Can Your Smartphone Save A Life? A Systematic Review of Mobile-Based Interventions For Suicide Prevention
Bibliographic record
Abstract
Mobile health (mHealth) apps are handy tools for tackling stigmatized mental health issues, including suicide. Mobile-based interventions for suicide prevention are easily accessible, increase the likelihood of honest reporting on sensitive topics and reduce stigma as compared to face-to-face or traditional interventions. Many mHealth apps for suicide prevention exist. However, the persuasive strategies employed in these apps and their efficacy remains unknown. To address this gap, we reviewed 80 suicide prevention apps available on app stores and in academic journals. We identified different persuasive strategies implemented in these apps using the Persuasive System Design (PSD) model. We also identified current trends within these apps, most and least-dominant implementations of persuasive strategies, effectiveness of apps, evaluation methods, and app content. We found that Personalization (n = 32) and Self-monitoring (n = 29) were the most-dominant strategies and Social Comparison, Social Role were the least-dominant strategies in suicide prevention apps. Based on our findings we discuss three major concerns in developing suicide prevention apps and offer recommendations for mitigating them. Our results show that persuasive strategies are a promising tool that can be used for designing suicide prevention apps. Our conclusions and recommendations will guide future work in suicide prevention app development and enhance the usability, effectiveness, and user-experience of such apps.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".