Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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JMIR formative research
Detection of Posttraumatic Stress Disorder With Rest-Activity Data: Machine Learning Approach Using Wearable and Self-Report Data
BACKGROUND: Growing evidence suggests that disruptions in rest-activity rhythms may serve as relevant markers of posttraumatic stress disorder (PTSD). Despite the emergence of machine learning methods applied to actigraphy and self-report data, few studies have used these approaches to identify individuals with clinically diagnosed PTSD. Prior work has focused on predicting probable PTSD based on self-report measures, yet discrepancies exist between clinical diagnoses and probable PTSD derived from self-reports. OBJECTIVE: This study explored whether wrist actigraphy and sleep logs could be used to accurately predict clinician-rated PTSD diagnosis and probable diagnosis of PTSD based on established self-report cutoffs (PTSD Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [PCL-5] ≥31 and ≥38) among trauma-exposed service members and veterans. We also explored which features were most strongly predictive of each outcome and whether models were able to predict PTSD diagnosis even when accounting for other mental health disorders. METHODS: Wrist actigraphy data and daily sleep logs were collected over 1 week from trauma-exposed male service members and veterans (N=36; mean age 41, SD 5.3 y). Candidate features were identified using univariate feature selection. Extreme gradient boosting models were trained using leave-one-subject-out cross-validation to predict the diagnosis of PTSD and probable diagnosis of PTSD based on 2 self-report cutoffs (PCL-5≥31 and ≥38). Performance metrics were then calculated at the person level. Linear regression was used to assess the discriminant validity of model-predicted scores and each PTSD outcome specifically, relative to other mental health diagnoses. RESULTS: Machine learning models predicting PTSD diagnosis and probable PTSD based on the PCL-5≥31 threshold demonstrated satisfactory performance in this sample. The diagnosis model achieved an area under the curve (AUC) of 0.83 (95% CI 0.61-1.00), with high accuracy (88%) and specificity (96%) and moderate sensitivity (63%). The PCL-5≥31 model yielded comparable performance (AUC=0.84, 95% CI 0.71-0.98) with balanced sensitivity (73%) and specificity (82%). For both models, a combination of subjective and objective features was the most impactful. These models were able to predict PTSD even when accounting for non-PTSD mental health diagnoses, as model-predicted scores were significantly associated with 2 outcomes: clinician-rated PTSD (B=0.19; P=.002) and probable PTSD based on a PCL-5≥31 cutoff (B=0.24; P=.003). In contrast, the model predicting probable PTSD based on the PCL-5≥38 threshold performed poorly (AUC=0.47, 95% CI 0.24-0.69), with a nonsignificant relationship between model-predicted scores and the outcome (B<0.01; P=.89). CONCLUSIONS: Both subjective and objective rest-activity features may improve the prediction of PTSD. Further research is needed to validate these findings and explore the use of integrating wearable sensor data and subjective information to support PTSD assessment.
20 May 2026
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Attenuating trauma- and cocaine-related intrusions by blocking memory reconsolidation with minocycline: protocol for a transdiagnostic randomized controlled trial
Background: Post-traumatic stress disorder (PTSD) and substance use disorders frequently co-occur, with their comorbidity linked to poorer treatment outcomes. Beyond this epidemiological overlap, PTSD and, in particular, cocaine use disorder (CUD) have also been conceptually linked, as maladaptive memory processes are thought to play a central role in both of their etiopathogeneses. PTSD and CUD are each characterized by intrusive memories - either trauma or cocaine related - that sustain distress and craving, respectively. These parallels have motivated transdiagnostic mechanistic research targeting memory reconsolidation. Matrix metalloproteinase-9 (MMP-9) is critically involved in synaptic plasticity and memory updating, and has been suggested as a pharmacological entry point for modulating reconsolidation-related processes.Objective: This study examines whether pharmacological interference with reconsolidation via MMP-9 inhibition by minocycline modulates trauma- and cocaine-related intrusive memories and their neurobiological correlates in PTSD and CUD.Method: In this monocentric, randomized, double-blind, placebo-controlled trial (NCT05902819), individuals with PTSD (n = 60) or CUD (n = 60) receive a single dose of minocycline - a tetracycline inhibiting MMP-9 - or placebo prior to an individualized, imagery-based reactivation of their most salient intrusive memory. Intrusive memories in day-to-day life are assessed using event-based ecological momentary assessment and pre-post intrusion questionnaires. Neurobiological correlates of maladaptive memory processing are indexed through functional magnetic resonance imaging and magnetic resonance spectroscopy, and peripheral biomarkers are obtained from blood, urine, and hair samples. Diagnostic status and comorbid symptoms are characterized using clinical interviews and standardized self-report measures.Conclusions: This trial adopts a mechanistic framework to investigate whether minocycline-augmented memory reactivation modulates intrusions and associated neurobiological markers in PTSD and CUD. By directly targeting candidate memory mechanisms shared by both disorders, it evaluates a transdiagnostic reconsolidation-based approach and is expected to inform models of intrusive memory persistence and updating. The findings may guide future development of memory-focused interventions in trauma- and addiction-related disorders. This randomized controlled trial evaluates a transdiagnostic, mechanism-focused intervention targeting maladaptive memory processes in PTSD and cocaine use disorder.Minocycline is used as an MMP-9 inhibitor to modulate intrusive trauma- and cocaine-related memories following brief imagery-based memory reactivation.The study integrates multimodal neurobiological assessments (fMRI, MRS, peripheral biomarkers) with ecological momentary intrusion measures to characterize changes in memory-related mechanisms.
14 May 2026
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