TRIQ
Smart, Adaptive Triathlon Training Backed by Science
Problem
Triathlon training requires complex scheduling, data analysis, and adaptations based on athlete performance. Existing tools lacked personalisation and failed to address the multidisciplinary nature of the sport.
Athletes struggled with managing training loads across swimming, cycling, and running while maintaining proper recovery and progress tracking. They needed a solution that could intelligently adapt to their unique needs and circumstances.
Research
User Interviews
We conducted interviews with 32 triathletes across various experience levels from beginners to elite competitors. Key findings revealed that:
- 78% struggled with balancing training across disciplines
- 92% wanted more personalised training adjustments
- 64% reported overtraining at least once per season
- 81% tracked metrics across multiple disconnected platforms
Competitive Analysis
Existing solutions either focused too broadly on general fitness or too narrowly on a single discipline. None effectively combined real-time adaptation with scientific principles specific to triathlon training.
Target Group: Recreational Triathletes
Target User Profile
- Gender: Primarily male (main buyer persona)
- Age: 30–55 years
- Income: Fixed income
- Language: Fluent in English
- Modality Experience: At least 1 year in swimming, cycling, and running
- Race Experience: Recreational; participated in at least one triathlon
- Race Ambition: Olympic Distance triathlon (no focus on podiums)
- Training Time: 6–12 hours per week
- Frequency: At least 5 training sessions per week
Design Process
User Journey Mapping
Comprehensive user journeys were created for different personas, identifying pain points and opportunities for AI-driven interventions.
Information Architecture
The app was built around three principles: Analyse, Adapt, and Progress. These informed navigation and feature structure.
Design Sprint
A 5-day sprint focused on visualizing multi-sport training data in a clear, actionable way to assist athlete decisions.
Iterative Testing
Three rounds of usability testing helped refine the UI using athlete feedback and performance tracking.
Prototypes
We created low-to-high fidelity prototypes, tested with real users and iterated accordingly.
HI-FI Prototype
Built in Figma to reflect real-world interface behavior and design fidelity.
Key Interface Innovations
- Unified training load visualization across sports
- AI-powered training suggestions from real-time data
- Adaptive recovery forecasts using biometric + training data
- Race prep simulator with course-specific plans
Results
94%
User Satisfaction
86%
Training Adherence
32%
Injury Reduction
4.8
App Store Rating
The TRIQ app transformed triathlon training, intelligently adapting to user needs with scientific accuracy.
Athletes saw better consistency, gains, and satisfaction.
Key Takeaways
AI-Enhanced Decision Making
ML models helped guide complex training with data-driven insight.
Visualisation Drives Compliance
Clear visual feedback increased engagement and understanding.
Personalization is Paramount
Custom adaptation was the highest-rated feature.
My Role
My Role
UI/Mobile Product Designer – Katlego Seabelo
- Home
- Calendar
- Insight
- Profile
Tools Used
Note: This was a one-time project engagement.