Biometric Data in Post-Traumatic Stress Disorder Detection: A Scoping Review of Digital Health Applications

CONTEXT: Post-traumatic stress disorder (PTSD) is mainly assessed through self-reports and clinician interviews, which can delay recognition and limit reach. Biometric markers captured using digital technologies may enable earlier and more objective detections. PURPOSE: To map biometric modalities used for PTSD detection in digital health, identify underused markers, characterise machine learning (ML)/artificial intelligence (AI) approaches, and assess sex-related analyses. METHODS: Guided by PRISMA-ScR, a protocol on the Open Science Framework was pre-registered and searches in PubMed, IEEE Xplore, and Google Scholar (2015-2025) were conducted. The full search string was: ("post-traumatic stress disorder" OR "PTSD") AND ("biometric data" OR "biosensor" OR "wearable technology") AND ("detection" OR "screening" OR "diagnosis" OR "monitoring") AND ("digital health" OR "mobile health" OR "AI-based" OR "machine learning"). Peer-reviewed human studies using biometric data with digital tools and/or ML/AI for PTSD detection were eligible. Of 3,312 records, 89 underwent full-text review, and 18 studies met the inclusion criteria. ANALYSIS: Data were categorised by biometric modality, digital platform (wearable devices, mobile applications, ML/AI systems), study population, and performance metrics (area under the curve, sensitivity, specificity). Findings were grouped thematically (physiological, neuroimaging, behavioural, genetic, multimodal) and synthesised narratively to identify trends, gaps, and the application of sex-stratified modelling. RESULTS: Most studies focused on physiological (e.g., heart rate, sleep) and neuroimaging (functional magnetic resonance imaging, electroencephalography) signals; behavioural and genetic modalities were underexplored. Data were frequently captured via wearables and mobile platforms, with ML commonly applied. Performance reporting was uneven, sex-stratified analyses were rare, and several promising modalities (e.g., eye-tracking, electrodermal activity) remain underused. CONCLUSION: Digital biometric approaches can detect PTSD; however, progress has been slowed by heterogeneous study designs, inconsistent reporting, and limited attention to sex differences. Establishing common reporting standards, evaluating multimodal models in real-world settings, and developing algorithms incorporating sex for more equitable screening are warranted.