Vape Quit Coach
An iOS app for quitting vaping
Ownership
Solo Designer & Developer
Team
Solo / small team
Key Result
4.8★ App Store Rating
Overview
An iOS app for smoking cessation built around tracking, coaching, and support during cravings and setbacks.
The Challenge
Many quitting apps rely on brittle streak systems and punitive framing. The goal here was to build something calmer and more usable during difficult moments.
Constraints
- •Behavior change support had to avoid shame mechanics and relapse punishment loops.
- •The app needed to be effective in high-stress, low-attention moments.
- •Solo development required disciplined scope and clear UX priorities.
Decision Log
Problem
Streak systems create brittle motivation and anxiety.
Decision
Shifted progress framing from perfect streaks to identity and trend signals.
Tradeoff
Less instantly gamified feedback.
Impact
More resilient long-term engagement after setbacks.
Problem
Craving moments are noisy and emotionally charged.
Decision
Used calm, low-stimulus intervention screens with short actions.
Tradeoff
Less visual spectacle during key moments.
Impact
Lower cognitive load when users need support most.
Approach
1. Behavioral Architecture
The product focuses on triggers, replacement habits, and support patterns rather than only on streak counting.
2. Progress as Identity
Progress is tracked in a way that still reflects forward movement after setbacks instead of resetting everything to zero.
3. Liminal Design
Intervention screens are intentionally low-stimulus so they stay usable during cravings and stress.
Outcome
The app shipped on iOS and has held a 4.8-star App Store rating.
4.8★
App Store Rating
100%
Solo Built
Learnings
- →Health products need a different interaction model than productivity tools.
- →Low-stimulus support flows matter more than high-energy motivation during cravings.
- →Solo work makes the product tradeoffs easier to see because no one else is making them for you.
Anti-Patterns Avoided
- ×Punitive streak resets and public shame nudges.
- ×Engagement loops optimized for app opens rather than quit outcomes.
- ×Clinical fear-based copy during relapse-adjacent moments.
Next Iterations
- →Adaptive intervention timing based on user-identified trigger windows.
- →Deeper longitudinal insights that preserve privacy and dignity.
Get In Touch
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