The Effects of an Exposure-Based Mobile App on Symptoms of Posttraumatic Stress Disorder in Veterans: Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Barriers to accessing in-person care can prevent veterans with posttraumatic stress disorder (PTSD) from receiving trauma-focused treatments such as exposure therapy. Mobile apps may help to address unmet need for services by offering tools for users to self-manage PTSD symptoms. Renew is a mobile mental health app that focuses on exposure therapy and incorporates a social support function designed to promote user engagement. OBJECTIVE: We examined the preliminary efficacy of Renew with and without support from a research staff member compared with waitlist among 93 veterans with clinically significant PTSD symptoms. We also examined the impact of study staff support on participant engagement with the app. METHODS: In a pilot randomized controlled trial, we compared Renew with and without support from a research staff member (active use condition) with waitlist (delayed use condition) over 6 weeks. Participants were recruited through online advertisements. The Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) was used to measure PTSD symptoms at pre, post, and 6-week follow-up. Usage data were collected to assess engagement with Renew. RESULTS: Results indicated a small effect size (d=-0.39) favoring those in the active use conditions relative to the delayed use condition, but the between-group difference was not significant (P=.29). There were no differences on indices of app engagement between the 2 active use conditions. Exploratory analyses found that the number of support persons users added to the app, but not the number of support messages received, was positively correlated with app engagement. CONCLUSIONS: Findings suggest Renew may hold promise as a self-management tool to reduce PTSD symptoms in veterans. Involving friends and family in mobile mental health apps may help bolster engagement with no additional cost to public health systems. TRIAL REGISTRATION: ClinicalTrials.gov NCT04155736; https://clinicaltrials.gov/ct2/show/NCT04155736.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".