GLORYFIT
Personalized routines from your data
We designed a mobile experience that transforms biometric data into adapted, accessible, and motivating routines. The goal was clear: generate enough trust for users to share sensitive information in exchange for recommendations that truly impact their health.
My role
- •My specific contribution:
- •Qualitative research on barriers to biometric data entry.
- •Design of onboarding flow and routine personalization.
- •Construction of the complete visual system.
- •Functional prototyping in Figma for Android, validated with real users.
The challenge
GloryFit came pre-installed on the Q18 smartwatch, an affordable wearable targeting a broad audience. Its promise: track steps, heart rate, sleep, and provide basic training routines.
But in practice, the problems became evident. The interface felt arbitrary, onboarding was chaotic, and routines were generic. There was no experience that truly connected with users.
From the start, our focus was to rethink this logic: how do we build an experience that invites rather than imposes?
Research and discovery
We began by mapping friction points in existing fitness apps. We conducted user interviews, data privacy surveys, and benchmarked platforms like Google Fit, Zepp, and MiFitness.
A clear pattern emerged: users were willing to share biometric data only when they perceived immediate value—not at the end of onboarding, but in the moment.
Design strategy
We made a key decision: demonstrate value before collecting data. The flow needed to be progressive without being slow, simple without being superficial.
We structured the experience around two key moments:
- First, gradual and justified information entry. We only asked for essentials upfront: age, activity level, and goals.
- Second, immediate visible personalization. Once users completed the basics, they instantly accessed the "My Routine" module.
The system behind it
We designed a personalization system that combined age, activity level, medical history, and goals.
- A sedentary user over 40 received a progressive, low-impact routine.
- An active user without injuries got a medium-to-high intensity routine.
- Users with injury history were shown low-risk exercises first.
We also built an algorithmic transparency module—each recommendation could display its underlying logic.
Experience design
The experience needed to convey clarity, control, and wellness—not glamour or "extreme fitness."
- Segmented buttons for activity level and time selection
- Dropdowns with real-world examples
- Animated tooltips explaining data usage
- Persistent FAB for quick routine access
- Progress indicators throughout onboarding
Visual system:
Soft greens as the primary color, neutral grays for information hierarchy, and Roboto typography for optimal Android readability.
Iterations and validation
During initial testing, we identified a critical issue: asking for sensitive data too early caused users to abandon the flow.
We solved this by reordering: first show a preliminary routine, then request sensitive data to refine it.
The result? More complete onboarding with a stronger sense of security and user control.
Validation and feedback
We validated with our target demographic: adults 35-55 looking to rebuild their fitness habits.
Key learnings
Timing matters as much as the data itself.
Transparency beats automation.
Control builds trust.
Clarity resonates with 35+ users.
Conclusion
GloryFit taught us a powerful lesson:
Trust isn't earned through polished visuals—it's built through interactions that deliver real value, respect user pace, and clearly explain their purpose.
