Currently submitted to: JMIR Formative Research
Date Submitted: May 20, 2026
Open Peer Review Period: May 21, 2026 - Jul 16, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Designing a Client-Facing Mobile Health Application for Survivors of Violence: Mixed Methods Human-Centered Design Study
ABSTRACT
Background:
Hospital-based violence intervention programs (HVIPs) play a critical role in reducing the long-term impacts of violence by linking survivors to resources that address social needs. However, HVIP effectiveness is often hindered by disorganized communication between Violence Prevention Professionals (VPPs) and their clients. Mobile health (mHealth) tools have the potential to improve communication, but none have been co-designed with survivors of violence to meet their unique needs. This study extends prior mHealth application (app) development aligned with VPP workflows and applies a Human-Centered Design (HCD) approach that deliberately centers the client-facing user experience as a core design input, ensuring that the mHealth app reflects clients' lived realities, preferences, and barriers to engagement.
Objective:
This study aims to design an mHealth app to improve communication and access to resources for survivors of violence using human-centered design (HCD) and iterative prototyping methods.
Methods:
We developed the client-facing component using human-centered design and integrated three ‘Ideation Phase’ methods: Participatory Design, Low-Fidelity Prototype Testing, and High-Fidelity Prototype Testing. The Participatory Design phase included semi-structured interviews and co-design activities with clients, followed by inductive qualitative analysis to inform the app's initial low-fidelity design. The Low-Fidelity Prototype Testing phase included guided user-testing interviews with probing questions about static wireframes, followed by inductive qualitative analysis to inform the app's initial high-fidelity design. The High-Fidelity Prototype Testing phase utilized the Rapid Iterative Testing and Evaluation (RITE) method. An impact ratio was calculated to quantify the proportion of identified usability issues that were successfully addressed through iterative design changes.
Results:
Fifteen unique clients participated across 16 testing sessions (one client participated in two phases). Participatory Design identified six key themes: (1) trust, (2) personal connection, (3) guidance in meaningful decision-making, (4) client empowerment, (5) intuitive and comprehensive design, and (6) dynamic journey and sense of progress. Low-Fidelity Testing reinforced these themes and identified two additional themes: (7) app personalization and (8) tailored resource curation. High-Fidelity Testing reinforced themes 1-8 and uncovered seven more: (9) celebration of client successes, (10) standardization of verbiage and design choices, (11) control over boundaries, (12) legitimization of experience, (13) accessibility of VPPs, (14) gratitude expression, and (15) integration with medical care. High-Fidelity Prototype Testing through RITE identified 98 actionable issues, with 89 addressed, achieving a 91% impact ratio. The final iteration of the app expanded from 12 initial low-fidelity wireframes to 32 refined high-fidelity wireframes designed to support client recovery from violent injury.
Conclusions:
The study used HCD and RITE to develop and refine a client-facing app tailored to survivors of violence receiving HVIP case management services. The 91% impact ratio indicates that RITE supported rapid prioritization and resolution of usability issues. The final version of the app integrates core features that enhance usability, empowerment, connection, decision-making, and support while ensuring accessibility and consistency for clients. This approach may be adaptable to developing other mHealth tools for specialized populations, though further research in other settings and with larger samples is needed to assess generalizability.
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