This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (2473) Registered Report (577) Usability of Apps and User Perceptions of mHealth (989) Violence including Domestic Violence/Abuse (125) Mindfulness and Meditation (212) Substance Abuse (427)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/60918, first published .
Evaluating an App-Based Intervention for Preventing Firearm Violence and Substance Use in Young Black Boys and Men: Usability Evaluation Study

Evaluating an App-Based Intervention for Preventing Firearm Violence and Substance Use in Young Black Boys and Men: Usability Evaluation Study

Evaluating an App-Based Intervention for Preventing Firearm Violence and Substance Use in Young Black Boys and Men: Usability Evaluation Study

Authors of this article:

Chuka Emezue1 Author Orcid Image ;   Dale Dan-Irabor2 Author Orcid Image ;   Andrew Froilan1 Author Orcid Image ;   Aaron Dunlap1 Author Orcid Image ;   Pablo Zamora1 Author Orcid Image ;   Sarah Negron1 Author Orcid Image ;   Janiya Simmons1 Author Orcid Image ;   Jayla Watkins1 Author Orcid Image ;   Wrenetha A Julion1 Author Orcid Image ;   Niranjan S Karnik3 Author Orcid Image
Article Authors Cited by Tweetations (3) Metrics

    Original Paper

    1Department of Women, Children and Family Nursing, Rush University Medical Center, Chicago, IL, United States

    2Humanities and Social Sciences, University of Missouri–Kansas City, Kansas City, MO, United States

    3Department of Psychiatry, University of Illinois Chicago, Chicago, IL, United States

    Corresponding Author:

    Chuka Emezue, MPA, MPH, PhD

    Department of Women, Children and Family Nursing

    Rush University Medical Center

    600 S Paulina St AAC Suite 1064A

    Armour Academic Center

    Chicago, IL, 60612

    United States

    Phone: 1 312 942 6151

    Email: chuka_emezue@rush.edu


    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

    JMIR Form Res 2024;8:e60918

    doi:10.2196/60918

    Keywords

    telemedicine; mobile app; adolescent; violence; substance-related disorder; African American; user-computer interface; software validation; software development; mobile phone 


    Background

    In 2021, a total of 84.8% of children who died by firearms were male, 49.9% were Black, and 82.6% were aged 15 to 19 years, with the majority of these deaths occurring through homicide (64.3%) [1]. During the same period, Black youth aged 18 to 24 years in US cities were 20 times more likely, and Hispanic youth were 4.4 times more likely, to be fatally shot compared with their non-Hispanic White peers, highlighting a severe racial disparity in firearm-related deaths [2-8]. In 2020, homicide rates among Black male individuals aged 10 to 44 years reached levels not seen since 1994 [2,9,10], as firearm-related deaths surpassed motor vehicle accidents as the leading cause of death in children and adolescents in the United States, driven primarily by increases in firearm homicides [1]. Similarly, research consistently shows a significant association between substance use and increased risk of firearm violence. Specifically, alcohol and drug use are linked to higher odds of firearm ownership, risky behaviors with firearms, and perpetration of firearm violence [11,12].

    Both firearm violence and co-occurring substance use have particularly severe consequences for young Black male individuals, who often endure the most intense psychological, physical, and psychosomatic effects related to firearm deaths and disabilities. These adverse outcomes can adversely diminish their overall well-being, health, quality of life, and future opportunities. Despite these associations, the relationship between specific substances and firearm violence is mixed [13,14].

    Young Black male individuals with assault-related injuries on adverse developmental trajectories encounter unique barriers to accessing and sustaining formal support systems, including mental health, behavioral, and medical services, across a prevention, treatment, and recovery continuum [15-18]. In urban areas with many behavioral programs, minority youth exhibit a 50% to 60% drop in mental and behavioral service use after the age of 17 years [19,20]. Overcoming these barriers necessitates innovative solutions tailored to their specific daily activities, environments, and needs.

    Digital health interventions (DHIs) present promising opportunities for delivering evidence-based prevention and treatment resources due to their accessibility, scalability, and potential for customization [16-19]. These interventions use digital tools and platforms to enhance health care delivery, patient outcomes, and overall well-being, which include but are not limited to smartphone apps, web-based platforms, wearable devices, virtual reality, gamification, and telemedicine systems. Similarly, several DHIs for substance use disorders have been well received by participants [21]. In contrast, current DHIs for youth and firearm violence prevention are still emergent. Existing technology-based solutions focus on secondary or tertiary prevention, such as crime control, surveillance, monitoring, offender communication, gunshot detection, and patient flagging systems [19,20]. Even so, few studies have examined the usability of DHIs for Black and African American youth [22].

    Our prior research with racial minority adolescents, including gun-carrying youth, found that they prefer and are motivated to engage in technology-enhanced interventions due to their consistent delivery, confidentiality, and convenience [13,17-19,21-25]. However, these interventions are typically implemented in emergency departments (EDs) or schools, addressing issues such as bullying or cyberbullying [26], teen dating violence [27], and disruptive behaviors [28,29]. ED-based interventions such as SafERteens [21,23,30], Project Ujima [24], Helping Individuals with Firearm Injuries [25], and Healing Hurt People [31] have effectively reduced aggression, victimization, trauma recidivism, rearrests, and violence-related outcomes for up to 1 year. Despite their potential, ED-based programs encounter substantial implementation obstacles, such as staff shortages, rapid ED workflows, reimbursement constraints, emphasis on secondary and tertiary prevention, and limited geographic reach.

    Social determinants of health (SDOHs), including provider biases, overcriminalization, concentrated poverty, historical mistrust of legal and medical actors, and limited educational and occupational opportunities, diminish the effects of these DHIs [15,32,33]. Structural violence, racism, and neighborhood disadvantages further exacerbate these SDOHs, discouraging young Black male individuals from engaging with prevention programs. Young Black male individuals exhibit 2 trauma-related avoidance strategies: service avoidance, where they steer clear of services or institutions with official records or police presence or provide false contact information to health care staff to evade criminal justice involvement [34-36], and experiential avoidance, characterized by suppressing unpleasant thoughts, memories, or feelings through maladaptive behaviors such as retaliation, pride in carrying weapons, firearm and other weapon-based violence, or involvement in gangs. Few interventions address these avoidance mechanisms as risk amplifiers [37-42], resulting in a revolving-door approach to firearm violence prevention, where young Black male individuals repeatedly cycle through violence-related programs without achieving lasting change. Emerging interventions must target multilevel SDOHs to create environments enabling young Black male individuals to thrive while simultaneously addressing and the psychological or cognitive risk factors that drive firearm violence [43].

    This Study

    This study assesses the usability of BrotherlyACT—a nurse-led, culturally tailored, multicomponent smartphone 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 (ACT), BrotherlyACT addresses crisis support gaps for young Black male individuals in underserved areas. Using a stepwise methodology combining usability testing (UT) and heuristic evaluation (HE), this study identifies and refines content, functionality, and user interface and user experience (UI/UX) design to enhance usability. This user-centered, iterative approach ensures alignment with the target group’s needs and preferences, supporting the intervention’s effectiveness and user engagement.


    Study Design

    Our study used a convergent mixed methods design to evaluate the usability of BrotherlyACT. Following CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth) guidelines, we conducted formative UT with 23 participants, comprising 15 young Black male users (aged 15-24 years), alongside 4 adult testers and 4 high school students from a youth advisory board, and HE with 8 mobile health (mHealth) and UI/UX experts from the University of Missouri’s Precision START (Smart Technologies and Applications for Rapid Translation) Lab. UT included using the System Usability Scale (SUS), think-aloud protocols, and cognitive walkthroughs, providing both quantitative and qualitative data on user experience and satisfaction. HE involved applying the Nielsen 10 Usability Heuristics [44], with experts identifying and scoring usability issues and recommending improvements for design consistency and navigation. This study and its previously published protocol [36] strictly adhered to the CONSORT-EHEALTH checklist (Multimedia Appendix 3).

    Participants: Inclusion and Exclusion Criteria

    To participate in the HE, expert testers were required to have prior experience in developing DHIs and to be willing to conduct controlled intervention testing on any device of their choice. There were no predefined exclusion criteria for heuristic testers, allowing a broad range of experts to contribute their insights. Both internal and external teams conducted the UT. External testers had to meet specific criteria: they had to identify as male and Black or African American, be aged between 15 and 24 years, have internet access, and be able to provide assent or consent (including parental consent where applicable). Service providers met the following criteria: aged 18 years or older (of any gender); with prior experience working with young Black male individuals affected by firearm violence and substance use; be of any race or ethnic origin; and be able to read and write in English.

    Exclusion criteria for young Black male individuals included active suicidal or homicidal behavior, being medically unstable, intoxication during recruitment, cognitive incapacity to provide informed consent, impairments (including emergent injuries), and being in police custody or incarceration during study enrollment. Internal UT testers had no specific eligibility criteria, allowing for diverse perspectives in the testing process.

    The BrotherlyACT App

    Overview

    The intervention begins with a single-session, brief motivational interviewing (MI) component (BrotherlyACT+MI) to boost young Black male individuals’ motivation, self-efficacy, retention, and treatment adherence. This brief MI-informed approach follows the Screening, Brief Intervention and Referral to Treatment model. A trained interventionist conducts the brief MI session, which is scripted for delivery by nontherapists in under 10 minutes, in a private, nonjudgmental setting. While not a full MI intervention (ie, it is MI informed), it incorporates all 4 MI principles—empathy, developing discrepancy (recognizing the gap between current behavior and future goals), rolling with resistance (anticipating pushback), and supporting self-efficacy—using 4 communication skills (ie, open-ended questions, affirmations, reflections, and summaries “OARS”). This brief session explores user goals (tracked on the app) and evokes “change talk” to personalize behavior change. Studies show MI achieves optimal results when paired with other therapies. Therefore, the MI session concludes with a warm introduction to the app, encouraging users to first complete video modules and use all app components.

    BrotherlyACT integrates life skills coaching (via brief video-based psychoeducational modules), safety planning tools, artificial intelligence (AI)–powered talk therapy chatbot, and zip code–enabled service connections. This intervention is designed to support low-income young male individuals (aged 15-24 years) living in underresourced communities. It aims to reduce the risks and consequences of firearm violence and early substance use while expanding access to evidence-based therapeutic support and resources. Our app developer, Hekademeia Research Solutions, created a prototype for the BrotherlyACT app using the Quasar Framework, Vue.js, and Typescript. The app has three main components: (1) video-based psychoeducational modules, (2) a safety planning toolkit, and (3) a service engagement and talk therapy AI-powered chatbot.

    Video-Based Psychoeducational Modules

    The app provides brief psychoeducational video lessons (Figure 1) on life skills, based on the 6 core principles of ACT, a third-wave cognitive behavioral therapy. Each module takes about 5 minutes on average to complete. Modules cover skills-based topics such as gun refusal, gang resistance, gun safety, alcohol and drug refusal, conflict resolution, nonretaliation, healthy and unhealthy relationships, mindfulness (emotional reactivity regulation), fostering positive future orientation, and adaptive coping strategies. These modules feature interactive elements such as cartoons, a real-life narrator, and graphics to enhance user engagement. Multiple-choice quizzes at the end of each module reinforce and practice skills. Users can complete all lessons at once or weekly while accessing other app features, with push notifications reminding them to finish incomplete modules. Completion rewards include medallions on a pseudonymized leaderboard, with plans to connect app achievements to tangible rewards such as cash incentives.

    ‎
    Figure 1. Screenshots of the BrotherlyACT prototype: psychoeducational modules.
    Safety Planning Toolkit

    Participants also have access to a safety planning toolkit that includes a 30-day mood trendline to track mood trends and triggers (Figure 2), and evidence-based risk assessment checklists (accessing risk for gang involvement, alcohol and drug use, and gun violence involvement using multiple choice-questions or a slide bar–weighted scoring system to create a risk score and simplified narratives explaining their risk levels; Figure 3). Participants can also access mindfulness-based stress reduction exercises (eg, guided square breathing) and a SMART (Specific, Measurable, Achievable, Relevant, Time-Bound) goal–setting feature that tracks 3 primary value-based objectives with suggested or user-defined, smaller goals. Personalized risk scores and narratives link users to preventive resources such as app modules or to the AI-powered chatbot called “DEVON (Digital Empowerment and Vital Resources Outreach Network) the Chatbot” (Figure 4). DEVON the Chatbot uses natural language processing and a question-answering system [45] powered by a deep learning model (OpenAI GPT-4.0) to provide violence and substance use reinforcement learning and talk therapy based on user inquiries.

    ‎
    Figure 2. Screenshot of the BrotherlyACT prototype: mood tracker.
    ‎
    Figure 3. Screenshot of the BrotherlyACT prototype: risk assessment.
    ‎
    Figure 4. Screenshot of the BrotherlyACT prototype: Digital Empowerment and Vital Resources Outreach Network (DEVON) the chatbot. LLM: large language model.
    Service Engagement and Talk Therapy AI-Powered Chatbot

    The app also connects young Black male individuals to local precrisis response services and navigational support to resources within a 50-mile (80.47 km) radius, determined by the user’s zip code (Figure 5). Users are provided with a list of vetted community-level crisis services addressing practical needs (eg, jobs, housing, legal, and childcare) and mental health needs (eg, Black therapists), ensuring 24/7 support. BrotherlyACT also includes a moderator-supervised peer support chat room for young Black male individuals to anonymously connect and share advice with peers experiencing similar challenges. In our prior study, young Black male individuals requested these add-ons to foster trust in behavioral services and enhance humanlike help-seeking with either a “real person” or a relatable avatar [46].

    ‎
    Figure 5. Screenshot of the BrotherlyACT prototype: zip code-based support.

    Ethical Considerations

    Ethical and safety considerations were approved by the institutional review board of the Rush University Medical Center (Office of Research Affairs: 21122902). All participants provided informed consent or assent via REDCap (Research Electronic Data Capture; Vanderbilt University), and all data were anonymized or deidentified to safeguard participant information. Parental consent was also obtained for participants under the age of 18 years. All participants, except the 4 adult internal testers, received a US $50 e-gift card as compensation.

    Procedures

    Methodology for UT

    Between September and December 2023, UT was conducted with 23 participants, comprising 15 young Black male individuals (aged 15-24 years), 4 adult testers, and 4 high school students participating in a 3-month youth advisory board and internship program at the EMERGE Innovations Lab within the Rush University Medical Center. A dual-pronged approach was used, combining qualitative usability evaluation using think-aloud protocols and cognitive walkthroughs [47]. Both methods involved potential end users verbalizing their thoughts while actively using the app (version 1.3.1), alongside guided tasks to assess ease of use. Qualitative interviews lasting 30 to 60 minutes were conducted in person or via Zoom (Zoom Video Communications), with audio recording and screen sharing where possible. Participants expressed their thoughts on the app’s content and features using a “think-aloud” protocol, while cognitive walkthroughs used scripted tasks to identify misunderstood content or phrases. We asked broad and nontechnical questions, such as feedback on the app’s least helpful aspect and guidance on navigation. Participants accessed the app via a URL on their personal devices or study iPad (Apple, Inc) devices, differing only in screen size. Both internal and external UT teams identified which features and content to retain, modify, or eliminate. Two master’s-level research assistants conducted usability tests via Zoom using a semistructured interview guide, incorporating think-aloud techniques and cognitive walkthroughs with scripted use cases.

    Next, all usability testers completed a quantitative assessment using the 10-item SUS [48,49], via REDCap [50]. The SUS is widely used to evaluate perceived usability, with studies showing a positive correlation between SUS scores and user engagement [51]. A minimum, average SUS score of 68 out of 100 is standard, with scores above 80.3 rated as excellent and in the top 10% [49,51]. The SUS is highly reliable (Cronbach α=0.90), with scores above 70 indicating usability [50,52].

    Methodology for HE

    The heuristic usability inspection occurred from April to May 2024, involving 8 mHealth expert testers experienced in developing and evaluating DHIs and related software. Each tester independently assessed the app using the Nielsen 10 Usability Heuristics, which is based on core interaction design principles [52-54]. These 8 expert testers, unfamiliar with the BrotherlyACT app, were recruited from the Precision START Lab at the University of Missouri-Columbia. They received a link to the app and were instructed to identify and assign severity ratings (using a 5-point severity ranking scale [SRS]) to areas where there were usability violations in the app. These issues were rated as follows: 0 (not a usability issue), 1 (cosmetic issue only—may be addressed if additional time is available), 2 (minor usability issue—priority should be given to resolving it), 3 (major usability issue—should be given high priority for resolution), and 4 (usability catastrophe—requires immediate action to rectify before product release). The testers could explore all app sections freely, without predefined use cases, unlike during the UT phase. In addition, heuristic testers provided written, open-ended narratives for each heuristic on REDCap to explain the issues they identified and offered recommended solutions. Examples of HE questions include “What were the issues you identified with the ‘Visibility of System Status’, and what are your recommendations?”

    Data Analysis

    UT Analysis

    Participants answered 5 pairs of contrasting questions in the 10-item SUS, each with one positive and one negative wording. For the SUS score, sums of odd-numbered (negative) questions formed X, and sums of even-numbered (positive) questions formed Y. The equations used to calculate the SUS was X0 = X – 5 and Y0 = 25 – Y. We calculated the SUS score by multiplying X0 and Y0 by 2.5. To convert the original scores of 0 to 4 to a 0 to 100 scale, we assigned each question a point value (eg, strongly disagree was given 1 point), added the points together, and multiplied by 2.5. Although scores can range from 0 to 100, they are typically ranked on a percentile basis. An SUS score >68 is considered above average [48]. We conducted a thematic analysis of qualitative UT data using a study-specific codebook divided into 8 categories: app navigation, module content, user interface design, app functionality, app interactivity, app user experience, cultural referencing, and miscellaneous codes. The lead author and 2 research assistants (AF and AD) performed inductive coding on the transcripts, applying this predetermined coding structure to evaluate necessary app modifications.

    HE Analysis

    After UT, mHealth specialists conducted an HE of BrotherlyACT using the Nielsen 10 Usability Heuristics, rated on a 5-point SRS. The 10 heuristic categories were divided into 22 subcategories, with expert recommendations and design solutions suggested for each. Evaluators examined system behaviors that violated the subheuristics. The Nielsen 10 Usability Heuristics form was adapted from a publicly available HE Workbook by the Nielsen Norman Group. Our objective was to identify and address any severe issues with scores of at least one on the SRS.


    Participant Characteristics

    A total of 23 participants, comprising 15 young Black male individuals (aged 15-24 years), 4 high school students, and 4 adults, participated in our formative UT. The average age of the participants in the young Black male group was 21 (SD 5.74; range 15-24) years. The mean age of the participants in the high school group was 17 years, and all 4 adult testers were older than 30 years. The 15 young Black male individuals in our sample were selected from a larger randomized controlled trial registered at ClinicalTrials.gov (NCT06359990), where the average age was 18.21 (SD 3.25) years, with most participants (68/312, 23.7%) being aged 15 years. All participants in our study were Black or African American, and only a small percentage identified as multiracial or Hispanic or Latinx (27/312, 11.34%). Most participants reported having some high school education (133/312, 42.7%), having a high school diploma (82/312, 26.3%), or being currently enrolled as students (179/312, 57.3%). In addition, 19.6% (61/312) of the participants worked part-time, which amounts to ≤35 hours per week.

    DHI experts (n=8) at the University of Missouri-Columbia’s Precision START Lab conducted our HE. The sociodemographic profile of DHI expert testers included 50% (4/8) identifying as White and 25% (2/8) identifying as Asian. Most had a PhD or professional degree (5/8, 60%), were female (4/8, 56%), and identified as assistant professor (4/8, 55%). The remaining expert testers comprised individuals with various functions: a data analyst and a PhD candidate in health informatics; a postdoctoral fellow; a nurse practitioner specializing in primary care, addiction, and chronic pain; a PhD student in nursing science; and a registered nurse.

    UT Quantitative and Qualitative Feedback

    The BrotherlyACT app achieved an average SUS score of 79, indicating near-excellent usability. This score corresponds to an A-minus grade and ranks the app in the 85th percentile for usability. All usability testers held a positive view of the prototype, giving it a score of 8 out of 10 (0=not at all likely and 10=extremely likely) for their level of satisfaction and the likelihood of recommending it to others. The qualitative interviews uncovered issues with the app that needed to be addressed, leading to 46 pages of codes and subcodes organized into 8 thematic areas: app navigation, module content, user interface design, app functionality, app interactivity, app user experience, cultural referencing, and miscellaneous codes. Participant feedback within these 8 thematic areas was categorized as negative, neutral, or positive (praising sections of the app), with some codes falling into more than one thematic area. The highest frequency of feedback was found in the “app user experience” area, which included a mix of positive and critical comments. Some substantial usability issues were that participants preferred real-life videos over the original cartoon videos; wanted characters in the videos to reflect them; and noted complications with using the app, with several features not particularly arranged to improve navigation. In response, we addressed the numerous usability issues they brought up. Importantly, all prior videos were revamped to feature a Black narrator and images that better reflected Black culture and the lived experiences of young Black male individuals. Users also requested a brief video explaining how to use the app, in place of the prior text description. In addition, daily reminders were recommended to ensure progress through the app sections. Adolescents found My Modules (ie, video-based psychoeducational modules) and the mindfulness-based stress reduction tools to be the most useful features of the app. Minor issues with other components of the app are organized into 8 thematic areas (Multimedia Appendix 1). A previous study captured predesign needs from young Black male individuals as target users between July 2022 and 2023 that went into this version of the app, and another study evaluated intervention “must-haves” for young Black male individuals impacted by bot violence and substance use [46]. We summarize individual feedback from our UT participants.

    HE Feedback

    Eight mHealth and UI/UX experts performed an HE on the app. Most of the app’s usability heuristics received a median score of less than 1, indicating no problems existed, showing the severity ranking results from our expert testers (Multimedia Appendix 2). The “Help users recognize, diagnose, and recover from errors” and “User control and freedom” heuristics had no violations. However, the “Help and documentation” heuristics had the highest violations, with a median score of 1.5. Previous research suggests that mHealth experts, due to their familiarity with digital platforms, tend to assign higher severity scores compared with unfamiliar testing groups, as was the case with our UI/UX experts [47]. Considering the feedback received, further actions were taken to address the concerns raised during the HE.

    Specific issues identified were primarily navigation and customization issues within the prototype, such as missing back buttons, missing undo and redo buttons, iconography choice, the need to make pages better navigable, the need for menu item relabeling (eg, relabeling “My Modules” to “My Lessons”), and the addition of a subpages list to the main menu list. HE evaluators were also concerned about the “Exit” button, relabeled as “Quick Exit,” and lauded this feature as a crucial safety and security element. Evaluators praised the style and content but struggled to navigate the “My Dashboard” panels (containing features such as My Mood Tracker, a quick overview of modules, goal trackers, ranking on a leaderboard, and a panel with daily positive quotes). Similarly, issues of output comprehension and navigability were noted with the Mood Tracker. We used the findings from the HE to refine the prototype and make it more user friendly.


    Principal Findings

    In this convergent mixed methods study, we assessed the usability of BrotherlyACT, a novel nurse-led, culturally tailored, digital intervention aimed at reducing youth and firearm violence and co-occurring substance use risk among young Black male individuals (aged 15 to 24 years). Through a stepwise methodology integrating HE and formative UT, we identified and refined content, functionality, and UI/UX design elements to optimize usability. BrotherlyACT achieved an average SUS score of 79, equivalent to an A-minus grade and placing it in the 85th percentile, indicating near-excellent usability. HE further validated these findings, with mHealth expert testers reporting minor errors and a highest severity score of 1.5 on a scale of 0 to 3 (with 3 indicating a major issue), underscoring that the app is well designed, user-friendly, and ready for effective use by its target audience with minimal adjustments needed. HE results showed that most usability heuristics received median scores of less than 1, indicating no key issues. However, the “Help and documentation” heuristic had the highest violation score of 1.5. Feedback highlighted navigation and customization challenges, such as missing buttons and unclear labeling.

    Qualitative UT interviews revealed some usability issues categorized into 8 thematic areas, including app navigation, user interface design, and cultural referencing. Key concerns included a preference for real-life videos featuring relatable characters, navigation challenges, and the need for instructional content on app use. In response, we revamped the app’s video content to feature Black narrators and added a user-guided how-to video. We have used this feedback from the UT and HE stages to refine the app prototype, improving its usability and ensuring it better meets the needs of young Black male individuals at risk for violence and substance use. To our knowledge, BrotherlyACT is the first evidence-based app addressing youth firearm violence and substance use using an ACT-based approach. These usability findings highlight its potential as a practical, effective clinical tool for engaging a high-risk, underserved population.

    Recent studies have explored UT for violence prevention DHIs, particularly in the context of intimate partner violence and sexual assault. mHealth apps have shown promise in addressing similar interpersonal violence issues, with usability evaluations indicating high user acceptance and effectiveness in screening, disclosure, and prevention [55-57]. Various usability evaluation methods, including questionnaires, interviews, and think-aloud protocols, have been used to assess these types of interventions [56]. However, existing DHIs for youth violence prevention have typically focused on survivors of partner or dating abuse [58,59], suicide prevention [60,61], surveillance and monitoring of youth cohorts [62], and technology-enabled violence against women and girls [62]. Earlier computer-based programs, such as SMART Talk, were successful in reducing beliefs supportive of violence and increasing intentions to use nonviolent strategies among middle school students [63-65]—often addressing internalizing and externalizing risk factors [56]. Other studies using multimedia to teach life skills (such as the Botvin Life Skills Training [66,67]) have shown that a comprehensive approach, including material on violence and the media, anger management, substance use, and conflict resolution skills, can be effective in reducing violence, delinquency, and drug use among students. These findings further highlight the potential of DHIs in primarily preventing youth violence and its antecedents [66,67], as well as in preventing substance use initiation and associated consequences [68-71].

    Building on this precedence, implementing BrotherlyACT can effectively reduce barriers to care that have historically impeded service use among young Black male individuals by providing continuous access to evidence-based resources and leveraging web-based or digital tools to close key equity gaps—an area of distinct need in the violence prevention literature [58]. Given the prevalence of structural racism and racial trauma impacting these youth, BrotherlyACT addresses critical gaps in mental health support, positioning itself as a primary prevention tool. By intervening before violence occurs, the app focuses on pivotal moments such as the onset of firearm carriage or early substance use—key turning points that can escalate from low-level aggression (eg, physical fighting and bullying) and psychological distress to more severe, high-lethality violence and substance use disorders [59]. The positive results from this UT and HE study set the stage for a pilot study to assess BrotherlyACT’s clinical efficacy in community health and emergency care settings. Specifically, by addressing firearm injuries and fatality risk outcomes such as attitudes toward guns and violence, reactive and proactive aggression, mental health outcomes (eg, psychological distress), and substance use (ie, onset, frequency, and attitudes).

    This study, although insightful, has limitations. The sample size, while sufficient for UT, may not represent the diversity of user experiences within at-risk young Black male populations. Our UT and HE were conducted in controlled environments, which may not reflect real-world conditions, such as in crisis situations. Future research should include more diverse samples and consider longitudinal assessments to evaluate long-term usability in real-world settings. User engagement and app functionality may change over time, revealing issues not evident in early testing stages. Factors such as internet connectivity, device types, app compatibility, data privacy, and users’ physical and emotional states could affect app usability and engagement—these dimensions may warrant further field testing in natural settings to provide ecologically valid insights into the app’s performance vis-à-vis user experience.

    In addition, this phase of our study did not explore the app’s efficacy in changing behavioral, cognitive, and mental health outcomes or its integration with existing support systems (ie, health care and community behavioral health settings). A pilot study is currently under way to fully understand the app’s clinical utility and efficacy in community health and emergency care environments. While BrotherlyACT shows promise as a digital intervention for at-risk young Black male individuals, addressing these limitations will be essential for optimizing its effectiveness and ensuring broader applicability and sustainability. The app’s cultural tailoring is a strength but may limit its applicability to other groups impacted by firearm violence (eg, American Indian and Alaska Native youth). The study’s main strength includes our use of a rigorous, convergent mixed methods approach and a comprehensive, multiphase UT protocol with multiple end-user groups with varying familiarity with DHIs.

    Conclusion

    In conclusion, BrotherlyACT shows promise as a user-friendly, culturally relevant, digital intervention to reduce youth violence and substance use among young Black male individuals. Positive usability scores and feedback from users and experts highlight the app’s potential to fill a critical gap in preventive care. With minor revisions and further testing, BrotherlyACT could be a vital tool in mitigating violence and promoting health equity within this vulnerable population. Adolescents and experts reported the benefits of this type of intervention for violence-involved young Black male individuals in clinical and community health settings. By enabling young Black male individuals to practice essential life skills (eg, emotional regulation, anger management, conflict resolution, and perspective taking); create a safety plan; and connect with reliable services, these types of interventions can empower young Black male individuals to manage risky behaviors and access tools and resources despite prevailing risk factors. Clinicians find the app useful for reaching susceptible youth from a primary prevention perspective and recommend using it alongside existing services as a take-home intervention. They note its benefits for both “treatment veterans” and those who are “treatment curious” due to its focus on ready-to-use life skills and behavior activation. Further investigation is necessary to assess BrotherlyACT’s effectiveness in addressing the outcomes outlined in our previously published protocol [36], primarily through implementation studies with larger youth samples in clinical and community-based settings.

    Acknowledgments

    The authors thank Dr Marlon Haywood; Mr Rickey Layfield (Urban Male Network, where most of our study population was recruited); and Lissette Ortiz and Jose Nietes (Chicago Hope Academy) for their support in youth consultations and recruitment coordination. They would also like to extend their appreciation to Hekademeia Research Solutions, Blake Anderson, and Shana Permenter for their assistance in designing and programming BrotherlyACT. The authors thank the Precision START (Smart Technologies and Applications for Rapid Translation) Lab members, Chicago Hope Academy students for evaluating the app’s initial iteration, and the young Black male individuals who participated in the study. The authors received funding from the Chicago Chronic Condition Equity Network through the National Institute on Minority Health and Health Disparities (grant 5P50MD017349-02); the Institute for Translational Medicine via National Institutes of Health/National Center for Advancing Translational Sciences (grants UL1TR002389, KL2TR002387, and TL1TR00238); and the Rush-BMO Institute for Health Equity Scholars Program at Rush University.

    Data Availability

    The data supporting the findings of this study are available from the corresponding author upon reasonable request. Due to the small sample size and identifiable affiliations with specific institutions, data access is restricted to protect participant confidentiality. Requests for data access will be reviewed in accordance with institutional policies on data sharing and participant privacy.

    Authors' Contributions

    Administrative support for the study was provided by AF and AD. Data analysis and interpretation were performed by CE. All authors were involved in study conception and design, data collection and assembly, writing of this paper, and final approval of this paper.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Summary of feedback generated from young Black male individuals during usability testing.

    DOCX File , 21 KB
    Multimedia Appendix 2

    Severity ranking scale results from expert testers.

    DOCX File , 26 KB
    Multimedia Appendix 3

    CONSORT-EHEALTH checklist (V.1.6.1).

    PDF File (Adobe PDF File), 1086 KB
    1. Roberts BK, Nofi CP, Cornell E, Kapoor S, Harrison L, Sathya C. Trends and disparities in firearm deaths among children. Pediatrics. Sep 01, 2023;152(3):e2023061296. [CrossRef] [Medline]
    2. Carter PM, Mouch CA, Goldstick JE, Walton MA, Zimmerman MA, Resnicow K, et al. Rates and correlates of risky firearm behaviors among adolescents and young adults treated in an urban emergency department. Prev Med. Jan 2020;130:105891. [FREE Full text] [CrossRef] [Medline]
    3. Bernardin ME, Spectorsky K, Gu H, Fairfax C, Cutler K. Child firearm injury circumstances and associations with violence intervention program enrollment. J Surg Res. May 2023;285:67-75. [CrossRef] [Medline]
    4. Sheats KJ, Irving SM, Mercy JA, Simon TR, Crosby AE, Ford DC, et al. Violence-related disparities experienced by black youth and young adults: opportunities for prevention. Am J Prev Med. Oct 2018;55(4):462-469. [FREE Full text] [CrossRef] [Medline]
    5. Basile KC, Clayton HB, DeGue S, Gilford JW, Vagi KJ, Suarez NA, et al. Interpersonal violence victimization among high school students - youth risk behavior survey, United States, 2019. MMWR Suppl. Aug 21, 2020;69(1):28-37. [FREE Full text] [CrossRef] [Medline]
    6. Reingle JM, Jennings WG, Maldonado-Molina MM. Risk and protective factors for trajectories of violent delinquency among a nationally representative sample of early adolescents. Youth Violence Juv Justice. Jul 16, 2012;10(3):261-277. [FREE Full text] [CrossRef] [Medline]
    7. Simon TR, Clayton HB, Dahlberg LL, David-Ferdon C, Kilmer G, Barbero C. Gun carrying among youths, by demographic characteristics, associated violence experiences, and risk behaviors - United States, 2017-2019. MMWR Morb Mortal Wkly Rep. Jul 29, 2022;71(30):953-957. [FREE Full text] [CrossRef] [Medline]
    8. Smith JR, Patton DU. Posttraumatic stress symptoms in context: examining trauma responses to violent exposures and homicide death among Black males in urban neighborhoods. Am J Orthopsychiatry. Mar 2016;86(2):212-223. [CrossRef] [Medline]
    9. Cunningham RM, Carter PM, Zimmerman M. The Firearm Safety Among Children and Teens (FACTS) consortium: defining the current state of the science on pediatric firearm injury prevention. J Behav Med. Aug 2019;42(4):702-705. [FREE Full text] [CrossRef] [Medline]
    10. Kegler SR, Simon TR, Zwald ML, Chen MS, Mercy JA, Jones CM, et al. Vital signs: changes in firearm homicide and suicide rates - United States, 2019-2020. MMWR Morb Mortal Wkly Rep. May 13, 2022;71(19):656-663. [FREE Full text] [CrossRef] [Medline]
    11. Chen D, Wu LT. Association between substance use and gun-related behaviors. Epidemiol Rev. Jan 13, 2016;38(1):46-61. [FREE Full text] [CrossRef] [Medline]
    12. Wintemute GJ. Alcohol misuse, firearm violence perpetration, and public policy in the United States. Prev Med. Oct 2015;79:15-21. [CrossRef] [Medline]
    13. Culyba AJ, Sigel E. Firearms and substance use: bringing synergy to counseling and intervention. Am J Drug Alcohol Abuse. May 03, 2020;46(3):263-265. [CrossRef] [Medline]
    14. McGinty EE, Choksy S, Wintemute GJ. The relationship between controlled substances and violence. Epidemiol Rev. Feb 11, 2016;38(1):5-31. [CrossRef] [Medline]
    15. Voith LA, Lee H, Salas Atwell M, King J, McKinney S, Russell KN, et al. A phenomenological study identifying facilitators and barriers to Black and Latinx youth's engagement in hospital-based violence intervention programs. Health Soc Care Community. Nov 08, 2022;30(6):e4873-e4884. [FREE Full text] [CrossRef] [Medline]
    16. Motley R, Banks A. Black males, trauma, and mental health service use: a systematic review. Perspect Soc Work (Houst). 2018;14(1):4-19. [FREE Full text] [Medline]
    17. Richardson JB, Wical W, Kottage N, Galloway N, Bullock C. Staying out of the way: perceptions of digital non-emergency medical transportation services, barriers, and access to care among young black male survivors of firearm violence. J Prim Prev. Feb 06, 2021;42(1):43-58. [CrossRef] [Medline]
    18. Ghafoori B, Barragan B, Palinkas L. Mental health service use among trauma-exposed adults: a mixed-methods study. J Nerv Ment Dis. Mar 2014;202(3):239-246. [FREE Full text] [CrossRef] [Medline]
    19. Belur J, Thornton A, Tompson L, Manning M, Sidebottom A, Bowers K. A systematic review of the effectiveness of the electronic monitoring of offenders. J Crim Justice. May 2020;68:101686. [CrossRef]
    20. Piza EL, Welsh BC, Farrington DP, Thomas AL. CCTV surveillance for crime prevention: a 40-year systematic review with meta-analysis. Criminol Public Policy. Mar 24, 2019;18(1):135-159. [CrossRef]
    21. Carter PM, Cunningham RM, Eisman AB, Resnicow K, Roche JS, Cole JT, et al. Translating violence prevention programs from research to practice: SafERteens implementation in an urban emergency department. J Emerg Med. Jan 2022;62(1):109-124. [FREE Full text] [CrossRef] [Medline]
    22. Wekerle C, Gilmore AK, Self-Brown S. Editorial: technology innovations for violence prevention, mental wellness and resilience among youth. Front Digit Health. Feb 19, 2024;6:1365726. [FREE Full text] [CrossRef] [Medline]
    23. Cunningham RM, Chermack ST, Zimmerman MA, Shope JT, Bingham CR, Blow FC, et al. Brief motivational interviewing intervention for peer violence and alcohol use in teens: one-year follow-up. Pediatrics. Jun 2012;129(6):1083-1090. [FREE Full text] [CrossRef] [Medline]
    24. Watkins J, Scoggins N, Cheaton BM, Nimmer M, Levas MN, Baumer-Mouradian SH, et al. Assessing improvements in emergency department referrals to a hospital-based violence intervention program. Inj Epidemiol. Sep 13, 2021;8(Suppl 1):44. [FREE Full text] [CrossRef] [Medline]
    25. Lyons VH, Floyd AS, Griffin E, Wang J, Hajat A, Carone M, et al. Helping individuals with firearm injuries: a cluster randomized trial. J Trauma Acute Care Surg. Apr 01, 2021;90(4):722-730. [FREE Full text] [CrossRef] [Medline]
    26. Chen Q, Chan KL, Guo S, Chen M, Lo CK, Ip P. Effectiveness of digital health interventions in reducing bullying and cyberbullying: a meta-analysis. Trauma Violence Abuse. Jul 21, 2023;24(3):1986-2002. [CrossRef] [Medline]
    27. Andrade TA, Sampaio MA, Donard V. Applying digital technologies to tackle teen dating violence: a systematic review. Trends Psychol. Apr 22, 2022;32(2):356-374. [CrossRef]
    28. Burkey MD, Hosein M, Morton I, Purgato M, Adi A, Kurzrok M, et al. Psychosocial interventions for disruptive behaviour problems in children in low- and middle-income countries: a systematic review and meta-analysis. J Child Psychol Psychiatry. Sep 06, 2018;59(9):982-993. [CrossRef] [Medline]
    29. Alsem SC, van Dijk A, Verhulp EE, de Castro BO. Using virtual reality to treat aggressive behavior problems in children: a feasibility study. Clin Child Psychol Psychiatry. Oct 20, 2021;26(4):1062-1075. [FREE Full text] [CrossRef] [Medline]
    30. Roche JS, Philyaw-Kotov ML, Sigel E, Eisman AB, Tzilos Wernette G, Resnicow K, et al. Implementation of a youth violence prevention programme in primary care. Inj Prev. Jun 29, 2022;28(3):231-237. [FREE Full text] [CrossRef] [Medline]
    31. Rich J, Harris EJ, Bloom SL, Rich L, Corbin T. Intervening in the community to treat trauma in young men of color. In: Heilbrun K, DeMatteo D, Goldstein NE, editors. APA Handbook of Psychology and Juvenile Justice. Washington, DC. American Psychological Association; 2016.
    32. Brown AF, Ma GX, Miranda J, Eng E, Castille D, Brockie T, et al. Structural interventions to reduce and eliminate health disparities. Am J Public Health. Jan 2019;109(S1):S72-S78. [CrossRef] [Medline]
    33. Voith LA, Atwell MS, Sorensen A, Thomas TJ, Coulton C, Barksdale EM. Identifying risk factors and advancing services for violently injured low-income black youth. J Racial Ethn Health Disparities. Aug 11, 2023;10(4):1809-1822. [FREE Full text] [CrossRef] [Medline]
    34. Brayne S. Surveillance and system avoidance: criminal justice contact and institutional attachment. Am Sociol Rev. Apr 04, 2014;79(3):367-391. [CrossRef]
    35. Voisin DR, Bird JD, Hardestry M, Shiu CS. African American adolescents living and coping with community violence on Chicago's southside. J Interpers Violence. Aug 18, 2011;26(12):2483-2498. [CrossRef] [Medline]
    36. Emezue C, Karnik NS, Reeder B, Schoeny M, Layfield R, Zarling A, et al. A technology-enhanced intervention for violence and substance use prevention among young black men: protocol for adaptation and pilot testing. JMIR Res Protoc. May 01, 2023;12:e43842. [CrossRef] [Medline]
    37. Dickinson J, Arthur J, Shiparski M, Bianca A, Gonzalez A, Erete S. Amplifying community-led violence prevention as a counter to structural oppression. Proc ACM Hum Comput Interact. Apr 22, 2021;5(CSCW1):1-28. [CrossRef]
    38. Corbin TJ, Tabb LP, Rich JA. Commentary on facing structural racism in emergency medicine. Acad Emerg Med. Oct 16, 2020;27(10):1067-1069. [FREE Full text] [CrossRef] [Medline]
    39. Heard-Garris N, Johnson TJ, Hardeman R. The harmful effects of policing-from the neighborhood to the hospital. JAMA Pediatr. Jan 01, 2022;176(1):23-25. [CrossRef] [Medline]
    40. Julion WA, Reed MA, Swanson B, Schoeny ME. The need for a bold research agenda to address structural racism. Nurs Outlook. Sep 2022;70(5):694-696. [CrossRef] [Medline]
    41. Krieger N. Methods for the scientific study of discrimination and health: an ecosocial approach. Am J Public Health. May 2012;102(5):936-944. [CrossRef] [Medline]
    42. Alvarez K, Polanco-Roman L, Samuel Breslow A, Molock S. Structural racism and suicide prevention for ethnoracially minoritized youth: a conceptual framework and illustration across systems. Am J Psychiatry. Jun 01, 2022;179(6):422-433. [FREE Full text] [CrossRef] [Medline]
    43. Lee LK, Fleegler EW, Goyal MK, Doh KF, Laraque-Arena D, Hoffman BD, et al. Firearm-related injuries and deaths in children and youth: injury prevention and harm reduction. Pediatrics. Oct 08, 2022:e2022060070. [CrossRef] [Medline]
    44. Nielsen J. Usability heuristics for user interface design. Nielsen Norman Group. URL: https://www.nngroup.com/articles/ten-usability-heuristics/ [accessed 2024-04-29]
    45. Bouziane A, Bouchiha D, Doumi N, Malki M. Question answering systems: survey and trends. Procedia Comput Sci. 2015;73:366-375. [CrossRef]
    46. Emezue CN, Enriquez M, Dougherty DS, Bullock LF, Bloom TL. Rural young males' acceptance and receptiveness to technology-based interventions for dating violence prevention: a qualitative descriptive study. J Adolesc. Oct 2021;92(1):137-151. [CrossRef] [Medline]
    47. Reeder B, Drake C, Ozkaynak M, Jones W, Marc D, David A, et al. Usability inspection of a mobile clinical decision support app and a short form heuristic evaluation checklist. In: Proceedings of the 13th International Conference, AC 2019, Held as Part of the 21st HCI International Conference on Augmented Cognition. 2019. Presented at: HCII '19; July 26-31, 2019:331-344; Orlando, FL. URL: https://link.springer.com/chapter/10.1007/978-3-030-22419-6_23 [CrossRef]
    48. Bangor A, Kortum PT, Miller JT. An empirical evaluation of the system usability scale. Int J Hum Comput Interact. Jul 30, 2008;24(6):574-594. [CrossRef]
    49. Brooke J. SUS: a 'quick and dirty' usability scale. In: Jordan PW, Thomas B, McClelland IL, Weerdmeester B, editors. Usability Evaluation in Industry. London, UK. CRC Press; 1996.
    50. Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O'Neal L, et al. The REDCap consortium: building an international community of software platform partners. J Biomed Inform. Jul 2019;95:103208. [FREE Full text] [CrossRef] [Medline]
    51. Alghabban WG, Hendley R. Perceived level of usability as an evaluation metric in adaptive e-learning: a case study with dyslexic children. SN Comput Sci. Apr 23, 2022;3(3):238. [FREE Full text] [CrossRef] [Medline]
    52. Nielsen J. Enhancing the explanatory power of usability heuristics. In: Proceedings of the 1994 SIGCHI Conference on Human Factors in Computing Systems. 1994. Presented at: CHI '94; April 24-28, 1994:152-158; Boston, MA. URL: https://dl.acm.org/doi/10.1145/191666.191729 [CrossRef]
    53. Nielsen J. Finding usability problems through heuristic evaluation. In: Proceedings of the 1992 SIGCHI Conference on Human Factors in Computing Systems. 1992. Presented at: CHI '92; May 3-7, 1992:373-380; Monterey, CA. URL: https://dl.acm.org/doi/10.1145/142750.142834 [CrossRef]
    54. Nielsen J, Molich R. Heuristic evaluation of user interfaces. In: Proceedings of the 1990 SIGCHI Conference on Human Factors in Computing Systems. 1990. Presented at: CHI '90; April 1-5, 1990:249-256; Seattle, WA. URL: https://dl.acm.org/doi/10.1145/97243.97281 [CrossRef]
    55. Gilmore AK, Davidson TM, Leone RM, Wray LB, Oesterle DW, Hahn CK, et al. Usability testing of a mobile health intervention to address acute care needs after sexual assault. Int J Environ Res Public Health. Aug 25, 2019;16(17):3088. [FREE Full text] [CrossRef] [Medline]
    56. Davis R, Gardner J, Schnall R. A review of usability evaluation methods and their use for testing eHealth HIV interventions. Curr HIV/AIDS Rep. Jun 11, 2020;17(3):203-218. [FREE Full text] [CrossRef] [Medline]
    57. Hughes C, Musselman EA, Walsh L, Mariscal T, Warner S, Hintze A, et al. The mPOWERED electronic learning system for intimate partner violence education: mixed methods usability study. JMIR Nurs. Jan 3, 2020;3(1):e15828. [FREE Full text] [CrossRef] [Medline]
    58. Carter PM, Zimmerman MA, Cunningham RM. Addressing key gaps in existing longitudinal research and establishing a pathway forward for firearm violence prevention research. J Clin Child Adolesc Psychol. Jun 04, 2021;50(3):367-384. [FREE Full text] [CrossRef] [Medline]
    59. Emezue C, Chase JA, Udmuangpia T, Bloom TL. Technology-based and digital interventions for intimate partner violence: a systematic review and meta-analysis. Campbell Syst Rev. Sep 27, 2022;18(3):e1271. [FREE Full text] [CrossRef] [Medline]
    60. Melia R, Francis K, Hickey E, Bogue J, Duggan J, O'Sullivan M, et al. Mobile health technology interventions for suicide prevention: systematic review. JMIR Mhealth Uhealth. Jan 15, 2020;8(1):e12516. [FREE Full text] [CrossRef] [Medline]
    61. Shin HD, Durocher K, Sequeira L, Zaheer J, Torous J, Strudwick G. Information and communication technology-based interventions for suicide prevention implemented in clinical settings: a scoping review. BMC Health Serv Res. Mar 23, 2023;23(1):281. [FREE Full text] [CrossRef] [Medline]
    62. Emezue C. Digital or digitally delivered responses to domestic and intimate partner violence during COVID-19. JMIR Public Health Surveill. Jul 30, 2020;6(3):e19831. [FREE Full text] [CrossRef] [Medline]
    63. Chikwe CF, Eneh NE, Akpuokwe CU. Conceptual framework for global protection against technology-enabled violence against women and girls. Int J Sci Res Arch. Mar 30, 2024;11(2):279-287. [CrossRef]
    64. Bosworth K, Espelage D, DuBay T, Dahlberg LL, Daytner G. Using multimedia to teach conflict-resolution skills to young adolescents. Am J Prev Med. 1996;12(5 Suppl):65-74. [Medline]
    65. Bosworth K, Espelage D, DuBay T. A computer-based violence prevention intervention for young adolescents: pilot study. Adolescence. 1998;33(132):785-795. [Medline]
    66. Botvin GJ, Griffin KW, Nichols TD. Preventing youth violence and delinquency through a universal school-based prevention approach. Prev Sci. Dec 2006;7(4):403-408. [CrossRef] [Medline]
    67. Williams C, Griffin KW, Mehta RK, Botvin GJ. Testing an evidence-based drug abuse and violence preventive approach adapted for youth in juvenile justice diversionary settings. Health Justice. Feb 02, 2021;9(1):3. [FREE Full text] [CrossRef] [Medline]
    68. Marsch LA. Leveraging technology to enhance addiction treatment and recovery. J Addict Dis. 2012;31(3):313-318. [FREE Full text] [CrossRef] [Medline]
    69. Marsch LA, Campbell A, Campbell C, Chen C, Ertin E, Ghitza U, et al. The application of digital health to the assessment and treatment of substance use disorders: the past, current, and future role of the National Drug Abuse Treatment Clinical Trials network. J Subst Abuse Treat. Mar 2020;112S:4-11. [FREE Full text] [CrossRef] [Medline]
    70. Marsch LA, Borodovsky JT. Technology-based interventions for preventing and treating substance use among youth. Child Adolesc Psychiatr Clin N Am. Oct 2016;25(4):755-768. [FREE Full text] [CrossRef] [Medline]
    71. Marsch LA, Bickel WK, Grabinski MJ. Application of interactive, computer technology to adolescent substance abuse prevention and treatment. Adolesc Med State Art Rev. Aug 2007;18(2):342-56, xii. [Medline]

    ‎
    ACT: Acceptance and Commitment Therapy
    AI: artificial intelligence
    CONSORT-EHEALTH: Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth
    DEVON: Digital Empowerment and Vital Resources Outreach Network
    DHI: digital health intervention
    ED: emergency department
    HE: heuristic evaluation
    mHealth: mobile health
    MI: motivational interviewing
    REDCap: Research Electronic Data Capture
    SDOH: social determinant of health
    SMART: Specific, Measurable, Achievable, Relevant, Time-Bound
    SRS: severity ranking scale
    START: Smart Technologies and Applications for Rapid Translation
    SUS: System Usability Scale
    UI/UX: user interface and user experience
    UT: usability testing

    Edited by A Mavragani; submitted 10.06.24; peer-reviewed by M Sadeqi Jabali; comments to author 20.08.24; revised version received 24.09.24; accepted 06.11.24; published 26.11.24.

    Copyright

    ©Chuka Emezue, Dale Dan-Irabor, Andrew Froilan, Aaron Dunlap, Pablo Zamora, Sarah Negron, Janiya Simmons, Jayla Watkins, Wrenetha A Julion, Niranjan S Karnik. Originally published in JMIR Formative Research (https://formative.jmir.org), 26.11.2024.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.