Dropout from evidence-based trauma treatment in a community mental health clinic serving victims of interpersonal violence

Dropout in a clinical trial for comorbid PTSD and MDD among US service members: Are pretreatment characteristics predictive?

OBJECTIVE

Despite effective treatment options for posttraumatic stress disorder (PTSD), many patients do not complete therapy. This includes U.S. active duty service members, yet factors linked to attendance in this population remain understudied and dropout remains difficult to predict. Additionally, most studies have not examined samples with PTSD and co-occurring major depressive disorder (MDD) despite high rates of comorbidity.

METHOD

The current study explored predictors of dropout among service members with comorbid PTSD and MDD (N = 94) randomized to cognitive processing therapy enhanced with behavioral activation (BA + CPT) or CPT as part of a clinical trial.

RESULTS

Using the Fournier approach, only two predictors were associated with lower dropout risk among over 20 examined: shorter duration between pretreatment assessment and Session 1 (p = .041) and past 3-month PTSD treatment engagement (p = .036).

CONCLUSION

Results suggest the possible utility of early momentum in starting therapy and leveraging recent treatment to improve attendance. However, this study also highlights the possible limitations of commonly assessed pretreatment factors in predicting attendance and current challenges in measuring dropout risk. Strategies to improve prediction, such as shifting focus to assess modifiable factors and processes more proximal to dropout during treatment, may be needed.Trial registration: ClinicalTrials.gov identifier: NCT02874131.

Advancing PTSD Diagnosis and the Treatment of Trauma in Humanitarian Emergencies via Mobile Health: Protocol for a Proof-of-Concept Non-Randomized Controlled Trial

BACKGROUND

Decentralized health systems in Low and Middle-Income Countries (LMICs) impacted by humanitarian crises lack resources and a qualified workforce to attend to the overwhelming demand for mental health care in emergencies. Innovative approaches that are safe, cost-effective, and scalable are needed to address the burden of traumatic stress brought by emergencies. High mobile phone ownership rates combined with the precision of neural, cognitive, and biometric measures of trauma and its feasible integration with Artificial Intelligence (AI) makes digital application (app) interventions a promising pathway to promote precision diagnosis and high-impact care.

OBJECTIVE

The aims of this study are to advance methods for the objective diagnosis and treatment of trauma in emergencies across LMICs by examining (i) neural, cognitive, and biometric markers and (ii) the efficacy of the eResilience App, a neuroscience-informed mobile health mental health app intervention, via changes in clinical symptomatology, cognitive performance, and brain activity.

METHODS

Trauma-exposed African refugees residing in Australia were selected for this study. A research software version of the eResilience App with advanced monitoring capabilities was designed for the trial. Participants completed the eResilience App at home during a seven-day period. Clinical, cognitive, and electrophysiological data were collected during baseline and post-test to examine biomarkers of trauma and the efficacy of the proposed digital intervention for the treatment of trauma and its potential outcomes including depression, anxiety, physical symptoms, self-harm, substance misuse, and cognitive impairment. In addition, biofeedback, wellbeing, and subjective stress data points were collected via the app during the treatment week, followed by clinical interviews at 1, 3, 6 and 12-months post-intervention.

RESULTS

Data collection was conducted between 2018 and 2020. A total n=100 participants exposed to war were screened, n= 75 were enrolled and assigned to a trauma-exposed control (n=38) or Posttraumatic Stress Disorder (PTSD) condition (n=37), and n= 70 completed all baseline, treatment, and post-test assessments. A total n=62 of the n=70 who completed the intervention opted to enrol in the 3, 6 and 12-month follow-ups. Data collection is complete, and results are being prepared for publication. If proven efficacious, this proof-of-concept clinical trial will inform fully powered randomized clinical trials in LMICs to further develop AI-powered, app-based diagnostic and prognostic features, and determine the app's cross-cultural efficacy for the treatment of trauma in emergency settings.

CONCLUSIONS

This protocol provides researchers with a comprehensive background of the study rationale, a detailed guideline for replication studies interested in examining the feasibility and the efficacy of the eResilience App across varied demographics, and a robust framework for investigations of low-cost objective diagnostic markers in mental health interventions. Methodological limitations and suggestions are also provided.

CLINICALTRIAL

Australian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12616001205426. Universal Trial Number (UTN): U1111-1180-0347.