Happy Screen

Screen time management app that uses machine learning to help the user to reduce their screen time in a smart way

Happy Screen

Overview

Happy Screen is a mobile app (and desktop extension) that helps the user to regulate their screen time without setting harsh limits and blocking apps. It is aimed towards young people who realise that they spend too much time in front of the screen. Happy Screen uses Machine Learning (ML) elements to reduce the setup process, 'learn' the users' screen habits and to give tips, suggestions and ideas on how to reduce the screen time gradually. Happy Screen was the final project of the master program Human-computer Interaction and Social media, Umeå University.

Process

Competitors research User research (survey) Sketching UIs Prototype v.1.0 User testing (focus groups) Prototype v.2.0 User testing (user interviews) (LINK for the prototype)

Challenge

Spending too much time in front of the screen is a serious problem that brings both mental and physical health problems to many people around the world. In the recent years people started to consider the overuse of screens as a problem. Same goes for many big corporations which incorporated various features in their software that will bring awareness and maybe some help to overcome the problem:

  • Instagram - “You’re all caught up” text in their feed
  • YouTube - statistics of total time watched
  • Facebook - “Your Activity” setting, reminder alert that pops up

The solution is not “just another screen time app” but something better - mobile app and desktop add-on that will track the time spend in more than one device and help the user to reduce it without forcing them to uninstall apps/delete profiles or keep the phone away.

Research

The research started with competitor analysis on a variety of screen time management apps and tools, their functions, popularity and the way they work. In order to gain a better understanding of the current landscape, I downloaded and tested 5 apps on Android phone and 5 on iOS device. The results showed different problems on both platforms - from visual inconsistency to serious issues and false time tracking. I surveyed 113 individuals from different age groups and locations on their screen time habits and experiences with existing screen time regulation apps. The results were absolutely unexpected, 52% of the responders (63) have never used apps to track/limit their screen time.

To identify problems with the existing apps that users experience, I decided to focus on a smaller target group of users who have been using screen time tracking apps. I interviewed 13 individuals, between 18 and 32 years old who have been using such apps in a span of 1 to 6 months.

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Ideation

Important part of the app and its name is the icon of a mobile device that will be used as a mascot and it will change its facial expressions based on the usage of the screen. Longer usage will make the icon cry and less/almost no usage will keep it smile. Happy Screen supports multiple devices and shows the total screen time of all devices or just the ones that are “turned on” on the Home screen. According to the research many users tend to switch to another device when they reach the limits.

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All existing solutions require the user to set limits which takes a lot of time. That is why Happy Screen will “learn” the habits of the user by incorporating machine learning (ML) and help them to set the right limit according to their current needs. It will save setup time. When it’s time to change the limits again, the app will send a notification.

Instead of “blocking” an app when the time limit is reached, its content will be “unusable”. Based on the type of the app (mainly images/videos/text or mix) the content will get different effects. For example, gradually blurring a news feed to the moment that it can’t be used, or applying some special effects on images, or maybe disabling some parts of the interface. Some of its features will remain active and Happy Screen will offer a few more minutes over the limit so the user can finish their task. I tested the ML elements by showing different visualisations of the effects and encouraging users to leave the device.

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Prototyping and testing

For this project I developed two high-fidelity prototypes:

  • Prototype 1.0 - tested with 3 focus groups of 3-4 people each
  • Prototype 2.0 - tested with face to face interviews with 4 potential users; LINK for the prototype

Both prototypes were designed with Sketch and the interactions were made with InVision.

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Reflections

Working on this project gave me the opportunity to think through a complex problem and try to develop a solution that aims to improve users’ digital health without forcing them to uninstall and stop using their favourite apps. I tried to keep the app with minimalistic user interface design in order to be easy to use. In terms of functionality it should do almost everything automatically and keep the user attention for a short period of time. All UI and UX solutions are based on the users’ needs and requirements.

Ultimately, I feel proud of all of the work and time I dedicated to this project. Despite the limited time frame and difficulties recruiting users for tests, I managed to create clean and minimalistic UI combined with UX patterns that will encourage the users to think more about this problem and hopefully help them to change their habits.

Time frame

Jan. 2019 - May 2019