Designing for Daily Dependence: UX Research on a Life-Sustaining Cardiac Device

Context

A cardiac pump keeps a patient alive. The internal mechanics are engineered for that job. But the external system (the controller, batteries, and carrying accessories) is what the patient actually lives with. It goes everywhere they go, every hour of every day: sleeping, showering, walking, managing a family, trying to feel like themselves.

A top global medical device company was developing a next-generation cardiac pump system. The internal mechanics were sound. The external system needed research across two distinct but equally critical problems. First, wearability: patients wear this device continuously, and comfort directly affects quality of life and long-term adherence. Second, alarm response: when the device signals a problem, patients need to respond correctly and quickly. For a life-sustaining device, a slow or confused response is not a usability failure. It is a safety risk.

My Role: Senior Human Factors Engineer (UX Research), embedded contractor via Goddard Technologies

Client: Top global medical device company (B2B2C)

Users: Patients with end-stage heart failure requiring long-term mechanical circulatory support

The Human Problem

This device does not get put away at the end of the day. It goes everywhere the patient goes, for the rest of their life. The research had to reflect that reality: not just how patients perform tasks in a lab, but how they actually live.

The external system created friction across two dimensions. On the wearability side: patients reported discomfort with the carrying system and controller mass during daily activities, creating adherence risk for a device they had no choice but to wear. On the alarm side: patients varied widely in how quickly and accurately they responded to alarms, and the reasons for that variation were not obvious from performance data alone. Lifestyle context, caregiver proximity, and social environment were shaping response behavior in ways that a standard usability test would not surface.

Both problems required research that went beyond task observation. The wearability problem needed iterative physical testing to quantify the tradeoff between battery capacity and comfort. The alarm problem needed behavioral research to understand not just what patients did, but why.

Approach

Phase 1: Early Risk Identification via CAD Review

Before any physical prototypes were fabricated, I participated in ergonomic reviews of three-dimensional CAD models with the Engineering and Industrial Design teams. The goal was to identify usability risks in the digital concepts before committing to expensive physical builds.

This phase was about asking research questions at the earliest possible moment: Can a patient grip this comfortably? Is the visual display accessible during daily activities? Does the button placement support error-free operation under stress?

Catching risks at this stage, before fabrication, is where research has the highest leverage. Design changes in CAD cost hours. The same changes after physical prototypes cost weeks and significant budget.

Phase 2: Wearability and Comfort Research

Patients wear the controller 24 hours a day, 7 days a week. That constraint makes wearability research fundamentally different from evaluating a product someone uses occasionally. Comfort, mass, and carrying configuration are not secondary concerns. They are primary usability variables with direct implications for whether patients can sustain use over time.

I led structured wearability evaluations comparing physical prototypes of varying mass and carrying configurations, including belt and shoulder bag systems, with real patients. Sessions included testing across varied activities: walking, seated use, and transitions between positions.

The core research tension was straightforward but not easy to resolve. Engineering needed larger batteries for longer device life, which meant more mass. Patients needed a system light enough to wear comfortably all day. Neither need was negotiable.

By identifying the mass threshold at which comfort ratings dropped significantly, the research gave Engineering and Industrial Design the data to find a design that satisfied both constraints, increasing battery capacity by 22% while maintaining a greater than 90% user acceptance rate.

Phase 3: Interface and Alarm Research

Alarm response was the highest-stakes research problem on the project. The team needed to choose between two alarm designs for a life-sustaining device, and that decision required more than performance data.

I led a mixed-methods study combining in-depth patient interviews with structured alarm response testing. The interviews were designed to surface what a task-based test cannot: the contextual factors that shape how patients actually respond to alarms in their daily lives.

The following data emerged:

  • Alarm response behavior was not simply a function of how clear or loud the alarm was. It was predicted by lifestyle and environmental factors: whether a caregiver was nearby, the social context the patient was in when an alarm fired, and how patients had mentally modeled their own response routines over time.

  • A patient who was socially active and frequently in public responded differently to the same alarm than a patient who spent most of their time at home with a caregiver present. Those behavioral patterns, combined with quantitative response time and accuracy data from the testing sessions, directly informed which alarm design was selected.

This was not a refinement decision. It was a patient safety decision grounded in both behavioral insight and performance evidence.

Impact

  • 25% improvement in task performance in the final round of testing following interface and alarm refinements.

  • 15% increase in alarm response speed. Mixed-methods research combining patient interviews and structured response testing directly informed alarm design selection for a critical patient safety decision.

  • 22% increase in battery capacity achieved while maintaining greater than 90% user acceptance rate, a direct result of wearability research quantifying the viable design space between patient comfort and engineering requirements.

  • 10% increase in comfort ratings following carrying system refinements.

  • Prototype costs reduced by identifying and mitigating usability risks through CAD review before fabrication.

Learnings I’ll take with me

  • What was hard: Both research problems required holding competing constraints in tension simultaneously. The wearability work meant finding the boundary between what engineering needed and what patients could tolerate. The alarm work meant understanding that the same alarm design would be experienced differently depending on who the patient was and how they lived. Neither problem could be solved with a single method or a single lens.

  • What I learned: For a device patients cannot remove, the research has to match the reality of continuous use, not just task completion in a controlled setting. Behavioral context (who is nearby, where the patient is, how they think about their own safety routines) is as predictive of real-world performance as the design itself. Surfacing that context required qualitative depth alongside quantitative measurement, and the combination produced a more defensible decision than either method would have alone.

  • Next: Formal human factors validation testing for FDA submission using the refined system components.