Promoting Engagement With Smartphone Apps for Suicidal Ideation in Young People: Development of an Adjunctive Strategy Using a Lived Experience Participatory Design Approach
Bibliographic record
Abstract
BACKGROUND: Suicide among young people is a worrying public health concern. Despite this, there is a lack of suitable interventions aligned with the needs of this priority population. Emerging evidence supports the effectiveness of digital interventions in alleviating the severity of suicidal thoughts. However, their efficacy may be undermined by poor engagement. Technology-supported strategies (eg, electronic prompts and reminders) have been deployed alongside digital interventions to increase engagement with the latter. However, evidence of their efficacy is inconclusive. User-centered design approaches may be key to developing feasible and effective engagement strategies. Currently, no study has been published on how such an approach might be expressly applied toward developing strategies for promoting engagement with digital interventions. OBJECTIVE: This study aimed to detail the processes and activities involved in developing an adjunctive strategy for promoting engagement with LifeBuoy-a smartphone app that helps young people manage suicidal thoughts. METHODS: Development of the engagement strategy took place in 2 phases. The discovery phase aimed to create an initial prototype by synthesizing earlier findings-from 2 systematic reviews and a cross-sectional survey of the broader mental health app user population-with qualitative insights from LifeBuoy users. A total of 16 web-based interviews were conducted with young people who participated in the LifeBuoy trial. Following the discovery phase, 3 interviewees were invited by the research team to take part in the workshops in the design phase, which sought to create a final prototype by making iterative improvements to the initial prototype. These improvements were conducted over 2 workshops. Thematic analysis was used to analyze the qualitative data obtained from the interviews and workshops. RESULTS: Main themes from the interviews centered around the characteristics of the strategy, timing of notifications, and suitability of social media platforms. Subsequently, themes that emerged from the design workshops emphasized having a wider variety of content, greater visual consistency with LifeBuoy, and a component with more detailed information to cater to users with greater informational needs. Thus, refinements to the prototype were focused on (1) improving the succinctness, variety, and practical value of Instagram content, (2) creating a blog containing articles contributed by mental health professionals and young people with lived experience of suicide, and (3) standardizing the use of marine-themed color palettes across the Instagram and blog components. CONCLUSIONS: This is the first study to describe the development of a technology-supported adjunctive strategy for promoting engagement with a digital intervention. It was developed by integrating perspectives from end users with lived experience of suicide with evidence from the existing literature. The development process documented in this study may be useful for guiding similar projects aimed at supporting the use of digital interventions for suicide prevention or mental health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".