Evaluating an App-Based Intervention for Preventing Firearm Violence and Substance Use in Young Black Boys and Men: Usability Evaluation Study
Bibliographic record
Abstract
BACKGROUND: Young Black male individuals are 24 times more likely to be impacted by firearm injuries and homicides but encounter significant barriers to care and service disengagement, even in program-rich cities across the United States, leaving them worryingly underserved. Existing community-based interventions focus on secondary and tertiary prevention after firearm violence has occurred and are typically deployed in emergency settings. To address these service and uptake issues, we developed BrotherlyACT-a nurse-led, culturally tailored, multicomponent app-to reduce the risk and effects of firearm injuries and homicides and to improve access to precrisis and mental health resources for young Black male individuals (aged 15-24 years) in low-resource and high-violence settings. Grounded in Acceptance and Commitment Therapy, the app provides life skills coaching, safety planning, artificial intelligence-powered talk therapy, and zip code-based service connections directly to young Black male individuals at risk for violence and substance use. OBJECTIVE: The primary aim of this study is to evaluate the usability, engagement, and satisfaction of BrotherlyACT among target young Black male users and mobile health (mHealth) experts, using a combination of formative usability testing (UT) and heuristic evaluation (HE). METHODS: Using a convergent mixed methods approach, we evaluated the BrotherlyACT app using HE by 8 mHealth specialists and conducted UT with 23 participants, comprising 15 young Black male users (aged 15-24 years), alongside 4 adult internal team testers and 4 high school students who were part of our youth advisory board. UT included the System Usability Scale and thematic analysis of think-aloud interviews and cognitive walkthroughs. HE involved mHealth experts applying the Nielsen severity rating scale (score 0-3, with 3 indicating a major issue). All testing was conducted via REDCap (Research Electronic Data Capture) and Zoom or in person. RESULTS: Qualitative usability issues were categorized into 8 thematic groups, revealing only minor usability concerns. The app achieved an average System Usability Scale score of 79, equivalent to an A-minus grade and placing it in the 85th percentile, indicating near-excellent usability. Similarly, the HE by testers identified minor and cosmetic usability issues, with a median severity score of 1 across various heuristics (on a scale of 0-3), indicating minimal impact on user experience. Overall, minor adjustments were recommended to enhance navigation, customization, and guidance for app users, while the app's visual and functional design was generally well received. CONCLUSIONS: BrotherlyACT was considered highly usable and acceptable. Testers in the UT stage gave the app a positive overall rating and emphasized that several key improvements were made. Findings from our UT prompted revisions to the app prototype. Moving forward, a pilot study with a pretest-posttest design will evaluate the app's efficacy in community health and emergency care settings. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/43842.
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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 | 0.044 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".