Engaging Caregivers in the Design of LifeLink: A Persuasive Mobile Application for Suicide Prevention
Bibliographic record
Abstract
Suicide is a complex phenomenon because in addition to the individual who is impacted, its effects seep into many lives including caregivers of the individual experiencing suicidal thoughts and those who die by suicide. Often, caregivers seek help everywhere, but face various challenges including long wait times, difficulty accessing resources quickly, personal struggles with mental health, and time sensitivity. Mobile-health interventions are a promising solution for this, as they are easily accessible, available, geographic location independent and affordable. Many mobile apps for suicide prevention exist. However, not much is known about their design and efficacy, and if these apps address the unique mental health needs of caregivers dealing with suicidal individuals. To address these gaps, we designed LifeLink, a persuasive mobile application (app) specifically for supporting caregivers of individuals experiencing suicidal thoughts. Firstly, we reviewed 25 studies to determine risk factors that are most strongly associated with suicide and considerations for developing technological interventions for suicide prevention. Secondly, we reviewed 80 suicide prevention apps from app stores and academic literature to identify gaps and different persuasive strategies’ implementations using the Persuasive System Design (PSD) model. Thirdly, using the findings from the two systematic reviews, we designed a low-fidelity prototype of LifeLink app, implementing various evidence-based persuasive strategies. Next, using a user-centered design approach, 45 caregivers evaluated the user experience of LifeLink. Finally, we conducted a survey and semi-structured interviews to understand caregivers’ needs when supporting an individual, their assessment of perceived persuasiveness of the app and implemented strategies. Our results reveal that all the persuasive strategies were effective and significantly persuasive. LifeLink was found to be easy to use, engaging, useful, easy to navigate, elicited positive user experience, was helpful and impactful for caregivers. We also conducted a thematic analysis of participants’ qualitative feedback to uncover more insights. Findings from our evaluation demonstrate the potential of LifeLink as a valuable tool for caregivers that can increase mental health literacy and foster a supportive environment.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".