Age-Friendly Hearing-Aid Self-Fitting

Project Overview

This project addresses the redesign of a mobile self-fitting flow for a hearing-aid application, reframing hearing-test calibration as a home-based, user-administered task. The challenge extends beyond UI simplification: it involves hearing loss, varying digital fluency among older users, caregiver support, environmental noise, and a multi-step calibration sequence that must remain reliable outside clinical settings. As a team-based healthcare interaction design project, my contribution centered on researching, redesigning, and evaluating the self-fitting and hearing-test experience for older adults.

Academic Framing

Age-Friendly Hearing-Aid Self-Fitting examines how older adults experience a mobile self-fitting hearing-aid system, and how interaction design can reduce the breakdowns that occur when professional, clinic-based fitting procedures are translated into home, self-administered software. As part of a cross-functional team, I contributed to research, redesign, and evaluation of the hearing test and self-fitting flow within a commercial hearing-aid application. The project treats hearing-aid self-fitting as a multi-actor system rather than a single-user interface problem: older users, caregivers, audiologists, hardware, and software each shape whether self-fitting succeeds. Working from field-informed research, stakeholder mapping, and assisted usability testing, the redesign introduced age-friendly interaction principles, segmented support paths for differing levels of digital fluency, and clearer environmental and feedback cues. This case grounds my broader research interest in how older adults move from initial access to confident, supported, and continued use of health-related technologies.

Research Question

How can self-fitting hearing-aid software be redesigned to reduce interaction breakdowns for older adults with varying digital fluency, while preserving the support roles of caregivers and audiologists?

Why This Case Matters for Aging Digital Inclusion

Hearing-aid self-fitting sits at the intersection of health technology and everyday digital interaction: it requires older adults to complete a multi-step, feedback-dependent task with limited margin for error, often without professional supervision. Failures in this context are not purely cosmetic interface problems; they can mean abandoned devices, persistent caregiver dependency, or reduced confidence in using assistive technology altogether. Studying this case surfaces how cognitive, sensory, physical, and contextual constraints interact to produce specific, observable interaction breakdowns. It also illustrates how segmented, assisted, and adjustable interaction paths can support digital inclusion without assuming a single, uniform older-adult user. In this case, the hearing-aid workflow made barriers and facilitators visible at the level of interaction: smartphone familiarity, caregiver mediation, feedback ambiguity, environmental noise, and confidence all shaped whether older users could move from initial access to sustained use.

Why self-fitting becomes difficult for older users

Project materials indicate that traditional clinic-based hearing-aid fitting involves a long, multi-step procedure, which the self-fitting product aimed to compress into a shorter, home-based process. Internal research suggested that this compression introduces new interaction risks, particularly for older users with limited smartphone experience or slower response times. The board visualizes a user base concentrated in older age groups, alongside early indicators of data-entry errors, help-seeking behavior, and first-time-use dropout. Face-to-face hearing-test observation further reveals the procedural complexity that self-fitting software must simplify without compromising accuracy.

Self-fitting as a five-role interaction system

The self-fitting experience is not contained within a single user's interaction with an app. Caregivers, audiologists, the mobile application, and the hearing aid itself each mediate part of the older user's experience, across first-time use, adaptation, support, and long-term use. Breakdowns can occur not only within the interface, but between these actors — for example, when a caregiver's operation does not match the user's actual hearing response. This stakeholder-aware framing aligns directly with digital-inclusion research that treats technology adoption as a relational, not purely individual, process.

Translating age-friendly research into interaction principles

Building on observed breakdowns, the team translated age-friendly usability research into a set of practical interaction principles, illustrated through a representative older-adult persona. These principles favor concrete over abstract cues, single clicks over swipe gestures, familiar patterns over novel ones, and multimodal feedback over single-channel feedback. Hardware-level interaction, such as battery and connection indicators, was addressed using the same principles, recognizing that age-friendly design extends beyond the app screen. This stage represents the guideline-translation work that connects research findings to reusable design parameters.

From previous flow issues to assisted testing and final optimization

The original hearing-test flow assumed users could complete a multi-step process independently, with limited tolerance for hesitation or error. Assisted testing sessions indicated limitations in memory (recalling instructions), response timing, caregiver-led operation, and the clarity of in-flow feedback. In response, the redesign restructured the flow to compress steps, introduce a one-time noise check, and separate users by smartphone familiarity and support condition. This stage represents the bridge between problem discovery and the final, segmented interaction redesign.

Segmented self-fitting flow for different support conditions

The final design separates users who are familiar with smartphones from those who are not, offering an assisted path with tester- or caregiver-led options where needed. Sound-field level detection was introduced to address environmental noise during testing, and on-screen guidance was clarified for both independent and assisted use. Outcome signals reported by the project team are described here cautiously, as aggregated, product-development indicators rather than findings from an independent or peer-reviewed study.

Research Takeaway