Engagement, Predictors, and Outcomes of a Trauma Recovery Digital Mental Health Intervention: Longitudinal Study
Bibliographic record
Abstract
Background Worldwide, exposure to potentially traumatic events is extremely common, and many individuals develop posttraumatic stress disorder (PTSD) along with other disorders. Unfortunately, considerable barriers to treatment exist. A promising approach to overcoming treatment barriers is a digital mental health intervention (DMHI). However, engagement with DMHIs is a concern, and theoretically based research in this area is sparse and often inconclusive. Objective The focus of this study is on the complex issue of DMHI engagement. On the basis of the social cognitive theory framework, the conceptualization of engagement and a theoretically based model of predictors and outcomes were investigated using a DMHI for trauma recovery. Methods A 6-week longitudinal study with a national sample of survivors of trauma was conducted to measure engagement, predictors of engagement, and mediational pathways to symptom reduction while using a trauma recovery DMHI (time 1: N=915; time 2: N=350; time 3: N=168; and time 4: N=101). Results Confirmatory factor analysis of the engagement latent constructs of duration, frequency, interest, attention, and affect produced an acceptable model fit (χ22=8.3; P=.02; comparative fit index 0.973; root mean square error of approximation 0.059; 90% CI 0.022-0.103). Using the latent construct, the longitudinal theoretical model demonstrated adequate model fit (comparative fit index 0.929; root mean square error of approximation 0.052; 90% CI 0.040-0.064), indicating that engagement self-efficacy (β=.35; P<.001) and outcome expectations (β=.37; P<.001) were significant predictors of engagement (R2=39%). The overall indirect effect between engagement and PTSD symptom reduction was significant (β=–.065; P<.001; 90% CI –0.071 to –0.058). This relationship was serially mediated by both skill activation self-efficacy (β=.80; P<.001) and trauma coping self-efficacy (β=.40; P<.001), which predicted a reduction in PTSD symptoms (β=−.20; P=.02). Conclusions The results of this study may provide a solid foundation for formalizing the nascent science of engagement. Engagement conceptualization comprised general measures of attention, interest, affect, and use that could be applied to other applications. The longitudinal research model supported 2 theoretically based predictors of engagement: engagement self-efficacy and outcome expectancies. A total of 2 task-specific self-efficacies—skill activation and trauma coping—proved to be significant mediators between engagement and symptom reduction. Taken together, this model can be applied to other DMHIs to understand engagement, as well as predictors and mechanisms of action. Ultimately, this could help improve the design and development of engaging and effective trauma recovery DMHIs.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".