A phased 9-week mixed-methods program that answered the question VP stakeholders were nervous to ask — will users accept a major UI redesign across Fitbit and Pixel Watch? The answer required Google's first quantitative design systems study across mobile and smartwatch.
In June 2024, the Google Health & Home team set out to update the UI across its health applications — mobile and smartwatch — to align with Material Design and expand business impact. The goal was a rebrand: new color profiles, updated visual language, a more coherent experience across Pixel Watch and the Fitbit mobile app.
But health applications carry a specific risk that other apps don't. Color in health UIs carries meaning — red signals warning, green signals success, yellow signals caution. Changing color profiles isn't just an aesthetic decision; it can affect whether users correctly understand their health data at a glance. VP-level stakeholders were nervous about change, and that nervousness needed to be resolved by research — not opinion.
The organizational stakes: This wasn't a small UI update. It was a cross-device rebrand that would touch Pixel Watch, the Fitbit mobile app, and health dashboards used by millions of users. Director-level support was blocked until research could de-risk the change.
The research challenge wasn't simple. The team needed two different types of answers at two different levels of evidence. First, they needed depth — a qualitative understanding of how users make sense of color in relation to health data, what mental models they hold, and which data types carry semantic meaning that must be protected. Second, they needed scale — statistically valid evidence that the new design would be well received, that it wouldn't harm usability, and that it would positively impact brand perception.
Neither question could answer the other. Qualitative research alone wouldn't give VP stakeholders the statistical confidence they needed. Quantitative research alone wouldn't explain why certain color profiles worked or failed.
I designed a phased three-study program to answer both — qualitative depth first, quantitative validation second, with an in-person lab study planned as a third phase for contextual device testing.
Before running scaled testing, I needed to build a qualitative foundation. The team had multiple new color profiles to evaluate — across mobile and watch — but no framework for understanding how users would interpret them in a health context. I ran 16 remote moderated in-depth interviews designed to surface that framework before a single line of quantitative survey code was written.
The qualitative study told us how users experience the new color profiles. Study 2 answered the executive question: will this redesign hurt the brand, damage trust, or reduce usability at scale? This required a different approach — brand-blind, between-subjects, statistically powered, designed to go across the organization.
I also had to solve a cross-functional alignment challenge before fieldwork could begin. To make these findings credible across Google's product areas, I aligned with the Material Design team to adopt published and validated measurement scales — ensuring that results from this study could be compared against, and socialized to, the broader Material Design ecosystem.
As the research findings would be socialized across the org, I needed to provide both statistics and extract findings into layperson terms — "what does it mean" for designers, PMs, and marketing.
— From the case study presentation, December 2024This program delivered two types of impact. The first was organizational: the research unblocked a stalled decision and gave senior stakeholders the evidence they needed to move forward confidently. The second was structural: it established a new measurement baseline that Google had never had before for cross-device health UI research.
The hardest part of this project wasn't the research methodology — it was the organizational challenge of running rigorous phased research at a pace that matched a product sprint cadence, while keeping VP stakeholders informed without overwhelming them with methodology.
What I learned: the best research communication for executive audiences isn't a summary of the methods — it's a reframing of the decision. The stakeholders weren't nervous about color profiles. They were nervous about making a public commitment to change that users might reject. Research's job was to either confirm that fear or dissolve it — and the way to do that was to use their language (brand, trust, behavioral intent) rather than ours (comprehension, semantic protection, t-test significance).
This is something I now build into every study from the start: who will make a decision based on this research, what they're afraid of, and how the findings will be framed to actually move them.