2025 DIA Annual Meeting: Designing Patient Friendly Digital Health Solutions

In an interview with Applied Clinical Trials, Brian Ongioni, chief product officer, uMotif, discussed how the company actively incorporates patient and site feedback throughout the digital health product lifecycle to ensure its tools are both accessible and intuitive. By involving end users early in the development process, uMotif aims to reduce burden on patients and clinical sites alike, while also ensuring that digital endpoints are usable, meaningful, and capable of standing up to regulatory scrutiny. Ongioni shared insights into the company's co-design strategies, real-world examples from clinical trials, and his perspective on the evolving role of artificial intelligence in enhancing patient-reported outcomes.

ACT: How does uMotif integrate patient and site feedback during the product development lifecycle to ensure digital health tools are accessible and intuitive for both stakeholders?

Ongioni: We have several dedicated committees—both site and participant or patient groups—that our product teams regularly engage with for feedback. This might involve reviewing a completely new scale or functionality, or it could mean taking a prototype or mock-up and gathering early input. By incorporating that feedback early in the software development lifecycle, we ensure that accessibility and usability are baked into the product from the start, rather than being added as an afterthought during study implementation.

ACT: Can you share an example of how co-designing with patients and sites has directly improved a feature or function of a uMotif platform?

Ongioni: One example that comes to mind is a trial we ran for patients with Parkinson’s disease. Before launching the study, we gave participants the opportunity to test the solution. That early engagement allowed us to design the platform in a way that significantly reduced their burden. For instance, Parkinson’s patients often struggle with small screens and precise navigation, so we adjusted the layout and allowed repositioning of specific application elements. These changes were instrumental in making the data collection process more accessible and ensuring the measurements could reliably support the study’s endpoint.

Full Interview Summary: uMotif integrates patient and site feedback throughout the product development lifecycle to ensure its digital health tools are accessible, intuitive, and truly reflective of end-user needs. This is achieved through dedicated patient and site committees that collaborate with product teams early in the software design process. Whether it involves evaluating new features or reviewing early-stage prototypes, these stakeholders provide input that is incorporated before study-specific implementations, ensuring accessibility is embedded from the outset rather than added later.

A notable example of this co-design approach occurred during a Parkinson’s disease trial. Patients were given access to the platform before the study launched, allowing uMotif to make critical adjustments to accommodate their specific motor challenges. This included redesigning screen navigation and repositioning measurement tools to reduce burden and ensure data could be reliably collected for the intended endpoints.

Balancing patient usability with site workflows is also a core focus. uMotif developed a “hub model” to streamline the experience for clinical sites, consolidating disparate systems into a unified platform. By observing and incorporating site input—such as understanding their daily routines and workflow priorities—the company optimized functionality to reduce administrative burden. This site-and-patient-first mindset extends to the validation of digital endpoints, where uMotif organizes separate workshops for patient and site committees to provide feedback on new instruments, ensuring their relevance, usability, and potential regulatory acceptability.

Looking ahead, uMotif sees artificial intelligence and machine learning playing an increasingly important role in refining patient-reported outcomes. These technologies could support the transition from paper to electronic formats and enhance scalability. While there’s ongoing skepticism, AI has the potential to help surface what matters most to patients by amplifying their voices throughout the development lifecycle, provided it is implemented thoughtfully and iteratively.