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PuzzleWalk app for iPhone and iPad


4.0 ( 5200 ratings )
Health & Fitness Games Puzzle
Developer: Indiana University
Free
Current version: 1.3.06, last update: 4 years ago
First release : 28 Mar 2019
App size: 36.43 Mb

PuzzleWalk is a gamified app where you can walk through cities around the world and solve spot the difference puzzles.
The principle of this app is a conversion algorithm between walking step and gaming time.
Your accumulated walking steps will be automatically converted to gaming time, so your walking step is key to be a PuzzleWalk master!

Your walking steps and puzzle points will give you a final score and make you eligible to receive a gift card every month!
More than a hundred of world cities are programmed along with a thousand of spot the difference puzzles.
Each city contains 5 to 10 puzzles depending on the level and each puzzle contains 5 different spots on two otherwise similar images.

*Notes
- Top 3 score-leaders will be given a monetary reward ($10 gift card) at the beginning of every month via email. Decision will be made based on total scores recorded on the leaderboard at midnight of the last day of the month.
- The puzzle game will be deactivated during nighttime (10 PM - 8 AM) to avoid sleep disturbances.
- Your walking step is well detected when you make continuous horizontal movement and subtle walking movement may not be detected by the app.
- The app will update your walking step data every 3 to 5 minutes if walking movement is detected. A walking step tracking page will help you monitor your daily, weekly, and monthly walking performance.

*More about PuzzleWalk
PuzzleWalk incorporates evidence-based gamification and Behavior Change Techniques (e.g. user instruction, self-monitoring on target performance, contingent rewards, and goal setting) to motivate people to be more physically active in a fun way.
The app was developed by the Indiana University School of Public Health and the School of Informatics, Computing, and Engineering researchers. The development of PuzzleWalk was funded by the Indiana University Collaborative Research Grants program.