Acceptability and Usability of a Digital Behavioral Health Platform for Youth at Risk of Suicide: User-Centered Design Study With Patients, Practitioners, and Business Gatekeepers
Bibliographic record
Abstract
BACKGROUND: Youth suicide rates are climbing, underscoring the need to improve clinical care. Personal smartphones can provide an understanding of proximal risk factors associated with suicide and facilitate consistent contact between patients and practitioners to improve treatment engagement and effectiveness. The Vira digital behavior change platform (Vira) consists of a patient smartphone app and a web-based practitioner portal (Vira Pro) that integrates objective mobile sensing data with Health Insurance Portability and Accountability Act (HIPAA)-compliant communication tools. Through Vira, practitioners can continuously assess patients' real-world behavior and provide clinical tools to enhance treatment via just-in-time behavior change support. OBJECTIVE: This study aimed to explore the acceptability and usability of the minimal viable product version of Vira through a user-centered design (UCD) approach and to identify barriers to implementing Vira in the context of an adolescent intensive outpatient program. METHODS: Over 2 iterative phases, feedback was gathered from adolescent patients (n=16), mental health practitioners (n=11), and business gatekeepers (n=5). The mixed methods UCD approach included individual semistructured interviews (eg, perspectives on treatment and attitudes toward digital tools), surveys (eg, usability), and unmoderated user testing sessions (eg, user experience). RESULTS: Overall, participants expressed optimism regarding Vira, particularly among adolescents, who showed high satisfaction with the app's interface and design. However, clinicians reported more mixed views, agreeing that it would be useful in treatment but also expressing concerns about the volume and displays of patient data in Vira Pro, workload management, and boundaries. Gatekeepers identified usability issues and implementation barriers related to electronic health records but also recognized Vira's potential to enhance treatment outcomes. Feedback from stakeholders informed several crucial changes to the platform, including adjustments to data-sharing protocols, user interface enhancements, and modifications to training methods. CONCLUSIONS: Vira has a high potential to improve patient engagement and improve clinical outcomes among high-risk youth. Iterative UCD and ongoing stakeholder engagement are essential for developing technology-based interventions that effectively meet the needs of diverse end users and align with clinical workflows.
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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 | 0.033 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".