mHealth in the Wild: Using Novel Data to Examine the Reach, Use, and Impact of PTSD Coach
Bibliographic record
Abstract
BACKGROUND: A majority of Americans (58%) now use smartphones, making it possible for mobile mental health apps to reach large numbers of those who are living with untreated, or under-treated, mental health symptoms. Although early trials suggest positive effects for mobile health (mHealth) interventions, little is known about the potential public health impact of mobile mental health apps. OBJECTIVE: The purpose of this study was to characterize reach, use, and impact of "PTSD Coach", a free, broadly disseminated mental health app for managing posttraumatic stress disorder (PTSD) symptoms. METHODS: Using a mixed-methods approach, aggregate mobile analytics data from 153,834 downloads of PTSD Coach were analyzed in conjunction with 156 user reviews. RESULTS: Over 60% of users engaged with PTSD Coach on multiple occasions (mean=6.3 sessions). User reviews reflected gratitude for the availability of the app and being able to use the app specifically during moments of need. PTSD Coach users reported relatively high levels of trauma symptoms (mean PTSD Checklist Score=57.2, SD=15.7). For users who chose to use a symptom management tool, distress declined significantly for both first-time users (mean=1.6 points, SD=2.6 on the 10-point distress thermometer) and return-visit users (mean=2.0, SD=2.3). Analysis of app session data identified common points of attrition, with only 80% of first-time users reaching the app's home screen and 37% accessing one of the app's primary content areas. CONCLUSIONS: These findings suggest that PTSD Coach has achieved substantial and sustained reach in the population, is being used as intended, and has been favorably received. PTSD Coach is a unique platform for the delivery of mobile mental health education and treatment, and continuing evaluation and improvement of the app could further strengthen its public health impact.
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How this classification was reachedexpand
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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".