Workout Intensity Settings
Designing for Safety, Personalization, and Predictability
Katalyst
2025
Summary
Role: Lead Product Designer
Scope: UX strategy, interaction design, user research
Tools: Figma, Miro, Mixpanel, Typeform
Timeline: ~3 months
Team: Product Manager, Frontend Engineer, Backend Engineer, Creative Director
I created a pre-workout setup and an in-workout adjustment system that gave users predictable, safe, and personalized intensity control during at-home EMS training. The solution reduced discomfort, improved trust, and kept trainer-programmed workouts flowing without disruption.
Problem & Context
Katalyst’s EMS training sessions rely on electrical impulses tailored to each user. At home, without an EMS technician present, users had limited control over intensity progression, and trainer-programmed changes could feel abrupt or unpredictable, leading to anxiety, skipped adjustments, and reduced confidence.
Users expressed uncertainty around safety and comfort at higher intensities, which we validated through post-workout surveys and interviews. The goal was to empower users to train independently while preserving the flow of trainer-designed workouts.
The Challenge
How might we make home EMS workouts safer and more personalized without breaking workout flow?
My Reponsibilities
I led the end-to-end UX process, including:
Leading UX strategy and design for pre-workout and in-workout control experiences
Turning user and trainer feedback into actionable design requirements
Collaborating with engineering to stay within firmware limits
Validating designs through usability testing with active members
Research & Insights
I combined:
User interviews & surveys on recorded-workout pain points
Trainer interviews to understand how intensity profiles are programmed
A/B testing on intensity profiles to measure perceived safety and usability
Usability testing with different profile configurations
Key insights:
Sudden intensity jumps were intimidating - users wanted gradual, predictable changes.
Many used personal workarounds, e.g., enabling trainer-led increases at the start, then disabling later.
Lack of transparency discouraged mid-session adjustments altogether.
Strategy & Prioritization
We focused on two high-impact opportunities:
Choose Before You Train: Let users select their preferred intensity progression before starting, reducing disruptive mid-workout changes.
Smooth & Visible Changes: Show upcoming intensity adjustments and apply them gradually for predictability and safety.
Constraints:
Built fully within existing firmware. Launched in 3 months without costly hardware changes.
Design Solutions
1. Pre-Workout Intensity Profiles
Added a step to choose one of three profiles: Standard, Early Ramp-Up, Late Boost
Added Easy Mode and Hard Mode for single-session end-intensity tweaks without changing the default user profile
Reduced need for manual mid-session changes
2. Smooth Intensity Ramp-Up
Displayed target intensity next to the current value when an adjustment occurred (either by the trainer or the user)
Applied changes over the next 4-second impulse instead of instantly, creating a smoother, safer shift
Results & Impact
28% decrease in mid-workout adjustments
85% positive feedback on smoother, more predictable changes
Qualitative feedback: users felt “in control” and “safer”
Supported retention goals by boosting trust in solo training - a key driver for long-term subscriptions
Increased user confidence, reduced drop-offs, and improved adoption of customized intensity settings
Collaboration
Engineering: Designed logic for gradual adjustments within firmware limits
Creative Director: Ensured visuals aligned with Katalyst’s premium, approachable brand
Trainers: Defined optimal progression patterns for different workout types
Reflection
Control equals confidence, and in at-home EMS training, confidence drives consistency. By giving users transparent, safe, and customizable control, we strengthened trust in solo sessions and positioned Katalyst as a professional-grade home training platform, critical for scaling beyond studios and sustaining subscription growth.
If extended, I’d explore adaptive intensity algorithms that auto-adjust using heart rate and performance data.