessment paradigm for PTSD using a contemporary theoretical framework with scalable deployment potential. Secondary objectives included examining whether remote assessment approaches could replicate previously identified differences between PTSD and control groups, collectively determining which components provide the greatest diagnostic utility. Method: We employed an exploratory cross-sectional multi-component design with 42 participants from Australian Defence Force and police backgrounds, validated against the CAPS-5 structured clinical interview. The paradigm incorporated cognitive tasks (dot probe, CRAFT), physiological assessments (heart rate, blood pressure, facial affect during trauma discussions and exposure), and longitudinal assessment (Ecological Momentary Assessment over four days). Data were analyzed using mixed-effects modeling to examine group differences across tasks, and machine learning classification to integrate multi-modal data for optimizing diagnostic accuracy. Models were evaluated with cross-validation techniques and interpretability analyses. Results: Several components demonstrated significant diagnostic value. The CRAFT task revealed PTSD participants responded significantly faster to disgust and fear faces compared to controls. During trauma narrative discussions, those with PTSD showed distinct emotional expression patterns, particularly slower resolution of negative affect. Linguistic analysis showed PTSD narratives contained significantly more negative emotional content, first-person singular pronouns, and less lexical diversity. EMA data revealed consistently higher symptom levels in the PTSD group across all time points. The machine learning classifier achieved an AUC of 0.75 on the test set. EMA-derived features, particularly symptom volatility metrics, contributed approximately 60% of the model’s predictive power, followed by narrative (25%) and acoustic features (10%). Discussion & Conclusions: This study demonstrates the feasibility and utility of a multi-modal digital assessment approach for PTSD. The strongest performing features were also those least burdensome participants. These findings suggest that scalable digital assessment paradigms may be able to effectively identify PTSD, potentially enabling earlier intervention. Future refinements should focus on optimizing the most diagnostically valuable components, particularly dynamic symptom monitoring and linguistic analysis, to develop lighter-weight assessment protocols suitable for frontline deployment.